Development, dissemination, and applications of a new terminological resource, the Q-Code taxonomy for professional aspects of general practice/family medicine

Abstract Background: While documentation of clinical aspects of General Practice/Family Medicine (GP/FM) is assured by the International Classification of Primary Care (ICPC), there is no taxonomy for the professional aspects (context and management) of GP/FM. Objectives: To present the development, dissemination, applications, and resulting face validity of the Q-Codes taxonomy specifically designed to describe contextual features of GP/FM, proposed as an extension to the ICPC. Development: The Q-Codes taxonomy was developed from Lamberts’ seminal idea for indexing contextual content (1987) by a multi-disciplinary team of knowledge engineers, linguists and general practitioners, through a qualitative and iterative analysis of 1702 abstracts from six GP/FM conferences using Atlas.ti software. A total of 182 concepts, called Q-Codes, representing professional aspects of GP/FM were identified and organized in a taxonomy. Dissemination: The taxonomy is published as an online terminological resource, using semantic web techniques and web ontology language (OWL) (http://www.hetop.eu/Q). Each Q-Code is identified with a unique resource identifier (URI), and provided with preferred terms, and scope notes in ten languages (Portuguese, Spanish, English, French, Dutch, Korean, Vietnamese, Turkish, Georgian, German) and search filters for MEDLINE and web searches. Applications: This taxonomy has already been used to support queries in bibliographic databases (e.g., MEDLINE), to facilitate indexing of grey literature in GP/FM as congress abstracts, master theses, websites and as an educational tool in vocational teaching, Conclusions: The rapidly growing list of practical applications provides face-validity for the usefulness of this freely available new terminological resource.

For the documentation of clinical data, the International Classification of Primary Care (ICPC-2) is used [13], often in conjunction with the International Classification of Diseases (ICD).
ICPC-2 has been developed to identify reasons for encounters, such as symptoms, acts performed or requested and diagnosis in daily practice. However, it was not constructed to reflect the professional context of GP/FM, such as organizational, managerial, ethical, environmental, educational, and investigational factors.
The absence of adequate normalized keywords for identifying specific professional contextual features of GP/FM is an impediment to proper indexing of grey literature in GP/FM.
More than 50% of the scientific outputs of general practitioners at congresses are never published [14,15]. Since McWhinney's famous initiative FAMLI [16,17], there is no knowledge management system dedicated to GP/FM grey literature (research which has not been published in academic journals, nor indexed by the major bibliographic databases) [18]. Consequently, study reports, congress abstracts, or collections of dissertations are often not globally available to researchers and practitioners, due to this lack of indexation [19]. For example, the Wonca Europe website maintains a database of more than 30 000 abstracts of conferences in GP/FM. However, these contents are not indexed, which hinders their utility to foster research and support the daily operations of GP/FM.
A taxonomy of professional contextual features of GP/FM has been developed to address this issue using present day technology for information storage and retrieval [20], such as machine learning, semantic web technologies and natural language processing (NLP).
This new taxonomy was named Q-Codes, in honour of the late Henk Lamberts, a Dutch professor of family medicine (University of Amsterdam), who proposed to use the letter Q (not yet used in ICPC) for the development of a rudimentary coding system for his departmental research library.

Aim
The aim of this background paper is: (i) to shortly describe the development of the Q-Codes taxonomy, designed to cover the professional contextual aspects of GP/FM; (ii) to present its dissemination tools available for accessing the taxonomy; and (iii) to describe a number of recent applications, as an exploration of the face-validity of this new terminological resource for primary care.  (Table 1) has been performed from a practicing GPs perspective, in a bottom-up approach, to identify relevant concepts focusing on the professional features of GP/FM (e.g. teaching, ethics, or environmental hazard issues). The choice of conferences was pragmatic. Abstracts needed to be available for analysis by the first author (MJ) in due time before the conference, so that the results could be presented and discussed with attendees [21]. The study of Kruschinski et al., on the content of 614 abstracts of the European General Practice Research Network used a similar methodology to delineate the aspects of GP/FM and was reused in the development process of the taxonomy [22].

Development of the taxonomy
Starting point for the analysis of the congress abstracts were the original Q-Codes created by H. Lamberts. To identify what is really at stake and respect the bottom-up approach, only themes addressed by GPs during the conferences have been classified. The data was analysed in a grounded theory approach with Atlas.ti software (http://atlasti.com/). Each abstract is read and code attributed along the classification ICPC-2 for clinical aspects and with a Q-Code for emerging professional contextual aspects. By grouping these identified concepts and their Q-Codes into subcategories and categories, gradually the taxonomy (a hierarchical ontological structure) takes shape. Cimino's standard set of desiderata for terminological resources was applied [23]. For a full description of this development, the reader is referred to a prior publication [24].
Each concept was illustrated by a carefully chosen sample of pertinent articles, to illustrate and deepen the understanding of the each individual Q-Code. Each Q-Code was linked to other terminological systems Twenty-four GPs and terminology experts from 10 countries were involved in the translation of the terms and scope notes into ten languages, thereby contributing to the further validation of the concept description of each Q-Code.
In the end, a taxonomy of 182 Q-Codes was created to represent professional contextual features of GP/FM. There are eight domains in the taxonomy: doctor's issues, patient's issues, patient categories, structure, knowledge management, and research and development. The two remaining domains are planetary health issues and medical ethics. Each domain is further divided into categories and subcategories (see the electronic annex for a full list of the 182 codes).

Dissemination of the taxonomy
The taxonomy with 182 Q-Codes in 10 languages was published as a semantic web resource on the multilingual, multi-terminology portal HETOP after free inscription (http://www.hetop.eu) [25] and in uniform resource identifier (URI) format ( Table 2).

Applications of the taxonomy
Numerous practical are currently in use or under development (see http://3cgp.docpatient.net for a constantly updated overview). We will limit ourselves here to describe examples of recent applications for three domains: facilitating bibliographic searching; facilitating indexing of congress abstracts and dissertations; and e-learning applications.

Facilitating bibliographic searching
Within the HeTOP website, a search and retrieval engine was built, which allows for multilingual bibliographic queries on PubMed. These queries facilitate the search for relevant literature on issues in primary care.
We will illustrate this with an example. Suppose that a medical student or general practitioner is interested in studies pertaining to the concept of 'work-life balance.' This concept is a Q-Code, but not a keyword in medical subject headings (MeSH), which makes it difficult to conduct a search in MEDLINE. By clicking on the Q-Code QD8 on http://www.hetop.eu/Q, relevant MeSH keywords will be suggested, with detailed information on their scope, and relationships in the MeSH hierarchy. In addition, a relevant search profile for this concept will be automatically provided, turning 182 Q-Codes into search filters for LiSSa (http://www. lissa.fr), a French publications database and MEDLINE (PubMed; Figure 1).
Trainees in GP/FM have used this search engine to explore concepts at stake during a consultation, and to have a prepared bibliography search for each important theme used in daily practice. An example of this approach is given in http://docpatient.net/3CGP/ QC/clinical_exercise.htm   (see http://ww.mgtfe.be (in French) and at the Department of General Practice of the University of Coimbra, Portugal). The results of long working hours devoted to the realization of an exciting final master thesis will remain unread, if not properly indexed. Students are offered ICPC, the Q-Code taxonomy, an indexing tool from the HeTOP portal, and a user guide to assist them in the task to index their work [26].

Q-Codes taxonomy and ICPC-2 are used to index the master theses in GP/FM in French-speaking Belgium
This approach is now reused by researchers from Houston University, Texas, US, for indexing of question-answer pairs in telehealth Brazil and by the Brazilian Society of Family and Community Medicine (SBMFC) to support the indexing of the congress abstracts of the next SBMFC congress (http://www. cbmfc2017.com.br/trabalhos/) [27].
Indexing of congress abstracts allows congress participants to identify more quickly particular contributions of interest during the conference. Researchers not attending the conference can also more easily pinpoint interesting contributions and their authors.
In addition, indexing all abstracts allows for a careful study of the scope and main points of the conference. As an example, we present shortly the results of such an analysis of the 212 abstracts of the congress of the French Society of teachers in GP/FM (CNGE), held in Lille in 2014. ICPC-2 was used to index clinical issues, and the Q-Codes taxonomy for professional contextual features. The distribution of themes of the QT domain (training and teaching) gives an insight in the abstracts presented ( Figure 2). In the abstracts presented at this congress, 45 address the general issue of quality assurance, 14 are about EBM and 38 about guidelines, etc.
In France, a multi-terminology concepts extractor (ECMT) is created for the full automation of the indexing process for conference abstracts, dissertations, and abstracts from the French LiSSa base [28]. A project to combine the Q-Codes taxonomy with this tool for application in the domain of GP/FM is initiated.

E-learning applications
In Vietnam, the Q-Codes taxonomy has been used in an e-learning programme for GP/FM students (http://3cgp.docpatient.net/action-research/q-codes-inan-e-learning-program-in-vietnam). In this project, natural language processing techniques are used to guide the students to the right Q-Codes for their documentation needs.

A taxonomy inspired by MeSH, focused on GP/FM
The Q-Codes taxonomy for professional aspects of GP/ FM has been developed as a new terminological resource for primary care. It is proposed as an extension to the clinically oriented International Classification for Primary Care (ICPC). This taxonomy is available on the web on http://www.hetop.eu/Q (free access) and in URI format (Table 1), as a professional terminological resource in 10 languages, and fit for semantic web applications. This taxonomy and its tools of dissemination are available at the point of care, to practicing physicians, researchers, trainers and trainees in GP/FM, as an illustration of the extent and complexity of this profession.
A growing list of recent applications of this taxonomy illustrates its rapid uptake, and provides facevalidity to its usefulness for scientific communications in the field of GP/FM, by facilitating indexing of dissertations, master theses and congress abstracts, and facilitating bibliographic searches in grey literature repositories of primary care and in general bibliographic systems such as MEDLINE.
The Q-Codes taxonomy has been developed from a strong empirical basis in a bottom-up approach, and applying terminological expertise and sound qualitative methods. To provide free access to the taxonomy, a database was published on the web, created with a standardized terminological structure and advanced semantic web techniques. Great effort was invested in linking and mapping the newly identified Q-Codes to existing concepts in existing terminologies, both linguistic (Babelnet) and conceptual (PubMed).
Further ontological research is needed to determine whether the two main rules of taxonomical thinking have been respected: completeness (all identified) and exclusivity (one place for each concept) [29]. A taxonomy is never complete and must evolve with the discipline. In this development process, we stopped at 182 Q-Codes, but future congress will certainly reveal new concepts.
The value of a taxonomy is determined by the quality and speed of the maintenance and updating process. Although Q-Codes have not been officially endorsed by the World Organization of Family Doctors International Classification Committee (WICC), the maintenance will be handled by a working group of WICC (http://www.ph3c.org/Q) who are also the custodians of ICPC-2.

An attempt to design a draft of the table of content of GP/FM
To the best of our knowledge, this is the first systematic, internationally accepted description of professional aspects in GP/FM. A historic predecessor was FAMLI, a short-lived extension of MeSH, available only in print and floppy disks, operational since 1980, and governed by Ian McWhinney (a founding father of GP/ FM) [16,17]. This effort was abandoned in 1992 [30]. This approach to the doctor's professional context should not be confused with the contextual approach of the patient's universe, as developed by Schrans [31], who studied the elements of the patient's life context (ideas, concerns, and expectations) that influence his or her state of health, the health problems offered to the doctor, with substantial impact on the process of care.
One could argue that the development of a profession-specific taxonomy for professional aspects of GP/ FM is unnecessary, as the aims for such a taxonomy could be met with existing resources, such medical subject headings (MeSH). However, the experience gained during the development of the taxonomy has shown that many profession-specific concepts are not properly represented in MeSH [32]. Furthermore, the learning curve for the correct use of a general and vast thesaurus is steeper than for the use of a simple profession-specific taxonomy.
A tool supporting the future of multicultural GP/FM The feasibility to translate the Q-Codes labelling and scope notes in ten languages illustrates that the taxonomy is robust to cultural diversity. The rapid acceptance of this new terminological tool and its integration in some recent applications is also an indication of face-validity. However, more in-depth research is needed to underpin the validity of this taxonomy for its intended uses. Studying the impact of the use of the taxonomy in search and indexing efficiency of medical students, general practitioners, librarians, and automated systems alike will be the next steps.
The use of automatic ontology learning techniques, relying on term extraction and semantic relation extraction, should be explored to identify concepts and relations from literature and incorporate them in the taxonomy, and facilitate the maintenance process.


extfieldsofstudy:
Medicine
Computer Science

text:
Predicting Heart Failure Patient Events by Exploiting Saliva and Breath Biomarkers Information

The aim of this work is to present a machine learning based method for the prediction of adverse events (mortality and relapses) in patients with heart failure (HF) by exploiting, for the first time, measurements of breath and saliva biomarkers (Tumor Necrosis Factor Alpha, Cortisol and Acetone). Data from 27 patients are used in the study and the prediction of adverse events is achieved with high accuracy (77%) using the Rotation Forest algorithm. As in the near future, biomarkers can be measured at home, together with other physiological data, the accurate prediction of adverse events on the basis of home based measurements can revolutionize HF management.

INTRODUCTION
Heart failure (HF) is a chronic life-threatening condition characterized by high rates of mortality and rehospitalizations. The European Society of Cardiology reports that 26 million people worldwide suffer from HF and 74% of them present at least one comorbidity [1]. HF is characterized by frequent re-admissions to hospital. HF accounts for 1-3% of all hospital admissions, while almost the 24% of hospitalized patients are re-hospitalized within a 30-day and the 46% within a 60-day, post discharge period.
Across the world, the 2-17% of patients admitted to hospital with HF die while in hospital and the 17-25% die within one year of admission [2]. The cost of HF management is driven by hospitalizations, corresponding to 1-2% of total healthcare expenditure.
The ability to accurately predict the aforementioned undesirable events enables the effective risk stratification of patients and allows the clinical decision making. This valuable prognostic information can guide the clinical experts in the adaptation of patient management and in the selection of the best treatment plan that should be followed. In turn, this is expected to improve the quality of care provided to the patients, while in parallel result in better health outcomes. Towards this direction, different factors have been studied for their predictive ability in HF morbidity and mortality, destabilizations and re-hospitalizations. In addition, several studies have been conducted focusing on the simultaneous examination of multiple factors using statistical methods (e.g. multi-variable Cox regression models). Such studies resulted in the creation of acknowledged in the clinical practice scores: (i) for the estimation of risk for mortality, the Heart Failure Survival Score [3], the Get With the guidelines score [4], the Seattle Heart Failure Model [5], the EFFECT [6], (ii) for rehospitalizations [7], and (iii) for morbidity [8].
Moreover, progress in analytical chemistry and biosensor development allows some of them to be detected in saliva and breath [43][44][45][46]. Uric Acid, Tumor Necrosis Factor Alpha (TNF-a), a-Amylase, Lactate, Cortisol and 8-iso-prostaglandin F2a, are among the most important saliva biomarkers, while Acetone (2-Propanon) and 2-methy-1,3-butadiene (isoprene) are indicative examples of breath biomarkers that play a key role in the patient diagnosis and prognosis.
The goal of this study is to introduce such biomarkers in the adverse event prediction process. Obtaining saliva and breath biomarkers is non -invasive and in a future setting can be performed at home [47], becoming on this way a significant tool for HF patient management. In our study, we employ these breath and saliva biomarkers in a machine learning approach which combines heterogeneous patient data (i.e. sociodemographic, clinical, sensor data and biomarkers) for the prediction of adverse events.

A. Dataset
The proposed method is evaluated using a dataset of 27 patients collected by the clinical center of the Universita Di Pisa (UNIPI), Italy within the framework of the HEARTEN project [47]. The criteria for patient selection are reported in Table I. The features recorded for each patient can be grouped to the following categories (Table II). Adherence Experts estimation regarding adherence of patients in terms of medication, activity, and nutrition and the prediction of the medication adherence risk of the patient which is extracted by the Adherence risk module of the HEARTEN project. Score Five scores are computed; European Heart Failure Self-care Behavior Scale 12-item scale for evaluating HF self-care [48], [49], Heart Failure Knowledge score that is related to HF knowledge in general, knowledge on HF treatment, symptoms recognition and occurrence [50], Get with the guidelines for estimating the in-hospital mortality [51], Seattle Heart Failure Model for predicting the 1-, 2-, and 3-year survival of HF patients [5], Minnesota Living with Heart Failure for providing feedback regarding the physical and emotional status of the HF patient [52].

Sensor data
Time and frequency domain Heart Rate Variability features extracted from the electrocardiogram (ECG), as well as respiration rate, weight and activity related data.
Based on clinical studies on biomarker behavior and influence, performed during the HEARTEN project, the following biomarkers are selected as most prominent marker compounds for monitoring HF conditions: (i) acetone in breath, (ii) cortisol and TNF-a in saliva. More specifically, acetone mirrors metabolism, as well as metabolic stress and the concentrations of acetone are elevated in HF patients compared to healthy subjects. A very significant increase in salivary cortisol levels is observed in peculiar cases when a sudden worsening of patients happened during hospitalization. In HEARTEN studies, after therapy adjustment, the cortisol decreased by a factor of about 2. As a consequence, cortisol was considered a good candidate for monitoring the HF patients. Additionally, chronic HF In total, 263 features are recorded for each patient (3 features corresponding to biomarkers, 151 features extracted from sensor data and 109 features corresponding to the other categories).
These features are recorded from the first time of patient's hospitalization until discharge, every second day. Thus, 141 instances are collected from all patients. It must be mentioned that the number of the hospitalizations days is not the same for all patients. On average, each stay lasts approximately 4 days. The dataset given as input to the proposed method is created with the assumption that the discharge instance of each patient is considered as event free ("no event" class), while the instance of the patient when the event was presented and/or the first hospitalization took place is considered as event ("event" class). This results to a set of 54 instances, with 25 corresponding to an event and 29 to a no event.

B. The proposed method
The proposed method consists of three steps: (i) preprocessing, (ii) feature selection, (iii) classification. A schematic representation of the proposed method is shown in Fig. 1 and a detailed description of each stage is provided below.
In the first step, missing values are addressed. Features with more than 60% of missing values are removed, since imputation of missing values cannot be performed due to the nature of the data.
Furthermore, features where the distribution between the values is greater than 80% are not retained. In the second step, the identification of features that can act as discriminators between the two expected situations (presence of an event or not) are selected following a wrapper approach [53], in combination with the classifiers employed in step 3. Two different approaches are tested. The first one (Fig. 1i) takes as input all the features, while in the second (Fig. 1ii), the method is applied separately to features extracted from sensor data and to features corresponding to categories (i)-(vii). Finally, in the third step, nine classifiers are employed and tested [54]

III. RESULTS
The proposed method is evaluated on a dataset of 54 instances, while the number of features is differentiated depending on the outcome of the feature selection step. The obtained results in terms of accuracy (Acc), sensitivity (Sens), specificity (Spec) and area under curve (AUC) are presented in Table III. Both approaches produce rather similar results (accuracy 76% and 77%, respectively), with 12 features to be finally selected in the first apporach and 23 features in the second approach. Rotation Forests (ROT) seem to be the best performing classification algorithm (see Table IV).
In order to evaluate the contribution of biomarkers in the prediction of adverse events, the following experiments are made: (i) all available features are given as input, (ii) features only from sensors are employed, (iii) features only from biosensors are utilized as predictors, and (iv) features from sensors and biosensors are met. The results without feature selection are presented in Table V

Case Model Acc
(i) All features without feature selection ROT 57% (ii) Features only from sensors without feature selection RF 60%

(iii) Features only from biosensors without feature selection
CART 66%

(iv) Features from sensors and biosensors without feature selection
ROT 59% presented in Table VI are extracted from the same experiments, as those presented in Table V, but this time following feature selection.
It should be noted that in case (iii) where only biomarkers are utilized, the feature selection approach is not applied due to the already small number of biomarkers used (i.e. three).
As shown in Tables V and VI, our approach yields superior results. The positive effect of feature selection is also clear (accuracy improvement from 66% to 77%).
A comparison of the proposed method with those reported in the literature (Table VII) cannot be directly performed since the studies reported in the literature: (i) predict the presence or not of one specific adverse event only (destabilizations, re-hospitalizations, mortality) and not the presence or not of HF adverse event in general like the proposed method, and (ii) do not utilize biomarkers. Focusing on specific adverse events, of course it has advantages for clinical practice; still it requires a much larger dataset. In this sense, this can be considered as a limitation of the proposed method. This will be addressed in the future through the data that will be collected during the pilot phase of the HEARTEN project. The utilization of breath and saliva biomarkers is the innovative feature of the proposed method.

IV. CONCLUSIONS
An automated method for the prediction of adverse events related to HF, utilizing information from saliva and breath biomarkers, is presented.
Different experiments are conducted in order the proposed method to be evaluated and the contribution of biomarkers to the prediction problem to be estimated. The results confirm the prediction ability of biomarkers either if they are employed as the only input (Acc: 66%) or in combination with other categories of features (Acc: 77%). Among the biomarkers, TNF-a is the one that presents the largest correlation with the prediction of an adverse event. However, the small number of instances does not allow the extraction of "safe" conclusions. The collection of biomarkers measurements from a larger number of patients will lead to a more in-depth evaluation.
As in the near future biomarkers can be measured at home, together with biosensor data, the accurate prediction of adverse events on the basis of home based measurements will revolutionize HF patient management. Such an approach can become the core of a chronic care model, allowing for early action by both, patients and physicians.
ACKNOWLEDGMENT This work is supported by the HEARTEN project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 643694.


s2fieldsofstudy:
Computer Science
Mathematics

text:
Robust multigrid methods for isogeometric discretizations of the Stokes equations

In recent publications, the author and his coworkers have proposed a multigrid method for solving linear systems arizing from the discretization of partial differential equations in isogeometric analysis and have proven that the convergence rates are robust in both the grid size and the polynomial degree. So, far the method has only been discussed for the Poisson problem. In the present paper, we want to face the question if it is possible to extend the method to the Stokes equations.

Introduction
Isogeometric analysis (IgA) was introduced by Tom Hughes et al. in the 2005, cf. [11], aiming to improve the connection between computer aided design (CAD) and finite element (FEM) simulation. In IgA, as in CAD software, B-splines and non-uniform rational B-splines (NURBS) are used for representing both the geometrical objects of interest and the solution of the partial differential equation (PDE) to be solved.
In IgA, mostly B-splines or NURBS of maximum smoothness are used, i.e., having a spline degree of p, the functions are p − 1 times continuously differentiable. Using such a function space, one obtains on the one hand the approximation power of high order functions, while on the other hand, unlike in standard high-order FEM, one does not suffer from a growth of the number of degrees of freedom.
From the computational point of view, the treatment of the linear systems arizing from the discretization with high spline degrees is still challenging as the condition number both of mass and stiffness matrices grows exponentially with the spline degree. In the early IgA literature, often finite element solvers have been transferred to IgA only with minimal adaptations. Numerical experiments indicate that such approaches result in methods that work well for small spline degrees, but their performance deteriorates as the degree is increased, often dramatically. In [10,9], the author and his coworkers have proposed multigrid methods which are provable robust in the polynomial degree and the grid size. Numerical experiments indicated that the proposed approach of subspace corrected mass smoothers seems to pay off (compared to multigrid methods with a standard Gauss-Seidel smoother) for polynomial degrees of four or five.
In the present paper, we want to discuss the extension of the subspace corrected mass smoothers beyond the case of the Poisson problem to the Stokes flow problem. Unlike for the Poisson problem, for the Stokes problem already the setup of a stable isogeometric discretization is non standard. As there have already been results in the literature, we refer to paper [1], which serves as a basis of the present paper. Alternative approaches can be found in [7,2,5] and others. After introducing discretizations, we discuss the setup of the preconditioner.
For the Poisson problem, the multigrid solver has been applied directly and as a preconditioner for the conjugate gradient method. For the case of a non-trivial geometry transformation, in [9] a conjugate gradient method, preconditioned with the multigrid method for the parameter domain, has been used. It has been shown that in this case the resulting method is robust both the grid size and the polynomial degree, but not in the geometry transformation.
There are a few approaches how to carry this over to the Stokes equations. The first possibility is to apply the multigrid method directly to the problem of interest (allat-once multigrid method), cf. [14] for a particularly popular method in standard FEM or [12] for a survey. As the results for the Poisson problem have indicated that a direct application of the multigrid method in the presence of a non-trivial geometry transformation is not optimal, we do not concentrate on that case. So, we consider a Krylov space method with an appropriate preconditioner, living on the parameter domain. In principle, this could be the Stokes problem on the physical domain, but such a choice (an indefinite preconditioner for an indefinite problem) typically requires the use of a GMRES method, whose convergence cannot be easily proven by considering the spectrum of the preconditioned system, cf. [8]. So, we consider elliptic preconditioners, particularly block-diagonal preconditioners. As the Stokes equations are well-posed in the Sobolev space H 1 (velocity) and the Lebesgue space L 2 (pressure), we just setup preconditioners for those spaces (operator preconditioning).
As we observe that the subspace corrected mass smoothers suffer significantly from the geometry transformation, we propose a variant (by incorporating an approximation to the geometry transformation) which leads for several experiments to a significant speedup. This paper is organized as follows. We will introduce the particular model problem in section 2 and discuss three kinds of discretizations for the mixed system in section 3. As a next step, in section 4, we propose a preconditioner. Finally, in section 5, we give the results of the numerical examples and draw some conclusions.

Model problem
Let Ω ⊆ R 2 be a simply connected domain with Lipschitz boundary ∂Ω and assume a force filed f to be given on Ω and boundary data to be given on ∂Ω. The Stokes flow model problem reads as follows. Find the velocity field u and the pressure distribution p such that hold on Ω and Dirichlet boundary conditions hold on ∂Ω. After homogenization, we obtain a mixed variational form, which reads as follows. Find u ∈ V := H 1 0 (Ω) and p ∈ Q := L 2 (Ω) such that (∇u, ∇v) a(u, v) Here, and in what follows L 2 (Ω), H 1 (Ω) and H 1 0 (Ω) are the standard Lebesgue and Sobolev spaces, and (·, ·) is the standard norm on L 2 (Ω).
Existence and uniqueness of the solution and its dependence of the data follows from Brezzi's theorem [3], which requires besides boundedness and H 1 -coercivity of a the inf-sup stability which is known to be satisfied for the Stokes problem, cf. [4].

Discretization
The discretization is done using a standard Galerkin approach, i.e., we replace the spaces V and Q by finite-dimensional subspaces V h and Q h . As for the continuous problem, existence and uniqueness of the solution can be shown by Brezzi's theorem.
Boundedness and H 1 -coercivity of a follow directly from the continuous problem, but the inf-sup stability for the discrete problem does not. Therefore, we have to guarantee that the discrete inf-sup condition is satisfied, which is actually a condition on the discretization. In the subsection 3.2, we will discuss discretizations satisfying this condition.
Assuming a particular discretization and a basis for the chosen space, one ends up with a linear system to be solved: For given f h , find x h such that and K h is a standard stiffness matrix and D h is a matrix representing the divergence.

Discretization in isogeometric analysis
Let S q p,h be the space of all q times continuously differentiable functions on (0, 1), which are piecewise polynomials of degree p on a (uniform) grid of size h = 1/n. As a basis for S q p,h we choose the classical basis of B-splines, see, e.g., [6]. For computational domains Ω ⊂ R 2 , we first define the spline spaces for the parameter domainΩ = (0, 1) 2 . On the parameter domain, we introduce the space of tensor-product splines, which reads as follows: where A ⊗ B denotes the linear span of all functions (x, y) → u(x)v(y), where u ∈ A and v ∈ B. Note that the restriction to two dimensions and to a uniform grid is only for ease of notation. The extension to three and more dimensions or to non-uniform grids is completely straight-forward. Assuming that physical domain Ω is the image of a B-spline or NURBS mapping we define the spline spaces on the physical domain typically using a classical pullback principle. More complicated domains are represented patch-wise, where for each patch a separate geometry transformation G exists. For simplicity, we do not discuss that in the present paper.

Stable discretizations for the Stokes problem
As mentioned above, it is required to set up the discretization such that the discrete inf-sup condition holds. We discuss this first for the parameter domain. Here, we follow the outline of the paper [1], where three spline space configurations have been proposed, which are variants of known stable spaces from standard finite elements: Taylor . All of them utilize the same grid for both the velocity and the pressure, which makes the implementation significantly easier compared to approaches that are based on setting up two different grids (like IgA-variants of the macro elements, cf. [2]). All of these discretizations follow the spirit of IgA, allowing to freely choose the underlying polynomial degree p. For all of them, the smoothness is in the order of the polynomial degree, which preserves the feature that the number of degrees of freedom is basically not increased when the polynomial degree is increased.
For the case of two dimensions, the spaces are given bŷ Observe that these spline spaces are nested, i.e., we haveV and (for n >> p) a ratio of 9 : 5 : 3 for the number of degrees of freedom. The extension of these definitions to three and more dimensions is straight-forward, cf. [1].
For all of these settings, the discrete inf-sup condition has been shown in [1]. For the Raviart-Thomas like splines, the discrete inf-sup condition cannot be proven if Dirichlet boundary conditions are present. As the method still seems to work well in practice, we include also the Raviart-Thomas discretization in our experiments.
The next step is to introduce the discretization for the physical domain. As outlined in the beginning of this section, the discretization, once introduced on the parameter domain, is typically defined on the physical domain just by direct composition: For the Stokes problem, as an alternative, the divergence preserving Piola transform has been proposed: where J G is the Jacobi matrix of G. The pressure distribution, which is a scalar quantity, is always mapped directly, i.e., in all cases we choose the direct composition In [1], the inf-sup stability has been shown if the Piola transform is used, for the Taylor-Hood like splines also if the direct composition is used. Again, we report also on the numerical results for the cases that are not covered by the convergence theory (direct composition for the Nédélec like and the Raviart-Thomas like splines).

Robust multigrid solvers
As outlined in the introduction, the multigrid preconditioner aims to represent the theoretical block-diagonal preconditioner where K h is the stiffness matrix, M h is the mass matrix and β > 0 is an accordingly chosen scaling parameter. As mentioned above and as discussed in detail in [9], we use as preconditioner for the problem on the physical domain the corresponding preconditioner, sayQ h , on the parameter domain. There, the matrices M h and K h are replaced byM h andK h , their counterparts on the parameter domain. Note that the stiffness matrix acts on the velocity variable, a vector-valued quantity, and that this matrix is block-diagonal on the parameter domain and, iff the direct composition is used, on the physical domain. In all cases, K h andK h are spectrally equivalent.
Instead of an exact inverse of the matrixQ h , we only need to realize an approximation to the application ofK −1 h andM −1 h to any given vector. The approximation of K −1 h is realized using one multigrid V-cycle with one pre-and one post-smoothing step of the subspace corrected mass smoother, as proposed in [9]. There, the algorithm was analyzed only for the case of splines of maximum smoothness, however it can be applied for any spline space and robustness in the polynomial degree can be guaranteed by a slight extension of the presented theory as long as the smoothness is in the order of the polynomial degree. As in the previous publications [10,9], the grid hierarchy is set up for a fixed polynomial degree and a fixed smoothness by just uniformly refining the grid. Using this approach, one obtains nested spaces, so the setup of the coarse-grid correction is trivial.
One of the key observations which was leading to the results in [10,9] was that the spectral equivalence of the mass matrix and its diagonal deteriorates if p is increased. This has also to be taken into account when constructing the preconditioner for the pressure variable. Analogously to the smoother, we realize the application ofM −1 h exactly, based on the tensor-product structure of the mass matrix.
The preconditioner is symmetric and positive definite and can therefore be applied in the framework of a MINRES iteration.

Numerical results
The numerical experiments have been performed using the C++ library G+SMO, see [13], both for the unit square, i.e., for a problem without geometry transformation, and for a quarter annulus {(x, y) ∈ R 2 + : 1 < x 2 + y 2 < 4}. For both problems, the problem has been constructed (with inhomogeneous right-hand-side and inhomogeneous Dirichlet boundary conditions) such that the exact solution is u h (x, y) = cos(5x + 5y) + sin(5x − 5y) −1 − cos(5x + 5y) + sin(5x − 5y) , In Table 1, we see number of MINRES steps required for reducing the initial error (measured in the ℓ 2 -norm of the solution vector) by a factor of 10 −6 ; cases where the memory was not enough are indicated with OoM. The discussion is done for all proposed discretization schemes. The need of the discussion of p-robust methods is easily observed when looking at the results for a standard preconditioner: We display the results if one multigrid V-cycle with Gauss-Seidel smoother is used for the velocity and one symmetric Gauss-Seidel sweep is used for the pressure (GS-MG). There, the number of iterations increases drastically if p is increased. As the approach is perfectly robust in the grid size h = 2 ℓ , we omit the numbers for finer grids. Compared to that approach, the preconditioner proposed in Section 4 (SCMS-MG) shows results which are robust both in the grid size and the polynomial degree and which works well for all discretizations. Although the iteration numbers are smaller than for the GS-MG preconditioner, one has to consider that the costs of the SCMS-MG preconditioner are significantly higher than those of the GS-MG preconditioner, so the proposed method only pays of iff higher polynomial degrees (starting from 4 or 5) are considered. We have chosen β = 0.05 and as damping parameter σ of the underlying smoother, cf. [9], either σ −1 = 0.04ĥ 2 (for Taylor-Hood and Nédélec) or 0.16ĥ 2 (for Raviart-Thomas), whereĥ is the grid size on the parameter domain. While some of the numbers might be improved by fine-tuning the parameters, the given tables for reasonable uniform choices show what one can expect for each of the methods.
In Table 2 we see how well the computed solution approximates the exact solution in terms of the L 2 -norm. Here, we have used the abovementioned solver, where the the stopping criterion has been chosen to reach either a relative error of 10 −10 or 100 iterations. We present the error between the computed solution and the known exact solution (for the pressure after projecting into the space of functions with vanishing mean). We observe that, for the same choice of the polynomial order p and the same grid size, the Taylor-Hood discretization yields to the smallest errors, for the cost of the largest number of degrees of freedom. For the Raviart-Thomas discretization (where the inf-sup condition cannot be shown for the chosen Dirichlet boundary conditions), we observe that the error for the velocity converges, while the error of the pressure stagnates at around 10 −2 . Observe moreover that for p = 5, the approximation on the coarsest grid was fine enough such that the approximation error could not be improved by refinement.
For the case of the quarter annulus, we distinguish between the results obtained by the direct composition (Table 3) and for the Piola transform (Table 4). Again,     we obtain first that GS-MG is robust in h, but the convergence deteriorates if the polynomial degree grows. As it leads to better results, we have set up the GS-MG on the physical domain. For the proposed SCMS-MG preconditioner, observe that the results behave similar to the results for the unit square, however the iteration counts are much larger, particularly if the Piola transform is used. For the direct composition, it is possible to improve the convergence significantly by replacing the mass and stiffness matrix on the parameter domain by a simple tensor-rank-one approximation of those matrices on the physical domain (SCMS-MG-geo). Note that the tensorrank-one approximation does not lead to any additional computational costs after the assembling phase. The extension of such a rank-one geometry approximation to the Piola transform is not yet known. For the original SCMS-MG preconditioner, have chosen β and σ as for the first model problem, just for the Raviart-Thomas smoother for the case with Piola transformation, we have chosen β = 0.0025. For the rank-one corrected version, we have chosen β = 0.01; the damping has been chosen based on an approximation for constants of the inverse inequality on the physical domain.
As in the case of standard finite elements, there are several possibilities to discretize the mixed formulation of the Stokes equations. Our experiments indicate that it might pay off to use the (in terms of degrees of freedom) more expensive variant of Taylor Hood discretizations than the other variants, particularly because it is known that this discretization also works for direct composition. The p-robust smoothers which we have proposed for for the Poisson problem can be carried over also to the Stokes flow problem, however it seems that further investigation is necessary concerning its application in the framework of non-trivial geometry transformations.


SECURITY AND PRIVACY APPROACH OF CLOUD COMPUTING ENVIRONMENT

Cloud computing model are obtaining ubiquitous authorization due to the heterogeneous convenience they provide. Although, the security & privacy problems are the main considerable encumbrance holding back the universal adoption of this new emerging technology. Various researches are concentrated on enhancing the security on Software as well as Hardware levels on the cloud. But these interpretations do not mainly furnish the complete security way and therefore the data security compute (measure) are still kept under the access control of service provider. Trusted Computing is another research concept. In actuality, these furnish a set of tools controlled by the third party technologies to secure the Virtual Machines from the cloud computing providers. These approaches provides the tools to its consumers to assess and monitor the aspects of security their data, they don’t allocate the cloud consumers with high control capability. While as the new emerging DCS approach aims to provide the security of data owners of their data. But the DCS approach concept is elucidate in many ways and there is not a standardized framework of cloud computing environment model for applying this approach.

INTRODUCTION
Cloud Computing is an internet based technology of computing, where the software, information as well as shared resources are provided to the cloud users on demand services as per the user requirements. Cloud computing is the most emerging technology, but the security as well as the privacy of data are the main issues of cloud computing. Cloud computing, one of the emerged and most powerful system used by both developers as well as users provides the environment development and the resources allocation to its users when need. Cloud resources are shared widely and accessed. The availability of cloud resources results vulnerable. Because when the unauthorized user gets access to the data may change the original data that causes the major security risk [1]..

CLOUD COMPUTING SECURITY TRENDS & DIRECTIONS
The fastest growing technology of Cloud computing environment is facing numerous hindrances those cannot be neglected directly by the traditional techniques. An appropriate technique should be adapted for the specific features of new computing paradigm [1]. There are numerous research directions available to address the cloud problem as per the type of problem. There is also a possibility when an untrusted provider of cloud computing can control the licit way by handling the cloud users' request operations to their advantage. So by protecting the data, mainly its integrity as well as the privacy from the both of providers and the external intruders will be expected to the bold result in the cloud security architecture that will stimulate widely adaptation of the cloud computing services [2]. The various security trends of cloud computing environment are shown in the figure 1.1.

PRESERVING PRIVACY OF DATA FROM CLOUD PROVIDERS
The integrity as well as the privacy factor of various cloud based applications is the main important requirements. The customers of cloud based computing are not only troubled of their data regarding the integrity and privacy from the unauthorized intruders, as they are also worried about the unrealized curious cloud providers [3]. In the cloud computing environment, the data of customer's are outsourced to the trusted or un-trusted cloud providers [4]. The un-trusted term is used to describe that the providers of cloud can't be fully believed. However they may not change the customers data instead they may compromise the user's data privacy either they may alter the standards for their benefits. The cloud computing service provider's may be contemplate as a honest by providing their valid users the security features related their data as well as process the authorized user's quires and retrieves the true results [5] . The encrypted data on cloud are totally secured from the users who do not have the valid credentials to access, even if they access the data can't be beneficial untill decryption [6]. The encrypting architecture of privacy before outsourcing the data on the cloud is shown in Figure 1.2. In the above architecture the data will stands in the encrypted shape & it can be decrypted by only the valid credential users. The decryption of data can be done only valid credentials cloud consumer's system. In the above design, the data privacy will remain independent for both the server trust as well as the SLA. Rather, the privacy protection of data only depends to the encryption algorithm techniques [7]. The other issues regarding that how the data owner and valid credential user's share as well as search their data that is encrypted. All these security measures should be done in a well defined manner so that the information should not get leaked to the intruders as well as the cloud providers [8]. The techniques of cryptography as well as the emerging data centric security approach may be the favorable solution to control the numerous challenges of cloud computing regarding the security.

SECURITY APPROACH TO CLOUD DATA SECURITY
In our approach, that we are going to discuss is Data Centric Security that stress the data security itself rather than the networks security etc. DCS is emerging speedily as the big enterprises are relying on digital information to run their big data and business. DCS allows overcome the organizations for the disconnection of Information technology securities and their business strategies. The DCS allows the mechanism to know about the stored data and also define the access policies of data on the cloud computing model [7]. The idea of DCS is based to furnish the security on cloud computing model at the data level. The owners of data are wholly responsible to define as well manage the security measures of data. These challenges can be accomplished without relying on the trusting the cloud computing provider [7]. Our proposed approach will use the various techniques for encryption and decryption. To overcome the management and computational expenses, the strategy of access control fulfillment and the symmetric key sharing of the data that are encrypted is accomplished by a well organized manner is based on the CRT. The enhancement of the security on the cloud paradigm to the data, the owner's should use a standard key itself to encrypt the data and then attach the unique key in a solid manner to the encrypted data. So that only the valid credential users can access the data with associated key. Moreover, the privacy can also be enhanced by keeping the authorized users identities hidden from the cloud computing provider [7]. Furthermore, secure search potential is the fundamental part on the encrypted data of our proposed solution as well as the necessary security standards, like the integrity etc. also attached to the data that are encrypted that will create the a secure file container, known as the DCS file [7]. This will allow only the valid user's for both to access as well as search the Data centric security file When we look about the traditional techniques of protecting data, the security is provided to the server side that stores the data. To protect the data on clod the strategies are controlled by the server of the admin. Hence this way of approach is known as a system-centric approach that is not worthy to protect the consumer's important data on the untrusted cloud paradigm. Another approach known as the DCS is predicted to be worthy fruitful for the services of cloud. The data centric security shows in common that the protection focus of the security is throughout the data and that kind of terminology can be used differently. In the cloud paradigm, the primary approach of DCS is to protect the data from inside, which will provide the best security of data at any level life time where the actual data is stored [9]. As we know that the virtual storage devices are storing the data of consumer's in the cloud computing. The Models Software As A Service and Data As A Service, customers only own the stored data and the cloud service providers own all the software and hardware that are involved itself for processing and storing in data are totally owned by the service providers. In other public models such as the IaaS and PaaS models, the consumer's also own the software applications for handling their data. So, therefore, the most valuable asset of the customer is their data, that may contain their sensitive information. The old security concept usually focuses about devices as well as the technologies used to hold and control the data. This traditional opinion is considered to be very harder by providing the accurate security for the secret as well as the sensitive data [10]. The research community of cloud focuses by securing the virtual machines as well as the operating systems that are hosting the services of cloud computing. There are various security standards based on the old security thought that focuses either virtual machine centric or system centric. All of the security thoughts are based on the concept of TC (Trusted Computing). The focus of the trusted computing group is to develop the standards, so as to make the consumers data as well as the software applications in a way that will be fully hidden from the admin of the cloud. The technology standards based on the trusted computing group provides the tools that are used by the cloud consumers to monitor their data location on the cloud. The focus of Trusted Computing Group and Trust Platform Module tools are not by providing the cloud consumers with the desired security & privacy control for their data. Instead, the concept of TC allows the customers to monitor the operations [4]. Various researchers have suggested the DCS Approach for overcoming the security related issues of data on cloud by switch the focus of securing customer's data to the data itself. However, in the concept of DCS approach there are numerous ideas regarding that how it can be applied to the cloud computing paradigm. In the DCS approach, security of data solutions for the cloud computing can be grouped on two standards: • The first standard of classification is based on level by which the security is provided at. • The second standard of classification is based on who is responsible for providing the security. The security function levels that can be provided in relation of data are illustrated in Figure 1.3. In simple term, the focuses of explanation for providing the security from outside the data level are arranged as the SCS (systemcentric security). And if the focus of solution is on a particular level, they will be classified as per their specific level. In Figure 1.2, the levels shown in the figure are average levels. However there can be extra or lesser levels based on the actual requirements and implementation in a practical system [7]. The Securing issue of data is involved itself from the cloud computing system by the adoption of the Data centric concept. Various studies of the Data Centric Security is based on providing the security features from within the data However, the DCS depends on TC technologies for assessing the environment trustworthiness hence the data security requires an outside data security measure. The data centric view of securing and preserving privacy of data in the cloud is presented by providing data confidentiality and privacy access to the data using either the TC approach or the DCS approach where data are self-protected. Although, when the data have been transferred on the cloud computing paradigm the researchers are not agreed regarding that who is responsible, i.e., the owner of data, the third party or the provider, for the data security maintenance under the concept of data centric security.

CONCLUSION
The main important problem of cloud computing paradigm is the security of data. Various security standards are used to ensure the security of data on the cloud. In this paper we have proposed the DCS approach to enhance the security as well as the privacy of Consumers data that are stored in a cloud computing environment systems. Our paper proposes the framework to the cloud paradigm by applying the Data centric approach to improve the security as well as the privacy of data. In our proposed approach data is encrypted before it is outsourced to the cloud and creates a file known as DCS file that retrains the encrypted data and only the valid users can access the DCS file. Hence, the data remains secure against possible security risks from inside and outside the cloud environment even against possible malicious actions by the cloud provider itself.

ACKNOWLEDGEMENT
First and Foremost, Praises and Thanks To Almighty Allah. The satisfaction that accompanies that the successful completion of any task would be incomplete without the mention of people whose ceaseless cooperation made it possible, whose constant guidance and encouragement crown all efforts with success. I am grateful to my guide Dr. Pratima Gautam for the guidance, inspiration and constructive suggestions that helped me in the preparation of this progress report. I am extremely thankful to my parents and my Wife who have been always a source of inspiration for their never-ending support and love throughout the completion of this Paper and I am also thankful my friends who have helped in successful completion of the partial report.


hMDAP: A Hybrid Framework for Multi-paradigm Data Analytical Processing on Spark

We propose hMDAP, a hybrid framework for large-scale data analytical processing on Spark, to support multi-paradigm process (incl. OLAP, machine learning, and graph analysis etc.) in distributed environments. The framework features a three-layer data process module and a business process module which controls the former. We will demonstrate the strength of hMDAP by using traffic scenarios in a real world.

INTRODUCTION
Data analysis has become a useful technique to organize, process, and analyze large amounts of data in order to obtain useful knowledge effectively such as hidden patterns, implicit correlations, future trends, customer preferences, valuable business information etc [3]. OLAP (online analytical processing) [6], as a key technology to provide rapid access to data (mostly relational data) for analysis via multidimensional structures, enables users (e.g., analysts, managers, executives etc.) to gain useful knowledge from data in a fast, consistent, interactive accessing way. There are many popular enterprise database management systems for supporting OLAP. For example, Oracle OLAP [8,18] is Oracle's current computing engine for online analytical processing. IBM company based on the DB2 database proposes the IBM DB2 OLAP Server [4,2] which can analyze the relational database quickly and directly. Microsoft also provides SQL Server Analytic Services (SSAS) [19,13] supporting for OLAP to analyze information, tables, and files scattered across multiple databases.
The characteristics of big data is not confined to only volume and velocity; it is also referred by the variety, variability and complexity of the data [11,7]. Due to the volume, variety and velocity at which the data grows, it is extremely difficult for organisations Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.

XXX XXX
c 2017 ACM. ISBN 978-1-4503-2138-9. DOI: XX.XXX/XXXX to process this data for timely and accurate decisions [1]. For this challenge, big data analysis [16] has become a tool to slove the problem. The primary goal of big data analysis is to help companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyze large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence programs [16]. Recently, many techniques have been successfully developped for providing big data analysis in various applications. For example, Oracle Bigdata [14] builds on Hadoop [10] through Oracle Direct Connector connecting Hadoop and Oracle databases. SQL Server 2012 [17] provides the extension service of OLAP and business intelligence on Hadoop to support big data analysis. IBM SmartCloud provides a Hadoop-based analytical software InfoSphere BigInsights [20] which can connect with IBM DB2. However, those existing techniques of big data analysis are mostly based on OLAP which is not effective to process data in various models (e.g., semi-structure [16]), they do not always bring highly accurate analysis due to the variety and variability of big data in a complicated application-for example, the real-time data on the performance of traffic applications or of mobile applications. Besides, how to process big data analysis efficiently is always an important problem when the scale of big data grows exponentially [5].
In this demonstration, we propose a hybrid framework for big data analysis on Apache Spark [12] (a high-performance computing architecture) which builds on HDFS of Hadoop. The framework features a three-layer data process module and a business process module which controls the former. Within this framework, we can support multi-paradigm data process (i.e., a technical connectivity between various disparate process [21]) in order to improve the accuracy of analysis, where various big data analysis techniques (incl. OLAP, machine learning, and graph analysis etc.) are interoperated to process the analysis of various applications of big data (incl. data cube [9], intelligent prediction, and complex network etc.) respectively. Moreover, our proposed framework built on Spark can process large-scale data efficiently. Finally, we implement hMDAP and demonstrate the strength of hMDAP by using traffic scenarios in a real world.

ARCHITECTURE
In Figure 1, we depict the architecture of our framework consisted of four parts: the storage management, the resource scheduling, the query analysis and the business process. In the following sections, we will introduce each part in detail.

Storage management
In Figure 2, there are two parts, the physical storage and the logical storage. The rapid growth of data makes the physical storage of data from single source storage to distributed storage. In order to solve the storage of multi-source data, we adopt the existing distributed file system. In our framework, it is HDFS (Hadoop Distributed File System [10]).
Besides, it products many types of data due to the different needs of applications, such as tables, texts, RCFile(the file type of Hive) and sequence data. In order to use these different types of data, we compose the abstract relational views by designing the metadata with semantics to convert data types to the relational data we can handle.

Resource scheduling
In our framework, the development is based on Spark and the module of the resource scheduling is assigned to Spark. The Figure 3 depicts the resource scheduling in our framework. We use MySQL [?] to query over relational database. The part of MLlib is Spark machine learning library. We call the functions in the library to compute. GraphX is the graph query module of Spark. We user it to query graphs and it provides a possibility to transform the different data formats to graph to query. The module of the query and analysis is located on the top of the framework. It is not only the entrance to provide services, but also provides the standard syntax and semantic specification of multiparadigm data analytical processing. At present, HiveQL is similar to the standard SQL, which is oriented to the classic OLAP task, and does not deal with the query language based on ML analysis and graph data analysis. On the basis of not changing the existing query language syntax standard, we develop a multi paradigm for large data fusion analysis query language expanded of machine learning(ML) and graph analysis.

Query analysis
Our big data analysis and processing of the query language is based on the improvement of the fusion of SQL and HiveQL in multi-paradigm. First of all, we analyze the support of HiveQL and SQL respectively and count the amount of operations which can be supported by the traditional relational algebra model. On the basis of the relational algebra model, we add other necessary operators to construct an extension of the algebraic language model, which can fully support the operation of HiveQL and standard SQL. For the operator with higher complexity, it is split into smaller sub operator or used other methods to optimize it. For the ML analysis, we count the commonly used analytical processing methods, such as classification and clustering, and define the abstract interfaces for common ML analysis processing methods. For the graph analysis processing, we also count the commonly used analytical processing methods, such as the shortest path algorithm, and define the abstract interfaces for them.
In this module, the framework also relates to the implementation of the OLAP on the relational database and ML and graph data processing tasks on the distributed framework. The traditional relational database query optimization method is no longer applicable to this situation. According to the different characteristics of relational storage management query engine and distributed file system of computing engine, we summarize the query information and optimize the performance. Firstly, we investigate the statistical index system used in traditional database and analyze the interaction between each index and the index in the system. Then, for each index in the index system of statistical information, we design efficient and accurate sampling methods to calculate the cost model in query optimization. According to the above statistics, we can also design a storage and maintenance programs which is easy to update and manage. And we may use the cost model in the traditional relational database to design a new cost model which can reflect the query cost of the mixed data. Figure 4 displays the query analysis. The main architectural components of the query analysis are Query and Data Analysis Process Tools(DAP Tools). In the first part, we can query by SQL or the function user defined as specified format. The DAP tools contain classical OLAP, DAP on machine learning and DAP on graph.

Business process
Our framework provides an analysis method for the large scale data analysis process. But in the face of complex business pro- cesses in different fields, we need the domain knowledge and according to the domain knowledge, we can design the multi-paradigm fusion of analysis task. We can draw lessons from the method of service composition in service oriented architecture design.
In this module, we need to do two things: developing a multi paradigm fusion analysis process orchestration language syntax and the complex business process scheduling method. In the first part, we need to analyze the patterns and characteristics of service orchestration language in service oriented architecture design and design an abstract model of the executable process. On the basis of the abstract model, we summarize the basic activities of complex business process analysis. Finally, we define the grammar of the business process. In the semantic, we need to research and analysis the meanings of basic business activities and define the start point,end point and the basic command. In the second part, we need to study and analyze complex business processes in practical applications. Then, we build complex business process models and refine the way to exchange messages in public business processes. After that, we need to control the interaction of each part of the resources through the interaction sequence of messages, achieving a reasonable call for each resource service. We still need to investigate the applicability of existing object-oriented design patterns. For the analysis of complex business process integration model, we design data business processes. We refine the design patterns in complex business processes based on the advantages and principles of existing design patterns.
In the real world, the business process model is complex and it takes a lot of time to analyze. The Figure 5 illustrates the details of the business process in our framework.
The user needs to write the configuration files before he or she submits the query. The format of configuration files are shown in Section 3. When the user submits a query to the framework, the query and analysis module in the framework starts to parse the user's query. This module parses queries according to predefined semantics, such as XML(Extensible Markup Language). The module transforms the user's query to two parts, the query over relational databases and the query in machine learning. We default that the user's query including the query over relational databases and the module determines whether or not to carry out the query in machine learning. We think that when the result of the query over relational databases is null, the framework begins to query in machine learning. After the analysis module, the framework uses the query over relational databases and the information about the databases which is read from the configuration files to query the relational databases. Then, the framework runs the query in machine learning. The input of machine learning is the result of querying by relational databases which the query statement is stored in the configuration files. And the parameters of the machine learning algorithm is also stored in the configuration files. When the framework gets the information of the machine learning algorithm, it starts to  train and calculate and the parameters of the training of the machine learning also comes from the configuration files. Finally, the framework makes a join of the results of two parts.

DEMONSTRATION
In this section, we present the interface of hMDAP based in Javascript, which communicate with the service in Java. We show the screenshot of hMDAP in Figure 6 and the configuration file we mentioned above in Figure 7.
The interface is composed as follows: • Configuration of Machine Learning: it is a text to input the path of the configuration file of the machine learning algorithm, such as parameters. • Configuration of Relation Database: it is a text to input the path of the configuration file of the relational databases, such as the user name. • Results: it is a text to display the results of the background.  And the details of the configuration file is as follows: • configuration: it is the beginning of the configuration file.
• input: it is the training dataset of the machine learning algorithm. • database: it indicates that the input dataset comes from the relational database as following information • url, user, password: they are the parameters to connect to the relational database, the location of the database, the user name and the password of the user. • sql: it is the statement to query the relational database.
• parameter: the contents under this label are the parameters of the machine learning algorithms except the input parameter. • value: a series of these labels are the values of the parameters. • algorithm: it is the name of algorithm. For example, the value of algorithm is KMeans and our framework runs the algorithm named KMeans which is defined in our library. User can customize the algorithm and give the location of the algorithm in this label. Before running the interface, the user should write two configuration files, the configuration of machine learning algorithms as Figure 7 and the configuration of relational databases that the contexts are the parts of <database> in Figure 7. When the user writes two files, he or she should write the paths of the files in the texts on the interface. Then, click the button Run. If the user wants to save the results, he or she clicks the button of Save. If the user don't need the results, he or she clicks the button of Cancel.

CONCLUSION
In this demonstration, we proposed hMDAP, a hybrid framework for large-scale data analytical processing to support multi-paradigm process on Spark. The multi-paradigm processing mechanism of hMDAP can provide the interoperability of data analytical process techniques to process data which might be not effectively handled if we only apply single data analytical process technique. On the other hand, hMDAP takes advantage of the high-performance of Spark in processing large-scale data. We believe that hMDAP provides a new approach to big data analysis in a multi-paradigm way.


extfieldsofstudy:
Physics
Computer Science

text:
Analysis of Artificial Neural Network in Erosion Modeling: A Case Study of Serang Watershed

Erosion modeling is an important measuring tool for both land users and decision makers to evaluate land cultivation and thus it is necessary to have a model to represent the actual reality. Erosion models are a complex model because of uncertainty data with different sources and processing procedures. Artificial neural networks can be relied on for complex and non-linear data processing such as erosion data. The main difficulty in artificial neural network training is the determination of the value of each network input parameters, i.e. hidden layer, momentum, learning rate, momentum, and RMS. This study tested the capability of artificial neural network application in the prediction of erosion risk with some input parameters through multiple simulations to get good classification results. The model was implemented in Serang Watershed, Kulonprogo, Yogyakarta which is one of the critical potential watersheds in Indonesia. The simulation results showed the number of iterations that gave a significant effect on the accuracy compared to other parameters. A small number of iterations can produce good accuracy if the combination of other parameters was right. In this case, one hidden layer was sufficient to produce good accuracy. The highest training accuracy achieved in this study was 99.32%, occurred in ANN 14 simulation with combination of network input parameters of 1 HL; LR 0.01; M 0.5; RMS 0.0001, and the number of iterations of 15000. The ANN training accuracy was not influenced by the number of channels, namely input dataset (erosion factors) as well as data dimensions, rather it was determined by changes in network parameters.

Introduction
Soil erosion is a process of soil displacement from one place to another, which becomes an indicator of declining quality of land, thus affecting the sustainability of agricultural production globally [1][2][3][4]. Prediction of erosion-prone area is important for evaluation of land use and to support decisions in making regulations on land management. The problem is there is high uncertainty of spatial data, greatly affecting the accuracy of prediction result, including erosion prediction. Spatial data is very complex and contains a lot of uncertainty, as evident in the nature, collection, and analysis technique of the data. Problems in the classification of non-linear spatial data with dimension of big data can be solved by artificial neural network approach [5]. The artificial neural network is one machine learning that is widely used in solving various problems of classification and prediction. An artificial neural network is a process of training input data to identify an output pattern, i.e. the target that has been determined by its ability to learn. Study of erosion of artificial neural network has been performed by several researchers by erosion-prone area prediction modeling with good resulting accuracy [6][7]. [8] Perform similar research, which is mapping landslide-prone area using artificial neural network with accuracy of prediction up to 82,92 %. The weakness of artificial neural network is incomprehensible supporting theoretical evidence in selecting a combination of parameter value of ANN, including ANN structure selected to produce effective results, making ANN difficult to apply [8][9]. The current research tested several parameter combinations to produce good accuracy. The algorithm used for execution was backpropagation. There are many artificial neural network algorithms that can be used in the training process. Backpropagation algorithm in multilayer perceptron (MLP) network is one that is widely utilized for complex problems. The previous researcher [10] compare the guided algorithms: Backpropagation, Self-Organizing Mapping and Simulated Annealing using Landsat TM imagery in the classification of land cover conclude that the backpropagation method obtains the most accurate precision compared to other algorithms. Some other researchers also prove that backpropagation algorithm is reliable in the classification of spatial data with good accuracy [8,[11][12][13].
Backpropagation algorithm uses error output to change the loading values backward. To fix this error, forward propagation phase must be performed in advance [14]. In addition to write algorithm selection, network operation is likewise influenced by the pattern of data input and the value of the network parameters, hence that the network can perform the function of generalization and memorization well. In this paper, the focus of study was comparing the combination of various input parameters of artificial neural network to get good classification results.

Method
The entire experiment was performed on Windows 8 (64-bit) operating system with i3 processor and 2 GB RAM. Artificial neural network training was performed on IDRISI using MLP (multilayer perceptron) tool. The training data consisted of 9 layers which are the factors of erosion control, i.e. erosivity (R), erodibility (K), length and slope (LS), land cover management (C), factors supporting land conservation practice (P) and four additional layers of remote sensing data, i.e. SPOT 5 multispectral imagery with four bands/channels that provided different spectral reflectance values for each land cover/land use. The neural network architecture in this modeling is as shown in Figure 1. Total data collected was 53 samples of which 30 samples were used as training data and 23 samples as test data for verification. The experiment conducted with 23 simulations by changing the values of network parameters, i.e. learning rate (LR), momentum (M), number of hidden layers (HL), number of iterations (I) and RMS Error (Table 1) The training results were evaluated using an overall accuracy calculation. One that is widely used in accuracy calculation of the classification is by preparing classification error matrix. The correlation between known reference data and classification results can be compared with error matrix. Overall accuracy was computed by dividing the number of right pixels (the number of main diagonal) with the total number of pixels in the error matrix [15].    Table 2 shows the highest training accuracy achieved in this study was 99.32%, occurred in ANN 14 simulation with combination of network input parameters of 1 HL; LR 0.01; M 0.5; RMS 0.0001, and the number of iterations of 15000. The previous researcher [16] have proved the accuracy of training reaches 91.9% when the iterations at 15000. The experimental results showed there was a simulation with low accuracy and some simulations that cannot be executed by artificial neural network because the combination of network input parameters was not considered ideal in the simulations ( Table 2)  Table 2 shows ANN 4 resulted in an error or "out of memory" whose results were not as expected. This condition occurred in the simulation with 2 HL, but cannot be said that only the hidden layer that affected on accuracy. The following are the effects of each network parameter against the level of accuracy. The first parameter, i.e. iteration, was the parameter that had great effect on accuracy. It can be seen from multiple simulations that had the same network parameter values, only differed in the value of iterations (Table 3)  Table 3 shows overtraining occurs in ANN 8 and ANN 9 simulations. ANN 8 with 5000 iterations had reached 98.58% accuracy, but when added by iterations in ANN 9 to 15000, the accuracy value was decreased. Basically, there was no provision or previous study mentioned the best number of iterations. The initial determination of the number of iterations will be a reference for the next simulation only when the first simulation is carried out. Thus, the thing to be considered when determining the number of iterations is the network is not stuck in overtraining, rather than what are the numbers of iteration that must be carried out in every training. Because the data pattern in each 5

1234567890
The case was different, the ideal treatment or artificial neural network architecture will also be different.
Overall, from 23 simulations that were carried out, the best iteration was 15000 since it occurred in some simulations. The optimal number of iterations was also strongly affected by the combination with other parameters, such as momentum and RMS [13]. The simulations had been carried out with two different momentum values tested in different iterations (Table 4). The changes of iteration carried out in the values of momentum in 0.5 and 0.9 showed M value = 0.5 higher accuracy by the addition of the number of iterations. The researcher performed the addition because of seeing the pattern of increased accuracy when the number of iterations added by the assumption of low accuracy because of the number of training was less (under training). An overview of accuracy changes at each iteration with the values of M = 0.5 and M = 0.9 is presented in Figure 2. The second parameter, i.e. learning rate, affected on the intensity of the training process. Learning rate becomes a problem when the number of training is too much, so that the system is not able to generalize well. Learning rate that is too high requires a longer time for continuous training, so that optimal convergence is not achieved [17]. Lower learning rate guarantees a decrease in gradient, but can increase the number of iterations. In the simulations carried out in the study, the most optimal learning rates producing the best accuracy was 0.001 and 0.01, as on previous research conducted by [13]. The correlation with accuracy and time was inversely related. The lower the value of learning rate, the higher the accuracy, and the longer the computation time. Otherwise, the higher the value of learning rate, the lower the accuracy, and the faster the computation time. The value of learning rate that was higher had been tested in the simulation conducted at ANN 7, i.e. 0.4 (Table 2), but the results of training cannot be executed. In contrast to those produced by [16], learning rate of 0.4 produces an accuracy of 75%. Generally, the value of LR been ranging from 0.001 to 1 [18]. The study had been attempted with LR 0001; 0.01 was quite optimal to use. Both values have also been proven by [13]. His study can yield the highest accuracy, but also can yield low accuracy if other input parameters are not correct. The third parameter, i.e. hidden layer, functions as a place where algorithm improves the load of input before comes to the output layer. ANN has knowledge rules that are difficult to formulate as seeing how much each of the parameters affect the accuracy, including hidden layer. Some simulations with 1 HL had very good accuracy, but contrarily, some simulations with 2 HL had better accuracy. Some of the following simulation shows the difference in accuracy between 1 HL and 2 HL (Table 5) Based on Table 5 above, the combination of parameters of LR 0.001; M 0.5; RMS 0.0001 would be optimal with 1 HL although changes were made to the number of iterations as in simulations ANN 1 and ANN 4, ANN 3 and ANN 5, ANN 18 and ANN 19. When the momentum parameter and the value of learning rate were changed, the resulting accuracies on some combinations with 2 HL were optimal, and in other combinations with 1 HL were sufficient. These conditions occurred in the simulations ANN 9 and ANN 11, ANN 20, and ANN 21. Overall, the best accuracy occurred with 1 HL. Some simulations with 2 HL achieved the highest accuracy as in ANN 11 and ANN 21, but accuracy with 1 HL on the same simulations and parameters had reached an accuracy of > 90%. So, it can be said that one hidden layer was sufficient to solve the problems with the characteristics of the data in this study.  [14] supports the above facts that 1 HL has been already quite optimized to use, because the more the number of HL, the lower the computing process; even in some cases, the computer cannot process them as in ANN 4 and ANN 5. The fourth parameter, i.e. momentum, functions to accelerate the convergence of the algorithm by forcing the load change process to continue to move, so that not being trapped in a local minimum, resulting in the training process to be faster. However, the training time in this study was not recorded since not been the focus of study. The difference in accuracy with different momentum values is presented in Table 6. Based on the observation on the results of simulations, the high value of learning rate required lower momentum, otherwise low value of learning rate required higher value of momentum to achieve optimal effectiveness and convergence during the training process. It can be seen from the simulation results of ANN 6, ANN 20, and ANN 21, in which the momentum, value of 0.9 in learning rate value of 0.001 was more optimal than the simulation using a momentum value of 0.5. While [11] give a rather good accuracy on the momentum value of 0.5.
The fifth parameter that affected the network was root mean square (RMS) error. RMS is the root mean square that occurs between the network output and target output. This study used two RMS values in simulations, i.e. 0.001 and 0.0001. From the test results, the highest accuracy on each combination occurred in RMS value of 0.0001. The analysis results of the entire simulation performed showed both the RMS values (0.001 and 0.0001) likely to yield better accuracy if the values of other network parameters (learning rate, hidden layers, iteration) were right through trial-and-error. The comparison of the accuracy, resulting in both RMS values is presented in the following Table 7  The number of iterations has a correlation with the value of the RMS. The small RMS error requires many iterations. Otherwise, the great RMS error requires fewer iterations, as proven by [13]. The same conclusion obtained in this study as described in the graphic of the results of simulations ANN 1 and ANN 2 (Figure 3). Test results on 23 simulations showed the highest accuracy ANN 14 simulation with overall accuracy reaching 99,32 %. This resulting accuracy is higher than that produced by [19] in the land use classification using artificial neural networks which resulted in the training accuracy by 95%. An image of classificationof ANN 14 is shown in Figure 4 where distribution of erosion class in the research site was dominated by the following erosion classes, namely very severe erosion (34.22%) spreading across most of the area of Kokap Sub-district, Girimulyo Sub-district, and some of the area of Pengasih Sub-district; severe erosion (20.75%) spreading all over Panjatan Sub-district, Pengasih Sub-district, and Nanggulan Sub-district; moderate erosion (29.78%) spreading across Pengasih Subdistrict, some of the area in Wates Sub-district, and Panjatan Sub-district; as well as slight erosion (10.81 %) and very slight erosion (4.46%) spreading all over Temon Sub-district and Wates Subdistrict. The percentages of distribution for each erosion class are shown in the histogram presented in

Conclusion
The artificial neural network is effectively used for the classification of spatial data, although trained by the small number of samples. The complexity of data input can be understood well by the network while training with the accuracy of 99.32%. The most influential parameter in this study was the number of iterations although generally, the right combination among network parameters was also an important concern. The parameter of hidden layer did not have a significant effect on the level of accuracy.