6454595
Generating Incremental ETL Processes Automatically
Incremental ETL processes are used for the incremental maintenance of data warehouses, which is generally designed by users with ETL tools. Using existing methods of incremental maintenance of materialized views for reference, we put forward an approach to generate an incremental ETL process automatically from the full ETL process in this paper. Existing researches are focused on the incremental maintenance of materialized views in such circumstances which involve the operators of selection, projection, join and aggregation but difference operators excluded. Since difference operators are used frequently in an ETL process, we first discuss incremental maintenance of materialized views defined with difference operators in detail
8249361
SUDEPHIC: Self-Tuning Density-Based Partitioning and Hierarchical Clustering
Clustering is one of the primary techniques in data mining, for which to find the user expecting result is a major issue. However, to dynamically specify the parameters for clustering algorithms presents an obstacle for users. This paper firstly introduces a novel density-based partitioning and hierarchical algorithm, which makes it easy to employ synthetic feedback mechanism in clustering. Additionally, by investigating into the relation between parameters and the clustering result, we propose a self-tuning technique for the setting of parameters. Meanwhile, the density distribution within a cluster can be expressed in the result for the user to specify the cluster’s feature. The algorithm is both evaluated in theory and practice. It outperforms many existing algorithms both in efficiency and quality.
30881426
Constraint-Based Graph Mining in Large Database
Currently, constraints are increasingly considered as a kind of means of user- or expert-control for filtering those unsatisfied and redundant patterns rapidly during the web mining process. Recent work has highlighted the importance of constraint-based mining paradigm in the context of frequent itemsets, sequences, and many other interesting patterns in large database. However, it is still not clear how to push various constraints systematically into graph mining process. In this paper, we categorize various graph-based constraints into several major classes and develop a framework CabGin (i.e. C onstr a int- b ased G raph M in ing) to push them into mining process by their categories. Non-monotonic aggregates like average also can be pushed into CabGin with minor revision. Experimental results show that CabGin can prunes a large search space effectively by pushing graph-based constraints into mining process.
10409272
Repairing Inconsistent XML Documents
XML document may contain inconsistencies that violate predefined integrity constraints, and there are two basic concepts for this problem: Repair is the data consistent with the integrity constraints, and also minimally differs from the original one. Consistent data is the data common for every possible repair. In this paper, first we give a general constraint model for XML, which can express functional dependencies, keys and multivalued dependencies. Next we provide a repair framework for inconsistent XML document with three basic update operations: node insertion, node deletion and value modification. Following this approach, we introduce the concept of repair for inconsistent XML document, discuss the chase process to generate repairs, and prove some important properties of the chase process. Finally we give a method to obtain the greatest lower bound of all possible repairs, which is sufficient for consistent data.
207162632
Utility-based anonymization for privacy preservation with less information loss
Privacy becomes a more and more serious concern in applications involving microdata. Recently, efficient anonymization has attracted much research work. Most of the previous methods use global recoding, which maps the domains of the quasi-identifier attributes to generalized or changed values. However, global recoding may not always achieve effective anonymization in terms of discernability and query answering accuracy using the anonymized data. Moreover, anonymized data is often used for analysis. As well accepted in many analytical applications, different attributes in a data set may have different utility in the analysis. The utility of attributes has not been considered in the previous methods.
 In this paper, we study the problem of utility-based anonymization. First, we propose a simple framework to specify utility of attributes. The framework covers both numeric and categorical data. Second, we develop two simple yet efficient heuristic local recoding methods for utility-based anonymization. Our extensive performance study using both real data sets and synthetic data sets shows that our methods outperform the state-of-the-art multidimensional global recoding methods in both discernability and query answering accuracy. Furthermore, our utility-based method can boost the quality of analysis using the anonymized data.
14792668
Personalization as a service: the architecture and a case study
Cloud computing has become a hot topic in the IT industry. Great efforts have been made to establish cloud computing platforms for enterprise users, mostly small businesses. However, there are few researches about the impact of cloud computing over individual users. In this paper we focus on how to provide personalized services for individual users in the cloud environment. We argue that a personalized cloud service shall compose of two parts. The client side program records user activities on personal de-vices such as PC. Besides that, the user model is also computed on the client side to avoid server overhead. The cloud side program fetches the user model periodically and adjusts its results accordingly. We build a personalized cloud data search engine prototype to prove our idea.
697583
Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows
MOTIVATION
Typical high-throughput genotyping techniques produce numerous missing calls that confound subsequent analyses, such as disease association studies. Common remedies for this problem include removing affected markers and/or samples or, otherwise, imputing the missing data. On small marker sets imputation is frequently based on a vote of the K-nearest-neighbor (KNN) haplotypes, but this technique is neither practical nor justifiable for large datasets.


RESULTS
We describe a data structure that supports efficient KNN queries over arbitrarily sized, sliding haplotype windows, and evaluate its use for genotype imputation. The performance of our method enables exhaustive exploration over all window sizes and known sites in large (150K, 8.3M) SNP panels. We also compare the accuracy and performance of our methods with competing imputation approaches.


AVAILABILITY
A free open source software package, NPUTE, is available at http://compgen.unc.edu/software, for non-commercial uses.
13205970
Performance analysis of several algorithms for processing joins between textual attributes
Three algorithms for processing joins on attributes of a textual type are presented and analyzed in this paper. Since such joins often involve document collections of very large size, it is very important to find efficient algorithms to process them. The three algorithms differ according to whether the documents themselves or the inverted files on the documents are used to process the join. Our analysis and simulation results indicate that the relative performance of these algorithms depends on the input document collections, the system characteristics and the input query. For each algorithm, the type of input document collection with which the algorithm is likely to perform well is identified.
9745553
Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development
Protein function prediction is one of the central problems in computational biology. We present a novel automated protein structure-based function prediction method using libraries of local residue packing patterns that are common to most proteins in a known functional family. Critical to this approach is the representation of a protein structure as a graph where residue vertices (residue name used as a vertex label) are connected by geometrical proximity edges. The approach employs two steps. First, it uses a fast subgraph mining algorithm to find all occurrences of family-specific labeled subgraphs for all well characterized protein structural and functional families. Second, it queries a new structure for occurrences of a set of motifs characteristic of a known family, using a graph index to speed up Ullman’s subgraph isomorphism algorithm. The confidence of function inference from structure depends on the number of family-specific motifs found in the query structure compared with their distribution in a large non-redundant database of proteins. This method can assign a new structure to a specific functional family in cases where sequence alignments, sequence patterns, structural superposition and active site templates fail to provide accurate annotation.
12735466
Position Based Unequal Error Protection for Image Transmission with Energy Constraint over Multirate XPD MIMO Sensor Networks
Multimedia delivery over Wireless Sensor Networks (WSNs) is energy critical. In this paper, we study an energy constrained cross layer optimization scheme to improve digital image quality in cross polarization discrimination (XPD) based Multiple Input Multiple Output (MIMO) WSNs. The contribution of this research can be summarized in three parts. First, link layer energy consumption and packet loss ratio in multirate XPD based MIMO WSN are modeled. Secondly, XPD based modulation optimization cross link and physical layer is proposed, achieving considerable energy efficiency. Finally, a new Position-Value (P- V) based Unequal Error Protection (UEP) scheme is studied and discussed in XPD MIMO WSNs, with more efficient resource allocation compared to traditional approaches. Simulation results show the proposed modulation optimization scheme improves energy efficiency considerably, and position based UEP achieves significant distortion-energy gain compared with traditional layer based resource allocation schemes in XPD MIMO WSNs.
45342895
Experiment Research on Feature Selection and Learning Method in Keyphrase Extraction
Keyphrases can provide a brief summary of documents. Keyphrase extraction, defined as automatic selection of important phrases within the body of a document, is important in some fields. Generally the keyphrase extraction process is seen as a classification task, where feature selection and learning model are the key problems. In this paper, different shallow features are surveyed and the commonly used learning methods are compared. The experimental results demonstrate that the detailed survey of shallow features plus a simpler method can more enhance the extraction performance.