16597693
On Conducting a Decision Group to Construct Semantic Decision Tables
Semantic Decision Table (SDT) was introduced to support collaborative decision making. Semantics, a group of decision makers, mutual understanding and collaborative environment(s) are at the heart of SDT. In practice, it is rather difficult to construct SDT within a decision group. We try to tackle this problem by modeling SDT within the context of information system, which is based on McGrath's Conceptual Framework for the Study of Group. Ontologies and DOGMA (Developing Ontology-Grounded Methods and Applications) approach to ontology engineering are proposed to store the semantics and mutual understanding for SDT. In this paper, we also propose a generic method of guiding a group to construct SDT, which is illustrated with an example of online customer management in the privacy domain.
32294667
Use Semantic Decision Tables to Improve Meaning Evolution Support Systems
Meaning Evolution Support Systems have been recently introduced as a real-time, scalable, community-basedcooperative systems to support the ontology evolution. In this paper, we intend to address the problems of accuracyand effectivenessin Meaning Evolution Support Systems in general. We use Semantic Decision Tables to tackle these problems. A Semantic Decision Table separates general decision rules from the processes, bootstraps policies and template dependencies in the whole system. Recently, DOGMA-MESS (" D eveloping O ntology G rounded M ethodology and A pplications" framework based " M eaning E volution S upport S ystems") is developed at VUB STARLab as a collection of meaning evolution support systems. We embed Semantic Decision Tables in DOGMA-MESS to illustrate our approach. Semantic Decision Tables play the roles in both top-downand bottom-upprocesses of the meaning evolution cycle. The decision rules that consist of templates dependency rules are mainly responsible for the top-down process execution. The bottom-up process execution relies on the ones that contain the concept lifting algorithms.
35426902
Towards Ontological Commitments with Omega -RIDL Markup Language
In the DOGMA (Developing Ontology-Grounded Methods and Applications) ontology engineering approach, ontology construction starts from an uninterpreted base of elementary fact types, called lexons, which are mined from linguistic descriptions. Applications that ontologically commit to such a lexon base are assigned a formal semantics by mapping the application symbols to paths in this lexon base. Besides specifying which concepts are used, we restrict how they may be used and queried with semantic constraints, or rules, based on the fact-based database modeling method NIAM/ORM. Such ontological commitments are specified in the Ω-RIDL1 language. In this paper we present the Ω -RIDL Markup Language and illustrate with a case from the field of Human Resources Management.
1436959
Towards Semantically Grounded Decision Rules Using ORM +
Recently, ontologies are proposed for many purposes to assist decision making, such as representing terminology and categorizing information. Current ontology-based decision support systems mainly contain semantically rich decision rules. In order to ground the semantics, we formalize those rules by committing them to domain ontologies. Those semantically grounded decision rules can represent the semantics precisely, thus improve the functionalities of many available rule engines. We model and visualize the rules by means of a novel extension of ORM. These rules are further stored in an XML-based markup language, ORM+ ML, which is a hybrid language of Rule-ML and ORM-ML. We demonstrate in the field of on-line customer management.
2555681
Judicial Support Systems: Ideas for a Privacy Ontology-Based Case Analyzer
Nowadays, ontology is applied as an integral part of many applications in several domains, especially in the world of law. The ontology based judicial support system is believed as a useful tool to support, for example, the legal argumentation assistant and legal decision taking in court. The privacy case analyzer is considered as one of the most interesting applications of ontology based privacy judicial support systems. The efficiency of privacy case analyzers depend on several factors such as how to tackle the problem of linking cases to legislations, how to imply the guidance of privacy principles, and how to improve the extraction of cases. This paper addresses those items and describes the research issues that will be investigated challenges of ontology based judicial support systems.
16344510
On Constructing, Grouping and Using Topical Ontology for Semantic Matching
An ontology topic is used to group concepts from different contexts (or even from different domain ontologies). This paper presents a pattern-driven modeling methodology for constructing and grouping topics in an ontology (PAD-ON methodology), which is used for matching similarities between competences in the human resource management (HRM) domain. The methodology is supported by a tool called PAD-ON. This paper demonstrates our recent achievement in the work from the EC Prolix project. The paper approach is applied to the training processes at British Telecom as the test bed.
38545491
Defining Re-usable Composite Aspect Patterns: An FDAF Based Approach
Architecting secure systems is an important and challenging problem. Solutions to model individual, or atomic, security capabilities have been proposed, including security patterns, component based, aspect-oriented, and service-oriented approaches. However, little work is available on how to model reusable compositions of security capabilities, where the security capabilities interact with each other and other parts of the system design. Here, an aspect-oriented approach to modeling composite aspects is presented. The approach is defined as an extension to the Formal Design Analysis Framework (FDAF). The FDAF metamodel is extended to support the static representation of composite aspects and an approach to defining the compositions is introduced. A composite aspect that provides Account Lockout with Selective Event Logging (ALSEL) capabilities is used as an example.
15542014
Efficient checkpointing of java software using context-sensitive capture and replay
Checkpointing and replaying is an attractive technique that has been used widely at the operating/runtime system level to provide fault tolerance. Applying such a technique at the application level can benefit a range of software engineering tasks such as testing of long-running programs, automated debugging, and dynamic slicing. We propose a checkpointing/replaying technique for Java that operates purely at the language level, without the need for JVM-level or OS-level support. At the core of our approach are static analyses that select, at certain program points, a safe subset of the program state to capture and replay. Irrelevant statements before the checkpoint are eliminated using control-dependence-based slicing; the remaining statements together with the captured run-time values are used to indirectly recreate the call stack of the original program at the checkpoint. At the checkpoint itself and at certain subsequent program points, the replaying version restores parts of the program state that are necessary for execution of the surrounding method. Our experimental studies indicate that the proposed static and dynamic analyses have the potential to reduce significantly the execution time for replaying, with low run-time overhead for checkpointing.
14166643
3D Model Simplification Method Based on ROI
Based on edge-collapse algorithm and ROI (region of interest), an effective 3D mesh simplification method is proposed. During the simplification procedure, this method could successfully preserve a high resolution for the 3D part that the user is interested in while the rest parts could meet the basic requirement with lower resolution. The experiments support the conclusion.
29100810
An Expert System Based Approach to Modeling and Selecting Requirement Engineering Techniques
The importance of requirements engineering (RE) has been raised numerous times in literatures. To choose suitable RE techniques for a particular project in a given situation is a challenging task, requiring substantial expertise and efforts. To help solving this problem, an expert system based approach is proposed. This expert system uses the knowledge from domain experts to model the causal factors of RE techniques. It can select suitable RE techniques for a software project. A web-based questionnaire is created in the first place to collect the expertise available in the community. The information collected by the questionnaire is analyzed and transformed into a new dataset for constructing a Bayesian Belief Network (BBN). The resulting BBN integrated with a GUI forms an expert system for RE techniques modeling and selection. Empirical study validates the transformed dataset and shows that the expert system outperforms other predictors in selecting suitable RE techniques in different RE phases.
22140835
The Algorithm of R Peak Detection in ECG Based on Empirical Mode Decomposition
A novel automatic R peak detection algorithm that is based on EMD (empirical mode decomposition) and adaptive threshold technique is presented. The R peaks are then identified by computing the first threshold and applying a set of adaptive thresholds that are not limited to a strict range. The proposed algorithm was able to detect R peak with high sensitivity when compared to other algorithms used in Physionet ECG database. Additionally, the new algorithm can successfully detect R peak for a wide variety of ECG shapes.