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identification, classification, problem, context, solution, role, artifact, relationship, guidance, management and evaluation [12]. In addition, these are differently covered by process patterns descriptions as it is shown in Fig. 2 most of them pay attention to the four main facets: context, solution, problem and relationships (15%, 14% and 13%). The lack of terminological consent refers to the problems of polysemy and synonymy addressed in labels used to describe patterns. Indeed, we find terms such as Consequences used to express a Resulting Context in PPL as well as a Guideline in Gnatz. Moreover, others different terms are being used to address the same concept such as Intention in RHODES to describe a pattern Problem, instead, the term Intent is used in Störrle. B. Unified Conceptualization of Process Patterns To overcome these lacks, a first step was to create a unified conceptualization of process patterns. Mappings efforts [12] were necessary to achieve this goal leading to a process patterns’ meta-model unifying patterns knowledge representations. Fig. 3 provides an abstract view of the meta-model structure in which we consider a process pattern information description from six facets: The identification facet encapsulates a set of properties identifying a pa ttern such as pattern name, author(s), keywords, pattern’s classification (type, category, abstraction level, and aspect) as well as pattern origin (project and participants) and pattern artifacts (used and/or produced). The core information is the main pattern facet embodying details about the well-known triplet: problem, context and solution. The relationships facet expresses how a pattern could interact with other patterns (e.g. similar patterns, refinement patterns, subsequent patterns, and antipatterns) The guidance facet refers to the support level provided by a pattern to be comprehended and used (e.g. known uses, example, literature, illustration, etc.) The evaluation facet provides feedbacks on pattern application (e.g. discussion, confidence, maturity, etc.) The management facet provides general information about a given pattern (e.g. version, creation-date) In order to validate the proposed meta-model, we adopted an ontology-based approach providing common and shared architecture and terminology for better patterns’ capitalization and reuse. The proposed ontology [12], named MetaProPOS and acronym for Meta Process Patterns’ Ontology for Software Development, aims to unify all th e proposed process patterns descriptions, providing thus semantic interconnection and composition of relevant and unified bodies of patterns knowledge. Artifact 4% Role 6% Guidance 11% Relationship 13% Identification 11% Figure 2. Architectural dissent in patterns descriptions [12]. Figure 3. Evaluation 3% Management 2% Abstract view of a unified process pattern. Because of space limitations, we could not give details of all the modeled facets. So we choose to illustrate this work by the relationships facet which is quiet important for the pattern reuse process. Fig. 4 shows the relationship hierarchy considered by the proposed meta-model. An alternative pattern is a pattern that could be applied as an alternative solution which can be different from a similar pattern. A refinement pattern should give details to an a bstract one. A consequent pattern is a pattern that is implied or that could be applied after a given pattern. An anti-pattern is a pattern that could not be applied with another given pattern. Figure 4. The relationship’s facet. Problem 13% Classification 8% Context 15% Solution 14% 507
IV. SCATTER In order to enhance patterns capitalization and reuse, a first step in this direction was to build up a unified conceptualization of process patterns via the proposed ontology MetaProPOS, as it is stated before. The next step is to demonstrate how it can be performed to fulfill our goal through the proposed approach SCATTER which, as the name implies, aims to help improve process patterns’ knowledge dissemination by means of a formal and semantic technique of patterns’ capitalization and reuse. As illustrated by Fig. 5, SCATTER comprises two major processes: A. Terminological, Semantic and Architectural Unification Process Given different collections of process patterns forming a patterns’ corpus, this main process consists of a tr iple unification effort. 1) Terminological unification: It aims to map between terms used as labels for a given pattern and the corresponding meta labels i n the proposed meta model. This is ensured by means of MetaProPOS on the one hand, and a text mining tool on other. The purpose of this phase is to recogn ize the terminology employed by a given pattern through key terms extraction. The unification’s result is an annotated pattern whose format is XM L. In order to reach th is target, we adopted a text mining approach. Indeed, there are already tools that are well recognized for their mastery in Natural Language Processing (NLP) namely: Open NLP [25], UIMA (Unstructured Information Management Architecture [26] and GATE (General Architecture for Text Engineering) [27]. Thus, we do not need to reinvent the wheel by rebuilding one from the scratch. This is why we choose to reuse, among these latter, GATE since it is open source and very well documented as well as used in research and industry. In this regard it should be n oted that the terminological unification phase is performed using the information extraction open source component of GATE which is ANNIE acronym for A Nearly- New Information Extraction system [28]. We should notice that in addition to GATE configuration, we have made a considerable extension to ANNIE’s system by adding Gazetteer lists such as “problem.lst”, “context.lst”, “relationship.lst and so on in order to help ANNIE’s system recognize key terms and concepts used in patterns’ descriptions. However, the use of these lists is necessary but insufficient to detect patterns segments, for this reason we added Jape rules such as “identification.jape” to capture the pattern’s identification facet in a pattern description, “guidance.jape” to identify the pattern’s guidelines, etc. As it is illustrated by Fig. 6, we used in a first step, the Sentence Splitter and the Tokeniser to perform a morphological analysis of unstructured patterns allowing the extraction of sentences and basic entities. Then in a next step, the POS Tagger is p erformed to associate grammatical category to tokens allowing thus recognition of various entities. The last step consists in patterns tagging by extracting pattern concepts. Ambler pattern PPDL pattern MetaProPOS Ontology WolF MetaProPOS Ontology Figure 5. P-Sigma pattern PROPEL pattern Converted patterns Format: .owl Terminological, Semantic and Architectural Unifications WordNet Similar Patterns Patterns’ corpus Störrle pattern UML-PP pattern Unified Form Patterns Warehouse (patterns + annotations) Patterns Mining Mega Patterns Gnatz pattern RHODES pattern Unified Architecture + Unified Terminology + Semantic Annotations Anti- Patterns Functional architecture of SCATTER. PROMENADE pattern PLMLx pattern Java code + JENA+ ANNIE + JAPE Rules Java code + Mining tool This step has recourse to previously generated results and uses the NE Transducer to extract named pattern’s entities (problem, context, solution, relationship, etc.) through new JAPE rules and Gazetteer lists. The components implied to achieve these steps are described as follows: a) Tokeniser: This component identifies various symbols in text documents (punctuation, numbers, symbols and different types). It applies basic rules to input text to identify textual objects. 508
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SEKE San Francisco Bay July 1-3 201
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Copyright © 2012 by Knowledge Syst
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The 24 th International Conference
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Zhenyu Chen, Nanjing University, Ch
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Riccardo Martoglia, University of M
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Poster/Demo Sessions Co-Chairs Fars
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Evaluating the Cost-Effectiveness o
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Program Understanding Evaluating Op
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An Empirical Study of Execution-Dat
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Computer Forensics: Toward the Cons
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Modeling Bridging KDM and ASTM for
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A Mapping Study on Software Product
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A proposal for the improvement of t
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Panel on Future Trends of Software
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een available for analysis. Social
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The rest of this paper is orga nize
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Now we assume is given, we first pr
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“iphone” and “blackberry” a
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nique estimate the impact set based
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Figure 1. An example to illustrate
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trategy, the results should support
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Figure 1. System Architecture would
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Figure 2. Sequence Diagram of Train
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deployed on Android phone and runs
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the ontology information is only us
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Both kinds of axioms lead to an inh
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engineer and achieving requirements
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elicitation and the act ivities con
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Step 2. Step 3. 1.2. Use of Procedu
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Figure 6.b. Task: Cognitive Analysi
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original User’s Discourse / Speec
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II. RELATED WORK Business processes
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test, we did not find any significa
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For the indicator “requirements a
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the model checkers SPIN [8] and NuS
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allow for the presentation of syste
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the requirement or design phases sh
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using the CR estimates against the
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Data Set TABLE II. DATA SETS Artifa
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Figure 4. Relative Error in the Est
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Take basic requirements from user a
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sent Semantic Web Ontologies. It co
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The rest of the paper is organized
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and the risk level. Existing assess
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diagram could give developers an in
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Figure 2. Co
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• Sensor(Lexical): at the top and
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An Overview of the RSLingo Approach
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Figure 2. Overview of the RSLingo a
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DETECTING EMERGENT BEHAVIOR IN DIST
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Definition 2: For a set of MSCs M,
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Stability of Filter-Based Feature S
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Algorithm 1: Threshold-Based Featur
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MI KS GM AUC PRC S2N RUS35 RUS50 RO
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80
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82
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84
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86
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Cloud Application Resource Mapping
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Fig. 1. UML Collaboration Diagram m
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Fig. 2. UML Activity Diagram modeli
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An Empirical Study of Software Metr
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TABLE I SOFTWARE DATASETS CHARACTER
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1) Classifiers: In this study, soft
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Progressive Clustering with Learned
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IV. DATA SOURCES We have been worki
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meta-data we assign to indicate sig
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Multi-Objective Optimization of Fuz
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uilding FNNs that satisfy different
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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test duration(s) for the scenario(s
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which are composed by the name and
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A Catalog of Patterns for Concept L
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assisted solution for lattice analy
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The complete lattice of the class s
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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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Fig. 6. CPU usage percentage of pre
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may introduce the extra learning cu
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[9] but no validation is provided i
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One of the st udents from Group B t
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scribed by the equation: where η i
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4 Model Checking DPN We use HyTech
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Recommender systems are usually cla
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such as one week and gradually dete
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•
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146
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Enforcing Contracts for Aspect-orie
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Table 1. Behavioral Rules in LSD [9
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1 public privileged aspect Update {
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Towards More Generic Aspect-Oriente
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vals must be denoted by some notion
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Aspect-Orientation in the Developme
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Table II SELECTED PRIMARY STUDIES #
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Evaluating Open Source Reverse Engi
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Table 1. Candidate reverse engineer
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Some tools may perform the needed f
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Coordination Model to Support Visua
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this CMV approach we employee three
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colored according to the Gradient T
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Improving Program Comprehension in
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The method used to support and eval
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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An Approach for Software Component
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properties. In particular, the foll
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Conference Dataset Anatomy Dataset
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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B. Monitoring Static Method Invocat
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undle which is responsible for pars
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An Exploratory Study of One-Use and
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Figure 1. Mindmap of Reuse Design P
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subject also caused outliers for re
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Choosing licenses in free open sour
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The important aspect is to provide
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that holds a model of each software
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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[10] N. Gunther, “Hit-and-run tac
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Section 5 d iscusses some related w
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model and the analysis results. The
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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(a) printtok (b) printtok2 (c) sche
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Gupta extended HGS and proposed the
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manage intellectual knowledge with
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D. Manage Training When any trainin
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TABLE II. COMPARISON BETWEEN THE EX
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B. Trace Semantics of DAP Fig. 1. A
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∀ [DR ′ ] []gen [; ] [D]□ [;
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transformation idea in MDA [13] . T
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MappingRule MappingTGtoHP { [Rule I
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executing a continuous transition,
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protocol of DATS we used is properl
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in Boolean search and reasoning has
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to be broken by expelling one of it
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where, among other things: 1. The t
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A never-ending language learner cal
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4. i 2 Learning(DEC, mex) via Bias
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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In addition to the rules, the knowl
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As described in the previous sectio
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the ontology hierarchy; learning a
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three different learning techniques
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TABLE IX. USING SVM FOR LEARNING tr
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Siemens set contains seven C progra
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The program tcas is not included be
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esults. To be more specific, they w
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According to IEEE [10], regression
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(a) Volatility (b) Complexity (c) R
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It mimics the test engineer knowled
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integrate the test results. • Aut
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No tify the cloud controller to get
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of execution-data classification. T
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Figure 1. Boxplots of average AUC r
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anches is more applicable than the
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To validate the proposed approach,
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contains broadcast of Subject to Ob
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S 1a=1; S 2b=5; cobegin { S 3read
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.
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298
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A functionality is the ability of a
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level); in UML/J2EE technology [11]
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• to provide a unified catalogue
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flexibility they provide to model s
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complete in zero-time, and thus hav
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figure assumes the experiment with
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could be a practical way of dealing
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and (iv) the reduced amount of line
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III.BUSINESS RULES FRAMEWORK FOR KN
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explanation for why the program wan
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320
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322
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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sequence. Each t i θ i in the firi
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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High-level Petri net Place Place Ty
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verification and thus is often limi
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(r 1 r 2 ...r k ) 1 denotes Path [r
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Fig. 3. Example a 3-compound weakly
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364
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366
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368
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necessary before verification. In t
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CTL formula as: AG (( jp EX( jp ad
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equirements level, like EA-Miner [1
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376
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378
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380
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II. RESOURCE-ORIENTED WORKFLOW MODE
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eventually triggers a co mmon task.
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Input: A resource oriented workflow
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access control exists, the action i
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(ACCDA), is customized to address a
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ICrudSchema and shown in Figure 3.
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Connectors for Secure Software Arch
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Non-repudiation security service pr
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anAsynchronousCustomer InterfaceCon
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How Social Network APIs Have Ended
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OSN Social Feed Unique Features Con
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users can control the reach of thei
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Computer Forensics: Toward the Cons
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In 2009, Cohen et al. in [4] have o
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dcterms:contributor, dcterms:creato
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A Holistic Approach to Software Tra
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Figure 1. Figure 1 shows an overvie
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B. Case study in a proprietary proj
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Pointcut Design with AODL Saqib iqb
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system attributes, methods and exec
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Feature modeling and Verification b
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hasOptionalPart hasPart hasMandato
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A Context Ontology Model for Pervas
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Figure 1. UML view of the model. Th
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Figure 3. Architecture for deliveri
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Ontology-based Representation of Si
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Figure 2. Upper Ontology fragment f
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I2Sim simulation model was transfor
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An ontology-based approach for stor
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fying the equivalences between conc
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• Homonymy conflicts: occur when
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Automatic Generation of Architectur
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we dealt w ith the verticals rules.
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Towards Architectural Evolution thr
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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Table 2. Impact set of C1,C2,C5 cha
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Page 487 and 488: Forecasting Fault Events in Power D
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Page 493 and 494: Testing Interoperability Security P
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Page 515 and 516: A Tiny Specification Metalanguage W
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Page 525 and 526: Figure 1. The process for engineeri
Page 527 and 528: Figure 4. The abstractions extracte
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Page 531 and 532: Algorithm 1: Algorithm to recursive
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Page 535: A. Pattern Reuse In patterns reuse
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Page 543 and 544: DC2AP Element and their Application
Page 545 and 546: 21. Consequences Example of use DC2
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Page 549 and 550: 2.3.1 Complementarity of MDD techno
Page 551 and 552: 3.2.2 EAggregateRelationship The EA
Page 553 and 554: cess. The proposal follows the ADM
Page 555 and 556: II. OVERVIEW OF MODAL TRANSITION SY
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Page 559 and 560: Suppose P = 〈S P , SP i , AI P ,
Page 561 and 562: Adaptive software should basically
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Page 567 and 568: Figure 1, a metamodel defines an XM
Page 569 and 570: Figure 3 is a feature diagram that
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Page 583 and 584: C. Dependent Variables and Measures
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Step 1: To start the process, the u
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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obtained from the categorization of
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Following the experimental methodol
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About the category “Improvements
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Identification Guidelines for the D
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created according to the preview re
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In our analysis, the “Government
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In the following, we present the re
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USE SCENARIO The application Vuelos
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[8] Mayhew, D., “The Usability En
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Interaction State Set Structure Dat
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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PROMETHEE methods [3] belong to a f
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comparison rule, a value of 1/9 is
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TABLE V: Supermatrix for the exampl
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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Table III SPEARMAN’S CORRELATION
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A Mapping Study on Software Product
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most relevant sources in software e
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TABLE IV TOOL’S CLASSIFICATION AC
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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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Figure 3: PlugSPL Feature Model Edi
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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It means that throughout the develo
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B. Instrumentation The experiment w
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• External Validity: W e have con
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II. THE PROPOSED TAXONOMY The taxon
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sub-dimension is categorized as, co
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A Proposal of Reference Architectur
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Figure. 1. Reference Architecture M
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A Variability Management Method for
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C. Correctness of configuration fil
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means patterns which are shown in s
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Tool Support for Anomaly Detection
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Figure 1. System overview of the SD
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Once the datasets were evaluated us
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Reconfiguration of Robot Applicatio
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data dependencies required for keep
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Spacemaker: Practical Formal Synthe
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Fig. 1. The ecommerce object model.
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Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
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software, based on revised requirem
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A Process-Based Approach to Improvi
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Appropriate processes m ust support
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Figure 2. Reordered layers of the k
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Automatic Acquisition of isA Relati
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III. VALIDATING THE DIMENSION HEADE
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The table title and the dimension s
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A light weight alternative for OLAP
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i.d. subsets of cubes focused on se
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Match production rules Enterprise S
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A Tool for Visualization of a Knowl
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other ontology visualization tools
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shown at Figure 5. Objectives are s
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Rendering UML Activity Diagrams as
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a = O1 → a → O2 b = O2 → b
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ecordProblem → A → reproducePro
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umlTUowl - A Both Generic and Vendo
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The tool’s ability to deal with S
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Elem. UML Example D 12 Harmonizing
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4) Associations: In the first test
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III. GRAPH REPRESENTATION OF CLASS
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as an add-in to popular UML m odeli
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742
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744
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746
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her necessities. However, the progr
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Figure 3. Global Log. of terms. The
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two stages. The first stage is focu
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754
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756
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758
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With the GDUC approach, we can mode
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Figure 6. Three proposals containin
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764
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766
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Proactive Two Way Mobile Advertisem
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information. If more than one adver
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Users can al so publish adv ertisem
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The COIN Platform: Supporting the M
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A-3
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Checking Contracts for AOP using XP
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Maicon B. da Silveira, 112 Aldo Dag
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Seyedehmehrnaz Mireslami, 70 Takao
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Wei Zhang, 422 Wenbo Zhang, 188 Zhi
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Desmond Greer Eric Gregoire Christi
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Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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