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from statement sequences. In this scope, KDM is recognized as a way of representing the source code, if only at the execution flow level. 2.2.2 Abstract Syntax Tree Metamodel (ASTM) Figure 2. KDM consists of 12 packages arranged into 4 layers As a complement, ASTM has been developed in accordance with the theory of languages to support the representation of source code. In fact, ASTM is composed of the GASTM (Generic Abstract Syntax Tree Metamodel), a standardized language-independent metamodel and the SASTM (Specific Abstract Syntax Tree Metamodel), a user-defined metamodel closely connected with a particular language (Java, COBOL and so on). 2.2. MDD technologies for modernization The concomitant use of KDM and ASTM requires a clear understanding of their current capabilities. 2.2.1 <strong>Knowledge</strong> Discovery Metamodel (KDM) The architecture of KDM is arranged into four layers, namely the Infrastructure Layer, Program Elements Layer, Runtime Resources Layer and Abstractions Layer (Figure 2). Each layer is dedicated to a particular viewpoint of an application. In this paper we are only interested in the two main layers: the Runtime Resources Layer and the Program Element Layer. These layers allow one to represent the user interfaces, data and code of the legacy application. The KDM Runtime Resources Layer The Runtime Resource Layer is composed of several packages (Data, UI, Event and Platform). However, we will concentrate on the Data and UI packages herewith. The first one is used to represent the organization of persistent data, especially to describe complex data repositories (e.g., record files, relational schemas, ...). Thesecond one is used to represent the structure of user interfaces and the dependencies between them in terms of interactions and sequences. The KDM Program Elements Layer Special attention is paid to the Program Elements Layer, whichis concerned with program-level artifacts. The Program Elements Layer is composed of the Code and Action packages. The Code package represents programming elements as determined by programming languages (data types, procedures, classes, methods, variables, etc.), while the Action package describes the low-level behavior elements of applications, including detailed control and data flows resulting Generic Abstract Syntax Tree Metamodel (GASTM) GASTM enables the representation of the code without any language specificity. GASTM contains all of the common concepts of existing languages in the form of metatypes. Parsing some files means instantiating these metatypes along with creating links in order to model semantic dependencies between text pieces. The goal of GASTM is to provide a basis for SASTM in order to later help users to define the domain-specific features of the code to be parsed. The key achievement is to avoid an unintelligible separation (especially in terms of representations) between the generic and specific characteristics of the code. Besides, the (annotated) distinction in models between generic versus specific parts, is highly valuable at processing time (see below). Specific Abstract Syntax Tree MetaModel (SASTM) SASTM is constructed through metatypes/metarelationships on the top of GASTM. This task is assigned to ASTM practitioners. It first leads to constructing a metamodel from scratch that is compatible with the legacy language/technology to be dealt with. The main goal of SASTM is to represent code peculiarities. Next, parsing the code amounts to distinguishing between generic and specific aspects, and thus instantiating GASTM or SASTM. The formal interrelation between the two metamodels ensures that models (or their respective instances which represent a given business case) are also consistently linked together based on (fully explicit) comprehensive links. 2.3. Realizing modernization OMG provides a set of standard specifications for software modernization but fails to provide a guideline for the practitionners. As such, we experimented on the aforesaid technologies while endeavoring to adhere to the – somewhat idealized – process depicted in Figure 1. 519
2.3.1 Complementarity of MDD technologies The complementarity of KDM and ASTM resides in the possible code level representations and the different operations that can be applied. On one hand, ASTM permits one to represent a given code source at procedure level, in the form of a syntax-tree whose production has involved a userdefined SASTM: code specificities are taken into account. On the other hand, KDM is used to represent the code at flow level (e.g., data inputs, data outputs, sequences). In fact, a flow level representation provides a direct support for flow analysis and the refactoring strategies thereof. In addition, any reverse engineering process relying on KDM models is reusable, whatever the source technology may be. So, ASTM deals with common parsing issues, while KDM deals with another viewpoint; thus creating the link with other facets like user interfaces, components and so on. 2.3.2 Discretization of the process The modernization process proposed in this paper is divided into three codified steps: 1. The first step consists in the abstraction of data, code and user interfaces from the legacy material. This is transformed into several technology-specific models. Practically speaking, for each artifact we define its own parser. The parsing outputs are concrete syntax trees (CSTs). Next, these CSTs are transformed into ASTs, each conforming to predefined SASTM metamodels. Starting from chunks of text and ending up as models, this global process is often called ”Injection” in the MDD jargon. 2. This second step consists in the transformation of SASTM models into KDM models, with respect to user interfaces, data and code packages. The goal of this model transformation is to eliminate all technological specificities of the input code model. For that purpose, a reformulation is sometimes required. 3. The third step consists in transforming the code-related and data-related KDM models into UML models, thus generating models in a widely accepted format with their associated graphical notation. There are no technical difficulties except that of choosing between the (existing) concurrent UML-like Java representations that are tolerated by today’s modeling tools. 2.3.3 Current limitations Our experience has led us to conclude that a modernization project is not a straightforward process, but instead a strongly iterative process, including the re-examination of the models’ parts and their mutual enrichment. We thereby advocate roundtrip capability as to enable information exchange and knowledge propagation within the process, at any level or step. At least two reasons explain this. First, real modernization requires incorporating additional or derived knowledge into models, either automatically or manually. The most prominent examples are the following: • Detection of code patterns. The purpose is to recognize sets of object codes that will facilitate refactorings, especially when targetting a completely new architecture. • Determination of components fate (and the traceability thereof). In accordance with application experts, the purpose is to stamp components that will be migrated and those that will be instead replaced by off-the-shelf components. • Extraction of business rules. The purpose is to gain a better comprehension of the business logic that underlies a huge amount of lines of code. • Multi-view modeling. The purpose is to set up the right semantic relationships between the interrelated views of the system that is being reversed (such as user interface, business code and those data structures used for persistence). Secondly, the strict discretization of the modernizing process envisioned by OMG is not realistic when aiming at providing a modernization CASE tool with a good user-experience level. Indeed, the way a user perceives the computer-aided modernization is an important question, from a tooling viewpoint. Typically, the new knowledge must be impacted to the code model and showed to the user. Thus, some code blocks within a code editor will be highlighted as patterns, while some others will be tagged as “to be replaced”, etc. In other words, the new knowledge derived from the KDM-level must be brought up to the ASTM-level. 3. Proposed research The observations above stress the necessity to take advantage of the two worlds, while providing roundtrip capability. This means preserving the support for architecture, data, user interfaces (even metrics) that is provided by KDM; as well as achieving the level of details allowed by ASTM. Therefore, we suggest that ASTM and KDM could be interrelated thanks to a bridge that we have dubbed SMARTBRIDGE. This bridge will ensure inter-relationships as well as intra-relationships. The former deal with links between two distinct metamodels: KDM and ASTM. The 520
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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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Figure 3. The PF and PTS results fo
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Forecasting Fault Events in Power D
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which the airport data did not cont
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Page 503 and 504: Model & Method Feasibility Evaluati
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Page 507 and 508: adequate to another criterion. This
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Page 515 and 516: A Tiny Specification Metalanguage W
Page 517 and 518: C. Syntax Extensions The expressive
Page 519 and 520: however, does not allow variables f
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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 and 536: A. Pattern Reuse In patterns reuse
Page 537 and 538: IV. SCATTER In order to enhance pat
Page 539 and 540: These latter would be clustered acc
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Page 543 and 544: DC2AP Element and their Application
Page 545 and 546: 21. Consequences Example of use DC2
Page 547: Legacy2Cloud logic. In this scope,
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
Page 557 and 558: (1) V □ (A) ⊆ V □ (B), V ♦
Page 559 and 560: Suppose P = 〈S P , SP i , AI P ,
Page 561 and 562: Adaptive software should basically
Page 563 and 564: A. Step S1 - Define the Business Pr
Page 565 and 566: F. Step S6 - Monitor at Run-Time Th
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 573 and 574: Pub-T Tc Tc Sub-T Sub-T Figu
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Page 579 and 580: proposed in this work requires the
Page 581 and 582: FTS is an important research area,
Page 583 and 584: C. Dependent Variables and Measures
Page 585 and 586: D. Conclusion Validity Conclusion v
Page 587 and 588: evaluation of team productivity, qu
Page 589 and 590: Figure 3: Process Fragment Example
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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
Page 749 and 750:
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
Page 781 and 782:
two stages. The first stage is focu
Page 783 and 784:
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
Page 799 and 800:
information. If more than one adver
Page 801 and 802:
Users can al so publish adv ertisem
Page 803 and 804:
The COIN Platform: Supporting the M
Page 805 and 806:
A-3
Page 807 and 808:
Checking Contracts for AOP using XP
Page 809 and 810:
Maicon B. da Silveira, 112 Aldo Dag
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Seyedehmehrnaz Mireslami, 70 Takao
Page 813 and 814:
Wei Zhang, 422 Wenbo Zhang, 188 Zhi
Page 815 and 816:
Desmond Greer Eric Gregoire Christi
Page 817 and 818:
Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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