SEKE 2012 Proceedings - Knowledge Systems Institute

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Three rules match with the expression in working memory: Rule 1, Rule 2 and Rule 3. I f we resolve conflicts in favour of the lowest-numbered rule, then Rule 1 will fire. This causes X to be bound to the value ‘Customer belongs to Bank1’ and the premise of Rule 1 to be placed in working memory (Fig 3). The system has thus chosen to explore the possible hypothesis that the customer belongs to Bank 1. Another way to look at this is that the system has selected an ‘Or’ branch in the And / Or graph in Fig 1. We also note that there are two premises to Rule 1, both of which must be satisfied to prove the conclusion true. These are ‘And’ branches of the search graph representing a d ecomposition of the problem (finding whether the customer belongs to Bank 1) into two sub problems (finding whether the PhoneNo matches with PhoneNo in Bank1 Knowledge Base, and Address Information matches with Address in Bank1 Knowledge Base). We m ay then fire Rule 4, whose conclusion matches with ‘Address Information matches with Address Information in Bank1 Knowledge Base’ causing its premises to be placed in working memory as in Fig 4. At this point, there are three entries in working memory that do not match with any rule conclusions. Our expert system will, in this situation, query the user customer directly about these sub goals. If the customer confirms all three of these as true, the expert system will have successfully determined that the customer belongs to Bank1. In this case, the use case will return the balance of the customer from Bank1. In our example, had we failed to determine that the customer belongs to Bank1, we would have needed to back up to the top level and try Rule 2 instead. Let us now consider a tr ace of the reasoning process. Suppose our expert system has an explanation facility, and let us assume that the consultation has reached the stage represented by the working memory configuration in Fig 4. The following dialogue begins with the computer asking the customer about the goals present in the working memory. Is your Phone Number 12345678? Yes Is your Address po_box_19_AB_Avenue_Kolkata? Yes Is your Name prakash_kumar? Why Here the system responds with a literal presentation of the rule under question. It has been established that: The Address Information matches with that in Bank1 Knowledge base, Therefore if The Name matches with that in Bank1 Knowledge base Then you are a customer of Bank1. Following the above trace, the deposit of c ustomer would be displayed. Because the customer answered yes to the first two questions, Rule 4 was able to fire, verifying the second premise of Rule 1. When the ‘why’ query was made, the system was trying to solve the first premise of Rule 1. It is to be noted that the explanation for the systems behavior is just a r estatement of the current rule under consideration. However, this provides an adequate 317
explanation for why the program wants to know whether the name is prakash_kumar. In the response to why query, the system states that it h as concluded that the Address Information matches with that in Bank1 Knowledge Base. Suppose the customer then asks: How the Address Information matches with that in Bank1 Knowledge Base The system will respond to how it concluded that the Address Information matches with that in Bank1 Knowledge base. The response is a tr ace of the reasoning that led to this conclusion, working back from the goal along the rules that support it to the user responses. This follows from Rule 4: If: PhoneNo matches with PhoneNo in Bank1 Knowledge Base, and Address matches with Address in Bank1 Knowledge Base Then: Address Information matches with Address Information in Bank1 Knowledge Base. PhoneNo provided by Customer Address provided by Customer The production system architecture provides an essential basis for these explanations. Each cycle of the control loop selects and fires another rule. The program may be stopped after each cycle and inspected. Because each rule represents a c omplete chunk of problem-solving knowledge, the current rule provides a context for the explanation. It can also be seen from the above how authentication of the user/customer is achieved my means of the Production Rule based expert system. The expert system can similarly be used for implementing role based authorization by defining appropriate production rules e.g. Rule 5 If Role matches with Role in Bank1 Knowledge Base, and Address Information matches with Address Information in Bank1 Knowledge Base Then Customer is authorized to access Bank1Knowledge Base The graphical representation uses predicate calculus expressions in list syntax. IV.BENEFITS OF USING BUSINESS RULES FRAMEWORK FOR KNOWLEDGE BASED SERVICE ORIENTED ARCHITECTURE The following benefits are realized when an Expert System based Business Rules Framework is used vis-àvis other approaches. Flexible configuration of Business Rules Highly Interactive Approach Explanation to the User regarding the conclusions reached With the provision of IVRS (Interactive Voice Response System), the proposed system will be of great help for visibility impaired people, since the system provides a simple step-by-step interactive user friendly process V.CONCLUSION Our proposed Business Rules Framework using Rule Based Expert System facilitates easy configuration of Business Rules for Business Process Modeling. The proposed Business Rules Framework is highly interactive in nature, and with the provision of IVRS (Interactive Voice Response System) will be of gr eat help for visibility impaired users, since the system provides a simple step-by-step interactive user friendly process. REFERENCES [1] Debasis Chanda, D Dutta Majumder, Swapan Bhattacharya, “Knowledge Based Service Oriented Architecture for M&A”, Accepted for Publication in SEKE 2010 (The 22nd Conference on Software Engineering and Knowledge Engineering), San Francisco Bay, USA, July 1-3, 2010 [2] Omar El-Gayar, Kanchana Tandekar, An XML-based schema definition for model sharing and reuse in a distributed environment, Decision Support Systems 43 (2007) 791–808 [3] Chua Fang Fang, Lee Chien Sing, Collaborative learning using service-oriented architecture: A framework design, Knowledge- Based Systems 22 (2009) 271–274 [4] Marcos L´opez-Sanz, Cesar J. Acuna, Carlos E. Cuesta, Esperanza Marcos, Modelling of Service-Oriented Architectures with UML, Electronic Notes in Theoretical Computer Science 194 (2008) 23– 37 [5] Claus Pahl, Semantic model-driven architecting of service-based software systems, Information and Software Technology 49 (2007) 838–850 [6] Sinuhe Arroyo, Miguel-Angel Sicilia, Juan-Manuel Dodero, Choreography frameworks for business integration: Addressing heterogeneous semantics, Computers in Industry 58 ( 2007) 487– 503 [7] David Chen, Guy Doumeingts, Francois Vernadat, Architectures for enterprise integration and interoperability: Past, present and future, Computers in Industry 59 (2008) 647–659 [8] George F Luger, AI Structures and Strategies for Complex Problem Solving, Pearson Education, Fourth Edition, 2006 318
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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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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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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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k v ( df i ) 0 , it means that the
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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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adequate to another criterion. This
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480
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482
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484
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A Tiny Specification Metalanguage W
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C. Syntax Extensions The expressive
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however, does not allow variables f
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derivation dependencies. The third
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model extension as proposed in [6].
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Figure 1. The process for engineeri
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Figure 4. The abstractions extracte
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way to detect something which could
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Algorithm 1: Algorithm to recursive
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obtain the input of experts on desi
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A. Pattern Reuse In patterns reuse
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IV. SCATTER In order to enhance pat
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These latter would be clustered acc
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described or even r etrieved, thus
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DC2AP Element and their Application
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21. Consequences Example of use DC2
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Legacy2Cloud logic. In this scope,
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2.3.1 Complementarity of MDD techno
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3.2.2 EAggregateRelationship The EA
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cess. The proposal follows the ADM
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II. OVERVIEW OF MODAL TRANSITION SY
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(1) V □ (A) ⊆ V □ (B), V ♦
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Suppose P = 〈S P , SP i , AI P ,
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Adaptive software should basically
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A. Step S1 - Define the Business Pr
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F. Step S6 - Monitor at Run-Time Th
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Figure 1, a metamodel defines an XM
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Figure 3 is a feature diagram that
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ecause the system of C-net model is
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Pub-T Tc Tc Sub-T Sub-T Figu
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Visual Studio Achievements, a Visua
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the project. This achievement has t
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proposed in this work requires the
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FTS is an important research area,
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C. Dependent Variables and Measures
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D. Conclusion Validity Conclusion v
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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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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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