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218 ATTILA VAJDA (1) long term projects. Researches like [6] based on historical data of 396 projects of IS- BSG data set, showed that there exist a relation between E and D, which is nonlinear in case of medium and large projects, but can be considered as linear in case of small projects. For small projects, simple duration estimation model is needed, which can be adjusted easily to the team, project type, and gives a correct result based on the estimated effort and team size. By using the model presented in this paper, we reduce the project uncertainty by allowing the manager to obtain more accurate project duration estimations. Based on the P id value the personal professional evaluation can be observed. The model can be further improved by taking into account external factors which can influence the team productivity factors like team experience, team motivation, technical knowledge, etc. and to develop a model for more accurate productivity predictions. References [1] A. W. Chow, B. D. Goodman, J. W. Rooney, C. D. Wyble, Engaging a corporate community to manage technology and embrace innovation, IBM Systems Journal, Vol. 46, No. 4, 2007 [2] Steve McConnell, Rapid Development, Microsoft Press, Washington, 1996 [3] Gibs.W.W. Software’s Chronic Crisis, Scientific American, September, 1994 [4] Boehm B., Abts C. Software Development Cost Estimation Approaches - A Survey, University of Southern California, Los Angeles, March 2000 [5] Temnenco V., Software Estimation, Enterprise-Wide, IBM The Rational Edge, Vol. June 2007 [6] Olign S., Bourque P., Abran A., Fournie B., Exploring the Relation Between Effort and. Duration in Software Engineering Projects, World Computer Congress 2000, Beijing, China [7] Heemstra F.J, Softwae cost estimation, Information and Software technology 34 (10): 627- 639,1992 [8] Kitchenham B.A.,Empirical studies of assumptions that underlie software cost estimation models, Information and Software technology 34 (4): 211-218,1992 (1) Sysgenic Group, Alunis 48, Targu Mures, Romania E-mail address: attila.vajda@sysgenic.com
KNOWLEDGE ENGINEERING: PRINCIPLES AND TECHNIQUES Proceedings of the International Conference on Knowledge Engineering, Principles and Techniques, KEPT2009 Cluj-Napoca (Romania), July 2–4, 2009, pp. 219–222 A FORMAL CONCEPT ANALYSIS APPROACH TO ONTOLOGY SEARCH IOAN ALFRED LETIA (1) AND MIHAI COSTIN (2) Abstract. Finding the most suited ontology for a given domain or problem is not an easy task but through our work here we are trying to ease this endeavor by providing a semiautomated search mechanism. Semiautomated, because we need our search target to be defined by an expert. Once that target defined, by combining the processing power of MAS with FCA, we analyse the search space in order to find the most compatible ontology with the initial target. 1. Motivation and Goal The main step for solving a certain problem has always been finding or creating the best tool for the job at hand, no matter what the domain was. In information science, our domain of interest, the tool in question is an ontology capable of mapping the problem domain with success. Finding the right ontology for a certain task has been even from the begining one of the theoretical strong-points of the semantic web but things are not as easy as it seems [8]. The right ontology is never easy to find and creating one is even harder. This, without any doubt, involves some sort of intelligent ontology search, a guided search that has an array of ontologies as search space. For this paper, we are using a predefined set of existing ontologies as search space. The process and the results themself can later on be applied to a larger scale, all being reduced afterwards to a scalability problem. We mentioned guided search because our process starts from a well defined domain. This domain must be described by some expert in the field using a set of concepts, concepts that will represent the search target. Our system uses this set of concepts in order to find the best suited ontology by expanding and matching it against the concepts found in the search space. 2. Proposed Solution Having the initial domain defined by an expert in the field, as to direct our search, the search agents can then start trying to find the concepts that match the initial set. 2000 Mathematics Subject Classification. 68T30, 94A15. Key words and phrases. Ontology, Formal Concept Analisys, Multiagent Systems, Search methods. 219 c○2009 Babe¸s-Bolyai University, Cluj-Napoca
Page 1 and 2: Invited Lectures CONTENTS H. Horace
Page 3 and 4: P. V. Borza, O. Gui, D. Dumitrescu,
Page 5: F. M. Boian, C. Aldea, On Evaluatin
Page 9 and 10: KNOWLEDGE ENGINEERING: PRINCIPLES A
Page 11 and 12: DECISION SUPPORT SYSTEM FOR SOFTWAR
Page 15 and 16: TRANSFORMING PROCEDURAL INTO OBJECT
Page 19 and 20: COMDEVALCO FRAMEWORK 191 3.1. Model
Page 21 and 22: COMDEVALCO FRAMEWORK 193 [2] Czibul
Page 23 and 24: RAPID PROTOTYPING OF CONVERSATIONAL
Page 25 and 26: RAPID PROTOTYPING OF CONVERSATIONAL
Page 27 and 28: (1) (1) (1)
Page 29 and 30: ∼
Page 31 and 32: EFFORT ESTIMATION BY ANALOGY USING
Page 33 and 34: EFFORT ESTIMATION BY ANALOGY USING
Page 35 and 36: SOFTWARE COST ESTIMATION MODEL BASE
Page 37 and 38: SOFTWARE COST ESTIMATION MODEL BASE
Page 41 and 42: ONTOLOGY DEVELOPMENT: A SOFTWARE EN
Page 45: DURATION ESTIMATION OF A WORK PACKA
Page 49 and 50: A FORMAL CONCEPT ANALYSIS APPROACH
Page 53 and 54: HIGH COUPLING DETECTION USING FUZZY
Page 57 and 58: EFFICIENT RECURSIVE PARALLEL PROGRA
Page 61 and 62: SKEPTICAL REASONING IN CONSTRAINED
Page 65 and 66: ALGEBRAIC MODEL FOR THE SYNCHRONOUS
Page 69 and 70: REVERSE ENGINEERING AND SIMULATION
Page 71 and 72: REVERSE ENGINEERING AND SIMULATION
Page 73 and 74: A PARALLELIZATION SCHEME OF SOME AL
Page 75 and 76: A PARALLELIZATION SCHEME OF SOME AL
Page 79 and 80: THE MULTI-OBJECTIVE REFACTORING SEL
Page 81 and 82: THE MULTI-OBJECTIVE REFACTORING SEL
Page 83 and 84: VIRTUAL REALITY REHABILITATION ENVI
Page 85: VIRTUAL REALITY REHABILITATION ENVI

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