This page is an old version of the Knowledge Management Lab's website. The new version is here.
 

The Knowledge Management Lab at the University of Toronto is a research group focusing on concepts, methods, and technologies supporting the management of knowledge. These include the representation, organization, acquisition, creation, usage, and evolution of knowledge in its many forms, and the modelling, analysis, and design of technical systems for supporting all facets of knowledge management.

An assortment of techniques for representing and managing codified knowledge has emerged from a number of areas in Computer Science (CS), notably Artificial Intelligence, Databases,  Software Engineering, and Information Systems.  Although there is no consensus on a notion of knowledge or knowledge-based processing in CS, the terms are used usually in contra-distinction with data or data processing, to highlight the needs

• to clarify the relationship between symbols stored in computers and what they represent in the world outside,
• to dissociate the manipulation of such symbolic representations from internal computer processing,
• to explicitly and formally deal with the semantics of such representations and manipulations, and
• to make effective use of meta-descriptions in treating these symbols and structures.

Our group, taking into account the contributions of many alumni, has actively participated in these developments.  Our group aims to continue such contributions.  Our current focus, however, is to broaden our horizon to deal not only with the treatment of formally represented, computer-stored knowledge, but, in recognizing the limitations of current techniques, to further develop them within the usage context of human, organizational, and social  processes.
 

• Conceptual modelling and knowledge representation - We seek appropriate conceptual schemas for representing complex knowledge domains.  We identify ontologies and define notations and representational and reasoning frameworks that are appropriate for the subject matter of the application area.  Our group has a long track record in developing information modelling techniques (see [Mylopoulos98] for a review) for a number of areas, including data modelling, requirements analysis and enterprise modelling.  Recent research tasks and advances include -
• an extensible conceptual modelling language, Telos [MBJK90], for supporting information system modelling.  The language has been adopted by many research groups world wide, and has at least three implementations.
• advancing beyond traditional static and dynamic ontologies to include intentional and social ontologies:
• the NFR framework [Chung93] - a goal-oriented framework for dealing with non-functional requirements , and
• the i* framework [Yu95] - an agent-oriented modelling framework
• a case-based reasoning framework, TA3 [Jurisica98], for dealing with flexible, scalable, multi-modal and multimedia case-based reasoning.
• techniques for dealing with ongoing change, heterogeneity and evolution, such as context and traceability mechanisms.
• Technologies and tools - We develop the computational technologies and tools needed to support the various modelling frameworks.  The tools provide analysis and reasoning support based on the semantics of the knowledge representation.  Repository technology that builds on techniques from  database research enables the effective storage and retrieval of large bodies of knowledge.
• Processes and methods - We study, model, and analyse usage processes in order to develop methods and guidelines.

Information and Knowledge Management - for Individuals, Groups, and Organizations

Sophisticated and powerful information and knowledge management tools will be the key enabling technologies for the knowledge economy of the immediate future.  We are interested in developing tools for information management; such tools may be used:
- by an individual to manage her personal information, e.g., technical knowledge, current tasks, references to information sources, archival information,?
- by a group working on a given project, e.g., a team of software engineers responsible for a legacy system who maintain collective information on the system, such as documentation, history, requirements, architecture, fixes,?
- by an organization who chooses to manage its ?corporate knowledge?, i.e., objectives, policies, business processes, projects, personnel, history, including information about its customers, markets, competitors, suppliers, ...

The information may already be available as data in existing databases or computer files (text, pictures, ...) or on the Web.  They may be in paper form or in people's heads.  In short, any information people, groups, or organizations use on a daily basis.  The key to managing complex information environments is to have a repository which maintains meta-information, i.e., information about information, such as where can one find a specific letter, who knows well a particular software module, or how up-to-date are the data we have about George in a database.  We also need flexible interfaces (both for users and systems) for loading information transparently (from databases or other documents), viewing information and updating information, also facilities for looking at the information at different levels of granularity.  An understanding of how information is used by people, groups and organizations (including psychological, sociological, or management dimensions) is also important.

We have been working on a large project focusing on information management for software development groups (in collaboration with the IBM Toronto lab), building both a repository, a schema as well as tools for populating the repository from source code files and other information sources [FHK+97]

Along another direction, we have been working on a project for managing web-based information, again building a (tentative) repository, a schema as well as tools for extracting information from the web and describing it through an interactive process [KGM98].  These techniques, especially those for dealing with meta-information, will be important as we have moved almost overnight into an age of open information systems triggered by the Internet, the Web and the proliferation of intranets, compared to the islands of information that had been the norm only a few years ago.  Further ahead, we anticipate the smooth cooperation among systems (in the midst of ongoing change) at the semantic and intentional levels will become top research priorities in the next few years.  Our group is among the active promoters of research in cooperative information systems [EUCAN].

Knowledge management as a management concept is also widely promoted in the business world today.  Intellectual capital is becoming a key differentiating factor for many enterprises in the highly competitive, global, post-industrial economy.  How  individuals and groups can generate, share, and utilize knowledge effectively is now viewed as crucial for organizational success.  The development of advanced knowledge management techniques in computing is highly complementary to this trend.  Current knowledge initiatives in enterprises typically rely heavily on generic groupware or intranet platforms, which have no knowledge of  domain concepts or semantics.  Our research aims to provide powerful yet flexible support through advanced modelling, repository technologies, and specialized processing.  As work content become increasingly complex and  knowledge intensive, many businesses and industries will find advanced technology support for knowledge management indispensable.  This is already becoming evident in domains such as software development, and advanced engineering design and manufacturing..
 
 

Information Systems Development, Software Engineering, and Requirements Engineering

The development of large scale software systems such as business information systems and real-time control systems (e.g., telecommunications systems) is one of the most challenging undertakings, and yet is the foundation of much of today's technology infrastructure. The burden of the system development task can be greatly reduced if appropriate support can be provided to ease the knowledge- and information-intensive undertaking.  A knowledge management approach needs to be founded on the suitable representation and use of software engineering knowledge [MBY97].

The DAIDA project [JMSV92] laid the foundations for a comprehensive information systems development environment, emphasizing the need to represent information about the subject world, the usage world, the system world, and the development world.  Repository technology is used to link the different kinds of information and to support development activities.

Understanding user and organizational needs and properly defining requirements is increasingly recognized as crucial to system success.  Our group has been active in the Requirements Engineering community since its earliest days.  Members of our group were among the first to advocate the explicit representation of knowledge about the world for requirements engineering [GMB82] [GMB94].  Knowledge structuring mechanisms including classification, generalization, aggregation, and time were provided to organize formally-represented knowledge about the world.  Some of these ideas appear in today's object-oriented analysis notations, often with less attention to the semantics and repository support.

Our recent work has led us to progress beyond object-oriented concepts to goal-oriented ones [MCY99].  One fundamental challenge that has been plaguing systems development has been the slippery nature of non-functional requirements.  Requirements such as accuracy, security, maintainability, evolvability, performance, costs, and so forth are known to be critical for system success.  Yet there had not been a systematic approach to assist developers to achieve these important qualities during the development process.  Earlier techniques had been limited to assessing these "-ilities" as attributes or properties after the fact.  The NFR Framework [Chung93] provides a systematic framework for representing non-functional requirements as goals and using them to guide systems development, providing support for detecting synergy and conflict among goals, managing tradeoffs, and for drawing on catalogues of generic design knowledge.

Requirements need to be understood within an organizational context.  Introducing or altering technical systems can impact stakeholders in complex ways.  The i* framework [Yu95a] offers an  agent-oriented approach to requirements engineering.  Agents in a social environment gain advantages through cooperation but also become vulnerable due to inter-dependencies.  By modelling the strategic dependencies among agents and reasoning about how they might advance or protect their interests, the i* framework provides a systematic approach for exploring strategic alternatives in defining systems requirements  [Yu97b].

Software architecture is one of the key areas in software engineering today, especially in light of the accelerating shift towards open, distributed, platform-based, and network-centric modes of computing.  We believe, as many have acknowledged, that much of the system-wide qualities are determined during high level architectural design.  We have been pursuing systematic approaches to architectural design using the goal-oriented approach of the NFR framework [CNY95] and more recently incorporating the agent-oriented approach of the i* framework [CGY99].

As our technical environment become increasing software-based, more and more of system development will have to contend with existing legacy systems.  A fundamental reason why  legacy systems are so hard to deal with is because knowledge about the system have not been kept once the development process was over.  Due to the lack of appropriate knowledge representation and management facilities, it is not surprising that much of that knowledge was never captured, let alone maintained.  Thus the knowledge management approach to software engineering is intended to prevent the fossilization of systems into legacy systems, treating system development as an ongoing process throughout a system's lifetime [DDJ+98].

For dealing with existing systems that are already legacy systems, we have developed techniques for software reengineering.  At the code level, we provide tools for extracting and reconstructing knowledge about existing software systems, and using this knowledge to convert software into new language and execution environments.  These techniques have been applied to convert legacy code to object-oriented code [CSER], and also to Y2K compliance.  A new project [IRIS] will develop techniques for reengineering software to operating in the increasingly network-centric computing environment.  A high-level knowledge-based requirements driven approach will be emphasized.

We also work on tools to support reverse engineering [D-FJ98]. This project covers applying information retrieval and knowledge-mining techniques to reverse engineering of legacy systems. In order to support a dynamic environment, we take an approach of integrating lightweight tools. Instead of forcing a user to use a specific and fixed environment, our approach provides basic information repository, which manages information extracted from the documentation and source code.
The system stores this information in a graph structure, it supports navigation through the repository, and modification of its structure and annotation. Results of our preliminary evaluation of the system on the small-size software are encouraging.

Case-based reasoning (CBR) systems require a design process that systematically produces high-quality applications. Beyond satisfying basic functional requirements for CBR, it is important to meet global quality factors, such as performance, accuracy and confidentiality, called non-functional requirements (NFRs). We use a goal-oriented, knowledge-based approach for aiding decision-support system development and usage, namely, we propose an approach for dealing with NFRs for CBR systems. In [JN98] show how quality can be built into a CBR system called TA3 [Jurisca98], using the "QualityCBR" approach, which integrates existing work on CBR and NFRs. We illustrate the use of the approach in a complex medical domain - in vitro fertilization [JGM+98].
 

Business Processes and Enterprise Modelling

One of the aims of the recent push for business process reengineering in many organizations is to ensure better alignment of information systems with business objectives.  Technology is seen as an enabler for business change, rather than as a means for making long-established processes more efficient through automation.  The information systems analyst of today therefore needs to have a much deeper understanding of business processes than before.  The i* framework provides a richer scheme for modelling, analyzing, and redesigning work processes [Yu95b][YML96].  It highlights the intentional (motivations, intents, rationales) aspects of relationships among strategic agents, complementing the traditional focus on activities and flows in conventional modelling schemes (e.g., IDEF) .  It therefore also allows greater attention to be paid to people and organizational issues [YM97].

The strategic orientation of i* also helps focus the modelling effort, so that enterprise modelling need not be an all-consuming activity, but can be targeted at critical areas. The research objective here is to provide ongoing support for aligning business goals with information systems, through systematic processes, and making use of computer-based knowledge management approaches and facilities.
 
 
 

Domain/Industry-Specific Areas
 

Biomedical Informatics
Biological research is generating data at an explosive rate. Analyzing this data and using it intelligently is a challenge because of its complexity, multiple interdependent factors, uncertainty of these dependencies, and continuous evolution. Biomedical computing involves the application of computational methods for the advancement of biological and medical science. Activities in this area range from data acquisition, robotics and laboratory analysis to the dissemination, storage and retrieval of knowledge. Modern biomedical computing is rooted in a broad range of application areas. Imaging needs from microscopy to mammography have motivated and relied on advances in imaging science. Medical data storage and access systems benefit from the study of information retrieval. Algorithms and software development are of key importance in areas such as genome sequence analysis and acquisition, which also depend on techniques from statistics and artificial intelligence. Medicine and the biological sciences already have an accumulation of extraordinarily large and complex data sets that are uninterpretable without the benefit of computational methods.
     The proposed medical application covers assisted reproductive technology for human infertility, namely in vitro fertilization (IVF). At each step of the IVF procedure, there are many dependent and independent variables, which may impact the chance of pregnancy. Thus, it is difficult for the clinician to discern trends and make informed decisions to optimize success rates for each individual infertile couple. Decision-support systems (DSSs) may enable IVF practitioners (1) to cope with the complexity of the domain during treatment planning and (2) to discover relationships between individual knowledge sources, which can then be used to improve pregnancy rates. The project is a multidisciplinary research in medical informatics. Its focus is on the development of object-based representations, context-based retrieval, and computer vision techniques, which will provide a framework for DSSs in medical domains. Knowledge-based DSSs are suited to disseminate experience, standardize procedures, lower cost, and increase the quality of health care services [JG97b, Jurisica98b]. This research has three specific aims: (1) design and implementation of tools for efficient, flexible and scalable case-based DSSs (preliminary results in [JG98,JG9]); (2) integration of the resulting system with image analysis techniques to facilitate image feature extraction (preliminary results in [GJ98]); and (3) evaluation of the prototype on medical domains, namely infertility treatment (preliminary results in [JMG+98]).
 

Industrial Control Processes
 

Telecommunications
 

From Legacy Systems to Modern Architectures CSER
Funding Agency: Consortium for Software Engineering Research
Principal Investigators: R. Holt, J. Mylopoulos, H. Mueller, K. Kontogiannis
Industry Partners: IBM Canada
Project Description

Agent-Oriented Software Architectures:  Models and Analysis Tools
Funding Agency:   Communications and Information Technology Ontario
Duration:  July 1998 - June 2000
Principal Investigators:  John Mylopoulos, Eric Yu, Yves Lesperance
Industry Partners:  Mitel Corp.
Project Description

Reengineering Software Systems for Network-Centric Computing
Funding Agency:  IRIS-3, a Federal Network of Centres of Excellence
Duration: April 1998 - March 2002
Principal Investigators:  John Mylopoulos, Hausi Mueller, Kostas Kontogiannis, Eric Yu
Industry Partners:  IBM, FSI, Techne
Project Description

Business Process Models and Tools
Funding Agency:   Information Technology Research Centre, Ontario
Duration:  1995 - 1998
Principal Investigators:  J. Mylopoulos, E. Yu, Y. Lesperance, H. Levesque, R. Reiter
Project Description

Organization Modelling
Funding Agency:  Natural Sciences and Engineering Research Council (NSERC) Individual Research Grant
Duration:  1996 - 2000
Principal Investigators:  E. Yu

Cooperative Information Systems
Funding Agency: European Commission (EU-Canada Collaboration project)
Duration:  1995 - 1998
Principal Investigators:  G DeMichelis, E. Dubois, M. Jarke, F. Matthes, J. Mylopoulos, K. Pohl, M. Papazoglou, J. Schmidt, C. Woo, E. Yu
Project Description

Decision Support for Biomedical Domains
Funding Agency:  Natural Sciences and Engineering Research Council of Canada (NSERC) Individual Research Grant
Duration: April 1998 - April 2002
Principal Investigators:  Igor Jurisica
Industry Partners: Toronto Center for Advanced Reproductive Technology, Toronto General Hospital
Project Description

Case-Based Reasoning: Knowledge Management and Applications
Funding Agency: Communications and Information Technology Ontario
Duration: July 1998 - July 2000
Principal Investigators: J. Glasgow, N. Cercone, R. Holte, I. Jurisica, S. Matwin,
Industry Partners: Toronto Center for Advanced Reproductive Technology, Toronto General Hospital, Kingston General Hospital, Hauptman-Woodward  Medical Research Institute, Molecular Mining, IBM Canada, Netron, Bell Canada, City of Regina,
Project Description

A Dynamic Repository for Software Maintenance and Evolution
Funding Agency: IBM Labs Canada
Duration:
Principal Investigators: H. Dayani-Fard, I. Jurisica
Industry Partners:
Project Description
 
 
 

• Telos
• the i* framework
• the NFR framework
• the TA3 framework
• Knowledge Base Management Systems

  Faculty Members
Prof. John Mylopoulos
Prof. Eric Yu
Prof. Igor Jurisica

  Affiliated members
Prof. Yves Lesperance, York University
Prof. Lawrence Chung, University of Texas, Dallas
Dr. Brian Nixon
Dr. Grigoris Karakoulas

  Post-Docs and Research Associates
Dr. Jianguo Lu
Niloo Hodjati

  Graduate students
Daniel Gross
Wincy Chan

  Contributing undergraduate students
Joseph Makuch
Iannis Tourlakis

  Recent Post-docs and Research Associates
Scott Kerr, Elizabeth D'Angelo, Suchun Wu, Avi Gal, Patrick Premont, Huaiqing Wang, Bryan Kramer, David Lauzon

  Recent contributing undergraduate students
Ying Shi, Jeff Lloyd, Conan Chan, Vincent Wu, Nelson Yu, Nick Zahariadis, Jane Foo, Godfrey Cheng, Seyil Yoon, Angela Lee.

  Recent visiting graduate students
Mike Bissener, Constant Backes, Sylvie Nigot  (University of Namur, Belgium)
Oscar Sjoeden, Fabian Tell  (Royal Institute of Technology, Sweden)
Lin Zheng, Mary Hu (City University of Hong Kong)
Hui Liu (University of Jyyvaskyla, Finland)
 
 

1999
• [MCY99]  J. Mylopoulos, L. Chung, E. Yu, "From Object-Oriented to Goal-Oriented Requirements Analysis", Communications of the ACM, January 1999.
• [CGY99] L. Chung, D. Gross, E. Yu, "Architectural Design to Meet Stakeholder Requirements", 1st Working IFIP Conference on Software Architecture,  February 22-24, 1999, San Antonio, Texas, USA.

1998
• [DDJ+98]  G. De Michelis, E. Dubois, M. Jarke, F. Matthes, J. Mylopoulos, M. Papazoglou, J. W. Schmidt, C. Woo, E. Yu, "A Three-Faceted View of Information Systems: The Challenge of Change", Communications of the ACM, December 1998.
• [D-FJ98] H. Dayani-Fard and I. Jurisica, "Reverse Engineering by Mining Dynamic Repositories". 5th Working Conference on Reverse Engineering (WCRE'98), IEEE, pp. 174-182, Hawaii, 1998.
• [GJ98] J. Glasgow and I. Jurisica, "Integration of case-based and image-based reasoning", American Association for Artificial Intelligence, AAAI Workshop on Case-Based Reasoning, pp. 67-74, Madison, WI, July 28, 1998.
• [JG98] I. Jurisica and J. Glasgow, "An efficient approach to iterative browsing and retrieval for case-based reasoning". Editor Angel Pasqual del Pobil, Jose Mira and Moonis Ali, Lecture Notes in Computer Science, IEA/AIE'98, Springer-Verlag, pp. 535-546, 1998.
• [JMG+98] I. Jurisica, J. Mylopoulos, J. Glasgow, H. Shapiro, and R. F. Casper, "Case-based reasoning in IVF: Prediction and knowledge mining". Artificial Intelligence in Medicine, 12(1), pp. 1-24, 1998.
• [JN98] I. Jurisica and B. Nixon, "Building quality into case-based reasoning systems". Lecture Notes in Computer Science, CAiSE'98, Springer-Verlag, pp. 363-380, 1998.
• [Jurisica98] I. Jurisica, "TA3: Theory, Implementation, and Applications of Similarity-Based Retrieval for Case-Based Reasoning". Ph.D. thesis, Dept. of Comp. Science, University of Toronto, 1998.
• [Jurisica98b] I. Jurisica, "Asynchronous Telemedicine: A Case-Based Reasoning Approach to Knowledge Sharing". In Information Technology in Community Health, Victoria, BC, November. 1-4, 1998.
• [KGM98]  S. Kerr, A. Gal, J. Mylopoulos, "Information Services for the Web: Building and Maintaining Domain Models", 3rd IFCIS Int. Conf. on Cooperative Information Systems, New York, N.Y., Aug. 1998.
• [MJY98]  J. Mylopoulos, I. Jurisica, E. Yu, "Computational Mechanisms for Knowledge Organization", 5th International ISKO Conference, August 25-29, 1998, Lille, France.
• [Mylopoulos98] J. Mylopoulos, "Information Modeling in the Time of the Revolution", Information Systems, 23 (3/4) pp. 127-155, Elsevier Science Permagon, 1998.  based on a keynote address presented at the Ninth Conference on Advanced Information Systems Engineering (CAiSE?97) in Barcelona, Catalunya on June 17, 1997. An earlier version of the paper, titled ?Characterizing Information Modeling Techniques for Information Systems Engineering?, is included in Bernus, P. and Schmidt, G., (eds.) Handbook on Architectures for Information Systems, Springer-Verlag, 1998
1997
• [CNY97] L. Chung, B. Nixon, and E. Yu, "Dealing with Change: An Approach Using Non-Functional Requirements", Requirement Engineering, Springer, vol. 1, no. 4, 1997.
• [FHK+97]  P.J. finnigan, R.C. Holt, I. Kalas, S. Kerr, J. Kontogiannis, H.A. Mueller, J. Mylopoulos, S.G. Perelgut, M. Stanley, K. Wong, "The Software Bookshelf", IBM Systems Journal, 36(4), pp. 564-593, 1997.
• [JG97] I. Jurisica and J. Glasgow, "Improving performance of case-based classification using context-based relevance". International Journal of Artificial Intelligence Tools. Special Issue of IEEE ITCAI-96 Best Papers. 6(4):511-536, 1997.
• [JG97b] I. Jurisica and K. M. Gupta, "Knowledge-based systems for decision support in health care". In Digital Knowledge Conference II, Toronto, 1997.
• [Jurisica97] I. Jurisica, "Similarity-Based retrieval for diverse Bookshelf software repository users", In IBM CASCON Conference, pages 224-235, Toronto, Canada, pp. 223-246, 1997.
• [Jurisica97b] I. Jurisica, "Supporting flexibility. A case-based reasoning approach". In The AAAI Fall Symposium. Flexible Computation in Intelligent Systems: Results, Issues, and Opportunities, pp. 111-116, Cambridge, Massachusetts, 1997.
• [MBY97] J. Mylopoulos, A. Borgida, and E. Yu, "Representing Software Engineering Knowledge", Automated Software Engineering, Kluwer Academic Publishers, vol. 4, no. 3, July 1997, pp. 291-317.
• [Yu97] E. Yu, "Towards Modelling and Reasoning Support for Early-Phase Requirements Engineering", 3rd IEEE Int. Symp. on Requirements Engineering (RE'97) Jan. 1997, Washington D.C., USA.
• [Yu97b]  E. Yu, "Why Agent-Oriented Requirements Engineering", 3rd Workshop on Requirements Engineering For Software Quality, June 1997, Barcelona, Catalonia.
• [YM97]  E. Yu and J. Mylopoulos, "Modelling Organizational Issues for Enterprise Integration," Int. Conf. on Enterprise Integration and Modelling Technology, Oct. 1997, Turin, Italy.
1996
• [JG96] I. Jurisica and J. Glasgow, "A case-based reasoning approach to learning control". In 5th Int. Conf. on Data and Knowledge Systems for Manufacturing and Engineering, pp. 361-384, Phoenix, AZ, 1996.
• [JG96b] I. Jurisica and Glasgow, "Case-Based Classification Using Similarity-Based Retrieval". In 8th IEEE Int. Conf. on Tools with Artificial Intelligence, pp. 410-419, Toulouse, France, 1996.
• Mylopoulos, J., Chaudhri, V., Plexousakis, D., Shrufi, A. and Topaloglou, T., "Building Knowledge Base Management Systems?, Very Large Databases Journal 5(4), October 1996
• [YML96] E. Yu, J. Mylopoulos and Y. Lesperance, "AI Models for Business Process Reengineering", IEEE Expert, August 1996, pp. 16-23.
1995
• A. Borgida, J. Mylopoulos, and R. Reiter "On the frame problem in procedure specifications", IEEE Transactions on Software Engineering, 21(10), Oct. 1995, pp.785-798. (based on Among-best-5-papers at ICSE'93) (ps)
• [CNY95] L. Chung, B. Nixon, and E. Yu, "Using Non-Functional Requirements To Select Among Architectural Alternatives", Int. Workshop on Architecture in Software Systems, May 1995, Seattle, Washington.
• [JS95] I. Jurisica and H. Shapiro, "Case-based reasoning system applied as an advisor for IVF practitioners". 51st Conf. of the American Society for Reproductive Medicine, Seattle, WA, 1995.
• [JG95] I. Jurisica and J. Glasgow, "Applying Case-Based Reasoning to Control in Robotics", 3rd Robotics and Knowledge-Based Systems Workshop, pp. 189-196, St. Hubert, Quebec, 1995.
• [Yu95a]  E. Yu, Modelling Strategic Relationships for Process Reengineering, Ph.D. thesis, Department of Computer Science, U. of Toronto, 1995.
• [Yu95b]  E. Yu, "Models for Supporting the Redesign of Organizational Work", Conf. on Organizational Computing Systems, Aug. 1995, Milpitas, California, pp. 225-236.
1994
• [GMB94]  S. Greenspan, J. Mylopoulos, and A. Borgida, "On Formal Requirements Modeling Languages: RML Revisited" (paper for invited plenary talk, on the occasion of receiving the ICSE n-10 award for Best Paper at ICSE'82), Proc. 16th Int. Conf. Soft. Eng., Sorrento, Italy, pp. 135-147, May 1994. (ps)
• [YM94] Yu, E. and Mylopoulos, J., "Understanding 'Why' in Software Process Modeling, Analysis and Design," Proceedings Sixteenth International Conference on Software Engineering, Sorrento, 1994.
1993
• [Chung93]  Chung, L. Representing and Using Non-Functional Requirements: A Process-Oriented Approach, Ph.D. thesis, Department of Computer Science, U. of Toronto, 1993.
• [Jurisica93] I. Jurisica, "Query Optimization for Knowledge Base Management Systems: A Machine Learning Approach". MSc thesis, Dept. of Comp. Sci., Univ. of Toronto, 1993.
• E. Yu, "Modeling Organizations for Information Systems Requirements Engineering," in First IEEE Int. Symp. on Requirements Eng. , pp. 34-41, Jan. 1993.
1992
• [GJ92] R. Greiner and Jurisica, "A statistical approach to solving the EBL utility problem". In National Conf. on Artificial Intelligence (AAAI-92), pp. 241-247, San Jose, CA, 1992.
• [JMSV92]  Jarke, M., Mylopoulos, J., Schmidt, J. and Vassiliou, Y., "DAIDA: An Environment for Evolving Information Systems," ACM Transactions on Information  Systems 10(1), 1992, pp. 1-50..
• Motschnig-Pitrik, R. and Mylopoulos, J., "Classes and Instances", Int. Journal of Intelligent and Cooperative Systems  1(1), April 1992.
• J. Mylopoulos, L. Chung and B. Nixon, "Representing and using non-functional requirements: a process-oriented approach", IEEE Transactions on Software Engineering, June 1992. pp. 483-497.
1991
• Kramer B. and Mylopoulos, J. "A Survey of Knowledge Representation", in The Encyclopedea of Artificial Intelligence, Shapiro, S.(ed.), John Wiley and Sons Inc., 2nd edition,  1991.
1990
• [MBJK90] Mylopoulos, J., Borgida, A., Jarke, M., and Koubarakis, M., "Telos: Representing Knowledge About Information Systems," ACM Transactions on Information Systems, October 1990.

 
Tutorials
• Janice Glasgow and Igor Jurisica. "Knowledge Mining and Discovery in Molecular Biology", Pacific Symposium on Biocomputing (PSB'99), Hawaii, January 4, 1999.
• Igor Jurisica and Janice Glasgow. "Data Mining and Knowledge Discovery", IBM CASCON Conference, Toronto, December 2, 1998.
• John Mylopoulos, Vinay Chaudhri, Igor Jurisica, Dimitris Plexousakis, Adel Shrufi, Thodoros Topaloglou, and Huaiqing Wang. "Development and Application of Knowledge Base Management Systems". Australian Joint Conference on AI, Canberra, Australia, November 1995.
• John Mylopoulos, Vinay Chaudhri, Igor Jurisica, Dimitris Plexousakis, Adel Shrufi, Thodoros Topaloglou, and Huaiqing Wang. "Knowledge Base Management Systems". International Joint Conference on AI, Montreal, Quebec, August 1995. Presented by J. Mylopoulos and T. Topaloglou.
• J. Mylopoulos, V. Chaudhri, I. Jurisica, D. Plexousakis, A. Shrufi, T. Topaloglou, and H. Wang. "Knowledge Base Management and its Application". IEEE Conference on AI Applications, IEEE Computer Society, San Antonio, TX, March 1994. Presented by Igor Jurisica and Huaiqing Wang.

 
pre-1990
• Mylopoulos, J. and Brodie, M., (eds.) Readings in Artificial Intelligence and Databases, Morgan-Kaufmann, 1988.
• M. Brodie and J. Mylopoulos, (eds.)  On Knowledge Base Management Systems: Integrating  artificial intelligence and database technologies, Springer-Verlag, New York, 1986.
• M. Brodie, J. Mylopoulos, and J. Schmidt, (eds.) On Conceptual Modelling: Perspectives from Artificial Intelligence, Databases and Programming Languages, Springer Verlag, 1984.
• Borgida, A., Greenspan, S. and Mylopoulos, J., "Knowledge Representation as a Basis for Requirements Specification", IEEE Computer 18(4), April 1985. Reprinted in Rich, C. and Waters, R., Readings in Artificial Intelligence and Software Engineering, Morgan-Kaufmann, 1987.
• S. Greenspan, J. Mylopoulos, and A. Borgida, "Capturing More World Knowledge in the Requirements Specification", Proc. of the Sixth International Conference on Software Engineering, Tokyo, 1982.  Reprinted in Freeman, P., and Wasserman, A. (eds.) Tutorial on Software Design Techniques, IEEE Computer Society Press, 1984. Also in R. Prieto-Diaz and G. Arango, Domain Analysis and Software Systems Modeling, IEEE Comp. Sci. Press, 1991.
• J. Mylopoulos, P. Bernstein, and H. Wong, "A language facility for designing interactive database-intensive applications", ACM Transactions on Database Systems, 5(2), June 1980.  Reprinted in Zdonik, S. and Maier, D., Readings in Object-Oriented Database Systems, Morgan-Kaufmann, 1989.
• H.J. Levesque and J. Mylopoulos, "A Procedural Semantics for Semantic Networks," in N.V. Findler (ed.), Associative Networks: Representation and Use of Knowledge by Computers, Academic Press, New York, pp. 93-120, 1979.

Roussopoulos, N., Mylopoulos, J., "Using Semantic Networks for Database Management", Proceedings of the First International Conference on Very Large Databases (VLDB'75), pp.144-172, September 1975.

CSC 340   Information Systems Analysis and Design
CSC 2106  Requirements Engineering

FIS 1341  Analyzing Information Systems
FIS 1342  Designing Information Systems
FIS 2101  Architecting Information, Systems, and Organizations
   (previously: Aligning Informational, Systems, and Organizational Architectures)
FIS 2103  Organizational Knowledge Management: Models and Technologies

recently offered Reading Courses:
- Analyzing Network-Centric Architectures
- Strategic Relationships Analysis

See also: recently completed theses.
 

The principal researchers of the KM lab are affiliated with the Department of Computer Science and Faculty of Information Studies at the University of Toronto. Students can carry out research under the Master's and Ph.D programs, at either Department or Faculty.

The lab has a number of ongoing research projects supported by industry research partners, the Ontario government, and the Canadian government. We seek highly qualified individuals to assist in conducting these research projects.

The group collaborates with many researchers and research groups worldwide. We welcome exchanges and collaborations with researchers and groups engaged in related work.
 

This page is under construction. Please visit again soon.

Last update of this page:  October 21, 1998.
Link to BUL KMLab added: September 30, 2002.