My main area of research can be categorized as that of Data Semantics in Cooperative Information Systems. This research area has been active for more than 20 years now, starting from the first Data Semantics Conference in Hasselt, Belgium (1985), and continuing with the CoopIS (Cooperative Information Systems) conference series. An IFIP working group (2.6) is devoted to this type of research, sponsoring the peer-reviewed Journal of Data Semantics as a leading avenue for publishing research results. My earlier contributions to this field involve the development of theoretical foundation for cooperative information systems interaction in a Web environment [7] (preceding the current trend of Web services), and application of active database techniques to enhance service utilization across cooperative information systems (see a well cited paper in CoopIS.96 [11] and a recepient of the best-paper award in CoopIS.98 [9], extended to a journal version at the International Journal on Cooperative Information Systems [5]).

In a paper at the VLDB Journal [3] we have modeled this inherent uncertainty of the process using fuzzy set theory with two practical outcomes. First, we have provided a theoretical justification to the popular use of the average fuzzy aggregator in the matching process. Second, we have set the foundation for the use of top-K schema mappings, rather than the common method of using just the best mapping. In this work we argue that the use of top-K mappings can assist in reducing the uncertainty of the process.

A paper at the Journal of Data Semantics in 2006 [2], builds upon this last observation, and propose a heuristic for schema matching that makes use of simultaneous evaluation of top-K mappings. This heuristic was empirically shown to increase the precision of the schema matching process by 25% on average.

As part of this line of research, a tool (OntoBuilder) for schema matching using ontological means was developed, and is publicly available at http://ie.technion.ac.il/OntoBuilder. An invited paper at the AI Magazine [6] describes the unique features of the algorithms employed by OntoBuilder and in particular the notion of element precedence in schemata. The original paper on OntoBuilder [10] is well cited. Also, OntoBuilder itself is well known. While there are several tools (both commercial and research-based, including one from Stanford University) named OntoBuilder, our tool comes up first in Google search and most of the references in Google are for our tool. OntoBuilder is also well cited in various online lists (e.g., OntologyMatching (http://www.ontologymatching.org/), Ziegler (http://www.ifi.unizh.ch/~pziegler/IntegrationProjects.html DigiCULT (http://www.digicult.info/pages/resources.php?t=10), SWgr (http://www.semanticweb.gr/index.php/SWgr:Community_Portal).

Resource monitoring: This line of research focuses on resource monitoring whenever the update pattern of a resource are known in stochastic terms only. In a paper at the Journal of the ACM from 2001 [4] we have established the theoretical foundation of managing resource monitoring in such a setting. Applying this model to specific policies in a caching environment is published at the ACM Transactions on Database Systems [1].

Workflows: Here, we focus on the management of exceptions in workflows. Exceptions, rare events that are hard to model at design time and manage at run-time, have been the focus of a PhD thesis of Mati Golani. In this work, we use graph theory to generate exceptions automatically, thus assisting a designer in de.ning exceptions handlers to various exceptions. Initial results were published BPM (Business Process Modeling), the competitive leading conference in workflows. [8]

[1] L. Bright, A. Gal, and L. Raschid. Adaptive pull-based policies for wide area data delivery. ACM Transactions on Database Systems (TODS), 2006.

[2] A. Gal. Managing uncertainty in schema matching with top-k schema mappings. Journal of Data Semantics, 2006. Accepted for Publication.

[3] A. Gal, A. Anaby-Tavor, A. Trombetta, and D. Montesi. A framework for modeling and evaluating automatic semantic reconciliation. VLDB Journal, 14(1):50.67, 2005.

[4] A. Gal and J. Eckstein. Managing periodically updated data in relational databases: A stochastic modeling approach. Journal of the ACM, 48(6):1141.1183, 2001.

[5] A. Gal, S. Kerr, and J.Mylopoulos. Information services for the web: Building and maintaining domain models. International Journal of Cooperative Information Systems (IJCIS), 8(4):227.254, 1999.

[6] A. Gal, G. Modica, H.M. Jamil, and A. Eyal. Automatic ontology matching using application semantics. AI Magazine, 26(1), 2005.

[7] A. Gal and J. Mylopoulos. Towards web-based application management systems. IEEE Transactions on Knowledge and Data Engineering (TKDE), 13(4):683.702, 2001.

[8] M. Golani, A. Gal - Flexible Business Process Management using Forward Stepping and Alternative Paths. Proceedings of the third Conference on Business Process Modeling (BPM'05). Nancy, France, September 6-8, 2005.

[9] S. Kerr, A. Gal, and J.Mylopoulos. Information services for the web: Building and maintaining domain models. In Proceedings of the Third IFCIS International Conference on Cooperative Information Systems (CoopIS.98), pages 4.13, NYC, NY, August 1998.

[10] G. Modica, A. Gal, and H. Jamil. The use of machine-generated ontologies in dynamic information seeking. In C. Batini, F. Giunchiglia, P. Giorgini, and M. Mecella, editors, Cooperative Information Systems, 9th International Conference, CoopIS 2001, Trento, Italy, September 5-7, 2001, Proceedings, volume 2172 of Lecture Notes in Computer Science, pages 433. 448. Springer, 2001.

[11] J. Mylopoulos, A. Gal, K. Kontogiannis, and M. Stanley. A generic integration architecture for cooperative information systems. In Proceedings of the First IFCIS International Conference on Cooperative Information Systems (CoopIS.96), pages 208.217, Brussels, Belgium, June 1996.