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A configurable process model is an integrated representation of multiple variants of a business process. It is designed to be individualized to meet a particular set of requirements. As such, configurable process models promote systematic reuse of proven or common practices. Existing notations for configurable process modeling focus on capturing tasks and control-flow dependencies, neglecting equally important aspects of business processes such as data flow, material flow and resource management. This paper fills this gap by proposing an integrated meta-model for configurable processes with advanced features for capturing resources involved in the performance of tasks (through task-role associations) as well as flow of data and physical artifacts (through task-object associations). Although embodied as an extension of a popular process modeling notation, namely EPC, the meta-model is defined in an abstract and formal manner to make it applicable to other notations.

Mobility of people and goods is essential in the global economy. The ability to track the routes and patterns associated with this mobility offers unprecedented opportunities for developing new, smarter applications in different domains. Much of the current research is devoted to developing concepts, models, and tools to comprehend mobility data and make them manageable for these applications. This book surveys the myriad facets of mobility data, from spatio-temporal data modeling, to data aggregation and warehousing, to data analysis, with a specific focus on monitoring people in motion (drivers, airplane passengers, crowds, and even animals in the wild). Written by a renowned group of worldwide experts, it presents a consistent framework that facilitates understanding of all these different facets, from basic definitions to state-of-the-art concepts and techniques, offering both researchers and professionals a thorough understanding of the applications and opportunities made possible by the development of mobility data.

We present a novel approach to probabilistic description logic programs for the Semantic Web in which disjunctive logic programs under the answer set semantics are tightly coupled with description logics and Bayesian probabilities. The approach has several nice features. In particular, it is a logic-based representation formalism that naturally fits into the landscape of Semantic Web languages. Tightly coupled probabilistic description logic programs can especially be used for representing mappings between ontologies, which are a common way of approaching the semantic heterogeneity problem on the Semantic Web. In this application, they allow in particular for resolving inconsistencies and for merging mappings from different matchers based on the level of confidence assigned to different rules. Furthermore, tightly coupled probabilistic description logic programs also provide a natural integration of ontologies, action languages, and Bayesian probabilities towards Web Services. We explore the computational aspects of consistency checking and query processing in tightly coupled probabilistic description logic programs. We show that these problems are decidable and computable, respectively, and that they can be reduced to consistency checking and cautious/brave reasoning, respectively, in tightly coupled disjunctive description logic programs. Using these results, we also provide an anytime algorithm for tight query processing. Furthermore, we analyze the complexity of consistency checking and query processing in the new probabilistic description logic programs, and we present a special case of these problems with polynomial data complexity. © 2009 Springer Berlin Heidelberg.

Automating the discovery of mappings between structured data sources is a long standing and important problem in data management. We discuss the rich history of the problem and the variety of technical solutions advanced in the database community over the previous four decades. Based on this discussion, we develop a basic statement of the data mapping problem and a general framework for reasoning about the design space of system solutions to the problem. We then concretely illustrate the framework with the Tupelo system for data mapping discovery, focusing on the important common case of relational data sources. Treating mapping discovery as example-driven search in a space of transformations, Tupelo generates queries encompassing the full range of structural and semantic heterogeneities encountered in relational data mapping. Hence, Tupelo is applicable in a wide range of data mapping scenarios. Finally, we present the results of extensive empirical validation, both on synthetic and real world datasets, indicating that the system is both viable and effective.

This article presents a technique to incrementally maintain materializations of ontological entailments. Materialization consists in precomputing and storing a set of implicit entailments, such that frequent and/or crucial queries to the ontology can be solved more efficiently. The central problem that arises with materialization is its maintenance when axioms change, viz. the process of propagating changes in explicit axioms to the stored implicit entailments. When considering rule-enabled ontology languages that are operationalized in logic databases, we can distinguish two types of changes. Changes to the ontology will typically manifest themselves in changes to the rules of the logic program, whereas changes to facts will typically lead to changes in the extensions of logical predicates. The incremental maintenance of the latter type of changes has been studied extensively in the deductive database context and we apply the technique proposed in [30] for our purpose. The former type of changes has, however, not been tackled before. In this article we elaborate on our previous papers [32, 33], which extend the approach of [30] to deal with changes in the logic program. Our approach is not limited to a particular ontology language but can be generally applied to arbitrary ontology languages that can be translated to Datalog programs, i.e. such as O-Telos, F-Logic [16] RDF(S), or Description Logic Programs [34]. © Springer-Verlag Berlin Heidelberg 2004.

This paper presents an attempt of exploiting advanced database technologies for the representation and the analysis of animal behavior. In this context, formalisms such as MADS and MuTACLP have been proposed as approaches representing and handling spatio-temporal information. These formalisms are experimented here in a case study, provided by biologists, concerning the study of animal behaviour. This paper copes with the case study from three different points of view. First of all the conceptual model of the problem is provided, by using MADS. Then, a subset of the problem is represented in MuTACLP, a logical model able to formalise and solve the queries the biologists are interested in. Then we experiment the support of commercial GIS technology for the analysis of spatio-temporal data.

International audience; The rapid expansion of WWW has unobtrusively influenced humans' socio-economical life patterns in the last decade. With the development of portable phones, PDAs and laptops, Humans cannot be satisfied anymore simply by rich and exact access to the information. They look today for the more dynamic and personalized services on the move. Thus, the ubiquitous computing approach [1] is becoming emergent and popular. As one of its significant components, especially due to its dynamical characteristics of the space, time and contexts, location-based services (LBS) [2][3][4] emphasize the ability to take into account the spatial, temporal and contextual characteristics of their interactions with the users to provide the most appropriate service based on the local environment. Their location-centric approach distinguishes them from the traditional research on web information retrieval and personal navigation [5][6]. A key point of improving LBS is how to tailor the contents and representation to individual's specific spatio-temporal context. In the literature, some research [6][7][8] has been using user profiles in information filtering. In this paper1, we attempt to flexibly specify the user profiles in XML Schema, and then apply them to refine the queries considering the particular space and time constraints, finally make the mapping between the pre-processed query and data. Simply stated, the user profile serves as a bridge between the generic queries from the diverse users and the heterogeneous data.

In electronic commerce scenarios, effectiveness and efficiency of business process execution are of paramount importance for business success. Even more than in traditional commerce scenarios, they determine the chances of survival of organizations in fast moving, highly competitive electronic markets. To obtain the required levels of effectiveness and efficiency, well-structured automated business process support is required. For typical business-to-consumer (B2C) electronic commerce, process support is usually of an intra-organizational nature. For business-to-business (B2B) electronic commerce, however, process support across organizational boundaries is often required as the basis for virtual enterprises.