Stéphane Bressan

Context interchange: new features and formalisms for the intelligent integration of information

The Context Interchange strategy presents a novel perspective for mediated data access in which semantic conflicts among heterogeneous systems are not identified a priori, but are detected and reconciled by a context mediator through comparison of contexts axioms corresponding to the systems engaged in data exchange. In this article, we show that queries formulated on shared views, export schema, and shared “ontologies” can be mediated in the same way using the Context Interchange framework. The proposed framework provides a logic-based object-oriented formalsim for representing and reasoning about data semantics in disparate systems, and has been validated in a prototype implementation providing mediated data access to both traditional and web-based information sources.

Context Knowledge Representation and Reasoning in the Context Interchange System

The Context Interchange Project presents a unique approach to the problem of semantic conflict resolution among multiple heterogeneous data sources. The system presents a semantically meaningful view of the data to the receivers (e.g. user applications) for all the available data sources. The semantic conflicts are automatically detected and reconciled by a Context Mediator using the context knowledge associated with both the data sources and the data receivers. The results are collated and presented in the receiver context. The current implementation of the system provides access to flat files, classical relational databases, on-line databases, and web services. An example application, using actual financial information sources, is described along with a detailed description of the operation of the system for an example query.

The Context Interchange mediator prototype

The <italic>Context Interchange</italic> strategy presents a novel approach for mediated data access in which semantic conflicts among heterogeneous systems are not identified a priori, but are detected and reconciled by a <italic>context mediator</italic> through comparison of <italic>contexts</italic>. This paper reports on the implementation of a Context Interchange Prototype which provides a concrete demonstration of the features and benefits of this integration strategy.

Indexing the edges—a simple and yet efficient approach to high-dimensional indexing

In this paper, we propose a new tunable index scheme, called iMinMax(<italic>&Ogr;</italic>), that maps points in high dimensional spaces to single dimension values determined by their maximum or minimum values among all dimensions. By varying the tuning “knob” <italic>&Ogr;</italic>, we can obtain different family of iMinMax structures that are optimized for different distributions of data sets. For a <italic>d</italic>-dimensional space, a range query need to be transformed into <italic>d</italic> subqueries. However, some of these subqueries can be pruned away without evaluation, further enhancing the efficiency of the scheme. Experimental results show that iMinMax(<italic>&Ogr;</italic>) can outperform the more complex Pyramid technique by a wide margin.

XOO7: applying OO7 benchmark to XML query processing tool

If XML is to play the critical role of the lingua franca for Internet data interchange that many predict, it is necessary to start designing and adopting benchmarks allowing the comparative performance analysis of the tools being developed and proposed. The effectiveness of existing XML query languages has been studied by many, with a focus on the comparison of linguistic features, implicitly reflecting the fact that most XML tools exist only on paper. In this paper, with a focus on efficiency and concreteness, we propose a pragmatic first step toward the systematic benchmarking of XML query processing platforms with an initial focus on the data (versus document) point of view. We propose XOO7, an XML version of the OO7 benchmark. We discuss the applicability of XOO7, its strengths, limitations and the extensions we are considering. We illustrate its use by presenting and discussing the performance comparison against XOO7 of three different query processing platforms for XML.

Rule-assisted prefetching in Web-server caching

Web servers manage large number of documents of widely variable sizes. Moreover, the access patterns on the documents may also c hange over time. While some documents are highly popular over a prolonged period of time, we expect newly added documents to increase in popularity while demand for most older documents decreases. It is therefore important to design e ective caching strategy at the web server. In this paper, we present our approach to the problem. Our main contribution lies in the design of a novel prefetching strategy, called RAP. RAP identi es a set of association rules from the Web servers access log. Unlike existing mining strategy, RAPs miner values recently added log records more than earlier log records. Based on the rules, RAP predicts and prefetches documents from users initial requests. We conducted extensive study to evaluate RAP. The results show that RAP signi cantly outperforms existing schemes. We also show that the mining and caching cost is relatively low.

Mining term association rules for automatic global query expansion: methodology and preliminary results

The authors are looking at the mining of association between terms for the automatic expansion of queries. The technique used for the discovery of the associations is association rule mining (R. Agrawal et al., 1996). The technique proposed is more flexible than previous techniques based on term co-occurrence since it takes into account not only the co-occurrence frequency but also the confidence and direction of the association rules. We have been able to consistently improve the effectiveness of the retrieval over the set of 48 test queries on the Associated Press 1990 news wires corpus of the TREC4 benchmark by query expansion using term association rules.

Fast Identity Anonymization on Graphs

Liu and Terzi proposed the notion of k-degree anonymity to address the problem of identity anonymization in graphs. A graph is k-degree anonymous if and only if each of its vertices has the same degree as that of, at least, k-1 other vertices. The anonymization problem is to transform a non-k-degree anonymous graph into a k-degree anonymous graph by adding or deleting a minimum number of edges.

Fast random graph generation

Today, several database applications call for the generation of random graphs. A fundamental, versatile random graph model adopted for that purpose is the Erdős-Rényi Γv,p model. This model can be used for directed, undirected, and multipartite graphs, with and without self-loops; it induces algorithms for both graph generation and sampling, hence is useful not only in applications necessitating the generation of random structures but also for simulation, sampling and in randomized algorithms. However, the commonly advocated algorithm for random graph generation under this model performs poorly when generating large graphs, and fails to make use of the parallel processing capabilities of modern hardware. In this paper, we propose PPreZER, an alternative, data parallel algorithm for random graph generation under the Erdős-Rényi model, designed and implemented in a graphics processing unit (GPU). We are led to this chief contribution of ours via a succession of seven intermediary algorithms, both sequential and parallel. Our extensive experimental study shows an average speedup of 19 for PPreZER with respect to the baseline algorithm.

Current approaches to XML management

The Extensible Markup Language has become the standard for information interchange on the Web. We study the data- and document-centric uses of XML management systems (XMLMS). We want to provide XML data users with a guideline for choosing the data management system that best meets their needs. Because the systems we test are first-generation approaches, we suggest a hypothetical design for a useful XML database that could use all the expressive power of XML and XML query languages.