Governing business compliance with regulations, laws, best practices, contracts, and the like is not an easy task, and so far there are only limited software products available that help a company to express compliance rules and to analyze its compliance state. We argue that todaypsilas SOA-based way of implementing and conducting business (e.g., using Web services and business process engines) lends itself very well to the development of a comprehensive compliance government solution that effectively aids companies in being compliant. In this paper, we contextualize the compliance problem in SOA-based businesses, we highlight which are the most salient research challenges that need to be addressed, and we describe our approach to compliance governance, spanning design, execution, and evaluation concerns.

Uncertainty is one of the major challenges related to the semantic gap in multimedia data description and retrieval. It is due not only to errors and imprecisions in content classification but also to the extended range of user queries. In this paper, an extension of fuzzy conceptual graphs, suitable for handling uncertainty in visual event description and retrieval, is presented. We deal with two types of graphs according to the sources of uncertainty. An extension of fuzzy spatial and temporal relationships is defined to capture imprecision in video content spatiotemporal descriptions. Moreover, similarity measures and matching algorithms are defined to assess the degree of match between the descriptions of video objects and the event model and then to detect events within video segments.

Behavior understanding and semantic interpretation of dynamic visual scenes have attracted a lot of attention in computer vision research community. Although the use of surveillance cameras has proliferated, the understanding of activities still remains complex. While users are mostly interested in high level and subjective semantics, only low level visual features can be extracted in a reliable way. This paper presents a novel framework for video guided behavior monitoring, built around the event modeling concept. It enables users to design their personal models of events combining elementary concept and low level features using expressive formalisms. The framework enables then detection of the events within video streams based on low level features extraction and manual annotations analysis, while taking in consideration uncertainty. Examples depicting content-based events modeling and detection from video surveillance are presented to illustrate the approach.

Interesting pattern mining is an important family of data mining problems with applications in many domains. In the last decade, many algorithms and benchmarks have been devised and many experimental studies have been performed for pattern mining problems. In this paper, we focus on the special class of pattern mining problems known to be ‘representable as sets’. This class of problem is important and have many applications such as in data mining, in databases, in artificial intelligence, or in software engineering.

The discovery of frequent patterns is a famous problem in data mining. While plenty of algorithms have been proposed during the last decade, only a few contributions have tried to understand the influence of datasets on the algorithms behavior. Being able to explain why certain algorithms are likely to perform very well or very poorly on some datasets is still an open question. In this setting, we describe a thorough experimental study of datasets with respect to frequent item sets. We study the distribution of frequent item sets with respect to item sets size together with the distribution of three concise representations: frequent closed, frequent free and frequent essential item sets. For each of them, we also study the distribution of their positive and negative borders whenever possible. From this analysis, we exhibit a new characterization of datasets and some invariants allowing to better predict the behavior of well known algorithms. The main perspective of this work is to devise adaptive algorithms with respect to dataset characteristics.

Understanding data semantics from existing relational databases is important for several applications such as database maintenance and analysis, database re-engineering, data warehouse design or query optimization. We present a tool called DBA Companion, which can be a help to deal with the understanding of existing relational databases. The prototype integrates algorithms dedicated to database analysis. This task rests on data mining techniques, which allow to design efficient algorithms. Emphasis is put on algorithm efficiency to be able to address operational situations, and then discover FDs and INDs satisfied in a database instance. The tool follows a loosely coupled approach with the underlying DBMS for algorithm execution. For instance, IARs are generated in the DBMS, which allows the user to modify them and to reiterate the analysis process from these new relations.

In the relational model, inclusion dependencies (INDs) convey many information on data semantics. They generalize foreign keys, which are very popular constraints in practice. However, one seldom knows the set of satisfied INDs in a database. The IND discovery problem in existing databases can be formulated as a data-mining problem. We underline that the exploration of IND expressions from most general (smallest) INDs to most specific (largest) INDs does not succeed whenever large INDs have to be discovered. To cope with this problem, we introduce a new algorithm, called Zigzag, which combines the strength of levelwise algorithms (to find out some smallest INDs) with an optimistic criteria to jump more or less to largest INDs. Preliminary tests, on synthetic databases, are presented and commented on. It is worth noting that the main result is general enough to be applied to other data-mining problems, such as maximal frequent itemsets mining.