André Flory

Adaptation des applications au contexte en utilisant les services web

The SECAS Project (Simple Environment for Context Aware Systems) is interested in the adaptation of applications to the context (preferences and environment of the user, used terminal ...). We aim to develop a platform which makes the data, the services and the user interface of applications adaptable to the various context situations. In this domain, researches have concentrated on how to capture context data and to carry it to the application. Our work focuses on the impact of context on the application. In this paper, we present the architecture of SECAS, a new definition of the context, our adaptation strategy and a case of study in the medical field.

Using business rules within a design process of active databases

Modeling behavior is an important task of the information system engineering process. This task is especially important when information systems are centered on active databases, which allow the replacement of parts of application programs with active rules. To relieve programmers from using either traditional or ad hoc techniques to design active databases, it is necessary to develop new techniques to model business rules. For that reason, inclusion of rules during analysis and design stages becomes an actual requirement. In this paper, we propose a uniform approach to modeling business rules (active rules, integrity constraints, etc. ). To improve the behavior specification, we extend the state diagrams that are widely used for dynamic modeling. This extension is a transformation of state transitions according to rule semantics. In addition, we outline new functionalities of Computer Aided System Engineering (CASE) to take into consideration the active database specificities. In this way, the designer can be assisted to control, maintain, and reuse a set of rules.

Extraction of Object-oriented Schemas from Existing Relational Databases: a Form-driven Approach

In this paper, we present our Form-driven approach for reverse engineering of relationa databases. This methodology uses the information extracted from both form structure and instances as a database reverse engineering input using an interaction with a user. Through a combination of forms structures and data instances analysis, forms relational sub-schemas and their constraints are derived. These relational sub-schemas are mapped to object sub-schemas, which will be merging into global object-oriented schema that presents the whole underlying databases. The resulting global object-oriented schema must be validated as a rich and correct representation of the application domain.

Transformation rules from semi-structured XML documents to database model

XML semi-structured documents provide a flexible and natural way to capture data. More often, users exchange documents with others or reuse their data in a wide range of tasks. A direct consequence is a growing need to manage semi-structured documents and to gain conventional database functionality. The aim of our research is the development of a comprehensive capturing and mapping data system that ensures flexible and well-adapted information capture and at the same time efficient information retrieval. A transformation process allows the mapping between the semi-structured document model and the traditional database model. It handles data extraction and data restructuring and deals with data mapping rules to feed traditional databases.

System for Medical Image Retrieval: The MIMS Model

The multifaceted description of image data presents a couple of problems for traditional information systems designed for textual data. Image databases require efficient and direct spatial search based on image objects and their relationships instead ofsom e cumbersome alphanumeric encodings oft he images. Current systems generally approach the problem from different views. Thus, they describe one image facet dependently on application domain. This paper presents a model for Medical Image Management System (MIMS) that allows physicians to retrieve images and required information by combining the strengths ofsev eral approaches. Via a convivial iconic interface, our system assign to each image object a graphical representation to describe image objects and their attributes. It allows also to automatically calculate their interrelations.

Information Warehouse for Medical Research

Data warehousing imposes itself as an attractive solution for centralizing and analyzing high quality data. In the medical research field, this technology can be used to validate assumptions and to discover trends on large amount of patient data. However, like other scientific complex data, medical data and especially raw sensor data need to be processed before becoming interpretable. The selection of the process mode is a key issue in the physicians reasoning. In our study, we propose a solution to gather data and processes into a single information warehouse. Our solution provides features for loading, modeling and querying the information warehouse. Stored data are multidimensional data (patient identity, therapeutic data...), raw sensor data (electrocardiogram, X-ray...) and processes. A prototype has been implemented and is illustrated in the cardiology domain to finalise processes in order to detect heart arrhythmia and acute myocardial ischemia that may lead to sudden cardiac death.

Adaptive context for medical information systems

With the considerable increase of data and the diversity of the user’s needs, the personalisation of information becomes a real challenge in medical information systems. In this context, we propose a technique to personalise medical information, based on a profile modeling. This technique consists in: 1) building user’s profile and 2) using these profiles in the medical information adaptation. The first step considers three dimensions: access right, skills, preferences. The second step consists in filtering data in response to user query according to profile content. Thus, users access relevant information personalized in the appropriate form at the right time with minimal user effort.

Static and dynamic reverse engineering of relational database applications: a form-driven methodology

We propose a form-based approach for reverse engineering of legacy relational database applications. The approach consists of three dependent steps: (1) acquisition of both structure and instances of forms; (2) extraction of the static aspect of the legacy database: through a combination of forms structures and data instance analysis, forms relational sub-schemas and their constraints are derived. These relational sub-schemas are mapped into object sub-schemas, which will be merged into global object schema that presents the whole underlying database; (3) extraction of the dynamic aspect of the legacy application: through the results of static aspect extraction, form behavior relationships are extracted in order to construct both methods of call graph of form and forms network.

Semantic Indexing for Intelligent Browsing of Distributed Data

We present in this paper a semantic indexing technics based on description logics. Data to be indexed are semantically organized as a Topic Map. The index is constructed according to data organization, user profile, and data distribution (association rules). This way leads to obtain a more efficient index and represents more semantics. The index is well adapted to jointly query and navigate in the topic map. DL allows to represent semantics and performs powerful reasoning. The index structure is based on subsumption relationships (for intra-concept indexing) and roles (for inter-concepts indexing).

Encapsulation of Intra-operation Parallelism in a Parallel Match Operator

This paper presents a new concept for relational parallel query optimization. We propose to encapsulate the intra-operation parallelism in a parallel match operator realizing most binary relational operations. Our approach integrates the optimization of this operator: a comparative study of the three main match algorithms — Nested Loops, Sort-Merge and Hash — is presented for shared-nothing architecture. It shows that database systems could profit by the implementation of all these algorithms. Futhermore, our match optimizer can be used by the query parallelizer in order to determine an optimal parallel execution strategy for each binary relational operation.