Yasser Hachaichi

AN AUTOMATIC METHOD FOR THE DESIGN OF MULTIDIMENSIONAL SCHEMAS FROM OBJECT ORIENTED DATABASES

A data warehouse (DW) is a large data repository system designed for decision-making purposes. Its design relies on a specific model called multidimensional. This multidimensional model supports analyses of huge volumes of data that trace the enterprises activities over time. Several design methods were proposed to build multidimensional schemas from either the relational data model or the entity-relationship data model. Almost all proposals that treated the object-oriented data model assume the presence of the data source UML class-diagram. However, in practice, either such a diagram does not exist or is obsolete due to multiple changes/evolutions of the information system. Furthermore, these few proposals require an intense manual intervention of the designer, which requires a high expertise both in the DW domain and in the object database domain. To overcome these disadvantages, this work proposes an automatic DW schema design method starting from an object database (schema and its instances). This method applies a set of extraction rules to identify multidimensional concepts and to generate star schemas. It is defined for the standard ODMG model and, thus, can be adapted with slight changes for other object database models. In addition, its extraction rules have the merit of being independent of the domain semantics. Furthermore, they automatically generate schemas classified according to their analytical potential; this classification helps the DW designer in selecting the most relevant schemas among the generated ones. Finally, being automatic, our method is supported by a toolset that also prepares for the automatic generation of the Extract Transform and Load procedures used to load the DW.

Conception assistée de MD : Une démarche et un outil

To assist the decisional designer in building data mart star schemes relying on a relational data source, we propose an approach supported by a tool. Our approach is independent of the semantic of any source information system; it classifies the relations in Rentities and R-associations and, is based on the structural semantics of the relations which is disseminated through the primary keys and referential constraints. This approach extracts facts, dimensions and hierarchies using a set of appropriate heuristics. A software tool baptized CAME is developed to support our proposed method; it constructs automatically star scheme data marts.

Modélisation multidimensionnelle de documents XML centrés-données

Today’s international nature of commerce forced the opening of corporal information systems (IS) to accept data required for both transactional and decisional processes. To ensure the interoperability of so opened IS, XML has been adopted as a de facto standard format for exchanged data. In order to accommodate this data format in the design of decision support systems, we propose in this paper an automatic method for the design of data mart schemas from XML documents. The proposed method has the merit of automatically identifying all multidimensional elements, classifying them in terms of their analytical potential, and tracing them to the source which facilitates the definition of ETL procedures. For this, our method analyzes the DTD of the source documents and consults a sample of XML documents to determine typing information when needed. It relies on a set of identification rules that are independent of the domain of the XML documents. Furthermore, being automatic, it is supported by a CASE toolset that facilitates its exploitation.

Decision Tree Transformation for Knowledge Warehousing

Explicit knowledge extracted from data, formalized tacit knowledge from experts or even knowledge existing in business sources may be in several heterogeneous formal representations and structures: as rules, models, functions, etc. However, a knowledge warehouse should solve this structural heterogeneity before storing knowledge. This requires specific tasks of harmonizing. This paper first presents our proposed definition and architecture of a knowledge warehouse, and then presents some languages for knowledge representations as particular the MOT (Modeling with Object Types) language. In addition, we suggest a metamodel for the MOT, and a metamodel for the explicit knowledge obtained using decision trees technique. As we aim to represent knowledge having different modeling formalisms into MOT, as a unified model, then we suggest a set of transformation rules that assure the move from the decision tree source model into the MOT target model. This work is still in progress, it is currently completed with tranformations for additional.

MOT Knowledge Model Integration Rules for Knowledge Warehousing

Abstract A knowledge warehousing process aims to build an intelligent decision support system. It collects, homogenizes, integrates and stores knowledge for a decision-making process. In this paper, we are interested in knowledge integration. More accurately, we propose an integration process for knowledge homogenized/modeled according to the MOT (Modeling with Object Types) knowledge model. This integration process consists of three ordered steps based on the type of schemas to integrate and their similarity. For this process, we define five integration rules based on semantic relationships between elements of MOT knowledge models, and then we develop an algorithm using these integration rules.

Association Rules Transformation for Knowledge Integration and Warehousing

Knowledge management process is a set of procedures and tools applied to facilitate capturing, sharing and effectively using knowledge. However, knowledge collected from organizations is generally expressed in various formalisms, therefore it is heterogeneous. Thus, a Knowledge Warehouse (KW), which is a solution for implementing all phases of the knowledge management process, should solve this structural heterogeneity before loading and storing knowledge. In this paper, we are interested in knowledge normalization. More accurately, we firstly introduce our proposed architecture for a KW, and then we present the MOT (Modeling with Object Types) language for knowledge representation. Since our objective is to transform heterogeneous knowledge into MOT, as a pivot model, we suggest a meta-model for the MOT and another for the explicit knowledge extracted through the association rules technique. Thereafter, we define eight transformation rules and an algorithm to transform an association rules model into the MOT model.

Hierarchy integration in the design of data warehouses

to their merger and/or collaboration with partners, todays enterprises often need to integrate several databases. As a result, their decision making process ends up analyzing data coming from various databases. While database integration has been thoroughly examined, it is only recently that the integration of multidimensional models has drawn attention. A multidimensional model is a data model that facilitates the analysis operations during the decision making process. It organizes data into facts that can be analyzed according to dimensions represented through hierarchies. This paper presents an integration process for the hierarchy concept. In particular, it proposes a set of basic integration operations and constraints that produce a multidimensional model that is loadable from different data sources. It illustrates the integration process through examples.