Ana Simonet

Algorithms and Support for Horizontal Class Partitioning in Object-Oriented Databases

Horizontal partitioning is a logical database design technique which facilitates efficient execution of queries by reducing the irrelevant objects accessed. Given a set of most frequently executed queries on a class, the horizontal partitioning generates horizontal class fragments (each of which is a subset of object instances of the class), that meet the queries requirements. There are two types of horizontal class partitioning, namely, primary and derived. Primary horizontal partitioning of a class is performed using predicates of queries accessing the class. Derived horizontal partitioning of a class is the partitioning of a class based on the horizontal partitioning of another class. We present algorithms for both primary and derived horizontal partitioning and discuss some issues in derived horizontal partitioning and present their solutions. There are two important aspects for supporting database operations on a partitioned database, namely, fragment localization for queries and object migration for updates. Fragment localization deals with identifying the horizontal fragments that contribute to the result of the query, and object migration deals with migrating objects from one class fragment to another due to updates. We provide novel solutions to these two problems, and finally we show the utility of horizontal partitioning for query processing.

Vertical fragmentation in distributed object database systems with complex attributes and methods

Object oriented database management systems are becoming popular and are being used in a large number of application domains, many of which are inherently distributed. Optimal application performance of a distributed object oriented database system requires class fragmentation and the development of allocation schemes to place fragments at distributed sites in order to minimize data transfer. Our approach is top down, and the entity of fragmentation is the class. Fragmentation algorithms have been proposed for the relational model, but the object model is relatively untouched. We present an algorithm for vertical fragmentation in a model consisting of complex attributes and complex methods. This type of fragmentation facilitates query decomposition, optimization, and parallel treatment for distributed object oriented database systems.

SIMS@REIN : un système d’information multi-sources pour l’insuffisance rénale terminale

In France, the prevalence of End-Stage Renal Disease (ESRD) is not precisely known. The sources of information are scattered and not coordinated. Consequently, care is ill adapted to meet the demand. The Multi-Source Information System is the basis of the Renal Epidemiology and Information Network (REIN). It is dedicated to improve and organise our medical and epidemiological knowledge of ESRD and to aid public health decision-making in this area. The proposed approach is based on the datawarehouses. This model allows a unified vision of scattered data into distinct databases, for a better management, be it particular (patient follow-up) or global (regional follow-up), with a finality of aid in decision-making. Several categories of problems were considered: the global conception of the information system, the organisation of the datawarehouse, which offers different viewpoints of the data, the integration of heterogeneous data coming from different sources, data exchange and definition of a specific ontology.

Objects with Views and Constraints: from Databases to Knowledge bases

We present an object model which embodies views as a primitive notion. Assertions play a prominent role in defining classes and views in a declarative manner, and in implementation issues, through the partitioning of the object space which is derived from their analysis. Assertions may be interpreted as integrity constraints or production rules according to whether the class of the object is known (databases), or has to be inferred from the known attribute values (knowledge bases).

Dealing with Missing Values in a Probabilistic Decision Tree during Classification

This paper deals with the problem of missing values in decision trees during classification. Our approach is derived from the ordered attribute trees method, proposed by Lobo and Numao in 2000, which builds a decision tree for each attribute and uses these trees to fill the missing attribute values. Our method takes into account the dependence between attributes by using Mutual Information. The result of the classification process is a probability distribution instead of a single class. In this paper, we present tests performed on several databases using our approach and Quinlans method. We also measure the quality of our classification results. Finally, we discuss some perspectives.

Bringing Together Description Logics and Database in an Object Oriented Model

Description logics have powerful deductive capabilities but rely on a weakly structured model. Databases provide storage optimization and offer better structuring capacities, especially within the object-oriented paradigm. We propose a model which offers the deductive capabilities of description logics and the structuring possibilities of the object approach. We distinguish between three types of classes : concrete classes, where objects are instantiated, virtual classes, which are subsets of concrete classes, corresponding to database object-preserving views, and abstract classes, which factor common properties of objects from different concrete classes. A schema in this model can be translated into a description logics schema and into a database schema. The former represents the deductive part and the latter deals with the storage aspect.

A Probabilistic Approach to Classify Incomplete Objects Using Decision Trees

We describe an approach to fill missing values in decision trees during classification. This approach is derived from the ordered attribute trees method, propo sed by Lobo and Numao in 2000, which builds a decision tree for each attribute and uses these trees to fill the missing attribute values. It is based on the Mutual Information between the attributes and the class. Our approach primarily extends this method on three points: 1) it does not impose an order of construction; 2) a probability distribution is used for each missing attribute instead of the most pro bable value; 3) the result of the classification process is a probability distribution i nstead of a single class. Moreover, our method takes the dependence between attributes into account. We present Lobo’s approach and our extensions, we compare them, and we discuss some perspectives.

An Ontology Driven Approach to Ontology Translation

Translating ontologies from a source language towards a target one is a required process both for the conception of a new ontology from existing ones (reuse) and for common use of an ontology by different knowledge based systems. The usual approach for translation is based upon the existence of a translator for each pair of languages from and to which the ontology must be translated. Therefore, several translating tools are necessary. We propose a translation approach which employs a unique translator no matter what source and target languages are involved. It relies on two principal components. The first one is a meta-language called M_Kif which extends the Kif language with the concept of meta-relation to describe representation ontologies. The second one is a pivot representation which unifies different styles of representations. The translation tool is an interpretation program of the meta-relation definitions specified in the source and target representation ontologies.

An approach to automatic ontology-based annotation of biomedical texts

Sharing and enriching of documents is expected and is made possible nowadays with tools enabling users to perform different kinds of annotations. We propose an Ontology-based approach to automate the semantic annotation of texts; Ontologies are represented in OWL (Web Ontology Language). OWL is supported by Semantic Web tools such as Racer for reasoning purpose and Jena. The tool for automatic semantic annotation supporting word-based and stem-based pre-indexing techniques is presented and its evaluation is made on three medical corpora both in English and French (brain disease area, cardiology and OHSUMED collection). The evaluation shows difference in the results obtained according to the pre-indixng mode used.

Towards a declarative approach for reusing domain ontologies

Abstract To ensure the reuse of domain ontologies, we propose an approach which uses a unique translator whatever the target system. It relies on a meta-language which allows a declarative description of any model based on frames, objects, relations, or description logics. Its basic concept is the meta-relation concept which, associated to a definition, represents the signification of the construct being defined.