Olfa Arfaoui

Using Conceptual Scaling for Indexing XML Native Databases

The essential purpose of indexing techniques is to find methods that ensure faster access to a given well-defined data and thus avoid a sequential scan of document. Two approaches are used for indexing XML documents: indexing based on the values and structural one. However, indexing XML documents for research purposes can be a complex task especially when we consider content and structure. The aim is to provide a combined structure while assuring hierarchical levels of data content and structure representation. In this paper, we propose to use conceptual scaling-based Formal Concept Analysis for indexing both content and structure.

Indexing Compressed XML Documents

XML data compression process seems to be inevitable to solve some problems related to the evolutionary growth of such data. Therefore, the indexing of compressed XML data, meanwhile, remains an important process and needs improvement and development in order to exploit the compressed data for querying and information retrieval. This work consists in studying and analyzing some suitable compressors to improve the indexing compressed XML documents process in order to query them later. We propose a new indexing process which leads in compressed XML data by re-indexing compressed XML data under XMill compressor.

An Ontology-driven MapReduce Framework for Association Rules Mining in Massive Data

Abstract To be competitive, companies need to be able to take advantage of the huge amounts of data, called also Big Data deluge, to predict what might happen in the future. In this way, predictive analytics play an important role for extracting useful information which may extend the business strategy and so gain competitive advantages. Predictive analytics involve data mining algorithms to discover knowledge from huge volumes of data. In this context, Association Rules (ARs) mining is considered as one of the most wide-spread data mining techniques. It is especially based on frequent itemsets mining process. However, when it comes to Big Data, ARs mining algorithms produce a huge amount of ARs, many of which are redundant and unuseful. To overcome this drawback, we propose a ontology-driven Map-Reduce Framework for ARs mining in massive data. Ontologies allow to filter the generated ARs and keep only useful ones. The filtering process is assured by a semantic pruning phase introduced in the Map-Reduce jobs in order to eliminate unuseful candidates from the computing of the Maximal Frequent Itemsets (MFI). This may allow a quantitative and especially qualitative reduction of the number of MFI and subsequently of the ARs. Extensive experiments on several datasets demonstrate the ability to handle massive data for mining ARs.

Ontology-based query refinement for XML information retrieval

The characteristics of XML (eXtensible Markup Language) documents have favored the need to develop specific and flexible querying systems while taking into account the coexistence of both structural and content information. The ultimate goal of these systems is to respond to different user expectations which tend to return appropriate answers to their preferences. However, people have often insufficient knowledge about XML data structure and contents, thus frequently obtaining empty answers or having to reformulate the queries several times. To solve this problem, we propose an ontology-based query refinement model for semi-structured information retrieval. It consists in reformulating a query by adding attributes from domain ontologies extracted from XML schemas.