Hadj Mahboubi

Fragmenting very large XML data warehouses via K-means clustering algorithm

XML data sources are gaining popularity in the context of Business Intelligence and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing complex and heterogeneous data. However, XML-native database systems currently suffer from limited performance, both in terms of volumes of manageable data and query response time. Therefore, recent research efforts are focusing on horizontal fragmentation techniques, which are able to overcome the above limitations. However, classical fragmentation algorithms are not suitable to control the number of originated fragments, which instead plays a critical role in data warehouses. In this paper, we propose the use of the K-means clustering algorithm for effectively and efficiently supporting the fragmentation of very large XML data warehouses. We complement our analytical contribution with a comprehensive experimental assessment where we compare the efficiency of our proposal against existing fragmentation algorithms.

Data mining-based fragmentation of XML data warehouses

With the multiplication of XML data sources, many XML data warehouse models have been proposed to handle data heterogeneity and complexity in a way relational data warehouses fail to achieve. However, XML-native database systems currently suffer from limited performances, both in terms of manageable data volume and response time. Fragmentation helps address both these issues. Derived horizontal fragmentation is typically used in relational data warehouses and can definitely be adapted to the XML context. However, the number of fragments produced by classical algorithms is difficult to control. In this paper, we propose the use of a k-means-based fragmentation approach that allows to master the number of fragments through its k parameter. We experimentally compare its efficiency to classical derived horizontal fragmentation algorithms adapted to XML data warehouses and show its superiority.

Expressing OLAP operators with the TAX XML algebra

With the rise of XML as a standard for representing business data, XML data warehouses appear as suitable solutions for Web-based decision-support applications. In this context, it is necessary to allow OLAP analyses over XML data cubes (XOLAP). Thus, XQuery extensions are needed. To help define a formal framework and allow much-needed performance optimizations on analytical queries expressed in XQuery, having an algebra at ones disposal is desirable. However, XOLAP approaches and algebras from the literature still largely rely on the relational model and/or only feature a small number of OLAP operators. In opposition, we propose in this paper to express a broad set of OLAP operators with the TAX XML algebra.

Towards the definition of spatial data warehouses integrity constraints with spatial OCL

Spatial Data Warehouses (SDWs) and Spatial OLAP systems are an effective solution to perform multidimensional spatial analysis on geographical data. However, the quality of such analysis heavily depends on the quality of stored data. Due to this, some works have been attempted to address the issues of data quality of SDWs using Integrity Constraints (ICs). In this paper, we propose a conceptual framework for SDW ICs based on two new classifications. Moreover, we describe the Model-Driven Architecture (MDA)-based specification and implementation of some IC classes using the Object Constraint Language (OCL) and an MDA-based code generator, called Spatial OCL2SQL

Semi-Automatic Design of Spatial Data Cubes from Simulation Model Results

In this paper the authors present a semi-automatic method for designing and generating spatial data cubes in order to visualize and analyze the results of simulation models. In the authors approach users choose their fact of analysis, then the system derives automatically a set of possible measures, dimensions of analysis, and generates the corresponding spatial data cubes. The analysis and visualization of the spatial data cubes are carried out using appropriate SOLAP tools. They also present the SimOLAP system, developed to validate their approach.

XWeB: the XML warehouse benchmark

With the emergence of XML as a standard for representing business data, new decision support applications are being developed. These XML data warehouses aim at supporting On-Line Analytical Processing (OLAP) operations that manipulate irregular XML data. To ensure feasibility of these new tools, important performance issues must be addressed. Performance is customarily assessed with the help of benchmarks. However, decision support benchmarks do not currently support XML features. In this paper, we introduce the XML Warehouse Benchmark (XWeB), which aims at filling this gap. XWeB derives from the relational decision support benchmark TPC-H. It is mainly composed of a test data warehouse that is based on a unified reference model for XML warehouses and that features XML-specific structures, and its associate XQuery decision support workload. XWeBs usage is illustrated by experiments on several XML database management systems.

Analyzing demographic and economic simulation model results: a semi-automatic spatial OLAP approach

In this paper, we present a semi-automatic SOLAP approach specially dedicated to the analysis of spatial model simulation results. We illustrate it on demographic and economic data of rural municipalities resulting from a model developed in the context of the European project PRIMA.

Indices in XML Databases.

With XML becoming a standard for business information representation and exchange, stor-ing, indexing, and querying XML documents have rapidly become major issues in database research. In this context, query processing and optimization are primordial, native-XML data-bases not being mature yet. Data structures such as indices, which help enhance performances substantially, are extensively researched, especially since XML data bear numerous specifici-ties with respect to relational data. In this paper, we survey state-of-the-art XML indices and discuss the main issues, tradeoffs and future trends in XML indexing. We also present an in-dex that we specifically designed for the particular architecture of XML data warehouses.

Query Performance Optimization in XML Data Warehouses

XML data warehouses form an interesting basis for decision-support applications that exploit complex data. However, native-XML database management systems (DBMSs) currently bear limited performances and it is necessary to research for ways to optimize them. In this chapter, we present two such techniques. First, we propose a join index that is specifically adapted to the multidimensional architecture of XML warehouses. It eliminates join operations while preserving the information contained in the original warehouse. Second, we present a strategy for selecting XML materialized views by clustering the query workload. To validate these proposals, we measure the response time of a set of decision-support XQueries over an XML data warehouse, with and without using our optimization techniques. Our experimental results demonstrate their efficiency, even when queries are complex and data are voluminous.