|Tipo di tesi||Tesi di dottorato di ricerca|
|Autore||OLARU, MARIUS OCTAVIAN|
|Titolo||Analisi di Data Warehouse Eterogenei e Integrazione Dimensionale|
|Titolo in inglese||Heterogeneous Data Warehouse Analysis and Dimensional Integration|
|Settore scientifico disciplinare||ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI|
|Corso di studi||Scuola di D.R. in INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT)|
|Data inizio appello||2014-02-17|
|Disponibilità||Accessibile via web (tutti i file della tesi sono accessibili)|
Il Data Warehouse (DW) è lo strumento principale di Business Intelligence per l'analisi di grandi molli di dati con lo scopo di estrare informazioni strategiche come supporto al processo decisionale.
The Data Warehouse (DW) is the main Business Intelligence instrument for the analysis of large banks of operational data for extracting strategic information in support of the decision making process. It is usually focused on a specific area of an organization. Data Warehouse integration is the process of combining multidimensional information from two or more heterogeneous DWs, and to present users an unified global overview of the combined strategic information from the DWs. The problem is becoming more and more frequent as the dynamic economic context sees many companies merges/acquisitions and the formation of new business networks, like co-opetition, where managers need to analyze all the involved parties and to be able to take strategic decisions concerning all the participants. The contribution of the thesis is to analyze heterogeneous DW environments and to present a dimension integration methodology that allows users to combine, access and query data from heterogeneous multidimensional sources. The integration methodology relies on graph theory and the Combined WordSense Disambiguation technique for generating semantic mappings between multidimensional schemas. Subsequently, schema heterogeneity is analyzed and handled, and compatible dimensions are uniformed by importing dimensional attributes from one dimension to another. This allows users from different sources to have the same overview of the local data, and increases local schema compatibility for drill-across queries. The dimensional attributes are populated with instance value by using a chase algorithm variant based on the RELEVANT clustering approach. Finally, several quality properties are discussed and analyzed. Dimension homogeneity/heterogeneity is presented from the integration perspective, and also the thesis presents the theoretical fundamental under which mapping soundness and consistency are guaranteed, meanwhile the mapping integration methodology coherency will be demonstrated. Furthermore, the integration methodology will be analyzed from a slowly changing dimensions perspective.