Jovan Varga

 

 

PhD in Computer Science, IT4BI-DC, Universitat Politècnica de Catalunya (UPC) and Aalborg Universitet (AAU)

 

 

Decision-making in the era of information society is based on the analysis of available data resources. There is an increasing number of publicly available data resources that represent a wealth of information to be explored. However, data exploration in these settings is often a tedious task due to the need for non-trivial technical skills (e.g., use of certain querying languages). Non-expert users need the assistance to navigate through this data landscape to perform their analysis. Traditional BI systems provide different user support functionalities, typically for querying and data visualization. These features are based on the exploitation of metadata artifacts (e.g., queries). The metadata are the fuel for different user assistance (e.g., query recommendation) algorithms and they directly determine the assistance possibilities. However, their management and organization are typically overlooked. Thus, my PhD thesis provided a metadata foundation for the user assistance features for next generation BI systems, especailly focusing on the exploratory OLAP context. Our claim is that the metadata need to be considered and handled as a first-class citizen. Moreover, in the novel settings the user wants to analyze data coming from external and non-controlled data sources. Therefore, the metadata need to be designed in a flexible and reusable manner to be utilized in the context of these new and heterogeneous data sources. In this context, we turn to the Semantic Web technologies that provide the means to interlink and share (meta)data among different systems and facilitate (meta)data integration tasks. Thus, my research involves areas of Multidimensional Modeling, OLAP, Business Intelligence, Semantic Web, Ontologies, Recommender Systems and others with the aim of creating semantic metadata artifacts to support automation and end-user data exploration.