The volume, velocity and uncontrolled variety of Big Data are changing the way data exploration for data-driven decision making is performed on top of Data Lakes. As data grows, novel methods are needed for data aggregation by means of indicators and multi-dimensional analysis of Data Lakes content, enabling exploration of data according to various dimensions, thus empowering users with diverse roles and competencies to capitalise on the available information. In this paper, we present a computer-aided approach (named PERSEUS, PERSonalised Exploration by User Support) for data exploration on top of a Data Lake. The approach is structured over three phases: (i) the construction of a semantic metadata catalog on top of the Data Lake; (ii) the creation of an Exploration Graph, based on metadata contained in the catalog, containing the semantic representation of indicators and analysis dimensions; (iii) the enrichment of the definition of indicators with personalisation aspects (based on users' profiles and preferences) to identify Exploration Contexts, in turn delimiting portions of the Exploration Graph for a personalised and interactive exploration of indicators. Results of an experimental evaluation in the Smart City domain are presented with the aim of demonstrating the feasibility of the approach.
Personalised Exploration Graphs on top of Data Lakes
Bianchini D.;De Antonellis V.;Garda M.
2024-01-01
Abstract
The volume, velocity and uncontrolled variety of Big Data are changing the way data exploration for data-driven decision making is performed on top of Data Lakes. As data grows, novel methods are needed for data aggregation by means of indicators and multi-dimensional analysis of Data Lakes content, enabling exploration of data according to various dimensions, thus empowering users with diverse roles and competencies to capitalise on the available information. In this paper, we present a computer-aided approach (named PERSEUS, PERSonalised Exploration by User Support) for data exploration on top of a Data Lake. The approach is structured over three phases: (i) the construction of a semantic metadata catalog on top of the Data Lake; (ii) the creation of an Exploration Graph, based on metadata contained in the catalog, containing the semantic representation of indicators and analysis dimensions; (iii) the enrichment of the definition of indicators with personalisation aspects (based on users' profiles and preferences) to identify Exploration Contexts, in turn delimiting portions of the Exploration Graph for a personalised and interactive exploration of indicators. Results of an experimental evaluation in the Smart City domain are presented with the aim of demonstrating the feasibility of the approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
