A Methodological Approach for enabling Personalised Smart City Data Exploration

In modern Smart Cities, the unpredictable growth and heterogeneity of the shared data is raising interest for data lakes repositories, due to their versatility and schema-on-read nature. Personalised data access methods are required to deal with the variety of users, their goals and preferences on available data, and need to be adapted to the unique characteristics of data lakes. Pay-as-you-go or on-demand solutions are advocated, where integration is progressively carried out, and methodologies to enable personalised data exploration are required. In this paper, we present a methodological approach to build users' profiles, in terms of context, determining the roles and activities of users while acting in the Smart City, and preferences expressed on indicators semantically derived from data lake sources. The proposed methodology includes: (a) the definition of preference constructors based on the semantic representation of indicators from data lake sources; (b) the definition of users' profiles, in terms of context and preferences; (c) the definition of a procedure to support domain experts and data analysts for enabling personalised exploration of indicators.