Personalised exploration graphs on semantic data lakes

Recently, organisations operating in the context of Smart Cities are spending time and resources in turning large amounts of data, collected within heterogeneous sources, into actionable insights, using indicators as powerful tools for meaningful data aggregation and exploration. Data lakes, which follow a schema-on-read approach, allow for storing both structured and unstructured data and have been proposed as flexible repositories for enabling data exploration and analysis over heterogeneous data sources, regardless their structure. However, indicators are usually computed based on the centralisation of the data storage, according to a less flexible schema on write approach. Furthermore, domain experts, who know data stored within the data lake, are usually distinct from data analysts, who define indicators, and users, who exploit indicators to explore data in a personalised way. In this paper, we propose a semantics-based approach for enabling personalised data lake exploration through the conceptualisation of proper indicators. In particular, the approach is structured as follows: (i) at the bottom, heterogeneous data sources within a data lake are enriched with Semantic Models, defined by domain experts using domain ontologies, to provide a semantic data lake representation; (ii) in the middle, a Multi-Dimensional Ontology is used by analysts to define indicators and analysis dimensions, in terms of concepts within Semantic Models and formulas to aggregate them; (iii) at the top, Personalised Exploration Graphs are generated for different categories of users, whose profiles are defined in terms of a set of constraints that limit the indicators instances on which the users may rely to explore data. Benefits and limitations of the approach are discussed through an application in the Smart City domain.