The resulting challenge for CHOROLOGOS is to formulate meaningful and useful query types, along with the theoretical properties which will enable pruning the search space effectively. These query types include reverse query operators (reverse top-k, reverse k-NN, etc.), why-not operators, queries that retrieve groups of objects (instead of single objects), complex joins, pattern queries, optimal location queries, and so on. Consequently, miscellaneous interesting query types have emerged, which raise challenges for query processing algorithms. Formulation of novel query types: The acquisition of massive complex data, described by spatial, temporal and textual dimensions, has motivated the research of novel query types, in order to retrieve data in flexible, expressive, and meaningful ways.As a result, the following main research and technological challenges need to be addressed by the project: The combination of spatio-textual data with spatio-temporal data at scale opens up new research directions, while at the same time challenges existing data processing solutions. Parallel processing of the proposed query types, towards scalable algorithms that make the analysis of vast-sized data sets feasible in practice.Efficient query processing algorithms following well-established methodologies, including filter-and-refine and branch-and-bound, aiming at fast delivery of accurate query results.Design of appropriate access methods that jointly index space, time, and text, in an appropriate way to support filtering of data that is irrelevant to the query at hand.Theoretical contributions in terms of properties and search bounds for the proposed query types, thus laying the foundations for efficient processing and search.Examples of such queries include similarity matching, pattern-based matching, as well as semantic similarity matching. Formulation of expressive query types that enable selection of underlying spatio-temporal-textual data based on diverse information needs, going beyond exact or syntactical matching and towards semantic retrieval.
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