Towards Progressively Querying and Mining Movement Data

We propose a research foundation for progressively mining and querying both movement data and patterns. Our proposal is based on an algebraic framework, referred to as 2W Model, that defines the knowledge discovery process as a progressive combination of mining and querying operators. The 2W Model framework provides the underlying procedural semantics for a language called MO-DMQL, that allows to progressively refine mining objectives. MO-DMQL extends conventional SQL in two respects, namely a pattern definition mechanism and the capability to uniformly manipulate both raw data and unveiled patterns. Also, an innovative computational engine, DAEDALUS, is introduced for processing MO-DMQL statements. The expressiveness and usefulness of the MO-DMQL language as well as the computational capabilities of DAEDALUS are qualitatively evaluated by means of a case study.

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