Towards a Semantic Indoor Trajectory Model

International audienceIn this paper we present a Semantic Indoor Trajectory Model aimed at supporting the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we are interested in combining aspects of state-of-the art semantic outdoor trajec-tory models, with a semantically-enabled hierarchical symbolic representation of the indoor space, which abides by OGC's In-doorGML standard. We drive the discussion on those modeling issues and details that have been overlooked so far or where our approach deviates from typical practices. We illustrate the modeling part with instantiations from the Louvre Museum in an effort to provide a pragmatic view of what a Semantic Indoor Trajectory Model ought to represent and ideally also how

Towards a semantic indoor trajectory model: application to museum visits

International audienceIn this paper we present a new conceptual model of trajectories, which accounts for semantic and indoor space information and supports the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we combine aspects of state-of-the-art semantic outdoor trajectory models, with a semantically-enabled hierarchical symbolic representation of the indoor space, which abides by OGC’s IndoorGML standard. We drive the discussion on modeling issues that have been overlooked so far and illustrate them with a real-world case study concerning the Louvre Museum, in an effort to provide a pragmatic view of what the proposed model represents and how. We also present experimental results based on Louvre’s visiting data showcasing how state-of-the-art mining algorithms can be applied on trajectory data represented according to the proposed model, and outline their advantages and limitations. Finally, we provide a formal outline of a new sequential pattern mining algorithm and how it can be used for extracting interesting trajectory patterns