Mining Spatio-temporal Data at Different Levels of Detail

Presented at the 11th AGILE International Conference on Geographic Information Science (AGILE 2008), Girona, Spain, 5-8 May 2008In this paper we propose a methodology for mining very large spatio-temporal datasets. We propose a two-pass strategy for mining and manipulating spatio-temporal datasets at different levels of detail (i.e., granularities). The approach takes advantage of the multi-granular capability of the underlying spatio-temporal model to reduce the amount of data that can be accessed initially. The approach is implemented and applied to real-world spatio-temporal datasets. We show that the technique can deal easily with very large datasets without losing the accuracy of the extracted patterns, as demonstrated in the experimental results.Science Foundation Ireland; Irish Research Council for Science, Engineering & TechnologyConference detailshttp://plone.itc.nl/agile/agile-conference

Exploratory spatio-temporal

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Multigranular spatio-temporal exploration: An application to on-street parking data

Traffic congestions cost billions of dollars to the society every year and are often aggravated by road users looking for parking. One way of alleviating the parking problem is providing decision makers of smart cities with powerful exploratory tools to analyse the data and find more effective solutions. This paper proposes a novel visual analytics tool for decision makers that allows multigranular spatio-temporal on-street parking data exploration. Even if the tool has been designed to deal with on-street parking data, it relies on a generic logic that makes it adaptable to more general spatio-temporal datasets

Massive Spatio-Temporal Mobility Data: An Empirical Experience on Data Management Techniques

The technological improvements within the Intelligent Transportation Systems, based on advanced Information and Communication Technologies (like Smartphones, GPS handhelds, etc.), has led to a significant increase in the availability of datasets representing mobility phenomena, with high spatial and temporal resolution. Especially in the urban scenario, these datasets can enable the development of “Smart Cities”. Nevertheless, these massive amounts of data may result challenging to handle, putting in crisis traditional Spatial Database Management Systems. In this paper we report on some experiments we performed to handle a massive dataset of about seven years of parking availability data, collected from the municipality of Melbourne (AU), being about 40 GB. In particular, we describe the results of an empirical comparison of the retrieval performances offered by three different off-the-shelf settings to manage these data, namely a combination of PostgreSQL + PostGIS with standard indexing, a clustered setup of PostgreSQL + PostGIS, and a combination of PostgreSQL + PostGIS + Timescale, a storage extension specialized in handling temporal data. Results show that the standard indexing is by far outperformed by the two other solutions, which anyhow have different trade-offs. Thanks to this experience, other researchers facing the problems of handing these kinds of massive mobility dataset might be facilitated in their task