Research Prototypes and Demos
CHOROCHRONOS
The Chorochronos Archive is a collection of moving object databases (MOD), and related algorithms that are used by the mobility data management and mining community for the empirical analysis and evaluation of mobility-centric query processing and mining algorithms. The primary goal of Chorochronos is to be used by students, educators, and researchers all over the world as a primary source of MOD-related research and applications. Funding support from the FP7/FET Coordination Action MODAP (Mobility, Data Mining, and Privacy; 2009-12; www.modap.org) and the ESF/COST MOVE (Knowledge Discovery from Moving Objects; 2009-13; http://move-cost.info/) projects is gratefully acknowledged. The success of the archive solely depends on the donors and creators of the databases and algorithms, therefore we are grateful to them. [more]
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HERMES: Moving Object Database
HERMES provides MOD functionality to OpenGIS-compatible state-of-theart Object-Relational DBMS. HERMES is designed to be used as a pure temporal or a pure spatial system, however, its main application is to support modeling and querying of moving objects. [more]
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HERMOUPOLIS: A Trajectory Generator for Simulating Generalized Mobility Patterns
During the last decade, the domain of mobility data mining has emerged providing many effective methods for the discovery of intuitive patterns representing collective behavior of trajectories of moving objects. Although a few real-world trajectory datasets have been made available recently, these are not sufficient for experimentally evaluating the various proposals, therefore, researchers look to synthetic trajectory generators. This case is problematic because, on the one hand, real datasets are usually small, which compromises scalability experiments, and, on the other hand, synthetic dataset generators have not been designed to produce mobility pattern driven trajectories. Motivated by this observation, we present Hermoupolis, an effective generator of synthetic trajectories of moving objects that has the main objective that the resulting datasets support various types of mobility patterns (clusters, flocks, convoys, etc.), as such producing datasets with available ground truth information. [more]
People involved: Nikos Pelekis, Christos Ntrigkogias, Panagiotis Tampakis, Stylianos Sideridis, Yannis Theodoridis
Private-HERMES: A Benchmark Framework for Privacy-Preserving Mobility Data Querying and Mining Methods
Mobility data sources feed larger and larger trajectory databases nowadays. Due to the need of extracting useful knowledge patterns that improve services based on users’ and customers’ behavior, querying and mining such databases has gained significant attention in recent years. However, publishing mobility data may lead to severe privacy violations. In this paper, we present Private-HERMES, an integrated platform for applying data mining and privacy-preserving querying over mobility data. The presented platform provides a two-dimension benchmark framework that includes: (i) a query engine that provides privacy-aware data management functionality of the in-house data via a set of auditing mechanisms that protect the sensitive information against several types of attacks, and (ii) a progressive analysis framework, which, apart from anonymization methods for data publishing, includes various well-known mobility data mining techniques to evaluate the effect of anonymization in the querying and mining results. The demonstration of Private-HERMES via a real-world case study, illustrates the flexibility and usefulness of the platform for supporting privacy-aware data analysis, as well as for providing an extensible blueprint benchmark architecture for privacy-preservation related methods in mobility data. [more]
People involved: Nikos Pelekis, Aris Gkoulalas-Divanis, Marios Vodas, Anargyros Plemenos, Despina Kopanaki, Yannis Theodoridis
