Stefano Spaccapietra | EPFL Graph Search

Stefano Spaccapietra is a full professor at EPFL, Switzerland, where he has been heading the database laboratory. He has been in academic positions all along his career. He got his PhD from the University of Paris VI, in 1978, where he first had his master in Computer Science in 1969. At that time, he has been teaching file systems, later turned into teaching database systems. He moved to the University of Burgundy, Dijon, in 1983 to take a professor position at the Institute of Technology. He left Dijon for EPFL in 1988.

Prof. Spaccapietra is a Fellow of the IEEE and recipient of the IFIP Silver Core Award and ER Award. He is an ER fellow.

He has been Editor-in-chief of the Journal of Data Semantics (LNCS subline), Springer.

He is member of the editorial boards of the Data and Knowledge Engineering Journal (Elsevier), the

Internet and Web Information Systems Journal (Kluwer), the Revue Internationale de Géomatique (Hermes), and the Computing Letters Journal (CoLe), VSP/Brill.

He was former Chair of the IFIP Working Group 2.6 "Databases" and of the ER Conferences Steering Committee.

Semantic Trajectories Modeling and Analysis

Maria Luisa Damiani, José Antonio Fernandes de Macedo, Christine Parent, Stefano Spaccapietra, Zhixian Yan

Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This survey provides the definitions of the basic concepts about mobility data, an analysis of the issues in mobility datamanagement, and a survey of the approaches and techniques for: (i) constructing trajectories from movement tracks, (ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and (iii) using data mining to analyze semantic trajectories and extract knowledge about their characteristics, in particular the behavioral patterns of the moving objects. Last but not least, the article surveys the new privacy issues that arise due to the semantic aspects of trajectories.

Assoc Computing Machinery2013

Semantic trajectories: Mobility data computation and annotation

Karl Aberer, Dipanjan Chakraborty, Christine Parent, Stefano Spaccapietra, Zhixian Yan

With the large-scale adoption of GPS equipped mobile sensing devices, positional data generated by moving objects (e.g., vehicles, people, animals) are being easily collected. Such data are typically modeled as streams of spatio-temporal (x,y,t) points, called trajectories. In recent years trajectory management research has progressed significantly towards efficient storage and indexing techniques, as well as suitable knowledge discovery. These works focused on the geometric aspect of the raw mobility data. We are now witnessing a growing demand in several application sectors (e.g., from shipment tracking to geo-social networks) on understanding the semantic behavior of moving objects. Semantic behavior refers to the use of semantic abstractions of the raw mobility data, including not only geometric patterns but also knowledge extracted jointly from the mobility data and the underlying geographic and application domains information. The core contribution of this article lies in a semantic model and a computation and annotation platform for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with segmentations and annotations. We also analyze a number of experiments we did with semantic trajectories in different domains.

2013

Semantic Trajectories: Mobility Data Computation and Annotation

Karl Aberer, Dipanjan Chakraborty, Christine Parent, Stefano Spaccapietra, Zhixian Yan

With the large-scale adoption of GPS equipped mobile sensing devices, positional data generated by moving objects (e.g., vehicles, people, animals) are being easily collected. Such data are typically modeled as streams of spatio-temporal (x,y,t) points, called ''trajectories''. In recent years trajectory management research has progressed significantly towards efficient storage and indexing techniques, as well as suitable knowledge discovery. These works focused on the geometric aspect of the raw mobility data. We are now witnessing a growing demand in several application sectors (e.g., from shipment tracking to geo-social networks) on understanding the {\it semantic'' behavior of moving objects. Semantic behavior refers to the use of semantic abstractions of the raw mobility data, including not only geometric patterns but also knowledge extracted jointly from the mobility data and the underlying geographic and application domains information. The core contribution of this paper lies in a ''Semantic Model'' and a ''Computation and Annotation Platform'' for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with annotations and segmentations. We also analyze a number of experiments we did with semantic trajectories in different domains.

Assoc Computing Machinery2012