Christine Parent’s research while affiliated with University of Lausanne and other places

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 data management, 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.

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.

For a long time, applications have been using data about the positions of the moving objects they are interested in. For example, city planning applications, in particular in the transportation and traffic management domains, have been observing and monitoring traffic flows to capture their characteristics, namely their importance and localization, with the aim to build better models for traffic regulation and to identify solutions for future development of the existing road network. Sociologists have also been examining the movement of cars equipped with GPS, focusing on individual cars rather than traffic flows, to understand the habits of their drivers. In the logistics domain, applications have been monitoring the localization of the parcels during their transportation from their source locations to their destinations. These applications use the data both to be able to locate a parcel at any time and to optimize the performance of the transportation and distribution strategy. Similar concerns rule the management of data tracking airline passengers and their luggage. Ecologists have been observing animals and, whenever possible, tracking them via transmitters and satellites, mainly to understand animals’ individual and group behaviors. Nowadays many enterprises are looking to extract information about their potential consumers out of the tracks left by their smartphones, electronic tablets, or access to social networks such as Flickr and Foursquare that record the geographic position of their users. Traditionally, data about movement have been captured using static facilities, for example, sensors producing traffic flow measures or detecting an animal’s presence. Data acquisition facilities changed drastically with the availability of embedded positioning devices (e.g., GPS).

Mobility data is becoming an important player in many application domains. Many techniques have been elaborated to extract statistical knowledge from the data sets gathering raw data tracks about the moving objects of interest to an application. These data tracks obey the physical-level specifications of the devices used for data acquisition (GPS, GSM, RFID, smart phones, and other sensors). Nowadays, interest has shifted from raw data tracks analysis to more application-oriented ways of analyzing more meaningful movement records suitable for the specific purposes of the application at hand. This trend has promoted the concept of semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This keynote paper intends to provide the foundations of a semantic approach to data about movement. It focuses on the definitions of the most important concepts about mobility data, concepts that are frequently used but rarely rigorously defined.

GPS devices allow recording the movement track of the moving object they are attached to. This data typically consists of a stream of spatio-temporal (x,y,t) points. For application purposes the stream is transformed into finite subsequences called {\em trajectories}. Existing knowledge extraction algorithms defined for trajectories mainly assume a specific context (e.g. vehicle movements) or analyze specific parts of a trajectory (e.g. stops), in association with data from chosen geographic sources (e.g. points-of-interest, road networks). We investigate a more comprehensive semantic annotation framework that allows enriching trajectories with any kind of semantic data provided by multiple 3rd party sources. This paper presents SeMiTri - the framework that enables annotating %all meaningful parts of trajectories for any kind of moving objects. Doing so, the application can benefit from a {\it \lq\lq semantic trajectory\rq\rq} representation of the physical movement. The framework and its algorithms have been designed to work on trajectories with varying data quality and different structures, with the objective of covering abstraction requirements of a wide range of applications. Performance of SeMiTri has been evaluated using many GPS datasets from multiple sources -- including both fast moving objects (e.g. cars, trucks) and people's trajectories (e.g. with smartphones). These two kinds of experiments are reported in this paper.

Mobility data is becoming an important player in many application domains. Many techniques have been elaborated to extract statistical knowledge from the data sets gathering raw data tracks about the moving objects of interest to an application. These data tracks obey the physical-level specifications of the devices used for data acquisition (GPS, GSM, RFID, smart phones, and other sensors). Nowadays, interest has shifted from raw data tracks analysis to more application-oriented ways of analyzing more meaningful movement records suitable for the specific purposes of the application at hand. This trend has promoted the concept of semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This keynote paper intends to provide the foundations of a semantic approach to data about movement. It focuses on the definitions of the most important concepts about mobility data, concepts that are frequently used but rarely rigorously defined.