Querying Spatio-temporal Patterns in Mobile Phone-Call Databases

Abstract — Call Detail Record (CDR) databases contain millions of records with information about cell phone calls, including the position of the user when the call was made/received. This huge amount of spatiotemporal data opens the door for the study of human trajectories on a large scale without the bias that other sources (like GPS or WLAN networks) introduce in the population studied. Also, it provides a platform for the development of a wide variety of studies ranging from the spread of diseases to planning of public transport. Nevertheless, previous work on spatiotemporal queries does not provide a framework flexible enough for expressing the complexity of human trajectories. In this paper we present the Spatiotemporal Pattern System (STPS) to query spatiotemporal patterns in very large CDR databases. STPS defines a regular-expression query language that is intuitive and that allows for any combination of spatial and temporal predicates with constraints, including the use of variables. The design of the language took into consideration the layout of the areas being covered by the cellular towers, as well as “areas ” that label places of interested (e.g. neighborhoods, parks, etc) and topological operators. STPS includes an underlying indexing structure and algorithms for query processing using different evaluation strategies. A full implementation of the STPS is currently running with real, very large CDR databases on Telefónica Research Labs. An extensive performance evaluation of the STPS shows that it can efficiently find complex mobility patterns in large CDR databases. I

Moving Object Trajectories Meta-Model And Spatio-Temporal Queries

In this paper, a general moving object trajectories framework is put forward to allow independent applications processing trajectories data benefit from a high level of interoperability, information sharing as well as an efficient answer for a wide range of complex trajectory queries. Our proposed meta-model is based on ontology and event approach, incorporates existing presentations of trajectory and integrates new patterns like space-time path to describe activities in geographical space-time. We introduce recursive Region of Interest concepts and deal mobile objects trajectories with diverse spatio-temporal sampling protocols and different sensors available that traditional data model alone are incapable for this purpose.Comment: International Journal of Database Management Systems (IJDMS) Vol.4, No.2, April 201

Towards a Scalable Dynamic Spatial Database System

With the rise of GPS-enabled smartphones and other similar mobile devices, massive amounts of location data are available. However, no scalable solutions for soft real-time spatial queries on large sets of moving objects have yet emerged. In this paper we explore and measure the limits of actual algorithms and implementations regarding different application scenarios. And finally we propose a novel distributed architecture to solve the scalability issues.Comment: (2012

Time Distortion Anonymization for the Publication of Mobility Data with High Utility

An increasing amount of mobility data is being collected every day by different means, such as mobile applications or crowd-sensing campaigns. This data is sometimes published after the application of simple anonymization techniques (e.g., putting an identifier instead of the users' names), which might lead to severe threats to the privacy of the participating users. Literature contains more sophisticated anonymization techniques, often based on adding noise to the spatial data. However, these techniques either compromise the privacy if the added noise is too little or the utility of the data if the added noise is too strong. We investigate in this paper an alternative solution, which builds on time distortion instead of spatial distortion. Specifically, our contribution lies in (1) the introduction of the concept of time distortion to anonymize mobility datasets (2) Promesse, a protection mechanism implementing this concept (3) a practical study of Promesse compared to two representative spatial distortion mechanisms, namely Wait For Me, which enforces k-anonymity, and Geo-Indistinguishability, which enforces differential privacy. We evaluate our mechanism practically using three real-life datasets. Our results show that time distortion reduces the number of points of interest that can be retrieved by an adversary to under 3 %, while the introduced spatial error is almost null and the distortion introduced on the results of range queries is kept under 13 % on average.Comment: in 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Aug 2015, Helsinki, Finlan

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies

Semantic Trajectories:Computing and Understanding Mobility Data

Thanks to the rapid development of mobile sensing technologies (like GPS, GSM, RFID, accelerometer, gyroscope, sound and other sensors in smartphones), the large-scale capture of evolving positioning data (called mobility data or trajectories) generated by moving objects with embedded sensors has become easily feasible, both technically and economically. We have already entered a world full of trajectories. The state-of-the-art on trajectory, either from the moving object database area or in the statistical analysis viewpoint, has built a bunch of sophisticated techniques for trajectory data ad-hoc storage, indexing, querying and mining etc. However, most of these existing methods mainly focus on a spatio-temporal viewpoint of mobility data, which means they analyze only the geometric movement of trajectories (e.g., the raw ‹x, y, t› sequential data) without enough consideration on the high-level semantics that can better understand the underlying meaningful movement behaviors. Addressing this challenging issue for better understanding movement behaviors from the raw mobility data, this doctoral work aims at providing a high-level modeling and computing methodology for semantically abstracting the rapidly increasing mobility data. Therefore, we bring top-down semantic modeling and bottom-up data computing together and establish a new concept called "semantic trajectories" for mobility data representation and understanding. As the main novelty contribution, this thesis provides a rich, holistic, heterogeneous and application-independent methodology for computing semantic trajectories to better understand mobility data at different levels. In details, this methodology is composed of five main parts with dedicated contributions. Semantic Trajectory Modeling. By investigating trajectory modeling requirements to better understand mobility data, this thesis first designs a hybrid spatio-semantic trajectory model that represents mobility with rich data abstraction at different levels, i.e., from the low-level spatio-temporal trajectory to the intermediate-level structured trajectory, and finally to the high-level semantic trajectory. In addition, a semantic based ontological framework has also been designed and applied for querying and reasoning on trajectories. Offline Trajectory Computing. To utilize the hybrid model, the thesis complementarily designs a holistic trajectory computing platform with dedicated algorithms for reconstructing trajectories at different levels. The platform can preprocess collected mobility data (i.e., raw movement tracks like GPS feeds) in terms of data cleaning/compression etc., identify individual trajectories, and segment them into structurally meaningful trajectory episodes. Therefore, this trajectory computing platform can construct spatio-temporal trajectories and structured trajectories from the raw mobility data. Such computing platform is initially designed as an offline solution which is supposed to analyze past trajectories via a batch procedure. Trajectory Semantic Annotation. To achieve the final semantic level for better understanding mobility data, this thesis additionally designs a semantic annotation platform that can enrich trajectories with third party sources that are composed of geographic background information and application domain knowledge, to further infer more meaningful semantic trajectories. Such annotation platform is application-independent that can annotate various trajectories (e.g., mobility data of people, vehicle and animals) with heterogeneous data sources of semantic knowledge (e.g., third party sources in any kind of geometric shapes like point, line and region) that can help trajectory enrichment. Online Trajectory Computing. In addition to the offline trajectory computing for analyzing past trajectories, this thesis also contributes to dealing with ongoing trajectories in terms of real-time trajectory computing from movement data streams. The online trajectory computing platform is capable of providing real-life trajectory data cleaning, compression, and segmentation over streaming movement data. In addition, the online platform explores the functionality of online tagging to achieve fully semantic-aware trajectories and further evaluate trajectory computing in a real-time setting. Mining Trajectories from Multi-Sensors. Previously, the focus is on computing semantic trajectories using single-sensory data (i.e., GPS feeds), where most datasets are from moving objects with wearable GPS-embedded sensors (e.g., mobility data of animal, vehicle and people tracking). In addition, we explore the problem of mining people trajectories using multi-sensory feeds from smartphones (GPS, gyroscope, accelerometer etc). The research results reveal that the combination of two sensors (GPS+accelerometer) can significantly infer a complete life-cycle semantic trajectories of people's daily behaviors, both outdoor movement via GPS and indoor activities via accelerometer