Showing papers by "Yannis Theodoridis published in 2012"

Segmentation and Sampling of Moving Object Trajectories Based on Representativeness

Abstract: Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub)trajectories in the MOD. In order to find the most representative subtrajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative subtrajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

Visually exploring movement data via similarity-based analysis

Abstract: Data analysis and knowledge discovery over moving object databases discovers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services Similarity search over trajectories is imperative for supporting such tasks Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type Aiming at providing a powerful toolkit for analysts, in this paper we propose a framework that provides several trajectory similarity measures, based on primitive (space and time) as well as on derived parameters of trajectories (speed, acceleration, and direction), which quantify the distance between two trajectories and can be exploited for trajectory data mining, including clustering and classification We evaluate the proposed similarity measures through an extensive experimental study over synthetic (for measuring efficiency) and real (for assessing effectiveness) trajectory datasets In particular, the latter could serve as an iterative, combinational knowledge discovery methodology enhanced with visual analytics that provides analysts with a powerful tool for "hands-on" analysis for trajectory data

EasyTracker: An Android Application for Capturing Mobility Behavior

Abstract: This paper presents Easy Tracker, a mobile application developed for the Android O/S that enable the storage, analysis and map visualization of routes of mobile users. Furthermore, it enable users to manually annotate part of their routes with labels describing their activity and behavior (e.g. "home having breakfast", "travelling by car to work", etc.). Of equal importance, the application encapsulates several state-of-the-art line simplification algorithms for compressing the trajectories drawn from collected GPS records, as well as segmenting trajectories into homogeneous parts in order to facilitate automatic auditing of the user's manual annotation.

Private-HERMES: a benchmark framework for privacy-preserving mobility data querying and mining methods

Abstract: 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.

FINGERPRINT: Summarizing Cluster Evolution in Dynamic Environments

Abstract: Monitoring and interpretation of changing patterns is a task of paramount importance for data mining applications in dynamic environments. While there is much research in adapting patterns in the presence of drift or shift, there is less research on how to maintain an overview of pattern changes over time. A major challenge is summarizing changes in an effective way, so that the nature of change can be understood by the user, while the demand on resources remains low. To this end, the authors propose FINGERPRINT, an environment for the summarization of cluster evolution. Cluster changes are captured into an "evolution graph," which is then summarized based on cluster similarity into a fingerprint of evolution by merging similar clusters. The authors propose a batch summarization method that traverses and summarizes the Evolution Graph as a whole and an incremental method that is applied during the process of cluster transition discovery. They present experiments on different data streams and discuss the space reduction and information preservation achieved by the two methods.

A Framework for Mobility Pattern Mining and Privacy- Aware Querying of Trajectory Data

Abstract: Existing approaches for privacy-aware mobility data sharing aim at publishing an anonymized version of the mobility dataset, operating under the assumption that most of the information in the original dataset can be disclosed without causing any privacy violations. In this paper, we assume that the majority of the information that exists in the mobility dataset must remain private and the data has to stay in-house to the hosting organization. To facilitate privacy-aware sharing of the mobility data we develop a trajectory query engine that allows subscribed users to gain restricted access to the database to accomplish various analytic tasks. The proposed engine (i) audits queries for trajectory data to block potential attacks to user privacy, (ii) supports range, distance, and k-nearest neighbor spatial and spatiotemporal queries, and (iii) preserves user anonymity in answers to queries by (a) returning a set of carefully crafted, realistic fakes trajectories, and (b) ensuring that no user-specific sensitive locations are reported as part of the returned trajectories. Along this direction, we also present Private-HERMES, a platform that provides a two-dimension benchmark framework that includes: (i) the aforementioned privacy-aware trajectory query engine 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.