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Mining Long, Sharable Patterns in Trajectories of Moving Objects
Geomatic ApS - Center for Geoinformatics.ORCID iD: 0000-0003-1164-8403
Aalborg University, Department of Computer Science.
2006 (English)In: Proceedings of the Third Workshop on Spatio-Temporal Database Management, STDBM 06, September 11, 2006, Seoul, South Korea / [ed] Christophe Claramunt and Ki-Joune Li and Simonas Šaltenis, CEUR , 2006, 49-58 p.Conference paper, Published paper (Refereed)
Abstract [en]

The efficient analysis of spatio–temporal data, generated by moving objects, is an es-sential requirement for intelligent locationbased services. Spatio-temporal rules can befound by constructing spatio–temporal baskets, from which traditional association rulemining methods can discover spatio–temporal rules. When the items in the baskets arespatio–temporal identifiers and are derived from trajectories of moving objects, the discovered rules represent frequently travelled routes. For some applications, e.g., an intelligent ridesharing application, these frequent routes are only interesting if they are long and sharable, i.e., can potentially be shared by several users. This paper presents a database projection based method for efficiently extracting such long, sharable requent routes.The method prunes the search space by making use of the minimum length and sharable requirements and avoids the generation of the exponential number of subroutes of long routes. A SQL–based implementation is described, and experiments on real life data show the effectiveness of the method.

Place, publisher, year, edition, pages
CEUR , 2006. 49-58 p.
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 174
Keyword [en]
spatio-temporal data mining, sharable frequent routes, SQL-based itemset mining
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-86103OAI: oai:DiVA.org:kth-86103DiVA: diva2:500437
Conference
The Third Workshop on Spatio-Temporal Database Management, STDBM 06, September 11, 2006, Seoul, South Korea
Note
QC 20120228Available from: 2012-02-13 Created: 2012-02-13 Last updated: 2012-02-28Bibliographically approved
In thesis
1. Spatio-Temporal Data Mining for Location-Based Services
Open this publication in new window or tab >>Spatio-Temporal Data Mining for Location-Based Services
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Largely driven by advances in communication and information technology, such as the increasing availability and accuracy of GPS technology and the miniaturization of wireless communication devices, Location–Based Services (LBS) are continuously gaining popularity. Innovative LBSes integrate knowledge about the users into the service. Such knowledge can be derived by analyzing the location data of users. Such data contain two unique dimensions, space and time, which need to be analyzed.

The objectives of this thesis are three–fold. First, to extend popular data mining methods to the spatio–temporal domain. Second, to demonstrate the usefulness of the extended methods and the derived knowledge in two promising LBS examples. Finally, to eliminate privacy concerns in connection with spatio–temporal data mining by devising systems for privacy–preserving location data collection and mining.

 

To this extent, Chapter 2 presents a general methodology, pivoting, to extend a popular data mining method, namely rule mining, to the spatio–temporal domain. By considering the characteristics of a number of real–world data sources, Chapter 2 also derives a taxonomy of spatio–temporal data, and demonstrates the usefulness of the rules that the extended spatio–temporal rule mining method can discover. In Chapter 4 the proposed spatio–temporal extension is applied to find long, sharable patterns in trajectories of moving objects. Empirical evaluations show that the extended method and its variants, using high–level SQL implementations, are effective tools for analyzing trajectories of moving objects.

Real–world trajectory data about a large population of objects moving over extended periods within a limited geographical space is difficult to obtain. To aid the development in spatio–temporal data management and data mining, Chapter 3 develops a Spatio–Temporal ACTivity Simulator (ST–ACTS). ST–ACTS uses a number of real–world geo–statistical data sources and intuitive principles to effectively generate realistic spatio–temporal activities of mobile users.

 

Chapter 5 proposes an LBS in the transportation domain, namely cab–sharing. To deliver an effective service, a unique spatio–temporal grouping algorithm is presented and implemented as a sequence of SQL statements. Chapter 6 identifies ascalability bottleneck in the grouping algorithm. To eliminate the bottleneck, the chapter expresses the grouping algorithm as a continuous stream query in a data stream management system, and then devises simple but effective spatio–temporal partitioning methods for streams to parallelize the computation. Experimental results show that parallelization through adaptive partitioning methods leads to speed–ups of orders of magnitude without significantly effecting the quality of the grouping. Spatio–temporal stream partitioning is expected to be an effective method to scale computation–intensive spatial queries and spatial analysis methods for streams.

 

Location–Based Advertising (LBA), the delivery of relevant commercial information to mobile consumers, is considered to be one of the most promising business opportunities amongst LBSes. To this extent, Chapter 7 describes an LBA framework and an LBA database that can be used for the management of mobile ads. Using a simulated but realistic mobile consumer population and a set of mobile ads, the LBA database is used to estimate the capacity of the mobile advertising channel. The estimates show that the channel capacity is extremely large, which is evidence for a strong business case, but it also necessitates adequate user controls.

 

When data about users is collected and analyzed, privacy naturally becomes a concern. To eliminate the concerns, Chapter 8 first presents a grid–based framework in which location data is anonymized through spatio–temporal generalization, and then proposes a system for collecting and mining anonymous location data. Experimental results show that the privacy–preserving data mining component discovers patterns that, while probabilistic, are accurate enough to be useful for many LBSes.

 

To eliminate any uncertainty in the mining results, Chapter 9 proposes a system for collecting exact trajectories of moving objects in a privacy–preserving manner. In the proposed system there are no trusted components and anonymization is performed by the clients in a P2P network via data cloaking and data swapping. Realistic simulations show that under reasonable conditions and privacy/anonymity settings the proposed system is effective.

Place, publisher, year, edition, pages
Department of Computer Science, Aalborg University, 2008. 198 p.
Series
Ph.D. Thesis, ISSN 1601-0590 ; 44
Keyword
spatio-temporal data mining; location-based services; location and trajectory anonymization
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-86310 (URN)
Public defence
2008-03-11, 0.2.13, Aalborg University, Selma Lagerløfs vej 300, Aalborg, 14:00 (English)
Opponent
Supervisors
Note
QC 20120215Available from: 2012-02-15 Created: 2012-02-13 Last updated: 2012-02-15Bibliographically approved

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