- Authors:
- Fosca Giannotti,
- Dino Pedreschi
The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data. This is a scenario of great opportunities and risks: on one side, mining this data can produce useful knowledge, supporting sustainable mobility and intelligent transportation systems; on the other side, individual privacy is at risk, as the mobility data contain sensitive personal information. A new multidisciplinary research area is emerging at this crossroads of mobility, data mining, and privacy. This book assesses this research frontier from a computer science perspective, investigating the various scientific and technological issues, open problems, and roadmap. The editors manage a research project called GeoPKDD, Geographic Privacy-Aware Knowledge Discovery and Delivery, funded by the EU Commission and involving 40 researchers from 7 countries, and this book tightly integrates and relates their findings in 13 chapters covering all related subjects, including the concepts of movement data and knowledge discovery from movement data; privacy-aware geographic knowledge discovery; wireless network and next-generation mobile technologies; trajectory data models, systems and warehouses; privacy and security aspects of technologies and related regulations; querying, mining and reasoning on spatiotemporal data; and visual analytics methods for movement data. This book will benefit researchers and practitioners in the related areas of computer science, geography, social science, statistics, law, telecommunications and transportation engineering.
Cited By
- Zhang H and Zhu Y (2020). A Method of Sanitizing Privacy-Sensitive Sequence Pattern Networks Mined From Trajectories Released, International Journal of Data Warehousing and Mining, 15:3, (63-89), Online publication date: 1-Jul-2019.
- Theodoropoulos G, Tritsarolis A and Theodoridis Y EvolvingClusters: Online Discovery of Group Patterns in Enriched Maritime Data Multiple-Aspect Analysis of Semantic Trajectories, (50-65)
- Theodoridis Y Learning from Our Movements – The Mobility Data Analytics Era Multiple-Aspect Analysis of Semantic Trajectories, (1-5)
- Damiani M, Hachem F, Issa H, Ranc N, Moorcroft P and Cagnacci F (2018). Cluster-based trajectory segmentation with local noise, Data Mining and Knowledge Discovery, 32:4, (1017-1055), Online publication date: 1-Jul-2018.
- Kuijpers B and Moelans B (2017). On the realisability of double-cross matrices by polylines in the plane, Journal of Computer and System Sciences, 86:C, (117-135), Online publication date: 1-Jun-2017.
Tong H, Wang F, Choudhury M and Obradovic Z (2016). Guest Editorial, ACM Transactions on Knowledge Discovery from Data, 10:4, (1-4), Online publication date: 27-Jul-2016.- Calabrese F, Ferrari L and Blondel V (2014). Urban Sensing Using Mobile Phone Network Data: A Survey of Research, ACM Computing Surveys, 47:2, (1-20), Online publication date: 8-Jan-2015.
- Fuchs G, Stange H, Hecker D, Andrienko N and Andrienko G (2015). Constructing semantic interpretation of routine and anomalous mobility behaviors from big data, SIGSPATIAL Special, 7:1, (27-34), Online publication date: 20-May-2015.
- Wu J, Claramunt C and Deng M (2015). An integrated qualitative and boundary-based formal model for a semantic representation of trajectories, SIGSPATIAL Special, 7:1, (35-42), Online publication date: 20-May-2015.
- Güting R, Valdés F and Damiani M (2015). Symbolic Trajectories, ACM Transactions on Spatial Algorithms and Systems, 1:2, (1-51), Online publication date: 5-Nov-2015.
- Estivill-Castro V and Nettleton D Privacy Tips Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, (1449-1456)
- Golubev A, Chechetkin I, Solnushkin K, Sadovnikova N, Parygin D and Shcherbakov M Strategway Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services, (1-4)
- Andrienko N, Andrienko G, Fuchs G and Jankowski P Visual analytics methodology for scalable and privacy-respectful discovery of place semantics from episodic mobility data Proceedings of the 2015th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part III, (254-258)
- Pelekis N, Theodoridis Y and Janssens D (2014). On the Management and Analysis of Our LifeSteps, ACM SIGKDD Explorations Newsletter, 15:1, (23-32), Online publication date: 17-Mar-2014.
- Dong Y, Yang Y, Tang J, Yang Y and Chawla N Inferring user demographics and social strategies in mobile social networks Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, (15-24)
- Yan Z, Chakraborty D, Parent C, Spaccapietra S and Aberer K (2013). Semantic trajectories, ACM Transactions on Intelligent Systems and Technology, 4:3, (1-38), Online publication date: 1-Jun-2013.
- Parent C, Spaccapietra S, Renso C, Andrienko G, Andrienko N, Bogorny V, Damiani M, Gkoulalas-Divanis A, Macedo J, Pelekis N, Theodoridis Y and Yan Z (2013). Semantic trajectories modeling and analysis, ACM Computing Surveys, 45:4, (1-32), Online publication date: 1-Aug-2013.
- Extracting Semantics of Individual Places from Movement Data by Analyzing Temporal Patterns of Visits Proceedings of The First ACM SIGSPATIAL International Workshop on Computational Models of Place, (9-16)
- Pelekis N, Gkoulalas-Divanis A, Vodas M, Plemenos A, Kopanaki D and Theodoridis Y Private-HERMES Proceedings of the 15th International Conference on Extending Database Technology, (598-601)
- Barth D, Bellahsene S and Kloul L Combining local and global profiles for mobility prediction in LTE femtocells Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems, (333-342)
- Trasarti R, Pinelli F, Nanni M and Giannotti F Mining mobility user profiles for car pooling Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, (1190-1198)
- Gkoulalas-Divanis A and Loukides G Revisiting sequential pattern hiding to enhance utility Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, (1316-1324)
- Brilhante I, de Macedo J, Renso C and Casanova M Trajectory data analysis using complex networks Proceedings of the 15th Symposium on International Database Engineering & Applications, (17-25)
- Gambs S, Killijian M and Núòez del Prado Cortez M (2011). Show Me How You Move and I Will Tell You Who You Are, Transactions on Data Privacy, 4:2, (103-126), Online publication date: 1-Aug-2011.
- Spaccapietra S and Parent C Adding meaning to your steps Proceedings of the 30th international conference on Conceptual modeling, (13-31)
- Nanni M, Trasarti R, Renso C, Giannotti F and Pedreschi D Advanced knowledge discovery on movement data with the GeoPKDD system Proceedings of the 13th International Conference on Extending Database Technology, (693-696)
- Ficek M, Pop T, Vláčil P, Dufková K, Kencl L and Tomek M Performance study of active tracking in a cellular network using a modular signaling platform Proceedings of the 8th international conference on Mobile systems, applications, and services, (239-254)
- Gambs S, Killijian M and del Prado Cortez M Show me how you move and I will tell you who you are Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, (34-41)
- Giannotti F, Nanni M, Pedreschi D, Pinelli F, Renso C, Rinzivillo S and Trasarti R Mobility data mining Proceedings of the Third International Workshop on Computational Transportation Science, (7-10)
- Pelekis N, Kopanakis I, Panagiotakis C and Theodoridis Y Unsupervised trajectory sampling Proceedings of the 2010th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part III, (17-33)
- Pelekis N, Kopanakis I, Panagiotakis C and Theodoridis Y Unsupervised trajectory sampling Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III, (17-33)
- Trasarti R, Rinzivillo S, Pinelli F, Nanni M, Monreale A, Renso C, Pedreschi D and Giannotti F Exploring real mobility data with M-atlas Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III, (624-627)
- Pelekis N, Kopanakis I, Panagiotakis C and Theodoridis Y Unsupervised trajectory sampling Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III, (17-33)
- Trasarti R, Rinzivillo S, Pinelli F, Nanni M, Monreale A, Renso C, Pedreschi D and Giannotti F Exploring real mobility data with M-atlas Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III, (624-627)
- Hotho A, Pedersen R and Wurst M Ubiquitous data Ubiquitous knowledge discovery, (61-74)
- Hotho A, Pedersen R and Wurst M Ubiquitous data Ubiquitous knowledge discovery, (61-74)
- Trasarti R, Rinzivillo S, Pinelli F, Nanni M, Monreale A, Renso C, Pedreschi D and Giannotti F Exploring real mobility data with M-Atlas Proceedings of the 2010th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part III, (624-627)
- Dodge S, Weibel R and Laube P (2009). Exploring movement-similarity analysis of moving objects, SIGSPATIAL Special, 1:3, (11-16), Online publication date: 1-Nov-2009.
- Kharrat A, Popa I, Zeitouni K and Faiz S Caractérisation de la densité de trafic et de son évolution à partir de trajectoires d'objets mobiles Proceedings of the 5th French-Speaking Conference on Mobility and Ubiquity Computing, (33-40)
- Ghys K, Kuijpers B, Moelans B, Othman W, Vangoidsenhoven D and Vaisman A Map matching and uncertainty Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, (468-471)
- Nergiz M, Atzori M, Saygın Y and Güç B (2009). Towards Trajectory Anonymization, Transactions on Data Privacy, 2:1, (47-75), Online publication date: 1-Apr-2009.
- Kuijpers B and Moelans B Towards a geometric interpretation of double-cross matrix-based similarity of polylines Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, (1-8)
Index Terms
- Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Reviews
Ruay-Shiung Chang
Cars let us go anywhere we want. Mobile phones let us talk on the move. Wireless networks let us access what we want, anywhere and at anytime. People are becoming nomadic. However, no matter where we go, no matter what we do, we leave traces or data behind. With this data, we can be traced, analyzed, followed, or even constantly monitored. The scenario in Orwell's 1984 is quite appalling, but modern technology could make it a reality if we do not pay attention. This book is a byproduct of a European project called geographic privacy-aware knowledge discovery and delivery (GeoPKDD). Its purpose is to demonstrate that it is possible to create useful mobility knowledge out of raw spatiotemporal data, by means of privacy-preserving data mining techniques. However, in the wrong hands, privacy-preserving techniques can turn into privacy-revealing techniques. This book consists of three parts. The first part, "Setting the Stage," introduces basic concepts and characteristics of mobile data and applications. Tracking and synthesizing wireless network data is emphasized in a single chapter. Collecting and analyzing data invariably involves privacy issues. Regulations, technologies, opportunities, and threats regarding privacy protection are also included in this part. The second part, "Mapping Moving Objects and Trajectory Data," focuses on handling trajectory data. It discusses its model, database system, warehouse, and its issues of privacy and security. Finally, the last part is concerned with data-mining for spatiotemporal and trajectory data. Knowledge discovery from so much data is not an easy task. Therefore, efficient and effective data mining algorithms must be devised. An interesting chapter introduces the visualization of trajectory data. A picture is worth a thousand words, and visualization can help people understand the data more easily. It is especially pointed out that the book has 12 figures in color. However, color seems unnecessary for some of them, while some of the black-and-white figures call out for color printing. For example, Figure 9.3 is fine in black-and-white, while Figure 1.1 would be easier to understand, and more interesting, if it were in color. To summarize, this is a pioneering book, and a must-read for anyone interested in this field. Please note that it only presents European viewpoints; the privacy issue, in particular, might be viewed quite differently across continents. Online Computing Reviews Service
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