After the introduction and development of the relational database model between 1970 and the 1980s, this model proved to be insufficiently expressive for specific applications dealing with, for instance, temporal data, spatial data and multi-media data. From the mid-1980s, this has led to the development of domain-specific database systems, the first being temporal databases, later followed by spatial database systems.
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References
T. Abraham. Knowledge Discovery in Spatio-Temporal Databases. Ph.D. Thesis, School of Computer and Information Science, Faculty of Information Technology, University of South Australia, 1999.
R. Agrawal, K.-I. Lin, H.S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proceedings of 21st International Conference on Very Large Data Bases (VLDB’95), pp. 490–501. Morgan Kaufmann, Los Altos, CA, 1995.
J. Alon, S. Sclaroff, G. Kollios, and V. Pavlovic. Discovering clusters in motion time-series data. In Proceedings of the 2003 Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’03), pp. 375–381. IEEE, Los Alamitos, CA, 2003.
R. Benetis, C.S. Jensen, G. Karciauskas, and S. Saltenis. Nearest and reverse nearest neighbor queries for moving objects. The Very Large Databases Journal, 15(3):229–249, 2006.
D. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In Proceedings of Knowledge Discovery and Delivery Workshop, pp. 359–370, 1994.
J. Biesterfeld, E. Ennigrou, and K. Jobmann. Neural networks for location prediction in mobile networks. In Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications (IWANNT’97), pp. 207–214, 1997.
T. Bozkaya, N. Yazdani, and Z.M. Özsoyoglu. Matching and indexing sequences of different lengths. In Proceedings of the 6th International Conference on Information and Knowledge Management (CIKM’97), pp. 128–135, 1997.
D.E. Brown, H. Liu, and Y. Xue. Mining preferences from spatial-temporal data. In Proceedings of the 1st International Conference on Data Mining (SDM’01), 2001.
H. Cao, N. Mamoulis, and D.W. Cheung. Mining frequent spatio-temporal sequential patterns. In Proceedings of the 5th International Conference on Data Mining (ICDM’05), pp. 82–89. IEEE, New Orleans, LA, 2005.
H. Cao, N. Mamoulis, and D.W. Cheung. Discovery of collocation episodes in spatiotemporal data. In Proceedings of the 6th International Conference on Data Mining (ICDM’06), pp. 823–827. IEEE, Hong Kong, China, 2006.
M. Chau, R. Cheng, B. Kao, and J. Ng. Uncertain data mining: An example in clustering location data. In Proceedings of the 10th Pacific–Asia Conference on Knowledge Discovery and Data Mining (PAKDD’06), pp. 199–204. Springer, Berlin Heidelberg New York, 2006.
C. Cheng, R. Jain, and E. van den Berg. Location prediction algorithms for mobile wireless systems. In B. Furht and M. Ilyas, editors, Wireless Internet Handbook: Technologies, Standards, and Applications, pp. 245–263. CRC, Boca Raton, 2003.
R. Cheng, D.V. Kalashnikov, and S. Prabhakar. Querying imprecise data in moving object environments. IEEE Transactions on Knowledge and Data Engineering, 16(9):1112–1127, 2004.
J.-P. Chilès and P. Delfiner. Geostatistics – Modeling Spatial Uncertainty. Wiley, London, 1999.
D. Chudova, S. Gaffney, E. Mjolsness, and P. Smyth. Translation-invariant mixture models for curve clustering. In Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining (KDD’03), pp. 79–88. ACM, New York, 2003.
G. Das, K.-I. Lin, H. Mannila, G. Renganathan, and P. Smyth. Rule discovery from time series. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD’98), pp. 16–22. AAAI, New York, 1998.
M. Ester, H.-P. Kriegel, and J. Sanders. Algorithms and applications for spatial data mining. In H.J. Miller and J. Han, editors, Geographic Data Mining and Knowledge Discovery, pp. 160–187. Taylor & Francis, London, 2001.
Fraunhofer Institut Intelligente Analyse- und Informationssysteme (IAIS). http://www.iais.fraunhofer.de, 2007.
S. Gaffney and P. Smyth. Trajectory clustering with mixture of regression models. In Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining (KDD’99), pp. 63–72. ACM, New York, 1999.
P. Geurts. Pattern extraction for time series classification. In Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD’01), pp. 115–127. Springer, Berlin Heidelberg New York, 2001.
F. Giannotti, M. Nanni, and D. Pedreschi. Efficient mining of temporally annotated sequences. In Proceedings of the 6th International Conference on Data Mining (SDM’06), pp. 346–357. SIAM, Bethesda, MD, 2006.
F. Giannotti, M. Nanni, D. Pedreschi, and F. Pinelli. Trajectory pattern mining. In Proceedings of the 13th International Conference on Knowledge Discovery and Data Mining (KDD’07). ACM, New York, 2007.
J. Gudmundsson, M.J. van Kreveld, and B. Speckmann. Efficient detection of motion patterns in spatio-temporal data sets. In Proceedings of the 12th International Workshop on Geographic Information Systems (GIS’04), pp. 250–257. ACM, New York, 2004.
M. Hadjieleftheriou, G. Kollios, P. Bakalov, and V.J. Tsotras. Complex spatio-temporal pattern queries. In Proceedings of the 31st International Conference on Very Large Data Bases (VLDB’05), pp. 877–888. ACM, New York, 2005.
M. Hadjieleftheriou, G. Kollios, D. Gunopulos, and V.J. Tsotras. On-line discovery of dense areas in spatio-temporal databases. In Proceedings of the 8th International Symposium on Advances in Spatial and Temporal Databases (SSTD’03), pp. 306–324. Springer, Berlin Heidelberg New York, 2003.
S.-Y. Hwang, Y.-H. Liu, J.-K. Chiu, and E.-P. Lim. Mining mobile group patterns: A trajectory-based approach. In Proceedings of the 9th Pacific–Asia Conference on Knowledge Discovery and Data Mining (PAKDD’05), pp. 713–718. Springer, Berlin Heidelberg New York, 2005.
V.S. Iyengar. On detecting space–time clusters. In Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining (KDD’04), pp. 587–592. ACM, New York, 2004.
P. Kalnis, N. Mamoulis, and S. Bakiras. On discovering moving clusters in spatio-temporal data. In Proceedings of 9th International Symposium on Spatial and Temporal Databases (SSTD’05), pp. 364–381. Springer, Berlin Heidelberg New York, 2005.
H.A. Karimi and X. Liu. A predictive location model for location-based services. In Proceedings of the 11th International Symposium on Geographic Information Systems (GIS’03), pp. 126–133. ACM, New York, 2003.
D. Katsaros, A. Nanopoulos, M. Karakaya, G. Yavas, O. Ulusoy, and Y. Manolopoulos. Clustering mobile trajectories for resource allocation in mobile environments. In Proceedings of the 5th International Symposium on Intelligent Data Analysis (IDA’03), pp. 319–329. Springer, Berlin Heidelberg New York, 2003.
E. Keogh and M. Pazzani. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD’98), pp. 239–241. ACM, New York, 1998.
A. Ketterlin. Clustering sequences of complex objects. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD’97), pp. 215–218. AAAI, New York, 1997.
K. Koperski and J. Han. Discovery of spatial association rules in geographic information databases. In Proceedings of the 4th International Symposium on Advances in Spatial Databases (SSD’95), pp. 47–66. Springer, Berlin Heidelberg New York, 1995.
K. Koperski, J. Han, and N. Stefanovic. An efficient two-step method for classification of spatial data. In Proceedings of the 8th International Symposium on Spatial Data Handling (SDH’98), pp. 45–55, 1998.
A.P. Kragh, B. Ornulf, G.D. Richard, and K. Niels. Statistical Models Based on Counting Processes. Springer Series in Statistics. Springer, Berlin Heidelberg New York, 1993.
M. Kulldorff. A spatial scan statistic. Communications in Statistics: Theory and Methods, 26(6):1481–1496, 1997.
K. Laasonen. Clustering and prediction of mobile user routes from cellular data. In Proceeding of 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’05), pp. 569–576. Springer, Berlin Heidelberg New York, 2005.
P. Laube and S. Imfeld. Analyzing relative motion within groups of trackable moving point objects. In Proceedings of 2nd International Conference on Geographic Information Science (GIS’02), pp. 132–144. Springer, Berlin Heidelberg New York, 2002.
P. Laube, M. van Kreveld, and S. Imfeld. Finding REMO – Detecting relative motion patterns in geospatial lifelines. In Proceedings of 11th International Symposium on Spatial Data Handling (SDH’04), pp. 201–214. Springer, Berlin Heidelberg New York, 2004.
J.-G. Lee, J. Han, and K.-Y. Whang. Trajectory clustering: A partition-and-group framework. In Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data (SIGMOD’07), pp. 593–604. ACM, New York, 2007.
Y. Li, J. Han, and J. Yang. Clustering moving objects. In Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining (KDD’04), pp. 617–622. ACM, New York, 2004.
Z. Li, M.H. Dunham, and Y. Xiao. STIFF: A forecasting framework for spatio-temporal data. In Mining Multimedia and Complex Data, pp. 183–198. Springer, Berlin Heidelberg New York, 2002.
Z. Li, L. Liu, and M.H. Dunham. Considering correlation between variables to improve spatiotemporal forecasting. In Proceedings of the 7th Pacific–Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD’03), pp. 519–531. Springer, Berlin Heidelberg New York, 2003.
B. Liang and Z.J. Haas. Predictive distance-based mobility management for multidimensional PCS networks. IEEE/ACM Transactions on Networking, 11(5):718–732, 2003.
S.C. Liou and Y.M. Huang. Trajectory predictions in mobile networks. International Journal of Information Technology, 11(11):109–122, 2005.
S. Ma, S. Tang, D. Yang, T. Wang, and J. Han. Combining clustering with moving sequential pattern mining: A novel and efficient technique. In Proceedings of the 8th Pacific–Asia Conference on Knowledge Discovery and Data Mining (PAKDD’04), pp. 419–423. Springer, Berlin Heidelberg New York, 2004.
N. Mamoulis, H. Cao, G. Kollios, M. Hadjieleftheriou, Y. Tao, and D. Cheung. Mining, indexing, and querying historical spatiotemporal data. In Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining (KDD’04), pp. 236–245. ACM, New York, 2004.
M. Nanni. Clustering Methods for Spatio-Temporal Data. Ph.D. Thesis, Computer Science Department, University of Pisa, 2002.
M. Nanni and D. Pedreschi. Time-focused density-based clustering of trajectories of moving objects. Journal of Intelligent Information Systems, 27(3):267–289, 2006.
G. Paaß and J. Kindermann. Current approaches to spatial statistics and Bayesian extensions. Technical Report, GMD – Forschungszentrum Informationstechnik, 2000.
C. Perng, H. Wang, S. Zhang, and S. Parker. Landmarks: A new model for similarity-based pattern querying in time series databases. In Proceedings of the 16th International Conference on Data Engineering (ICDE’00), pp. 33–42. IEEE, San Diego, CA, 2000.
D. Pfoser and C.S. Jensen. Capturing the uncertainty of moving-object representations. In Proceedings of the 6th International Symposium on Advances in Spatial Databases (SSD’99), pp. 111–132. Springer, Berlin Heidelberg New York, 1999.
D. Pfoser, C.S. Jensen, and J. Theodoridis. Novel approaches in query processing for moving object trajectories. In Proceedings of the 26th International Conference Very Large Databases (VLDB’00), pp. 395–406. Morgan Kaufmann, Los Altos, CA, 2000.
D. Pokrajac and Z. Obradovic. Improved spatial-temporal forecasting through modelling of spatial residuals in recent history. In Proceedings of the 1st International Conference on Data Mining (SDM’01), 2001.
S. Saltenis, C.S. Jensen, S.T. Leutenegger, and M.A. Lopez. Indexing the positions of continuously moving objects. In Proceedings of the International Conference on Management of Data (SIGMOD’00), pp. 331–342. ACM, New York, 2000.
L. Song and X. He. Evaluating next-cell predictors with extensive Wi-Fi mobility data. IEEE Transactions on Mobile Computing, 5(12):1633–1649, 2006.
J. Sun, D. Papadias, Y. Tao, and B. Liu. Querying about the past, the present, and the future in spatio-temporal databases. In Proceedings of the 20th International Conference on Data Engineering (ICDE’04), pp. 202–213. IEEE, Los Alamitos, CA, 2004.
Swiss Poster Research Plus. http://www.spr-plus.ch, 2007.
Y. Tao and D. Papadias. Time-parameterized queries in spatio-temporal databases. In Proceedings of the International Conference on Management of Data (SIGMOD’02), pp. 334–345. ACM, New York, 2002.
Y. Tao, D. Papadias, and J. Sun. The TPR*-tree: An optimized spatio-temporal access method for predictive queries. In Proceedings of 29th International Conference on Very Large Data Bases (VLDB’03), pp. 790–801. Morgan Kaufmann, Los Altos, CA, 2003.
Y. Tao, J. Sun, and D. Papadias. Analysis of predictive spatio-temporal queries. ACM Transactions on Database Systems, 28(4):295–336, 2003.
G. Trajcevski, O. Wolfson, K. Hinrichs, and S. Chamberlain. Managing uncertainty in moving objects databases. ACM Transactions on Database Systems, 29(3):463–507, 2004.
A. Vautier, M.-O. Cordier, and R. Quiniou. An inductive database for mining temporal patterns in event sequences. In ECML/PKDD Workshop on Mining Spatial and Temporal Data, 2005.
M. Vlachos, D. Gunopulos, and G. Das. Rotation invariant distance measures for trajectories. In Proceedings of the 10th ACM International Conference on Knowledge Discovery and Data Mining (KDD’04), pp. 707–712. ACM, New York, 2004.
M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, and E.J. Keogh. Indexing multi-dimensional time-series with support for multiple distance measures. In Proceedings of the 9th ACM International Conference on Knowledge Discovery and Data Mining (KDD’03), pp. 216–225. ACM, New York, 2003.
M. Vlachos, G. Kollios, and D. Gunopulos. Discovering similar multidimensional trajectories. In Proceedings of the 18th International Conference on Data Engineering (ICDE’02), pp. 673–684. IEEE, San Jose, CA, 2002.
Y. Wang, E.-P. Lim, and S.-Y. Hwang. On mining group patterns of mobile users. In Proceedings of the 14th International Conference on Database and Expert Systems Applications (DEXA’03), pp. 287–296. Springer, Berlin Heidelberg New York, 2003.
J. Yang and M. Hu. TrajPattern: Mining sequential patterns from imprecise trajectories of mobile objects. In Proceedings of 10th International Conference on Extending Database Technology (EDBT’06), pp. 664–681. Springer, Berlin Heidelberg New York, 2006.
G. Yavas, D. Katsaros, Ö. Ulusoy, and Y. Manolopoulos. A data mining approach for location prediction in mobile environments. Data and Knowledge Engineering, 54(2):121–146, 2005.
P. Zhang, Y. Huang, S. Shekhar, and V. Kumar. Correlation analysis of spatial time series datasets: A filter-and-refine approach. In Proceedings of the 7th Pacific–Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD’03), pp. 532–544. Springer, Berlin Heidelberg New York, 2003.
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Nanni, M., Kuijpers, B., Körner, C., May, M., Pedreschi, D. (2008). Spatiotemporal Data Mining. In: Giannotti, F., Pedreschi, D. (eds) Mobility, Data Mining and Privacy. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75177-9_11
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