Selective Materialization: An Efficient Method for Spatial Data Cube Construction
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
A Prime Number Labeling Scheme for Dynamic Ordered XML Trees
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Warehousing and Analyzing Massive RFID Data Sets
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient storage scheme and query processing for supply chain management using RFID
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Hermes – a framework for location-based data management
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
A novel incremental principal component analysis and its application for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Warehousing and querying trajectory data streams with error estimation
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
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Trajectory data streams are huge amounts of data pertaining to time and position of moving objects generated by different sources continuously using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks). Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. Moreover, spatial data streams poses interesting challenges both for their proper definition and acquisition, thus making the mining process harder than for classical point data. In this paper, we address the problem of trajectory data streams On Line Analytical Processing, that revealed really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose an end to end framework in order to make the querying step quite effective. We performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed techniques.