Data cube approximation and histograms via wavelets
Proceedings of the seventh international conference on Information and knowledge management
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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
Efficient OLAP Operations in Spatial Data Warehouses
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Multidimensional data modeling for location-based services
The VLDB Journal — The International Journal on Very Large Data Bases
Spatio-Temporal Aggregation Using Sketches
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Historical spatio-temporal aggregation
ACM Transactions on Information Systems (TOIS)
Spatio-temporal data warehouses using an adaptive cell-based approach
Data & Knowledge 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
Approximate Aggregations in Trajectory Data Warehouses
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Hermes – a framework for location-based data management
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Frequent spatio-temporal patterns in trajectory data warehouses
Proceedings of the 2009 ACM symposium on Applied Computing
Regions of interest in trajectory data warehouse
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
SeTraStream: semantic-aware trajectory construction over streaming movement data
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
A visual analytics system for metropolitan transportation
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Visually exploring movement data via similarity-based analysis
Journal of Intelligent Information Systems
A metaphoric trajectory data warehouse for Olympic athlete follow-up
Concurrency and Computation: Practice & Experience
Visual Mobility Analysis using T-Warehouse
International Journal of Data Warehousing and Mining
Semantic trajectories: Mobility data computation and annotation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
On the Management and Analysis of Our LifeSteps
ACM SIGKDD Explorations Newsletter
Intelligent Data Analysis
| Hi-index | 0.00 |
The flow of data generated from low-cost modern sensing technologies and wireless telecommunication devices enables novel research fields related to the management of this new kind of data and the implementation of appropriate analytics for knowledge extraction. In this work, we investigate how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information. In particular, we focus our research on three issues that are critical to trajectory data warehousing: (a) the trajectory reconstruction procedure that takes place when loading a moving object database with sampled location data originated e.g. from GPS recordings, (b) the ETL procedure that feeds a trajectory data warehouse, and (c) the aggregation of cube measures for OLAP purposes. We provide design solutions for all these issues and we test their applicability and efficiency in real world settings.