Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
A Framework for Generating Network-Based Moving Objects
Geoinformatica
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Warehousing and Analyzing Massive RFID Data Sets
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate mining of frequent patterns on streams
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Building real-world trajectory warehouses
Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
Hermes – a framework for location-based data management
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Building the data warehouse of frequent itemsets in the DWFIST approach
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Mining uncertain data for frequent itemsets that satisfy aggregate constraints
Proceedings of the 2010 ACM Symposium on Applied Computing
Frequent itemset mining of uncertain data streams using the damped window model
Proceedings of the 2011 ACM Symposium on Applied Computing
A metaphoric trajectory data warehouse for Olympic athlete follow-up
Concurrency and Computation: Practice & Experience
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In this paper we present an approach for storing and aggregating spatio-temporal patterns by using a Trajectory Data Warehouse (TDW). In particular, our aim is to allow the analysts to quickly evaluate frequent patterns mined from trajectories of moving objects occurring in a specific spatial zone and during a given temporal interval. We resort to a TDW, based on a data cube model, having spatial and temporal dimensions, discretized according to a hierarchy of regular grids, and whose facts are sets of trajectories which intersect the spatio-temporal cells of the cube. The idea is to enrich such a TDW with a new measure: frequent patterns obtained from a data-mining process on trajectories. As a consequence these patterns can be analysed by the user at various levels of granularity by means of OLAP queries. The research issues discussed in this paper are (1) the extraction/mining of the patterns to be stored in each cell, which requires an adequate projection phase of trajectories before mining; (2) the spatio-temporal aggregation of patterns to answer roll-up queries, which poses many problems due to the holistic nature of the aggregation function.