research-article

Frequent spatio-temporal patterns in trajectory data warehouses

Authors:

C. SilvestriAuthors Info & Claims

SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing

Pages 1433 - 1440

https://doi.org/10.1145/1529282.1529603

Published: 08 March 2009 Publication History

Get Access

Abstract

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.

References

[1]

R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB, pages 487--499, 1994.

Digital Library

Google Scholar

[2]

T. Brinkhoff. A framework for generating network-based moving objects. Geoinformatica, 6(2): 153--180, 2002.

Digital Library

Google Scholar

[3]

C. Giannella, J. Han, J. Peri, X. Yan, and P. Yu. Mining frequent patterns in data streams at multiple time granularities. In NSF Workshop on Next Generation Data Mining, 2003.

Google Scholar

[4]

F. Giannotti, M. Nanni, D. Pedreschi, and F. Pinelli. Trajectory pattern mining. In Proc. KDD, pages 330--339, 2007.

Digital Library

Google Scholar

[5]

H. Gonzalez, J. Han, X. Li, and D. Klabjan. Warehousing and Analyzing Massive RFID Data Sets. In Proc. ICDE, page 83, 2006.

Digital Library

Google Scholar

[6]

H. Kargupta and K. Sivakumar. Existential Pleasures of Distributed Data Mining. In Data Mining: Next Generation Challenges and Future Directions. AAAI/MIT Press, 2004.

Digital Library

Google Scholar

[7]

C. Lucchese, S. Orlando, P. Palmerini, R. Perego, and F. Silvestri. kDCI: a Multi-Strategy Algorithm for Mining Frequent Sets. In Proc. FIMI, 2003.

Google Scholar

[8]

G. Marketos, E. Frentzos, I. Ntoutsi, N. Pelekis, A. Raffaetà, and T. Theodoridis. Building Real-World Trajectory Warehouses. In MobiDE, pages 8--15, 2008.

Digital Library

Google Scholar

[9]

R. S. Monteiro, G. Zimbrao, H. Schwarz, B. Mitschang, and J. M. de Souza. Building the Data Warehouse of Frequent Itemsets in the DWFIST Approach. In Proc. ISMIS, pages 294--303, 2005.

Digital Library

Google Scholar

[10]

S. Orlando, R. Orsini, A. Raffaetà, A. Roncato, and C. Silvestri. Trajectory Data Warehouses: Design and Implementation Issues. Journal of Computing Science and Engineering, 1(2): 240--261, 2007.

Crossref

Google Scholar

[11]

N. Pelekis, Y. Theodoridis, S. Vosinakis, and T. Panayiotopoulos. Hermes - A Framework for Location-Based Data Management. In Proc. EDBT, 2006.

Digital Library

Google Scholar

[12]

C. Silvestri and S. Orlando. Approximate Mining of Frequent Patterns on Streams. Int. Journal of Intelligent Data Analysis, 11(1): 49--73, 2007.

Digital Library

Google Scholar

[13]

I. H. Witten, A. Moffat, and T. C. Bell. Managing Gigabytes: Compressing and Indexing Documents and Images. Morgan Kaufmann Publishers, 1999.

Digital Library

Google Scholar

Cited By

View all

Hu GDuan NZhu J(2016)Lightweight road network learning for efficient trajectory pattern mining2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)10.1109/SOLI.2016.7551666(83-88)Online publication date: Jul-2016
https://doi.org/10.1109/SOLI.2016.7551666
Leonardi LOrlando SRaffaetà ARoncato ASilvestri CAndrienko GAndrienko N(2014)A general framework for trajectory data warehousing and visual OLAPGeoinformatica10.1007/s10707-013-0181-318:2(273-312)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1007/s10707-013-0181-3
Elragal ARaslan H(2014)Analysis of Trajectory Data in Support of Traffic Management: A Data Mining ApproachAdvances in Data Mining. Applications and Theoretical Aspects10.1007/978-3-319-08976-8_13(174-188)Online publication date: 2014
https://doi.org/10.1007/978-3-319-08976-8_13
Show More Cited By

Index Terms

Frequent spatio-temporal patterns in trajectory data warehouses
1. Information systems
  1. Data management systems
    1. Database administration
  2. Information systems applications

Recommendations

Closed frequent similar pattern mining

The concept of closed frequent similar pattern mining is introduced.Several lemmas to prune the search space are introduced and proved.A novel closed frequent similar pattern mining algorithm (CFSP-Miner), is proposed.CFSP-Miner is more efficient than ...
Item-centric mining of frequent patterns from big uncertain data
Abstract
High volumes of wide varieties of valuable data of different veracity (e.g., imprecise and uncertain data) can be easily generated or collected at a high velocity for various knowledge-based and intelligent information & engineering systems in ...
Mining uncertain data for constrained frequent sets
IDEAS '09: Proceedings of the 2009 International Database Engineering & Applications Symposium

Data mining aims to search for implicit, previously unknown, and potentially useful pieces of information---such as sets of items that are frequently co-occurring together---that are embedded in data. The mined frequent sets can be used in the discovery ...

Comments

Information & Contributors

Information

Published In

SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing

March 2009

2347 pages

ISBN:9781605581668

DOI:10.1145/1529282

Conference Chairs:
Sung Y. Shin
South Dakota State University, United States
,
Sascha Ossowski
University Rey Juan Carlos, Spain

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Sixth Framework Programme

Conference

SAC09

Sponsor:

SIGAPP

SAC09: The 2009 ACM Symposium on Applied Computing

March 8, 2009 - March 12, 2008

Hawaii, Honolulu

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
462
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Hu GDuan NZhu J(2016)Lightweight road network learning for efficient trajectory pattern mining2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)10.1109/SOLI.2016.7551666(83-88)Online publication date: Jul-2016
https://doi.org/10.1109/SOLI.2016.7551666
Leonardi LOrlando SRaffaetà ARoncato ASilvestri CAndrienko GAndrienko N(2014)A general framework for trajectory data warehousing and visual OLAPGeoinformatica10.1007/s10707-013-0181-318:2(273-312)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1007/s10707-013-0181-3
Elragal ARaslan H(2014)Analysis of Trajectory Data in Support of Traffic Management: A Data Mining ApproachAdvances in Data Mining. Applications and Theoretical Aspects10.1007/978-3-319-08976-8_13(174-188)Online publication date: 2014
https://doi.org/10.1007/978-3-319-08976-8_13
Porto Fde C. Moura Ada Silva FBassini APalazzi DPoltosi Mde Castro LCameron L(2012)A metaphoric trajectory data warehouse for Olympic athlete follow-upConcurrency and Computation: Practice & Experience10.1002/cpe.186924:13(1497-1512)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1002/cpe.1869
Leung CJiang FChu WWong WPalakal MHung C(2011)Frequent itemset mining of uncertain data streams using the damped window modelProceedings of the 2011 ACM Symposium on Applied Computing10.1145/1982185.1982393(950-955)Online publication date: 21-Mar-2011
https://dl.acm.org/doi/10.1145/1982185.1982393
Xia YWang GZhang XKim GBae H(2011)Spatio-temporal Similarity Measure for Network Constrained Trajectory DataInternational Journal of Computational Intelligence Systems10.1080/18756891.2011.97278554:5(1070-1079)Online publication date: Sep-2011
https://doi.org/10.1080/18756891.2011.9727855
Gibaud AThomin PSallez Y(2011)Foresee, a fully distributed self-organized approach for improving traffic flowsSimulation Modelling Practice and Theory10.1016/j.simpat.2011.01.00319:4(1096-1117)Online publication date: Apr-2011
https://doi.org/10.1016/j.simpat.2011.01.003
Leung CHao BBrajczuk DShin SOssowski SSchumacher MPalakal MHung C(2010)Mining uncertain data for frequent itemsets that satisfy aggregate constraintsProceedings of the 2010 ACM Symposium on Applied Computing10.1145/1774088.1774305(1034-1038)Online publication date: 22-Mar-2010
https://dl.acm.org/doi/10.1145/1774088.1774305
Marketos GTheodoridis Y(2010)Ad-hoc OLAP on Trajectory DataProceedings of the 2010 Eleventh International Conference on Mobile Data Management10.1109/MDM.2010.63(189-198)Online publication date: 23-May-2010
https://dl.acm.org/doi/10.1109/MDM.2010.63

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Closed frequent similar pattern mining

Item-centric mining of frequent patterns from big uncertain data

Mining uncertain data for constrained frequent sets

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations