article

Multimedia data warehouses: a multiversion model and a medical application

Authors:

Anne-Muriel Arigon,

Maryvonne Miquel,

Anne TchounikineAuthors Info & Claims

Multimedia Tools and Applications, Volume 35, Issue 1

Pages 91 - 108

https://doi.org/10.1007/s11042-007-0118-7

Published: 01 October 2007 Publication History

Abstract

In field such as Cardiology, data used for clinical studies is not only alphanumeric, but can also be composed of images or signals. Multimedia data warehouse then must be studied in order to provide an efficient environment for the analysis of this data. The analysis environment must include appropriate processing methods in order to compute or extract the knowledge embedded into raw data. Traditional multidimensional models have a static structure which members of dimensions are computed in a unique way. However, multimedia data is often characterized by descriptors that can be obtained by various computation modes. We define these computation modes as "functional versions" of the descriptors. We propose a Functional Multiversion Multidimensional Model by integrating the concept of "version of dimension." This concept defines dimensions with members computed according to various functional versions. This new approach integrates different computation modes of these members into the proposed model, in order to allow the user to select the best representation of data. In this paper, a conceptual model is formally defined and a prototype for this study is presented. A multimedia data warehouse in the medical field has been implemented on a therapeutic study on acute myocardial infarction

References

[1]

1. Agrawal R, Gupta A, Sarawagi S (1995) Modeling multidimensional databases. IBM Research Report, IBM Almaden Research Center, September 25p.

[2]

2. Blaschka M, Sapia C, Höfling G (1999) On schema evolution in multidimensional databases. In: Data Warehousing and Knowledge Discovery, First International Conference, DaWaK '99, Florence, Italy, August 30-September 1, 1999, Proceedings. Lecture Notes in Computer Science 1676 Springer, ISBN 3- 540-66458-0.

[3]

3. Body M, Miquel M, Bédard Y, Tchounikine A (2003) Handling evolutions in multidimensional structures. In: Proceedings of the 19th International Conference on Data Engineering, March 5-8, 2003, Bangalore, India. IEEE Computer Society, ISBN 0-7803-7665-X.

[4]

4. Cabibbo L, Torlone R (1998) A logical approach to multidimensional databases. In: Advances in Database Technology--EDBT'98, 6th International Conference on Extending Database Technology, Valencia, Spain, March 23-27, 1998, Proceedings. Lecture Notes in Computer Science 1377 Springer, ISBN 3-540-64264-1.

[5]

5. Chaudhuri S, Dayal U (1997) An overview of data warehousing and OLAP technology. SIGMOD Record, 26(1):65-74.

Digital Library

[6]

6. Chevalier P, Rodriguez C, Bontemps L, Miquel M, Kirkorian G, Rousson R, Potet F, Schott JJ, Baró I, Touboul P (2001) Non-invasive testing of acquired long QT syndrome: evidence for multiple arrhythmogenic substrates. Cardiovasc Res 50(2):386-398 May.

[7]

7. Eder J, Koncilia C (2001) Evolution of dimension data in temporal data warehouses. In: Proceedings of the Data Warehousing and Knowledge Discovery, Third International Conference, DaWaK 2001, Munich, Germany, September 5-7, 2001, Lecture Notes in Computer Science 2114 Springer, ISBN 3-540-42553.

[8]

8. Gyssens M, Lakshmanan LVS (1997) A foundation for multi-dimensional databases. In: Proceedings of the 23rd International Conference on Very Large Data 9. Bases, August 25-29, 1997, Athens, Greece. Morgan Kaufmann, ISBN 1-55860-470-7.

Digital Library

[9]

9. Han J, Kamber M (2001) Data mining, concepts and techniques. Morgan Kaufmann.

[10]

10. Hristovski D, Rogac M, Markota M (2000) Using data warehousing and OLAP in public health care. In: Proceedings AMIA Symp: 369-373.

[11]

11. Hurtado C, Mendelzon AO, Vaisman A (1999) Maintaining data cubes under dimension updates. In: Proceedings of the 15th International Conference on Data Engineering, 23-26 March 1999, Sydney, Australia, IEEE Computer Society Press.

[12]

12. Hurtado C, Mendelzon AO, Vaisman A (1999) Updating OLAP dimensions. In: Proceedings of the ACM Second International Workshop on Data Warehousing and OLAP, November 6, 1999, Kansas City, Missouri, USA, Proceedings. ACM.

[13]

13. Huyn N (2001) Data analysis and mining in the life sciences. Sigmod Record, 30(3):76-85 September.

Digital Library

[14]

14. Inmon WH (eds) (1996) Building the data warehouse, 3rd edn, Wiley, New York.

[15]

15. Isken MW, Littig SJ, West M (2001) A data mart for operations analysis. J Healthc Inf Manag (JHIM) 15 (2):Summer.

[16]

16. Jarke M, List T, Köller J (2000) The challenge of process data warehousing. In: Proceedings of 26th International Conference on Very Large Data Bases, September 10-14, 2000, Cairo, Egypt. Morgan Kaufmann, ISBN 1-55860-715-3.

[17]

17. Kamp V, Wietek F (1997) Database system for multidimensional data analysis. In: Proceeding of the International Database Engineering and Application Symposium 1997 (IDEAS) Concordia University, Montreal, Canada, August 25-27.

[18]

18. Kimball R (1996) The data warehouse toolkit. Wiley, New York.

[19]

19. Lehner W (1998) Modeling large OLAP scenarios. In: Proceedings of the 1998 International Conference on Extending Database Technology, Valencia, Spain.

[20]

20. Li C, Sean Wang X (1996) A data model for supporting on-line analytical processing. In: Proceedings of the Fifth International Conference on Information and Knowledge Management, November 12-16, 1996, Rockville, MD, USA. ACM.

Digital Library

[21]

21. Mendelzon AO, Vaisman A (2000) Temporal queries in OLAP. In: Proceedings of 26th International Conference on Very Large Data Bases, September 10-14, 2000, Cairo, Egypt. Morgan Kaufmann, ISBN 1-55860-715-3.

[22]

22. Niinimäki J, Selén G, Kailajärvi M, Grönroos P, Irjala K, Forsström JJ (1996) Medical data warehouse, an investment for better medical care. Medical Informatics in Europe (MIE'96), Copenhagen, Denmark, August.

[23]

23. Pedersen TB, Jensen C (1998) Research issues in clinical data warehousing. In: 10th International Conference on Scientific and Statistical Database Management, Proceedings, Capri, Italy, July 1-3, 1998. IEEE Computer Society, ISBN 0-8186-8575-1.

Digital Library

[24]

24. Pedersen TB, Jensen C, Dyreson C (2001) A foundation for capturing and querying complex multidimensional data. Inf Syst 26(5): Special issue: Data Warehousing.

[25]

25. Tchounikine A, Miquel M, Flory A (2001) Information warehouse for medical research. In: Data Warehousing and Knowledge Discovery, Third International Conference, DaWaK 2001, Munich, Germany, September 5-7, 2001, Proceedings. Lecture Notes in Computer Science 2114 Springer, ISBN 3-540-42553-5.

[26]

26. Tikekar RV, Fotouhi F, Ragan D (1995) Storage and retrieval of medical images from data warehouses. Digital Image Storage and Archiving Systems.

[27]

27. Vassiliadis P (1998) Modeling multidimensional databases, cubes and cube operations. In: 10th International Conference on Scientific and Statistical Database Management, Proceedings, Capri, Italy, July 1-3, 1998. IEEE Computer Society, ISBN 0-8186-8575-1.

Digital Library

[28]

28. Vassiliadis P, Sellis T (1999) A survey of logical models for OLAP databases. SIGMOD Record, 28 (1):64-69, March.

Digital Library

[29]

29. You J et al. (2001) On hierarchical multimedia information retrieval. In: Proceedings of the 2001 International Conference on Image Processing (ICIP 2001), Thessaloniki, Greece, October 7-10 IEEE.

[30]

30. Zaïane OR, Han J, Li ZH, Hou J (1998) Mining multimEdia data. In: Proceedings of CASCON'98: Meeting of Minds, pp. 83-96, Toronto, Canada, November.

[31]

31. Zhang H et al. (2001) Developing a digital mammography data warehouse. Medical Imaging.

[32]

32. Zücker R, Kietz JU, Vaduva A (2001) Mining mart: metadata-driven preprocessing. In: Principles of Data Mining and Knowledge Discovery, 5th European Conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001, Proceedings. Lecture Notes in Computer Science 2168 Springer, ISBN 3-540-42534-9.

Cited By

Mertz NJónsson BDuane ARossetto LBailer WSchoeffmann KLokoč J(2022)NeoCubeProceedings of the 2nd International Workshop on Interactive Multimedia Retrieval10.1145/3552467.3554799(9-16)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1145/3552467.3554799
Jónsson BDuane AMertz N(2022)Relational Database Performance for Multimedia: A Case StudyProceedings of the 19th International Conference on Content-based Multimedia Indexing10.1145/3549555.3549558(186-190)Online publication date: 14-Sep-2022
https://dl.acm.org/doi/10.1145/3549555.3549558
Teixeira JAnnibal LFelipe JCiferri RCiferri C(2018)A similarity-based data warehousing environment for medical imagesComputers in Biology and Medicine10.1016/j.compbiomed.2015.08.01966:C(190-208)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1016/j.compbiomed.2015.08.019
Show More Cited By

Index Terms

Multimedia data warehouses: a multiversion model and a medical application
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
2. Information systems
  1. Data management systems
    1. Information integration
      1. Data warehouses
  2. Information systems applications
    1. Decision support systems
      1. Data warehouses
    2. Multimedia information systems

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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 35, Issue 1

October 2007

120 pages

ISSN:1380-7501

Issue’s Table of Contents

Copyright © Copyright © 2007 Springer Science+Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 October 2007

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Cited By

Mertz NJónsson BDuane ARossetto LBailer WSchoeffmann KLokoč J(2022)NeoCubeProceedings of the 2nd International Workshop on Interactive Multimedia Retrieval10.1145/3552467.3554799(9-16)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1145/3552467.3554799
Jónsson BDuane AMertz N(2022)Relational Database Performance for Multimedia: A Case StudyProceedings of the 19th International Conference on Content-based Multimedia Indexing10.1145/3549555.3549558(186-190)Online publication date: 14-Sep-2022
https://dl.acm.org/doi/10.1145/3549555.3549558
Teixeira JAnnibal LFelipe JCiferri RCiferri C(2018)A similarity-based data warehousing environment for medical imagesComputers in Biology and Medicine10.1016/j.compbiomed.2015.08.01966:C(190-208)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1016/j.compbiomed.2015.08.019
Jónsson BEiríksdóttir ÁWaage LTómasson GSigurthórsson HAmsaleg L(2014)M3 + P3 + O3 = Multi-D Photo BrowsingProceedings of the 20th Anniversary International Conference on MultiMedia Modeling - Volume 832610.1007/978-3-319-04117-9_40(378-381)Online publication date: 6-Jan-2014
https://dl.acm.org/doi/10.1007/978-3-319-04117-9_40
Akaichi JBen Attouch RRokne JFaloutsos C(2013)Disease evolution visualization through historized versions of medical imagesProceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/2492517.2500322(972-975)Online publication date: 25-Aug-2013
https://dl.acm.org/doi/10.1145/2492517.2500322

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