Cited By
View all- Hesabi ZTari ZGoscinski AFahad AKhalil IQueiroz C(2015)Data Summarization Techniques for Big Data—A SurveyHandbook on Data Centers10.1007/978-1-4939-2092-1_38(1109-1152)Online publication date: 17-Mar-2015
Towards realistic sampling: generating dependencies in a relational database
Article No.: 12, Pages 1 - 6
Abstract
Managing large amounts of information is one of the most expensive, time-consuming and non-trivial activities and it usually requires expert knowledge. In a wide range of application areas, such as data mining, histogram construction, approximate query evaluation, and software validation, handling exponentially growing databases has become a difficult challenge, and a subset of the data is generally preferred. As a solution to the current challenges in managing large amounts of data, database sampling from the operational data available has proved to be a powerful technique. However, none of the existing sampling approaches consider the dependencies between the data in a relational database. In this paper, we propose a novel approach towards constructing a realistic testing environment, by analyzing the distribution of data in the original database along these dependencies before sampling, so that the sample database is representative to the original database.
References
[1]
IBM Optim Integrated Data Management. http://www-01.ibm.com/software/data/data-management/optim-solutions/.
[2]
Oracle Database Performance Tuning Guide. http://docs.oracle.com/cd/B19306_01/server.102/b14211/stats.htm#PFGRF003.
[3]
SELECT - Oracle Documentation. http://docs.oracle.com/cd/B19306_01/server.102/b14200/statements_10002.htm.
[4]
J. Bisbal and J. Grimson. Consistent database sampling as a database prototyping approach. Journal of Software Maintenance, 14(6): 447--459, Nov. 2002.
[5]
J. Bisbal, J. Grimson, and D. Bell. A formal framework for database sampling. Inf. Softw. Technol., 47(12): 819--828, Sept. 2005.
[6]
J. Bisbal, B. Wu, D. Lawless, and J. Grimson. Building consistent sample databases to support information system evolution and migration. In Proceedings of the 9th International Conference on Database and Expert Systems Applications (DEXA'98), volume 1460 of Lecture Notes in Computer Science, pages 196--205. Springer-Verlag, 1998.
[7]
V. T. Chakaravarthy, V. Pandit, and Y. Sabharwal. Analysis of sampling techniques for association rule mining. In Proceedings of the 12th International Conference on Database Theory, ICDT '09, pages 276--283, New York, NY, USA, 2009. ACM.
[8]
S. Chaudhuri, G. Das, and U. Srivastava. Effective use of block-level sampling in statistics estimation. In Proceedings of the 2004 ACM SIGMOD international conference on Management of data, SIGMOD '04, pages 287--298, New York, NY, USA, 2004. ACM.
[9]
G. John and P. Langley. Static versus dynamic sampling for data mining. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pages 367--370. AAAI Press, 1996.
[10]
H. Köhler, X. Zhou, S. Sadiq, Y. Shu, and K. Taylor. Sampling dirty data for matching attributes. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, SIGMOD '10, pages 63--74, New York, NY, USA, 2010. ACM.
[11]
F. Olken. Random Sampling from Databases. PhD thesis, University of California at Berkeley, 1993.
[12]
C. R. Palmer and C. Faloutsos. Density biased sampling: an improved method for data mining and clustering. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data, SIGMOD '00, pages 82--92, New York, NY, USA, 2000. ACM.
[13]
F. Provost, D. Jensen, and T. Oates. Efficient progressive sampling. In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pages 23--32. ACM Press, 1999.
[14]
H. Toivonen. Sampling large databases for association rules. In Proceedings of the 22th International Conference on Very Large Data Bases, VLDB '96, pages 134--145, San Francisco, CA, USA, 1996. Morgan Kaufmann Publishers Inc.
[15]
M. J. Zaki, S. Parthasarathy, W. Li, and M. Ogihara. Evaluation of sampling for data mining of association rules. Technical report, Rochester, NY, USA, 1996.
Index Terms
- Towards realistic sampling: generating dependencies in a relational database
Recommendations
VFDS: An Application to Generate Fast Sample Databases
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge ManagementLarge amounts of data often require expensive and time-consuming analysis. Therefore, highly scalable and efficient techniques are necessary to process, analyze and discover useful information. Database sampling has proven to be a powerful method to ...
A formal framework for database sampling
Database sampling is commonly used in applications like data mining and approximate query evaluation in order to achieve a compromise between the accuracy of the results and the computational cost of the process. The authors have recently proposed the ...
Generation of test databases using sampling methods
ISSTA 2013: Proceedings of the 2013 International Symposium on Software Testing and AnalysisPopulating the testing environment with relevant data represents a great challenge in software validation, generally requiring expert knowledge about the system under development, as its data critically impacts the outcome of the tests designed to ...
Comments
Information & Contributors
Information
Published In
January 2013
772 pages
ISBN:9781450319584
DOI:10.1145/2448556
Copyright © 2013 ACM.
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]
Sponsors
- SIGAPP: ACM Special Interest Group on Applied Computing
- SKKU: SUNGKYUNKWAN UNIVERSITY
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 January 2013
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
ICUIMC '13
Sponsor:
- SIGAPP
- SKKU
ICUIMC '13: The 7th International Conference on Ubiquitous Information Management and Communication
January 17 - 19, 2013
Kota Kinabalu, Malaysia
Acceptance Rates
Overall Acceptance Rate 251 of 941 submissions, 27%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 198Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)0
Reflects downloads up to 17 Oct 2024
Other Metrics
Citations
Cited By
View all- Hesabi ZTari ZGoscinski AFahad AKhalil IQueiroz C(2015)Data Summarization Techniques for Big Data—A SurveyHandbook on Data Centers10.1007/978-1-4939-2092-1_38(1109-1152)Online publication date: 17-Mar-2015
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in