Two Database Related Interpretations of Rough Approximations : Data Organization and Query Execution

• Autorzy: Ślęzak D. , Synak P. , Wojna A. , Wróblewski J.
• Języki publikacji: EN

Abstrakty

EN

We present analytic data processing technology derived from the principles of rough sets and granular computing. We show how the idea of approximate computations on granulated data has evolved toward complete product supporting standard analytic database operations and their extensions. We refer to our previous works where our query execution algorithms were described in terms of iteratively computed rough approximations. We explain how to interpret our data organization methods in terms of classical rough set notions such as reducts and generalized decisions.

Słowa kluczowe

EN

analytic data processing systems; rough-granular computational models

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2013
• Tom: Vol. 127, nr 1-4
• Strony: 445--459
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Ślęzak D.

• Institute of Mathematics, University of Warsaw, ul. Banacha 2, 02-097 Warsaw, Poland
• Infobright Inc., ul. Krzywickiego 34 lok. 219, 02-078 Warsaw, Poland

autor

Synak P.

• Infobright Inc., ul. Krzywickiego 34 lok. 219, 02-078 Warsaw, Poland

autor

Wojna A.

• Infobright Inc., ul. Krzywickiego 34 lok. 219, 02-078 Warsaw, Poland

autor

Wróblewski J.

• Infobright Inc., ul. Krzywickiego 34 lok. 219, 02-078 Warsaw, Poland

Bibliografia

• [1] Aggarwal, C. C., Han, J., Wang, J., Yu, P. S.: On Clustering Massive Data Streams: A Summarization Paradigm, in: Data Streams: Models and Algorithms (C. C. Aggarwal, Ed.), Springer, 2007, 9-38.
• [2] Beaubouef, T., Petry, F. E.: Incorporating Rough Data in Database Design for Imprecise Information Representation, in: Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam, Volume 2 (A. Skowron, Z. Suraj, Eds.), vol. 42 of Intelligent Systems Reference Library, Springer, 2013, 137-155.
• [3] Deshpande, A., Ives, Z. G., Raman, V.: Adaptive Query Processing, Foundations and Trends in Databases, 1(1), 2007, 1-140.
• [4] Duntsch, I., Gediga, G.: Uncertainty Measures of Rough Set Prediction, Artificial Intelligence, 106(1), 1998, 109-137.
• [5] Fagin, R.: Multivalued Dependencies and a New Normal Form for Relational Databases, ACM Transactions on Database Systems, 2(3), 1977, 262-278.
• [6] Idreos, S., Groffen, F., Nes, N., Manegold, S., Mullender, K. S., Kersten, M. L.: MonetDB: Two Decades of Research in Column-oriented Database Architectures, IEEE Data Engineering Bulletin, 35(1), 2012,40-45.
• [7] Lipski Jr., W.: On Databases with Incomplete Information, Journal of the ACM, 28(1), 1981, 41-70.
• [8] Liu, Q.: Approximate Query Processing, in: Encyclopedia of Database Systems (L. Liu, M. T. Ozsu, Eds.), Springer, 2009, 113-119.
• [9] Nguyen, H. S.: Approximate Boolean Reasoning: Foundations and Applications in Data Mining, LNCS Transactions on Rough Sets, 5, 2006, 334-506.
• [10] Pawlak, Z.: Rough Sets, International Journal of Parallel Programming, 11(5), 1982, 341-356.
• [11] Pawlak, Z., Skowron, A.: Rough Sets: Some Extensions, Information Sciences, 177(1), 2007, 28-40.
• [12] Pedrycz, W.: Granular Computing - Analysis and Design of Intelligent Systems, CRC Press, 2013.
• [13] Peters, G., Crespo, F., Lingras, P., Weber, R.: Soft Clustering - Fuzzy and Rough Approaches and Their Extensions and Derivatives, Internation Journal of Approximate Reasoning, 54(2), 2013, 307-322.
• [14] Skowron, A., Grzymała-Busse, J.: From Rough Set Theory to Evidence Theory, in: Advances in the Dempster Shafer Theory of Evidence (R. R. Yaeger, M. Fedrizzi, J. Kacprzyk, Eds.), Wiley, 1994, 193-236.
• [15] Slezak, D., Kowalski, M., Eastwood, V., Wróblewski, J.: Methods and Systems for Database Organization, US Patent 8,266,147 B2, 2012.
• [16] Slezak, D., Synak, P., Borkowski, J., Wroblewski, J., Toppin, G.: A Rough-columnar RDBMS Engine - A Case Study of Correlated Subqueries, IEEE Data Engineering Bulletin, 35(1), 2012, 34-39.
• [17] Slezak, D., Toppin, G., Kowalski, M., Wojna, A.: System and Method for Managing Metadata in a Relational Database, US Patent 8,521,748, 2013.
• [18] Slezak, D., Wroblewski, J., Eastwood, V., Synak, P.: Brighthouse: An Analytic Data Warehouse for Ad-hoc Queries, Proceedings of the VLDB Endowment, 1(2), 2008, 1337-1345.
• [19] Swiniarski, R. W., Skowron, A.: Rough Set Methods in Feature Selection and Recognition, Pattern Recognition Letters, 24(6), 2003, 833-849.
• [20] White, P. W., French, C. D.: Database System with Methodology for Storing a Database Table by Vertically Partitioning all Columns of the Table, US Patent 5,794,229, 1998.