|Article title||DATA STRUCTURE COMPOSITION FOR THE DATA PROCESSING BY THE RECONFIGURABLE COMPUTING SYSTEMS|
|Authors||S. A. Butenkov|
|Section||SECTION IV. RECONFIGURABLE AND NEURAL NETWORK COMPUTING SYSTEMS|
|Month, Year||08, 2018 @en|
|Abstract||The processes of accumulation, compression, storage, extraction, processing and analysis of data are traditionally considered in various sections of theoretical informatics. To solve applied problems of technical implementation of these stages of working with data, methodologically different approaches are used, based on heterogeneous mathematical data models, and, accordingly, technically different software and hardware. At the same time, the optimization of the construction of data processing facilities is considered at each stage separately and using particular mathematical data models. This leads the developers of complex data processing systems to a situation in which, in addition to the actual processing, it is necessary to carry out the processes of converting the data presentation forms for the next stage of processing. Such intermediate conversions of data formats require a significant consumption of hardware resources and time, especially in the case of large amounts of data (Big Data). In a number of our works, a new mathematical apparatus for presenting and processing the data, based on the theory of algebraic systems for granular (integrated) data representation, has been introduced, developed and applied in new computing facilities. The new approach implements the ideas of the granular computing machine introduced by Lotfi Zadeh. It organically includes all the specified stages of working with data (on a uniform mathematical and algorithmic basis) and allows wide use of effective algorithms of linear complexity (greedy algorithms) in tasks related to data storage and processing. A new mathematical representation of data allows the data to be compressed naturally at all stages of processing at the expense of the basic properties of the informational granulation methodology. Since the methods based on the most typed algorithms of granular computations (without cycles and branching) are effectively implemented on reconfigurable high-performance computing systems, the present paper proposes structural solutions for implementing efficient algorithms of processing the granular data in the “fast algorithms”class for the granular computings built by the machines reconfigurable means.|
|Keywords||Granular computing; reconfigurable computing system; information granulation; matroid; greedy algorithm; algebraical system.|
|References||1. Lin T.Y., Yao Y.Y. and Zadeh L.A. (eds.). Data Mining, Rough Sets and Granular Computing. Physica-Verlag, Heidelberg, 2002.
2. Lin T.Y. Granular Computing: Structures, Representations, Applications and Future Directions, In: the Proc. of 9th International Conference, RSFDGrC 2003, Chongqing, China, May 2003, Lecture Notes on Artificial Intelligence LNAI 2639, Springer-Verlag, 16-24.
3. Kalyaev I.A., Levin I.I., Semernikov E.A., Shmoylov V.I. Rekonfiguriruemye mul'tikonveyernye vychislitel'nye struktury [Multiconference reconfigurable computing structure]. Rostov-on-Don: Izd.-vo YuNTS RAN, 2009, 344 p.
4. Zadeh L.A. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems, 1997, Vol. 90, pp. 111-127.
5. Yao Y.Y. Granular computing: basic issues and possible solutions, Proceedings of the 5th Joint Conference on Information Sciences, 2000, pp. 186-189.
6. Butenkov S.A., Zhukov A.L. Informatsionnaya granulyatsiya na osnove izomorfizma algebraicheskikh sistem [Information granulation based on isomorphism of algebraic systems], Sb. trudov Mezhdunarodnoy algebraicheskoy konferentsii, posvyashchennoy 80-letiyu so dnya rozhdeniya A.I. Kostrikina, Nal'chik, 12-18 iyulya 2009 g. [Proceedings of the international algebraic conference, devoted to the 80th anniversary of the birth of A. I. Kostrikin, on July
12-18, 2009], pp. 206-209.
7. Butenkov S. Granular Computing in Image Processing and Understanding, In Proceedings of IASTED International Conference on Artificial Intelligence and Applications “AIA 2004”, Innsbruk, Austria, February 10-14, 2004.
8. Butenkov S.A. Metody informatsionnoy granulyatsii v parallel'nykh vychisleniyakh [Methods of information granulation in parallel computing], Materialy 3-y Vserossiyskoy nauchno-tekhnicheskoy konferentsii «SKT-2014», 29 sentyabrya-4 oktyabrya 2014 g., Divnomorskoe, Gelendzhik [.Materials of the 3rd all-Russian scientific and technical conference "SKT-2014", September 29-October 4, 2014, Divnomorskoye, Gelendzhik], Vol. 1, pp. 99-104.
9. Butenkov S., Zhukov A., Nagorov A., Krivsha N. Granular Computing Models and Methods Based on the Spatial Granulation, XII Int. Symposium «Intelligent Systems», INTELS’16, 5-7 October 2016, Moscow, Russia. Elsevier Procedia Computer Science. 103, 2017, pp. 295-302.
10. Pedrysz W. Granular Computing – the emerging paradigm, Journal of Uncertain Systems, 2007, Vol. 1, No. 1, pp. 38-61.
11. Zadeh L.A. From Computing with Numbers to Computing with Words – From Manipulation of Measurements to Manipulation of Perceptions, IEEE Transactions on Circuits and Systems, 1999, Vol. 45, pp. 105-119.
12. Zadeh L.A. Soft computing and fuzzy logic, IEEE Software, 1994, Vol. 11, Nos. 1-6,
13. Mal'tsev A.I. Algebraicheskie sistemy [Algebraic system]. Moscow: Nauka, 1970, 392 p.
14. Krivsha N., Krivsha V., Beslaneev Z., Butenkov S. Greedy algorithms for Granular Computing Problems in Spatial Granulation Technique, XII Int. Symposium «Intelligent Systems», INTELS’16, 5-7 October 2016, Moscow, Russia. Elsevier Procedia Computer Science, 2017, 103, pp. 303-307.
15. Butenkov S.A. The development of Intelligent Data Analysis paradigm for the Theory of Information Granulation, Proceedings of IV International Conference “Integrated models and Soft Computing in Artificial Intellect”, Colomna, May 28-30 2007, Vol. 1, pp. 188-194.
16. Butenkov S.A. Robust Representation and Processing for weakly structured information, In Proceedings of IEEE-sponsored International Conference on Artificial Intelligence “AIS 2004”, Divnomorskoe, Russia, September 5-10, 2004, pp. 89-91.
17. Butenkov S.A. Nemetricheskiy podkhod v zadachakh granulyatsii dannykh [Non-metric approach in data granulation problems], Nauchnye trudy SWorld [Scientific works of SWorld], Issue 4 (41). Ivanovo: Nauchnyy mir, 2015, 104 p, Vol. 2, pp. 91-99.
18. Rogozov Yu.I., Butenkov S.A., Nagorov A.L., Beslaneev Z.O. Modeli dannykh na osnove teorii informatsionnoy granulyatsii [Data models based on the theory of information granulation], Trudy Pyatoy Mezhdunarodnoy konferentsii «Sistemnyy analiz i informatsionnye tekhnologii» SAIT-2013, Krasnoyarsk, 19-25 sentyabrya 2013 g. [Proceedings of the Fifth international conference "System analysis and information technologies" SAIT-2013, Krasnoyarsk, 19-25 September 2013], Vol. 2, pp. 395-398.
19. Butenkov S.A., Krivsha V.V., Al'-Douyani S.KH.S. Postroenie sistemy nechetkikh otnosheniy vzaimnogo polozheniya na dekartovykh granulakh [Construction of a system of fuzzy relations of mutual position on Cartesian granules], Trudy mezhdunarodnoy nauchno-tekhnicheskoy konferentsii «Iskusstvennye intellektual'nye sistemy» (IEEE AIS’06) [Proceedings of the international scientific and technical conference "Artificial intelligent systems" (IEEE AIS'06)]. Moscow: Fizmatlit, 2006, Vol. 2, pp. 99-105.
20. Vatolin D., Ratushnyak A., Smirnov M., Yukin V. Metody szhatiya dannykh [Data compression methods]. Moscow: DIALOG-MIFI, 2003, 384 p.