Classification of methods for the big data analytics

Authors:

Oleh Veres, Roman Olyvko

Information Systems and Networks Department, Lviv Polytechnic National University, 12, S. Bandery Str., Lviv, 79013, Ukraine, Oleh.M.Veres@lpnu.ua

This article describes the features of classification methods and technologies, analytics Big data. Described group of methods and technologies, analytics Big data that are graded according to the functional relationships and formal model of information technology. The problem of the definition of ontology concepts analytics Big data.

1. Maier-Shenberher V. Bolshie dannye. Revoliutsiia, kotoraia izmenit to, kak my zhivem, rabotaem i myslim, Viktor Maier-Shenberher, Kennet Kuker ; transl. from English Inny Haidiuk, M. : Mann, Ivanov i Ferber, 2014, 240 p.

2. Bolshie dannye i analitika [Electronic resource], Access mode: http://www-03.ibm.com/systems/ru/technicalcomputing/bigdata.html

3. Aheeva A. Analitiki predupredili ob opasnosti bolshikh dannykh [Electronic resource], Anna Aheeva, Access mode: http://bigdata.cnews.ru/news/top/2015-10-23_eksperty_predosteregayut_ot_....

4. Nazvany prichiny tormozheniia rynka bolshikh dannykh [Electronic resource], Access mode: http://bigdata.cnews.ru/news/top/2015-11-20_analitiki _otsenili_tempy_rosta_ mirovogo_rynka.

5. Koen Dzh. MOHuchie sposobnosti: novye priemy analiza bolshikh dannykh [Electronic resource], Dzheffri Koen, Braien Dolen, Mark Danlep, Dzhozef Khellerstein, Keileb Velton; transl. from English Serhei Kuznetsov, Access mode: http://citforum.ru/database/articles/mad_skills/

6. History and evolution of big data analytics [Electronic resource], Access mode:https://www.sas.com/en_us/insights/analytics/big-data-analytics.html

7. Mitchell R. 8 big trends in big data analytics [Electronic resource], Robert L. Mitchell, Computerworld, OCT 23, 2014, Access mode :http://www.computerworld.com/article/ 2690856/big-data/8-big-trends-in-big-data-analytics.html

8. Bol- shie dannye (Big Data) [Electronic resource], Access mode: http://tadviser.ru/a/125096.

9. Inmon W. H. Big Data – getting it right: A checklist to evaluate your environment, [Electronic resource], W. H. Inmon., DSSResources.COM, 2014, Access mode: http://dssresources.com/papers/features/ inmon/inmon01162014.htm.

10. Shakhovska N. B. Orhanizatsiia velykykh danykh u rozpodilenomu seredovyshchi, N. B. Shakhovska, Yu. Ya. Boliubash, O. M. Veres, Obchysliuvalna tekhnika ta avtomatyzatsiia: [zb. nauk. pr. DonNTU], Donetsk, 2014, P. 147–155, (Visnyk, DonNTU ; No 2 (27).

11. Shakhovska N. B. Big Data Federated Repository Model, N. B. Shakhovska, Yu. Ja. Bolubash, O. M. Veres, The Experience of Designing and Application of CAD Systems in Microelectronics (SADMS’2015) Proc. ofthe XIII-thInt. Conf., (Polyana-Svalyava (Zakarpattya), Ukraine, 24-27 February, 2015), Lviv: Publishing Lviv Polytechnic, 2015, P. 382–384.

12. Veres O. Elements of the Formal Model Big Data, Oleh Veres, Natalya Shakhovska, Perspektyvni tekhnolohii i metody proektuvannia MEMS: materialy KhI mizhnar. konf. MEMSTECH2015, 2–6 veresnia 2015, Lviv, Nats. un-t "Lviv. politekhnika", Lviv: Vyd-vo Lviv. politekhniky, 2015, P. 81–83.

13. Shakhovska N. Data space architecture for Big Data managering, N. Shakhovska, O. Veres, Y. Bolubash, L. Bychkovska-Lipinska, Xth International Scientific and Technical Conference "Computer Sciences and Information Technologies" (CSIT’2015), R. 184–187, Lviv, 2015. DOI: 10.1109/STC-CSIT.2015.7325461

14. Shakhovska N. Generalized formal model of Big Data, N. Shakhovska, O. Veres and M. Hirnyak,, ECONTECHMOD: an international quarterly journal on economics of technology and modelling processes, vol. 5, no. 2, 2016, R. 33–38.

15. Shakhovska N. Big Data Information Technology and Data Space Architecture, N. Shakhovska, O. Veres, Y. Bolubash, Sensors & Transducers, vol. 195, no. 12. R. 69–76, 2015.

16. Barsehian A. A. Analiz dannykh i protsessov, A. A. Barsehian, M. S. Kupriianov, I. I. Kholod, M. D. Tess, S. I. Elizarov, 3-e izd. pererab. i dop, SPb., BKhV-Peterburh, 2009, 512 p.

17. Paklin N. B. Biznes-analitika: ot dannykh k znaniiam (+ SD), N. B. Paklin, V. I. Oreshkov, SPb., Piter, 2009, 624 p.

18. Diuk V. Data Mining: uchebnyi kurs (+CD), V. Diuk, A. Samoilenko, SPb., Piter, 2001, 368 p.

19. Manyika J. Big data: The next frontier for innovation, competition, and productivity, Manyika James. Mc Kinsey Global Institute, June, 2011, 156 p.

20. ZhuravlevIu. I. Raspoznavanie. Matematicheskie metody. Prohrammnaia sistema. Prakticheskie primeneniia, Iu. I. Zhuravlev, V. V. Riazanov, O. V. Senko, M. : Fazis, 2006, 176 p.

21. Zinovev A. Iu. Vizualizatsiia mnohomernykh dannykh, A. Iu. Zinovev, Krasnoiarsk: Izd. Krasnoiarskoho hos. tekhn. un-ta, 2000, 180 p.

22. Chubukova I. A. Data Mining: tutorial, I. A. Chubukova, M. : Internet-universitet informatsionnykh tekhnolohii: BINOM: Laboratoriia znanii, 2006, 382 p.

23. Sytnyk V. F. Intelektualnyi analiz danykh (deitamaininh): tutorial /V. F. Sytnyk, M. T. Krasniuk, K., KNEU, 2007, 376 p.

24. Ian H. Witten. Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, Mark A. Hall, 3rd Edition, Morgan Kaufmann, 2011, 664 c.

25. Marr B. Big Data: Using SMART Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance, Bernard Marr, John Wiley&Sons Ltd, 2015, 256 p.

26. Einav L. The Data Revolution and Economic Analysis [Electronic resource], Liran Einav, Jonathan Levin, NBER Working PaperNo. 19035, 2013, Access mode : http://www.nber.org/chapters/P.12942.pdf

27. Vaniashin A. Za bolshimi dannymi sledit PANDA, A. Vaniashin, A. Klimentov, V. Korenkov, Superkompiutery. 2013, No 3 (11), P. 56–61

28. Serov D. Analitika "bolshikh dannykh" – novye perspektivy [Electronic resource], Denis Serov, "StorageNews", No 1 (49), 2012, Access mode : http://www.storagenews.ru/49/EMC_BigData_49.pdf

29. Ronen Sh. Links that speak: The global language network and its association with global fame [Electronic resource], Shahar Ronen, Bruno Gonçalves, Kevin Z. Hu, Alessandro Vespignani, Steven Pinker, César A. Hidalgo, PNAS, Vol. 111, No. 52, 2014, Access mode : http://stevenpinker.com/files/pinker/files /pnas_hildago_et_al_global_language_network_2014.pdf

30. Aflalo Y. Spectral multidimensional scaling [Electronic resource], Yonathan Aflalo, Ron Kimmel, PNAS, vol. 110, no. 45, November 5, 2013, Access mode :http://www.cs.technion.ac.il/~ron/PAPERS/Journal/AflaloKimmelPNAS2013.pdf

31. Gadepally V. Big Data Dimensional Analysis [Electronic resource], Vijay Gadepally, Jeremy Kepner. arXiv:1408.0517v1, Access mode : https://arxiv.org/pdf/1408.0517v1.pdf

32. Weinstein M. Analyzing Big Data with Dynamic Quantum Clustering [Electronic resource], M. Weinstein, F. Meirer, A. Hume, Ph. Sciau, G. Shaked, R. Hofstetter, E. Persi, A. Mehta, D. Horn. arXiv:1310.2700, Access mode : https://arxiv.org/ftp/arxiv/papers/1310/1310.2700.pdf.

33. Paklin, N. B. Biznes-analitika: ot dannykh k znaniiam [Text] : tutorial, N. B. Paklin, V. I. Oreshkov, 2-e izd., ispr, SPb. : Piter, 2013, 702 p.

34. Zheliazny D. Hovori na iazyke diahramm : posobie po vizualnym kommunikatsiiam dlia rukovoditelei, D. Zheliazny, M. : Institut kompleksnykh stratehicheskikh issledovanii, 2004. –220 p.

35. Roem D. Praktika vizualnoho myshleniia. Orihinalnyi metod resheniia slozhnykh problem, D. Roem, M . : Mann, Ivanov i Ferber, 2014, 396 p.

36. Tafti E. Predstavlenie informatsii [Electronic resource], E. Tafti, Access mode : http://envisioninginformation.daiquiri.ru/15

37.Iau N. Iskusstvo vizualizatsii v biznese. Kak predstavit slozhnuiu informatsiiu prostymi obrazami, N. Iau, M. : Mann, Ivanov i Ferber, 2013, 352 p.

38. Iliinsky N. Designing Data Visualizations, N. Iliinsky, J. Steele, Sebastopol :O’Reilly, 2011, 110 p.

39. Krum R. Cool infographics: effective communication with datavisualization and design, R. Krum, Indianapolis: Wiley, 2014, 348 p.

40. Tiuki Dzh. Analiz rezultatov nabliudenii: razvedochnyi analiz, Dzh. Tiuki; ed. V. F. Pi- sarenko, M., Mir, 1981, 693 p.

41. Alper C. New Software for Visualizing the Past, Presentand Future [Electronic resource], C. Alper, K. Brown, G. R. Wagner, DSSResources.COM, 09/23/2006. –Access mode :http://dssresources.com/papers/ features/alperbrown&wagner/alperbrown&wagner 9212006.html

42. Barsehian A. A. Analiz dannykh i protsessov: tutorial, A. A. Barsehian, M. S. Kup- riianov, I. I. Kholod, M. D. Tess, S. I. Elizarov, – 3-e izd. pererab. i dop, SPb., BKhV-Peterburh,2009, 512 p.

43. Text Mining [Electronic resource], Access mode: http:/ /statsoft.ru/home/textbook/modules/sttextmin. html#index

44. Lande D. Hlubinnyi analiz tekstov: tekhnolohiia effektivnoho analiza tekstovykh dannykh [Electronic resource], Dmitrii Lande. –Access mode: http://visti.net/~dwl/art/dz/

45. Barsehian A. A. Tekhnolohii analiza dannykh. Data Mining, Visual Mining, Text Mining, OLAP, A. A. Barsehian, M. S. Kupriianov, V. V. Stepanenko, I. I. Kholod, 2-e izd. pererab. i dop, SPb., BKhV-Peterburh, 2007, 384 p.

46. Liniuchev P. Text Mining: sovremen- nye tekhnolohii na informatsionnykh rudnikakh [Electronic resource], Pavel Liniuchev, PC Week/RENo 6 (564), 27 fevralia – 5 marta 2007, Access mode:https://www.pcweek.ru/idea/article/detail.php?ID=82081

47. Pleskach V. L. Informatsiini systemy i tekhnolohii na pidpryiemstvakh : pidruchnyk, V. L. Pleskach, T. H. Zatonatska, K. : Znannia, 2011, 718 p.

48. Stounbreiker M. MapReduce i parallelnye SUBD: druzia ili vrahi? [Electronic resource], Maikl Stounbreiker, Deniel Abadi, Devit Devitt, Sem Medden, Erik Paulson, Endriu Pavlo, Aleksandr Razin ; transl. from English Serhei Kuznetsov, Communications of the ACM, vol. 53, no. 1, January 2010, Access mode: http://citforum.ru/database/articles/mr_vs_dbms-2/

49. Berezin A. Map-Reduce na primere MongoDB [Electronic resource], Anton Berezin, Access mode:https://habrahabr.ru/post/184130/

50. Lebedenko E. Tekhnolohiia GoogleMapReduce: razdeliai i vlastvui [Electronic resource], Evhenii Lebedenko, Access mode :http://www.computerra.ru/82659/mapreduce/

51. Pavlo E. Sravnenie podkhodov k krupnomasshtabnomu analizu dannykh [Electronic resource], Endriu Pavlo, Erik Paulson, Aleksandr Razin, Deniel Abadi, Devid Devitt, Semiuel Medden, Maikl Stounbreiker; transl. from English Serhei Kuznetsov, Access mode :http://citforum.ru/database/articles/mr_vs_dbms/2.shtml

52. BigData ot A do Ia. Chast 1: Printsipy raboty s bolshimi dannymi, paradihma MapReduce [Electronic resource] , Access mode:https://habrahabr.ru/company/dca/blog/267361/

53. Big data ot A do Ia. Chast 3: Priemy i stratehii razrabotki MapReduce-prilozhenii [Electronic resource], Access mode:https://habrahabr.ru/company/dca/blog/270453/

54. Havrilova T. A. Bazy znanii intellektualnykh sistem, T. A. Havrilova, V. F. Khoroshevskii, SPb. : Piter, 2000, 384 p.

55. Havrilova T. A. Ontolohiia dlia izucheniia inzhenerii znanii, Trudy Mezhdunarodnoi nauchno-prakticheskoi konferentsii KDS-2001, 2001.

56. Havrilova T. A. Ontolohicheskii podkhod k upravleniiu znaniiami pri razrabotke korporativnykh informatsionnykh sistem, Novosti iskusstvennoho intellekta, 2003, No 2, P. 24–30.

57. Lytvyn V. V. Bazy znan intelektualnykh system pidtrymky pryiniattia rishen: monograph, V. V. Lytvyn; Ministerstvo osvity i nauky, molodi ta sportu Ukrainy, Natsionalnyi universytet "Lvivska politekhnika", Lviv : Vyd-vo Lvivskoi politekhniky, 2011, 240 p.