THE INVESTIGATION OF THE OPPORTUNITIES OF BIG DATA ANALYTICS AS ANALYTICS-AS-A-SERVICE IN CLOUD COMPUTING FOR OIL AND GAS INDUSTRY - Problems of Information Technology

THE INVESTIGATION OF THE OPPORTUNITIES OF BIG DATA ANALYTICS AS ANALYTICS-AS-A-SERVICE IN CLOUD COMPUTING FOR OIL AND GAS INDUSTRY - Problems of Information Technology

THE INVESTIGATION OF THE OPPORTUNITIES OF BIG DATA ANALYTICS AS ANALYTICS-AS-A-SERVICE IN CLOUD COMPUTING FOR OIL AND GAS INDUSTRY - Problems of Information Technology

THE INVESTIGATION OF THE OPPORTUNITIES OF BIG DATA ANALYTICS AS ANALYTICS-AS-A-SERVICE IN CLOUD COMPUTING FOR OIL AND GAS INDUSTRY - Problems of Information Technology

THE INVESTIGATION OF THE OPPORTUNITIES OF BIG DATA ANALYTICS AS ANALYTICS-AS-A-SERVICE IN CLOUD COMPUTING FOR OIL AND GAS INDUSTRY - Problems of Information Technology
THE INVESTIGATION OF THE OPPORTUNITIES OF BIG DATA ANALYTICS AS ANALYTICS-AS-A-SERVICE IN CLOUD COMPUTING FOR OIL AND GAS INDUSTRY - Problems of Information Technology
AZERBAIJAN NATIONAL ACADEMY OF SCIENCES

№1, 2016

THE INVESTIGATION OF THE OPPORTUNITIES OF BIG DATA ANALYTICS AS ANALYTICS-AS-A-SERVICE IN CLOUD COMPUTING FOR OIL AND GAS INDUSTRY

Ramiz M. Alıguliyev, Yadigar N. Imamverdiyev , Fargana J. Abdullayeva

The increase in the volume of data generated in oil and gas industry has led to serious problems in this sector. In this paper, the challenges posed by big data in oil and gas industry, current state of implementation of big data analytics in this sphere are analyzed. The platforms of Big data analytics produced by large organizations for oil and gas industry and the experience of the largest oil and gas companies of the world in big data analytics are studied. Big data analysis techniques and the goals of using cloud computing for big data analytics are analyzed. Suggestions and recommendations for the realization of big data analytics as Analytics-as-a-Service in the oil and gas industry are given. (pp. 9-22)

Keywords: Big data analytics, OLAP, cloud computing, Analytics-as-a-Service, oil and gas exploration and production, Hadoop, MapReduce, data science
DOI : 10.25045/jpit.v07.i1.02
References
  • Cuzzocrea A., Song I.Y., Davis K.C. Analytics over large-scale multidimensional data: the big data revolution! / Proc. of the ACM 14th International Workshop on Data Warehousing and OLAP, 2011, pp. 101–104.
  • Chen C.L. Zhang C.Y. Data-intensive applications, challenges, techniques and technologies: A survey on big data // Information Science, 2014, vol.275, pp.314–327.
  • Obama Administration Unveils “Big data” initiative: Announces $200 million in new R&D investments, 2012, 4 p.
  • http://www.technologyreview.com/news/427876/big-oil-goes-mining-for-big-data/
  • Manyika J., Chui M., Brown B., Bughin J., Dobbs R., Roxburgh C., Byers A.H. Big Data: The next frontier for innovation, competition, and productivity, 2011, 156 p.
  • Brulé M., Tapping the power of Big Data for the oil and gas industry, IBM Software White Paper for Petroleum Industry, 2013, 8 p.
  • Big Data in the Cloud: Converging technologies, Intel IT Center, 2015, 12 p.
  • Demirkan H., Delen D. Leveraging the capabilities of service-oriented decision support systems: putting analytics and big data in cloud // Decision Support Systems, 2013, vol.55, pp.412–421.
  • Pandey S., Nepal S., Cloud computing and scientific applications - big data, scalable analytics and beyond // Future Generation Computer Systems, 2013, vol.29, pp.1774–1776.
  • Hashem I.A., Yaqoob I., Anuar N., Mokhtar S., Gani A., Khan S.U. The rise of “big data” on cloud computing: Review and open research issues // Information Systems, 2015, vol.47, pp.98–115.
  • Zulkernine F., Bauer M., Aboulnaga A. Towards cloud-based analytics-as-a-service (CLAaaS) for big data analytics in the cloud / Proc. of the IEEE International Congress on Big Data, 2013, pp.62–69.
  • What is advanced analytics? http://www-01.ibm.com/software/data/infosphere/what-is-advanced-analytics/
  • Russom P. Big data analytics, TDWI research, 2011, 35 p.
  • Feblowitz J. Big data in upstream oil and gas, IDC energy insights, 2013, 45 p.
  • Liebowitz J. Business analytics: an introduction, 2013, 288 p.
  • Buyya R., Yeo C.S., Venugopal S., Broberg J., Brandic I. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility // Future Generation Computer Systems, 2009, 25, no.6, pp.599–616.
  • Yan W., Brahmakshatriya U., Xue Y., Gilder M., Wise B. p-PIC: Parallel power iteration clustering for big data // Journal of Parallel and Distributed Computing, 2012, vol.73, no.3, pp.352–359.
  • Kim Y., Shim K., Kim M., Lee J.S. DBCURE-MR: an efficient density-based clustering algorithm for large data using MapReduce // Information Systems, 2014, vol.42, pp.15–35.
  • Song J., Guo C., Wang Z., Zhang Y., Yu G., Pierson J. HaoLap: Aa Hadoop based OLAP system for big data // Journal of Systems and Software, 2015, vol.102, pp.167–181.
  • Kajdanowicz T., Kazienko P., Indyk W. Parallel processing of large graphs // Future Generation Computer Systems, 2014, vol.32, pp.324–337.
  • Malewicz G., Austern M., Bik A., Dehnert J., Horn I., Leiser N., Czajkowski G. Pregel: A system for large-scale graph processing / Proc. of the International Conference on Management of Data, 2010, pp.135–146.
  • Ordonez C., Mohanam N., Garcia A.C. PCA for large data sets with parallel data summarization // Distributed and Parallel Databases, 2014, v32,no.3, pp.377–403.
  • Fiore S., D’Anca A., Palazzo C., Foster I., Williams D.N., Aloisio G. Ophidia: Toward big data analytics for eScience / Proc. of the International Conference on Computational Science, 2013, pp.5–7.
  • Wu X., Zhu X., Wu G., Ding W., Data mining with big data // IEEE Transactions on Knowledge and Data Engineering, 2014, vol.26, no.1, pp.97–107.
  • Steed C.A., Ricciuto D.M., Shipman G., Smith B., Thornton P.E., Wang D., Williams D.N. Big data visual analytics for earth system simulation analysis // Computers & Geosciences, 2013, vol.61, pp.71–82.
  • Truyens M., Eecke P.V. Legal aspects of text mining // Computer Law & Security Review, 2014, vol.30, no.2, pp.153–170.
  • Chen C.L., Zhang C.Y. Data-intensive applications, challenges, techniques and technologies: a survey on big data // Information Science, 2014, vol.275, pp.314–327.
  • Kambatla K, Kollias G, Kumar V., Grama A. Trends in big data analytics // Journal of Parallel and Distributed Computing, 2014, vol.74, no.7, pp.2561–2573.
  • Hsu C., Li G., Niu W., Batten L., Dorronsoro B., Danoy G., Bouvry P., Katz D.S., Zhang Z. Intelligent big data processing // Future Generation Computer Systems, 2014, vol.36, 452
  • Alguliyev R.M., Hajirahimova M.S. The phenomenon of “Big data”: Problems and opportunities // Information Technology Problems, 2014, №2, pp.3–16.
  • Alguliyev R.M., Hajirahimova M.S. “Big data” technologies / The problems of electronic government building, 1st Republican scientific-practical conference proceedings, 2014, pp. 214-127.
  • Dean J, Ghemawat S. MapReduce: a flexible data processing tool // Communications of the ACM, 2010, vol.53, no.1, pp.72–77.
  • Shvachko K., Hairong K., Radia S., Chansler R. The Hadoop distributed file system / Proc. of the IEEE 26th Symposium on Mass Storage Systems and Technologies, 2010, pp.1–10.
  • Talia D. Clouds for scalable big data analytics // Computer, 2013, vol 46, no.5, pp.98–101.
  • Zheng Z., Zhu J., Lyu M.R. Service-generated big data and big data-as-a-service: An overview / IEEE 2nd International Congress on Big Data, 2013, pp.403–410.
  • Sangvai P. Impact of big data in oil and gas industry / Proc. 10th Biennial international Conference & Exposition, 2013, pp.439–440.
  • Hems A., Soofi A., Perez E. Drilling for new business value. How innovative oil and gas companies are using big data to outmaneuver the competition, A Microsoft White Paper, 2013, 12 pp.
  • Taneja P., Wate P. Big Data enabled digital oil field / Computer Society of India Communications, 2013, pp.18–20.
  • Baaziz A., Quoniam L. Big data in upstream oil and gas, IDC energy insights, How to use Big Data technologies to optimize operations in Upstream Petroleum Industry // International Journal of Innovation, 2013, vol.1, no.1, pp.1–9.
  • Big Data for the oil and gas industry, Issue 5/4, TechConnect, 6 p.
  • Tapping the power of big data for the oil and gas industry, IBM Software, 2013, 8 pp.
  • Dayal U. Akatsu M, Gupta C. Expanding global big data solutions with innovative analytics // Hitachi Review, 2014, vol.63, no.6, pp.333–339.
  • Big data in oil and gas: how to tap its full potential, Hitachi, WebTech Q&A Session, 2013, 45 pp.
  • Perrons R.K., Hems A. Cloud computing in the upstream oil & gas industry: a proposed way forward // Energy Policy, 2013, pp. 732–737.
  • Feblowitz J., The big deal about big data in upstream oil and gas. Paper & Presentation, IDC Energy Insights, 2012.
  • Nicholson R., Big data in the Oil & Gas Industry, IDC Energy Insights, 2012.