On big data-guided upstream business research and its knowledge management

Abstract The emerging Big Data integration imposes diverse challenges, compromising the sustainable business research practice. Heterogeneity, multi-dimensionality, velocity, and massive volumes that challenge Big Data paradigm may preclude the effective data and system integration processes. Business alignments get affected within and across joint ventures as enterprises attempt to adapt to changes in industrial environments rapidly. In the context of the Oil and Gas industry, we design integrated artefacts for a resilient multidimensional warehouse repository. With access to several decades of resource data in upstream companies, we incorporate knowledge-based data models with spatial-temporal dimensions in data schemas to minimize ambiguity in warehouse repository implementation. The design considerations ensure uniqueness and monotonic properties of dimensions, maintaining the connectivity between artefacts and achieving the business alignments. The multidimensional attributes envisage Big Data analysts a scope of business research with valuable new knowledge for decision support systems and adding further business values in geographic scales.

[1]  Emc Education Services.  Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data , 2015 .

[2]  Vasant Dhar,et al.  Editorial - Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research , 2014, Inf. Syst. Res..

[3]  Nicholas R. Jennings,et al.  On agent-based software engineering , 2000, Artif. Intell..

[4]  Alistair Fletcher,et al.  Interval probability process mapping as a tool for drilling decisions analysis—the R&D perspective , 2004 .

[5]  Cornelius Puschmann,et al.  Metaphors of Big Data , 2014 .

[6]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .

[7]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[8]  Saumyadipta Pyne,et al.  Big Data Analytics: Methods and Applications , 2016 .

[9]  Shastri L. Nimmagadda,et al.  On new emerging concepts of petroleum digital ecosystem , 2012, WIREs Data Mining Knowl. Discov..

[10]  Peter Rob,et al.  Database systems : design, implementation, and management , 2000 .

[11]  Tom Fawcett,et al.  Data science for business , 2013 .

[12]  Vijay K. Vaishnavi,et al.  Design Science Research Methods and Patterns: Innovating Information and Communication Technology , 2007 .

[13]  Rudi Studer,et al.  How to structure and access XML documents with ontologies , 2001, Data Knowl. Eng..

[14]  Mayumi Hori,et al.  Applying XML Web Services into Health Care Management , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[15]  Jagdish S. Rustagi,et al.  Introduction to Statistical Methods , 1985 .

[16]  Li Guoyu.  World Atlas of Oil and Gas Basins , 2011 .

[17]  Rob Mattison.  Data warehousing - strategies, technologies, and techniques , 1996 .

[18]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[19]  Stephan Kudyba,et al.  Big Data, Mining, and Analytics: Components of Strategic Decision Making , 2014 .

[20]  Jan vom Brocke,et al.  Comparing Business Intelligence and Big Data Skills , 2014, Business & Information Systems Engineering.

[21]  Helmut Krcmar,et al.  Big Data , 2014, Bus. Inf. Syst. Eng..

[22]  Jan Pries-Heje,et al.  FEDS: a Framework for Evaluation in Design Science Research , 2016, Eur. J. Inf. Syst..

[23]  Ben Kei Daniel,et al.  Big Data and Learning Analytics in Higher Education , 2017 .