Deployment of a Descriptive Big Data Model

Big Data is an emerging research topic. The term remains fuzzy and jeopardizes to become an umbrella term. Straight forward investigations are inhibited since the research field is not well defined, yet. To identify a common understanding, experts have been interviewed. Hereby, the findings are coded and conceptualized until a descriptive Big Data model is developed by using Grounded Theory. This provides the basis for the model’s deployment. Here, academic publications and practical implementations marked as Big Data are classified. It becomes evident that Big Data is use-case driven and forms an interdisciplinary research field. Even not all papers belong to this research field. The findings become confirmed by the practical implementations. The chapter contributes to the intensive discussion about the term Big Data in illustrating the underlying area of discourse. A classification to set the research area apart from others can be achieved to support a goal oriented research in future.

[1]  B MilesMatthew,et al.  Qualitative Data Analysis , 2009, Approaches and Processes of Social Science Research.

[2]  Stephan Hartmann,et al.  The World as a Process: Simulations in the Natural and Social Sciences , 1996 .

[3]  Carsten Felden,et al.  Big Data - A State-of-the-Art , 2012, AMCIS.

[4]  Keith Gordon,et al.  What is Big Data , 2013 .

[5]  U. Flick.  An Introduction to Qualitative Research , 1998 .

[6]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[7]  Maximilian Röglinger,et al.  Big Data , 2013, Bus. Inf. Syst. Eng..

[8]  A. Strauss,et al.  Basics of qualitative research: Grounded theory procedures and techniques. , 1992 .

[9]  Ledion Bitincka,et al.  Experiences with workload management in splunk , 2012 .

[10]  Chen Li,et al.  Inside "Big Data management": ogres, onions, or parfaits? , 2012, EDBT '12.

[11]  Roy H. Campbell,et al.  Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory , 2011, FAST.

[12]  B. Glaser.  Theoretical Sensitivity: Advances in the Methodology of Grounded Theory , 1978 .

[13]  A. Strauss,et al.  The Discovery of Grounded Theory , 1967 .

[14]  Daniel Freedman,et al.  Fast Mean Shift by compact density representation , 2009, CVPR.

[15]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[16]  AlaviMaryam,et al.  Review: Knowledge management and knowledge management systems , 2001 .

[17]  John Krumm,et al.  User-Generated Content , 2008, IEEE Pervasive Comput..

[18]  Stefanie Nowak,et al.  Using one-class SVM outliers detection for verification of collaboratively tagged image training sets , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[19]  Steven Jones,et al.  Reflections on the use of grounded theory in interpretive information systems research , 2003, ECIS.

[21]  Dorothy E. Leidner,et al.  Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues , 2001, MIS Q..

[22]  Anne Laurent,et al.  High Performance Pre-computing: Prototype Application to a Coastal Flooding Decision Tool , 2012, 2012 Fourth International Conference on Knowledge and Systems Engineering.

[23]  Stephan Hartmann,et al.  The World as a Process , 1996 .

[24]  Hui Liu,et al.  Parallel option pricing with BSDEs method on MapReduce , 2011, 2011 3rd International Conference on Computer Research and Development.

[25]  Renato Pajarola,et al.  A Survey of Compressed GPU-Based Direct Volume Rendering , 2013, Eurographics.

[26]  Zhiwei Xu,et al.  RCFile: A fast and space-efficient data placement structure in MapReduce-based warehouse systems , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[27]  Thomas L. Sterling,et al.  A High-Performance Computing Forecast: Partly Cloudy , 2009, Computing in Science & Engineering.

[28]  R. Grant,et al.  Environments: Organizational Capability as Knowledge Integration , 2022 .

[29]  J. Barney,et al.  The resource-based view of the firm: Ten years after 1991 , 2001 .

[30]  Edmund Kohlwey,et al.  Leveraging the Cloud for Big Data Biometrics: Meeting the Performance Requirements of the Next Generation Biometric Systems , 2011, 2011 IEEE World Congress on Services.

[31]  Michael L. Brodie,et al.  The meaningful use of big data: four perspectives -- four challenges , 2012, SGMD.

[32]  Galit Shmueli,et al.  Predictive Analytics in Information Systems Research , 2010, MIS Q..

[33]  Xiaozhou Li,et al.  Reliability analysis of deduplicated and erasure-coded storage , 2011, PERV.

[34]  Trevor Wood-Harper,et al.  Systems development as a research act , 1999, J. Inf. Technol..

[35]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[36]  John Elder,et al.  Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications , 2012 .

[37]  Alexander S. Szalay,et al.  GrayWulf: Scalable Software Architecture for Data Intensive Computing , 2009 .

[38]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[39]  Jean-Paul Van Belle,et al.  Web Mining for Strategic Intelligence: South African Experiences and a Practical Methodology , 2007 .

[40]  Joshua B. Plotkin,et al.  Spatiotemporal correlations in criminal offense records , 2011, TIST.