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From Data to Knowledge Analytics:Capabilities and Limitations


Affiliations
1 KAnOE - Center for Knowledge Analytics and Ontological Engineering, PES University, Bangalore 560085, India
     

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Data analytics is playing a central role in deriving useful information from large amounts of data available online in a variety of domains and applications. Analytics enploys a wide array of methods ranging from classical statistical techniques to those exploiting the visual and cognitive capabiUties of human users. In spite of all its capabilities, analytics at present seems to suffer from significant limitations in deahng with unstructured data and knowledge. This article explores the Umitations and defines key requirements to be met by fiiture developments in analytics. The article concludes with a sketch of tme knowledge analytics which is capable of delivering insights from knowledge structures, not just tabular data.

Keywords

Analytics, Unstructured Data, Knowledge, Capabilities, Limitations.
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  • Davenport, Thomas H. and Jeanne G. Harris. (2007). Competing on Analytics: The New Science of Winning. Boston, Mass: Harvard Business School Press.

  • Steve Lavalle, et al, (2011). Big Data, Analytics and The Path from Insights to Value. MIT Sloan Management Review Rep., 52(2).

  • Adrian Slywotzky and John Drzik. (2005). Managing Uncertainty: Countering the Biggest Risk of All. Hanvard Business Review Rep.

  • The Analytics Store. (2014). Irish Data Analytics Landscape Survey 2014-2015. The Analytics Store Rep.

  • Endert, Alex, et al. (2014). The human is the loop: new directions for visual analytics." Journal of intelligent information systems. 43(3): 411-435.

  • Hitzler, Pascal, and Krzysztof Janowicz. (2013). Linked Data, Big Data, and the 4th Paradigm. Semantic Web. 4(3): 233-235.

  • Shneiderman, Ben. (1996). The eyes have it: A task by data type taxonomy for information visualizations. Visual Languages, Proceedings IEEE Symposium on. IEEE, 1996.

  • Keim, Daniel, et al. (2006). Challenges in visual data analysis. Information Visualization, 2006. IV 2006. Tenth International Conference on. IEEE, 2006.

  • Rajesh Bordawekar. (2015). Analysing Analytics, Mass: Morgan and ClayPool Publishers.

  • Leskovec, Jure. (2011). Social media analytics: tracking, modeUng and predicting the flow of information through networks. Proceedings of the 20th international conference companion on World Wide Web. ACM.

  • James Kobielus. Social Media Analytics vs Social Network Analysis: Is there a real difference or Are you seeing double?, http://blogs.forrester.com/james_kobielus/10-07-07-sociaI_media_analytics_vs_social_network_analysis_there_real_difference_or_are_you_seeing_double

  • John Scott (2000). Social Network Analysis: A Handbook, Sage PubUcations.

  • Stanley Wasserman and Katherine Faust. (1994). Social Network Analysis: Methods and Applications, Cambridge University Press.

  • Scott Etkin, Analytics Trends 2015: Q and A with Deloitte's John Lucker. http://datainformed.com/analytics-trends-2015-q-and-a-with-john-lucker/

  • Buckland, Michael K. and Fredric C. Gey (1994). The relationship between recall and precision. X457S, 45(1): 12-19,

  • Mahesh, Kavi. (1999). Text Retrieval Quality: A Primer. Oracle Technology Network (Technet.Oracle.com), Oracle Corporation Redwood Shores, California, USA, 1999, http://www.oracle.con)/technetwoik/testcontent/imt-quality-092464.html

  • Tamara Franklin, (2015). The State of Content Analytics 2015. EcontentMag.

  • Siemens, George, and Ryan SJ d Baker. (2012). Learning analytics and educational data mining: towards communication and coUaboratioa Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, 2012.

  • Text Analytics, http://www.medalUa.com/text-analytics/

  • Pritchard, Alan. (1969). Statistical bibliography or bibliometrics? yoMraa/ of documentation 25: 348-349.

  • Nalimov, V.V. and Mul'chenko, Z.M. (1989). Study of Science Development as an Information Process. Scientometrics, 15: 33-43.

  • Sengupta, I.N. (1992). Bibhometrics, informetrics, scientometrics and librametrics: an overview. Libri, 42(2): 75-98.

  • Almind, Tomas C. and Peter Ingwersen. (1997). Informetric analyses on the World Wide Web: Methodological approaches to 'webometrics'. Jowraa/ of documentation 53(4): 404-426.

  • Priem, J.; Taraborelli, D.; Groth, P. and Neylon, C. (2010). Altmetrics: A manifesto, 26. http://altmetrics.org/manifesto

  • Colazzo, Dario, et al. (2014). RDF analytics: lenses over semantic graphs. Proceedings Of The 23rdInternational Conference On World Wide Web. ACM.

  • Seth Grimes, Breakthrough Analysis, http://breaktliroughaiialvsis.com/2008/08/01/unstructured-data-and-the-80-percent-rule/

  • Blumberg, R. and Atre, S. (2003). The problem with unstructured data. DM Review, 13: 42-49.

  • Cunningham, Hamish. (2002). GATE, a general architecture for text engineering. Computers and the Humanities, 36(2): 223-254.

  • Ferrucci, David, and Adam Lally. (2004). UIMA: an architectural approach to unstructured information processing in tlK corporate research environment. Natural Language Engineering, 10(3-4): 327-348.

  • Linked Data, http://www.w3.org/standards/semanticweb/data

  • Soergel, Dagobert. (1999). The rise of ontologies or the reinvention of classification.X4S75 50(12): 1119-1120.


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  • From Data to Knowledge Analytics:Capabilities and Limitations

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Authors

Pallavi Karanth
KAnOE - Center for Knowledge Analytics and Ontological Engineering, PES University, Bangalore 560085, India
Kavi Mahesh
KAnOE - Center for Knowledge Analytics and Ontological Engineering, PES University, Bangalore 560085, India

Abstract


Data analytics is playing a central role in deriving useful information from large amounts of data available online in a variety of domains and applications. Analytics enploys a wide array of methods ranging from classical statistical techniques to those exploiting the visual and cognitive capabiUties of human users. In spite of all its capabilities, analytics at present seems to suffer from significant limitations in deahng with unstructured data and knowledge. This article explores the Umitations and defines key requirements to be met by fiiture developments in analytics. The article concludes with a sketch of tme knowledge analytics which is capable of delivering insights from knowledge structures, not just tabular data.

Keywords


Analytics, Unstructured Data, Knowledge, Capabilities, Limitations.

References