Business Intelligence and Analytics: Research Directions

Business intelligence and analytics (BIA) is about the development of technologies, systems, practices, and applications to analyze critical business data so as to gain new insights about business and markets. The new insights can be used for improving products and services, achieving better operational efficiency, and fostering customer relationships. In this article, we will categorize BIA research activities into three broad research directions: (a) big data analytics, (b) text analytics, and (c) network analytics. The article aims to review the state-of-the-art techniques and models and to summarize their use in BIA applications. For each research direction, we will also determine a few important questions to be addressed in future research.

[1]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[2]  Ramanathan V. Guha,et al.  Semantic search , 2003, WWW '03.

[3]  Jay F. Nunamaker,et al.  A Visual Framework for Knowledge Discovery on the Web: An Empirical Study of Business Intelligence Exploration , 2005, J. Manag. Inf. Syst..

[4]  Noshir S. Contractor,et al.  The Emergence of Multidimensional Networks , 2009, J. Comput. Mediat. Commun..

[5]  Jimmy J. Lin,et al.  Smoothing techniques for adaptive online language models: topic tracking in tweet streams , 2011, KDD.

[6]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[7]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[8]  Barbara Wixom,et al.  Developing an Enterprise Business Intelligence Capability: The Norfolk Southern Journey , 2011, MIS Q. Executive.

[9]  Michael R. Lyu,et al.  SoRec: social recommendation using probabilistic matrix factorization , 2008, CIKM '08.

[10]  Hsinchun Chen,et al.  CyberGate: A Design Framework and System for Text Analysis of Computer-Mediated Communication , 2008, MIS Q..

[11]  Sanjay Ghemawat,et al.  MapReduce: a flexible data processing tool , 2010, CACM.

[12]  Cosma Rohilla Shalizi,et al.  Homophily and Contagion Are Generically Confounded in Observational Social Network Studies , 2010, Sociological methods & research.

[13]  Mason A. Porter,et al.  Communities in Networks , 2009, ArXiv.

[14]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[15]  D. Butler.  Data, data everywhere... , 2005, Nature Structural &Molecular Biology.

[16]  Barbara Wixom,et al.  Continental Airlines Flies High with Real-Time Business Intelligence , 2004, MIS Q. Executive.

[17]  Hsinchun Chen,et al.  EBizPort: Collecting and analyzing business intelligence information , 2004, J. Assoc. Inf. Sci. Technol..

[18]  Hsinchun Chen,et al.  AI and Opinion Mining , 2010, IEEE Intelligent Systems.

[19]  Shonali Krishnaswamy,et al.  Mining data streams: a review , 2005, SGMD.

[20]  Yizhou Sun,et al.  Mining Heterogeneous Information Networks: Principles and Methodologies , 2012, Mining Heterogeneous Information Networks: Principles and Methodologies.

[21]  Neil Savage,et al.  Gaining wisdom from crowds , 2012, Commun. ACM.

[22]  Hans-Georg Kemper,et al.  Business Intelligence – BI , 2002 .

[23]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[24]  Ana Paula Appel,et al.  HADI: Mining Radii of Large Graphs , 2011, TKDD.

[25]  Lise Getoor,et al.  Link mining: a survey , 2005, SKDD.

[26]  Barbara Wixom,et al.  The Current State of Business Intelligence , 2007, Computer.

[27]  Robert W. Zmud,et al.  Advanced Business Intelligence at Cardinal Health , 2005, MIS Q. Executive.

[28]  Ee-Peng Lim,et al.  Predicting Item Adoption Using Social Correlation , 2011, SDM.

[29]  Soumen Chakrabarti,et al.  Learning to rank networked entities , 2006, KDD '06.

[30]  L. Nelson.  Data, data everywhere. , 1997, Critical Care Medicine.