Business Intelligence Domain and Beyond

Faced with the challenge of organizing massive, diverse collections of data, Enterprises look to Business Intelligence to transform this data into useful information, allowing more effective and efficient production. As a result, Business Intelligence theories and technologies are the focus of an increasing amount of substantial investment from Enterprises seeking to maintain a competitive advantage. This paper explores recent literature of the Business Intelligence domain and provides a few stimulating and innovate theories and practices. The authors explore several state-of-the-art studies related to the future trends and challenges of Business Intelligence as well as the surrounding technologies, such as data warehousing and cloud computing, that drive it.

[1]  A Min Tjoa,et al.  Sense & response service architecture (SARESA): an approach towards a real-time business intelligence solution and its use for a fraud detection application , 2005, DOLAP '05.

[2]  Wolfgang Ketter,et al.  Business intelligence gap analysis: a user, supplier and academic perspective , 2010, ICEC '10.

[3]  Syed Mansoor Sarwar,et al.  Real-time data warehousing for business intelligence , 2010, FIT.

[4]  Surajit Chaudhuri,et al.  An overview of business intelligence technology , 2011, Commun. ACM.

[5]  Olivia Parr Rud,et al.  Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy , 2009 .

[6]  Il-Yeol Song,et al.  Toward total business intelligence incorporating structured and unstructured data , 2011, BEWEB '11.

[7]  Robert L. Grossman,et al.  Data mining standards initiatives , 2002, CACM.

[8]  G. M. Muriithi,et al.  A conceptual framework for delivering cost effective business intelligence solutions as a service , 2013, SAICSIT '13.

[9]  Chrisna Jooste,et al.  Usability evaluation guidelines for business intelligence applications , 2013, SAICSIT '13.

[10]  Kevin Wilkinson,et al.  Data integration flows for business intelligence , 2009, EDBT '09.

[11]  Morten Middelfart.  Improving business intelligence speed and quality through the OODA concept , 2007, DOLAP '07.

[12]  Felix Wortmann,et al.  An architecture for ad-hoc and collaborative business intelligence , 2010, EDBT '10.

[13]  Lei Li,et al.  Bringing business intelligence to healthcare informatics curriculum: a preliminary investigation , 2014, SIGCSE.

[14]  Richard J. Goeke,et al.  Leveraging the flexibility of your data warehouse , 2007, CACM.

[15]  Hsinchun Chen,et al.  Business Intelligence and Analytics: Research Directions , 2013, TMIS.

[16]  Daswin De Silva,et al.  Chronic disease management: a business intelligence perspective , 2011 .

[17]  Carlos Gameiro.  Implementation of business intelligence tools using open source approach , 2011, OSDOC '11.

[18]  Vojtech Svátek,et al.  Semantic annotation and linking of competitive intelligence reports for business clusters , 2008, OBI '08.

[19]  Goutam Kumar Saha.  Business intelligence computing issues , 2007, UBIQ.

[20]  Antonio Ferrández Rodríguez,et al.  Model-driven restricted-domain adaptation of question answering systems for business intelligence , 2011, BEWEB '11.

[21]  Henning Baars,et al.  Decision support for partially moving applications to the cloud: the example of business intelligence , 2013, HotTopiCS '13.