TY - GEN
T1 - Enhancing business intelligence applications with value-driven feedback and recommendation
AU - Kolodner, Yoav
AU - Even, Adir
PY - 2009/12/1
Y1 - 2009/12/1
N2 - Business intelligence (BI) systems support activities such as data analysis, managerial decision making, and businessperformance measurement. Our research investigates the integration of feedback and recommendation mechanisms (FRM) into BI solutions. We define FRM as textual, visual, and/or graphical cues that are embedded into front-end BI tools and guide the end-user to consider using certain data subsets and analysis forms. Our working hypothesis is that the integration of FRM will improve the usability of BI tools and increase the benefits that end-users and organizations can gain from data resources. Our first research stage focuses on FRM based on assessment of previous usage and the associated value gain. We describe the development of such FRM, and the design of an experiment that will test the usability and the benefits of their integration. Our experiment incorporates value-driven usage metadata - a novel methodology for tracking and communicating the usage of data, linked to a quantitative assessment of the value gained. We describe a high-level architecture for supporting the collection, storage, and presentation of this new metadata form, and a quantitative method for assessing it.
AB - Business intelligence (BI) systems support activities such as data analysis, managerial decision making, and businessperformance measurement. Our research investigates the integration of feedback and recommendation mechanisms (FRM) into BI solutions. We define FRM as textual, visual, and/or graphical cues that are embedded into front-end BI tools and guide the end-user to consider using certain data subsets and analysis forms. Our working hypothesis is that the integration of FRM will improve the usability of BI tools and increase the benefits that end-users and organizations can gain from data resources. Our first research stage focuses on FRM based on assessment of previous usage and the associated value gain. We describe the development of such FRM, and the design of an experiment that will test the usability and the benefits of their integration. Our experiment incorporates value-driven usage metadata - a novel methodology for tracking and communicating the usage of data, linked to a quantitative assessment of the value gained. We describe a high-level architecture for supporting the collection, storage, and presentation of this new metadata form, and a quantitative method for assessing it.
KW - Business Intelligence
KW - Data Warehouse
KW - Decision Support Systems
KW - Metadata
KW - Recommender Systems
UR - http://www.scopus.com/inward/record.url?scp=84870810107&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84870810107
SN - 9781615675814
T3 - 15th Americas Conference on Information Systems 2009, AMCIS 2009
SP - 1481
EP - 1489
BT - 15th Americas Conference on Information Systems 2009, AMCIS 2009
T2 - 15th Americas Conference on Information Systems 2009, AMCIS 2009
Y2 - 6 August 2009 through 9 August 2009
ER -