Eliciting data semantics via top-down and bottom-up approaches: challenges and opportunities

  • Authors:

  • Lois Delcambre;Vijay Khatri;Yair Wand;Barbara Williams;Carson Woo;Mark Zozulia

  • Affiliations:

  • Portland State University, USA/ Indiana University, USA University of British Columbia, Canada/ Hill-Rom Company, USA University of British Columbia, Canada/ Deloitte Consulting;Portland State University, USA/ Indiana University, USA University of British Columbia, Canada/ Hill-Rom Company, USA University of British Columbia, Canada/ Deloitte Consulting;Portland State University, USA/ Indiana University, USA University of British Columbia, Canada/ Hill-Rom Company, USA University of British Columbia, Canada/ Deloitte Consulting;Portland State University, USA/ Indiana University, USA University of British Columbia, Canada/ Hill-Rom Company, USA University of British Columbia, Canada/ Deloitte Consulting;Portland State University, USA/ Indiana University, USA University of British Columbia, Canada/ Hill-Rom Company, USA University of British Columbia, Canada/ Deloitte Consulting;Portland State University, USA/ Indiana University, USA University of British Columbia, Canada/ Hill-Rom Company, USA University of British Columbia, Canada/ Deloitte Consulting

  • Venue:
  • ER'06 Proceedings of the 25th international conference on Conceptual Modeling
  • Year:
  • 2006

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Abstract

Data semantics can be defined as the meaning and use of data [2]. In the context of databases, data semantics refers to the set of mappings from a representation language to agreed-upon concepts in the real world [1]. Eliciting and capturing data semantics can enable better management of the enterprise data. Additionally, elicitation of data semantics can enhance understanding of applications and result in reduced maintenance and testing costs along with improved administration of applications. “Bad” data, or data whose semantics are not known or are not clear, is considered a major cause of failures such as “botched marketing campaigns, failed CRM and data warehouse projects, angry customers, and lunkhead decisions” [3]. To investigate the practical challenges and to propose future research opportunities, this discussion panel, moderated by Vijay Khatri and Carson Woo, will present: 1) views from Management Information Systems (MIS) and Computer Science (CS) research as well as 2) methods, tools and approaches employed in practice.