Schema Mapping as Query Discovery
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
On schema matching with opaque column names and data values
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Information-theoretic tools for mining database structure from large data sets
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Essential sqlalchemy
Statistics Using SAS Enterprise Guide
Statistics Using SAS Enterprise Guide
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As data sources and data size proliferate, knowledge discovery from 'Big Data' is starting to pose several challenges. In this paper, we address a specific challenge in the practice of enterprise knowledge management while extracting actionable nuggets from diverse data sources of seemingly related information. In particular, we address the challenge of archiving knowledge gained through collaboration, dissemination and visualization as part of the data analysis inference and decision-making lifecycle. We motivate the implementation of an enterprise data discovery and knowledge recorder tool called SEEKER based on a real-world case study. We motivate the implementation of an enterprise data discovery and knowledge recorder tool called SEEKER Schema Exploration and Evolving Knowledge Entity Recorder based on the queries and the analytical artifacts that are being created by analysts as they use the data. We show how the tool serves as a digital record of institutional domain knowledge and as documentation for the evolution of data elements, queries and schemas over time. As a knowledge management service, a tool like SEEKER saves enterprise resources and time by 1 avoiding analytic “silos” i.e., a separate set of data that is not included in an enterprise's data administration, 2 expediting the process of multi-source data integration and 3 intelligently documenting discoveries from collaborating analysts.