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A central interest of my entire career has been concerned with increasing the scale of problems that can be solved by AI often by finding inspiration in database techniques being developed to manage the ever increasing volume of data encountered in business organizations. A natural complement has been to look to AI methods for inspiration to solve problems in data management. The first group of papers concerns doing both at the same time.
Data Integration
An unsupervised instance matcher for schema-free RDF data. J. Web Semant. 35: 102-123 (2015)
Schema matching over relations, attributes, and data values. SSDBM2014: 28:1-28:12
An Unsupervised Algorithm for Learning Blocking Schemes. ICDM 2013: 340-349
SPHINX: Schema integration by example. J. Intell. Inf. Syst. 29(2): 145-184(2007)
A Tractable Query Cache by Approximation. SARA 2002: 14
Database research inspired at least in part by AI methods
AI research inspired at least in part by database methods
Ultrawrap: SPARQL execution on relational data, J. F. Sequeda and Daniel P. Miranker, Journal of Web Semantics, Vol. 22, 2013, pp. 19-39.
"An algorithmic basis for integrating production systems and large databases," D. P. Miranker and D. A. Brant, [1990] Proceedings. Sixth International Conference on Data Engineering, Los Angeles, CA, USA, 1990, pp. 353-360, doi: 10.1109/ICDE.1990.113488.
"The organization and performance of a TREAT-based production system compiler," D. P. Miranker and B. J. Lofaso, in IEEE Transactions on Knowledge and Data Engineering, vol. 3, no. 1, pp. 3-10, March 1991, doi: 10.1109/69.75882.
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