Browsing by Author "Naughton, Jeffrey"
Now showing items 3-7 of 7
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Database Support for Matching: Limitations and Opportunities
Kini, Ameet; Shankar, Srinath; DeWitt, David; Naughton, Jeffrey (University of Wisconsin-Madison Department of Computer Sciences, 2005)A match join of R and S with predicate theta is a subset of the theta join of R and S such that each tuple of R and S contributes to at most one result tuple. Match joins and their generalizations arise in many scenarios, ... -
On the Integration of Structure Indexes and Inverted Lists
Kaushik, Raghav; Krishnamurthy, Rajasekar; Naughton, Jeffrey; Ramakrishnan, Raghu (University of Wisconsin-Madison Department of Computer Sciences, 2003)We consider the problem of how to combine structure indexes and inverted lists to answer queries over a native XML DBMS, where the queries specify both path and keyword constraints. We augment the inverted list entries to ... -
RDBMS Index Support for Sparse Data Sets
Beckmann, Jennifer; Chu, Eric; Naughton, Jeffrey (University of Wisconsin-Madison Department of Computer Sciences, 2006)Maintenance costs and storage overheads incurred by indexes often limit the number of indexes created per table in an RDBMS. For sparse data, where a table may have hundreds of attributes, indexing only a few attributes ... -
A Survey of the Existing Landscape of ML Systems
Kumar, Arun; McCann, Robert; Naughton, Jeffrey; Patel, Jignesh M. (2015-11-27)We survey the existing landscape of ML systems to identify gaps that motivate our vision of a unifying abstraction to support the iterative process of model selection and lay a principled foundation for model selection ... -
To Join or Not to Join? Thinking Twice about Joins before Feature Selection
Kumar, Arun; Naughton, Jeffrey; Patel, Jignesh M.; Zhu, Xiaojin (2015-11-27)Closer integration of machine learning (ML) with data processing is a booming area in both the data management industry and academia. Almost all ML toolkits assume that the input is a single table, but many datasets are ...