E
Eugene J. Shekita
Researcher at IBM
Publications - 92
Citations - 11061
Eugene J. Shekita is an academic researcher from IBM. The author has contributed to research in topics: Query optimization & XML database. The author has an hindex of 46, co-authored 92 publications receiving 10959 citations. Previous affiliations of Eugene J. Shekita include Google & University of Wisconsin-Madison.
Papers
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Patent
Systems, methods and computer program products for probing a hash table for improved latency and scalability in a processing system
TL;DR: In this article, the hash table is queried by comparing hash values with multiple slots in each chunk, such that if a value in a chunk equals the hash value of the compressed input key, then a match is declared and a vector is returned with a significant bit of a matching slot in the bucket set to a value.
Journal ArticleDOI
Technical note-- XTABLES: Bridging relational technology and XML
TL;DR: This work presents the modified figures and other queries, along with SQL commands that can be used to generate the sample data described in the original paper and those produced by the modified queries.
Patent
Synchronizing an auxiliary data system with a primary data system
Ronald J. Barber,Harish Deshmukh,Ning Li,Bruce G. Lindsay,Sridhar Rajagopalan,Roger C. Raphael,Eugene J. Shekita +6 more
TL;DR: In this paper, the primary data system and the auxiliary data system are synchronized for the purpose of processing data requests sent from the primary system that were not processed by the auxiliary system.
Jaql: A Scripting Language for Large Scale Semistructured Data Analysis
Kevin Scott Beyer,Vuk Ercegovac,Rainer Gemulla,Andrey Balmin,Mohamed Y. Eltabakh,Carl-Christian Kanne,Fatma Ozcan,Eugene J. Shekita +7 more
TL;DR: Jaql as discussed by the authors is a declarative scripting language for analyzing large semistructured datasets in parallel using Hadoop's MapReduce framework, which is used in IBM's InfoSphere BigInsights [5] and Cognos Consumer Insight [9] products.
Proceedings ArticleDOI
Static score bucketing in inverted indexes
TL;DR: This paper shows that a new index organization based on static score bucketing significantly improves in index build performance while having minimal impact on the quality of search results.