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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Proceedings ArticleDOI
Storing and querying ordered XML using a relational database system
Igor Tatarinov,Stratis D. Viglas,Kevin Scott Beyer,Jayavel Shanmugasundaram,Eugene J. Shekita,Chun Zhang +5 more
TL;DR: This paper shows that XML's ordered data model can indeed be efficiently supported by a relational database system, and proposes three order encoding methods that can be used to represent XML order in the relational data model, and also proposes algorithms for translating ordered XPath expressions into SQL using these encoding methods.
Proceedings ArticleDOI
Scaling distributed machine learning with the parameter server
Mu Li,David G. Andersen,Jun Woo Park,Alexander J. Smola,Amr Ahmed,Vanja Josifovski,James Long,Eugene J. Shekita,Bor-Yiing Su +8 more
TL;DR: In this paper, the authors propose a parameter server framework for distributed machine learning problems, where both data and workloads are distributed over worker nodes, while the server nodes maintain globally shared parameters, represented as dense or sparse vectors and matrices.
Proceedings ArticleDOI
Improved histograms for selectivity estimation of range predicates
TL;DR: A taxonomy of histograms that captures all previously proposed histogram types and indicates many new possibilities is provided, which introduces novel choices for several of the taxonomy dimensions, and derive new histograms types by combining choices in effective ways.
Proceedings ArticleDOI
A comparison of join algorithms for log processing in MaPreduce
TL;DR: Key implementation details of a number of well-known join strategies in MapReduce are described and a comprehensive experimental comparison of these join techniques on a 100-node Hadoop cluster is presented.
Journal ArticleDOI
Efficiently Publishing Relational Data as XML Documents
Jayavel Shanmugasundaram,Eugene J. Shekita,Rimon Barr,Michael J. Carey,Bruce G. Lindsay,Hamid Pirahesh,Berthold Reinwald +6 more
TL;DR: The results of an experimental study show that constructing XML documents inside the relational engine can have a significant performance benefit and show the superiority of having the relational engines use what is called an “outer union plan” to generate the content of an XML document.