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Elke A. Rundensteiner
Researcher at Worcester Polytechnic Institute
Publications - 600
Citations - 12367
Elke A. Rundensteiner is an academic researcher from Worcester Polytechnic Institute. The author has contributed to research in topics: Query optimization & Computer science. The author has an hindex of 55, co-authored 561 publications receiving 11439 citations. Previous affiliations of Elke A. Rundensteiner include IBM & University of California, Irvine.
Papers
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Proceedings ArticleDOI
Hierarchical parallel coordinates for exploration of large datasets
TL;DR: A multi-resolution view of the data via hierarchical clustering is developed, and a variation of parallel coordinates is used to convey aggregation information for the resulting clusters.
Proceedings ArticleDOI
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
TL;DR: This paper defines visual clutter as any aspect of the visualization that interferes with the viewer's understanding of the data, and presents the concept of clutter-based dimension reordering, an attribute that can significantly affect a visualization's expressiveness.
Journal ArticleDOI
Hierarchical encoded path views for path query processing: an optimal model and its performance evaluation
TL;DR: This paper proposes a hierarchical encoded path view (HEPV) model, and presents complete solutions for all phases of the HEPV approach, including graph partitioning, hierarchy generation, path view encoding and updating, and path retrieval.
Patent
System and method for synchronizing and/or updating an existing relational database with supplemental XML data
TL;DR: A system and a method for synchronizing and updating a relational database with supplemental data in which the relational database has a set of tables defined by a relational schema is proposed in this article.
Proceedings ArticleDOI
Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets
TL;DR: DOSFA is an interactive hierarchical dimension ordering, spacing and filtering approach based on dimension hierarchies derived from similarities among dimensions that is scalable multi-resolution approach making dimensional management a tractable task.