Elke A. Rundensteiner mostly deals with Data mining, Data visualization, Visualization, Data stream mining and Information retrieval. Elke A. Rundensteiner has included themes like Scalability, Theoretical computer science and Set in her Data mining study. Her Visualization research includes themes of Variable and Cluster analysis.
Her work deals with themes such as Query plan, Data stream, Distributed computing and State, which intersect with Data stream mining. Her Information retrieval research is multidisciplinary, incorporating perspectives in XML, High dimensional data sets and Data exploration. The concepts of her Query optimization study are interwoven with issues in Query language and Joins.
Elke A. Rundensteiner spends much of her time researching Data mining, Information retrieval, Theoretical computer science, Database and Artificial intelligence. The study incorporates disciplines such as Set and Outlier in addition to Data mining. Her studies examine the connections between Information retrieval and genetics, as well as such issues in XML validation, with regards to Programming language.
Elke A. Rundensteiner works in the field of Database, namely Data warehouse. Her research in Artificial intelligence intersects with topics in Machine learning and Natural language processing. Elke A. Rundensteiner works mostly in the field of Data stream mining, limiting it down to topics relating to Distributed computing and, in certain cases, Schema, as a part of the same area of interest.
Her scientific interests lie mostly in Artificial intelligence, Machine learning, Deep learning, Natural language processing and Visual analytics. Her Artificial intelligence study which covers Task that intersects with Class, Variety and Social media. Elke A. Rundensteiner interconnects Feature extraction and Data collection in the investigation of issues within Machine learning.
The various areas that Elke A. Rundensteiner examines in her Visual analytics study include Domain, Use case, Multi feature and Data science. Her Visualization research is multidisciplinary, incorporating elements of Information retrieval and Human–computer interaction. Her Data mining research incorporates elements of Reliability and Scalability.
Elke A. Rundensteiner mainly investigates Artificial intelligence, Machine learning, Natural language processing, Task and Data mining. Her research integrates issues of Domain, Layer and Identification in her study of Artificial intelligence. Her study in the field of Test set, Statistical classification and Support vector machine also crosses realms of Mean squared prediction error.
Her Natural language processing research includes elements of Structure, Timeline and Focus. Her Task study also includes fields such as
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Hierarchical parallel coordinates for exploration of large datasets
Ying-Huey Fua;Matthew O. Ward;Elke A. Rundensteiner.
ieee visualization (1999)
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
W. Peng;M.O. Ward;E.A. Rundensteiner.
ieee symposium on information visualization (2004)
Multiview: A Methodology for Supporting Multiple Views in Object-Oriented Databases
Elke A. Rundensteiner.
very large data bases (1992)
Hierarchical encoded path views for path query processing: an optimal model and its performance evaluation
N. Jing;Y.-W. Huang;E.A. Rundensteiner.
IEEE Transactions on Knowledge and Data Engineering (1998)
Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations
Yun-Wu Huang;Ning Jing;Elke A. Rundensteiner.
very large data bases (1997)
Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets
Jing Wang;Wei Peng;M.O. Ward;E.A. Rundensteiner.
ieee symposium on information visualization (2003)
System and method for synchronizing and/or updating an existing relational database with supplemental XML data
Wang-Chien Lee;Gail Anne Mitchell;Elke Angelika Rundensteiner;Xin Zhang.
Maintaining data warehouses over changing information sources
Elke A. Rundensteiner;Andreas Koeller;Xin Zhang.
Communications of The ACM (2000)
Runtime Semantic Query Optimization for Event Stream Processing
Luping Ding;Songting Chen;E.A. Rundensteiner;J. Tatemura.
international conference on data engineering (2008)
Dynamic plan migration for continuous queries over data streams
Yali Zhu;Elke A. Rundensteiner;George T. Heineman.
international conference on management of data (2004)
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