What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Operating system
- Database
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.
Her most cited work include:
- Hierarchical parallel coordinates for exploration of large datasets (363 citations)
- Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering (233 citations)
- System and method for synchronizing and/or updating an existing relational database with supplemental XML data (195 citations)
What are the main themes of her work throughout her whole career to date?
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.
She most often published in these fields:
- Data mining (31.12%)
- Information retrieval (14.34%)
- Theoretical computer science (14.16%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (12.41%)
- Machine learning (6.82%)
- Deep learning (2.27%)
In recent papers she was focusing on the following fields of study:
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.
Between 2018 and 2021, her most popular works were:
- Automatic emotion detection in text streams by analyzing Twitter data (30 citations)
- FARE: Diagnostics for Fair Ranking using Pairwise Error Metrics (14 citations)
- Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding. (14 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Programming language
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
- Class which connect with Social media, Microblogging, Supervised learning and Recurrent neural network,
- Variety and related Rank, Representation, Open problem and Base.
Her work on Data stream mining is typically connected to Kernel density estimation as part of general Data mining study, connecting several disciplines of science.
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