What is she best known for?
The fields of study she is best known for:
- Database
- Artificial intelligence
- Programming language
Her scientific interests lie mostly in Theoretical computer science, Information retrieval, Data mining, Data integration and Data mapping.
Her Theoretical computer science research integrates issues from Semantics, Object-relational mapping and Schema.
Her study in Information retrieval is interdisciplinary in nature, drawing from both Rewriting and SQL.
Her research investigates the connection with Data mining and areas like Scalability which intersect with concerns in Cluster analysis and Set.
As a member of one scientific family, she mostly works in the field of Data integration, focusing on Data exchange and, on occasion, Programming language, Algorithmics and Conjunctive query.
Her study on Data mapping also encompasses disciplines like
- XML that connect with fields like Data warehouse,
- Information integration and related Schema migration.
Her most cited work include:
- Data exchange: semantics and query answering (1069 citations)
- Translating web data (442 citations)
- Schema Mapping as Query Discovery (432 citations)
What are the main themes of her work throughout her whole career to date?
Renée J. Miller mainly investigates Information retrieval, Data mining, Data integration, Theoretical computer science and World Wide Web.
Her studies deal with areas such as Open data, Set and Database as well as Information retrieval.
Her Data mining study integrates concerns from other disciplines, such as Machine learning, Cluster analysis, Scalability and Data modeling.
Her work deals with themes such as Ontology-based data integration, Data exchange, Metadata and Data science, which intersect with Data integration.
Her work carried out in the field of Data exchange brings together such families of science as Programming language, Data mapping, Algorithmics, XML and Schema mapping.
The study incorporates disciplines such as Range, Semantics and Schema in addition to Theoretical computer science.
She most often published in these fields:
- Information retrieval (29.81%)
- Data mining (23.60%)
- Data integration (18.01%)
What were the highlights of her more recent work (between 2014-2021)?
- Data mining (23.60%)
- Data science (11.18%)
- Information retrieval (29.81%)
In recent papers she was focusing on the following fields of study:
Renée J. Miller focuses on Data mining, Data science, Information retrieval, Open data and Scalability.
Renée J. Miller performs integrative study on Data mining and Data quality in her works.
The Data science study combines topics in areas such as Visualization, Data integration and Data management.
Her Data integration research is multidisciplinary, incorporating elements of Data exchange and Data mapping.
Her biological study spans a wide range of topics, including Table, Set, Statistical model and Benchmark.
Process and Greedy algorithm is closely connected to Theoretical computer science in her research, which is encompassed under the umbrella topic of Scalability.
Between 2014 and 2021, her most popular works were:
- Combining quantitative and logical data cleaning (74 citations)
- Table union search on open data (46 citations)
- The iBench integration metadata generator (44 citations)
In her most recent research, the most cited papers focused on:
- Database
- Programming language
- Artificial intelligence
The scientist’s investigation covers issues in Data science, Data mining, Scalability, Open data and Data integration.
Her research integrates issues of Software versioning and Metadata management in her study of Data science.
Her Functional dependency study in the realm of Data mining interacts with subjects such as Metric.
Her work in Scalability addresses subjects such as Theoretical computer science, which are connected to disciplines such as Greedy algorithm.
Her Data integration research incorporates elements of Data mapping, Data element, Metadata and Data management.
Her Data element study combines topics from a wide range of disciplines, such as Schema evolution, Field, Data exchange and Data warehouse.
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