What is she best known for?
The fields of study she is best known for:
- Database
- Operating system
- Programming language
Her main research concerns Database, Schema, Query language, View and Information retrieval.
Her Database design and Relational database study in the realm of Database interacts with subjects such as Structure.
Her studies in Database design integrate themes in fields like Document type definition and Data warehouse.
Laura M. Haas combines subjects such as Programming language and Query optimization with her study of Query language.
In her work, Data management is strongly intertwined with Set, which is a subfield of Query optimization.
In the field of Information retrieval, her study on Schema mapping overlaps with subjects such as Key.
Her most cited work include:
- Apparatus, system, and method for database provisioning (910 citations)
- Optimizing Queries Across Diverse Data Sources (460 citations)
- Schema Mapping as Query Discovery (432 citations)
What are the main themes of her work throughout her whole career to date?
Laura M. Haas spends much of her time researching Database, World Wide Web, Data science, Information retrieval and Query optimization.
She has researched Database in several fields, including Data structure and Stream processing.
As a member of one scientific family, Laura M. Haas mostly works in the field of World Wide Web, focusing on Distributed database and, on occasion, Deadlock prevention algorithms, Concurrency control and Distributed algorithm.
Her Data science research integrates issues from Data integration, Information integration, Data management and Big data.
Her research in Information retrieval intersects with topics in Interface, Document Structure Description and Natural language processing.
The concepts of her Query optimization study are interwoven with issues in Query language, Query expansion, Middleware and View.
She most often published in these fields:
- Database (33.94%)
- World Wide Web (19.27%)
- Data science (15.60%)
What were the highlights of her more recent work (between 2009-2018)?
- Data science (15.60%)
- Big data (5.50%)
- Data integration (8.26%)
In recent papers she was focusing on the following fields of study:
Laura M. Haas mostly deals with Data science, Big data, Data integration, Database and World Wide Web.
Her work investigates the relationship between Data science and topics such as Data management that intersect with problems in Engineering ethics and Field.
Her Database study incorporates themes from Theoretical computer science, Set and Stream processing.
The various areas that Laura M. Haas examines in her Theoretical computer science study include Information retrieval, Schema and Tracing.
Her Set research is multidisciplinary, incorporating perspectives in Relational database, Data mining, Distributed database and Competitive intelligence.
Her biological study spans a wide range of topics, including Independence, Process design and Software engineering.
Between 2009 and 2018, her most popular works were:
- SECRET: a model for analysis of the execution semantics of stream processing systems (91 citations)
- The Beckman report on database research (62 citations)
- The Beckman Report on Database Research (46 citations)
In her most recent research, the most cited papers focused on:
- Database
- Operating system
- Programming language
Her primary scientific interests are in Theoretical computer science, Big data, Database research, Stream processing and Key.
Laura M. Haas interconnects Information retrieval, Schema and Tracing in the investigation of issues within Theoretical computer science.
Her Big data research incorporates elements of Data life cycle, Cloud computing, World Wide Web and Data science.
Along with Database research, other disciplines of study including Data management and Scalability are integrated into her research.
The Stream processing study combines topics in areas such as Variation, Database, Window, Semantics and Range.
Her Key research spans across into fields like Data stream mining and Semantic heterogeneity.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
