What is she best known for?
The fields of study she is best known for:
- Database
- Artificial intelligence
- Operating system
Sudha Ram mainly focuses on Marketing, Data mining, Management science, Information retrieval and Semantic data model.
Her study in the field of Early adopter also crosses realms of Public relations.
Sudha Ram has included themes like Variety, Artificial intelligence and Machine learning in her Data mining study.
Her studies in Management science integrate themes in fields like Normalized systems, Artifact, Design science and Problem domain.
Her Semantic data model research is multidisciplinary, incorporating perspectives in Data modeling, Conceptual schema, Distributed database and Database design.
Sudha Ram combines subjects such as Serializability, Fragmentation, Concurrency control and Database theory with her study of Distributed database.
Her most cited work include:
- Design science in information systems research (7502 citations)
- Consumer Resistance to Innovations: The Marketing Problem and its solutions (631 citations)
- The iPlant Collaborative: Cyberinfrastructure for Plant Biology. (353 citations)
What are the main themes of her work throughout her whole career to date?
Her main research concerns World Wide Web, Information retrieval, Data science, Knowledge management and Artificial intelligence.
Her work carried out in the field of World Wide Web brings together such families of science as Ontology and Semantics.
Her studies link Schema with Information retrieval.
Her study connects Expert system and Knowledge management.
The study incorporates disciplines such as Machine learning and Natural language processing in addition to Artificial intelligence.
Her Semantic data model research includes themes of Distributed database and Database design.
She most often published in these fields:
- World Wide Web (13.60%)
- Information retrieval (12.00%)
- Data science (11.60%)
What were the highlights of her more recent work (between 2012-2020)?
- Artificial intelligence (10.40%)
- Big data (4.00%)
- Social media (4.80%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Artificial intelligence, Big data, Social media, Design science and Machine learning.
Her Artificial intelligence study combines topics in areas such as Data modeling, Predictive analytics and Natural language processing.
Her Big data research is multidisciplinary, incorporating perspectives in Quality assessment and Knowledge management.
While the research belongs to areas of Design science, Sudha Ram spends her time largely on the problem of Library science, intersecting her research to questions surrounding Business process management, Method engineering, Design science research and System integration.
Her Machine learning research is multidisciplinary, incorporating elements of Educational institution and Data science.
In her research, Sudha Ram performs multidisciplinary study on Database design and Conceptual framework.
Between 2012 and 2020, her most popular works were:
- Predicting Asthma-Related Emergency Department Visits Using Big Data (93 citations)
- The relational model is dead, SQL is dead, and I don't feel so good myself (45 citations)
- Large-Scale Network Analysis for Online Social Brand Advertising (36 citations)
In her most recent research, the most cited papers focused on:
- Database
- Artificial intelligence
- Operating system
Sudha Ram mostly deals with Big data, World Wide Web, Social media, Data modeling and Artificial intelligence.
Her Big data study integrates concerns from other disciplines, such as Urbanization, Bridging, Quality assessment and Knowledge management.
Her work deals with themes such as Listing, Relational model and Pairwise comparison, which intersect with World Wide Web.
Sudha Ram regularly ties together related areas like Medical emergency in her Social media studies.
The various areas that Sudha Ram examines in her Data modeling study include Social influence, Event, Intelligent decision support system and Data mining.
Her biological study spans a wide range of topics, including Tree and Machine learning.
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