EDBT 2026 Demo / reviewers in the wild / expert
Shankar Sundaresan
dblp:32/3909
· DBLP profile ↗
4ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 3Artificial intelligence and machine learning · 2 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Database system architecture and tuning · 87% Data mining · 13% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology |
0.0 | 1 | 1997 | Database Design with Common Sense Business Reasoning and Learning · ACM Trans. Database Syst. 1997 |
Database system architecture and tuning
database design |
0.0 | 1 | 1997 | Database Design with Common Sense Business Reasoning and Learning · ACM Trans. Database Syst. 1997 |
Database system architecture and tuning › database design
database design tools |
0.0 | 1 | 1997 | Database Design with Common Sense Business Reasoning and Learning · ACM Trans. Database Syst. 1997 |
Data mining
knowledge acquisition |
0.0 | 1 | 1997 | Database Design with Common Sense Business Reasoning and Learning · ACM Trans. Database Syst. 1997 |
Methods — techniques the papers use, named apart from their topics
hierarchical context-dependent knowledge base · 0.0distance function · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Parallel teams for knowledge creation: Role of collaboration and incentives
Shankar Sundaresan, Justin Zhang 0001 |
Decis. Support Syst. | 1 |
| 1998 | An Ontology-Based Expert System for Database DesignabstractAlthough it is possible to encode a great deal of process knowledge about database design into a system, experience has shown that the contribution of a human designer extends beyond his or her knowledge of database design techniques. The next step in the evolution of automated database design tools is to incorporate knowledge and reasoning capabilities to support this higher level of participation. Doing so requires some understanding of what different terms mean. This paper presents an ontology that can be used as a surrogate for the meaning of words in a database design system to simulate the contributions that a designer would make based on his or her general knowledge. The ontology classifies a term into one or more categories such as person, abstract good or tradable document. It is comprised of a semantic network, a knowledge base containing information on the meaning of terms that have been classified, an expert system knowledge-acquisition component, and a distance measure for assessing the distance between the meanings of terms. The ontology was tested by different types of users on a variety of problems and was shown to be quite effective. Veda C. Storey, Debabrata Dey, Harald Ullrich, Shankar Sundaresan |
Data Knowl. Eng. | 4 |
| 1997 | An Ontology for Database Design Automation
Veda C. Storey, Harald Ullrich, Shankar Sundaresan |
ER | 3 |
| 1997 | Database Design with Common Sense Business Reasoning and LearningabstractAutomated database design systems embody knowledge about the database design process. However, their lack of knowledge about the domains for which databases are being developed significantly limits their usefulness. A methodology for acquiring and using general world knowledge about business for database design has been developed and implemented in a system called the Common Sense Business Reasoner, which acquires facts about application domains and organizes them into a a hierarchical, context-dependent knowledge base. This knowledge is used to make intelligent suggestions to a user about the entities, attributes, and relationships to include in a database design. A distance function approach is employed for integrating specific facts, obtained from individual design sessions, into the knowledge base (learning) and for applying the knowledge to subsequent design problems (reasoning). Veda C. Storey, Roger H. L. Chiang, Debabrata Dey, Robert C. Goldstein, Shankar Sundaresan |
ACM Trans. Database Syst. | 5 |