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Keh-Chiang Yu

dblp:99/4540 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 1988
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Databases, data management, data science and information retrieval · 1

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.

Human-computer interaction and pervasive computing
1 paper
Collaborative and social computing · 100%
Databases, data mining, and information retrieval
1 paper
Data models and query languages · 50% Information retrieval · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing › computer-supported cooperative work
collaborative applications
0.011988
Object Lens: A "Spreadsheet" for Cooperative Work · ACM Trans. Inf. Syst. 1988
Collaborative and social computing
cooperative work
0.011988
Object Lens: A "Spreadsheet" for Cooperative Work · ACM Trans. Inf. Syst. 1988
Information retrieval › document organization
hypertext
0.011988
Object Lens: A "Spreadsheet" for Cooperative Work · ACM Trans. Inf. Syst. 1988
Data models and query languages
object-oriented database
0.011988
Object Lens: A "Spreadsheet" for Cooperative Work · ACM Trans. Inf. Syst. 1988

Methods — techniques the papers use, named apart from their topics

semistructured objects · 0.0rule-based agents · 0.0rule-based agent · 0.0
YearPublicationVenuePosition
1988 Object Lens: A "Spreadsheet" for Cooperative Work
abstract
Object Lens allows unsophisticated computer users to create their own cooperative work applications using a set of simple, but powerful, building blocks. By defining and modifying templates for various semistructured objects, users can represent information about people, tasks, products, messages, and many other kinds of information in a form that can be processed intelligently by both people and their computers. By collecting these objects in customizable folders, users can create their own displays which summarize selected information from the objects in table or tree formats. Finally, by creating semiautonomous agents, users can specify rules for automatically processing this information in different ways at different times. The combination of these primitives provides a single consistent interface that integrates facilities for object-oriented databases, hypertext, electronic messaging, and rule-based intelligent agents. To illustrate the power of this combined approach, we describe several simple examples of applications (such as task tracking, intelligent message routing, and database retrieval) that we have developed in this framework.
Kum-Yew Lai, Thomas W. Malone, Keh-Chiang Yu
ACM Trans. Inf. Syst.3
1987 An investigation on the nature of decision support system software
Michael Szu-Yuan Wang, Keh-Chiang Yu
Decis. Support Syst.2
1982 An empirical study of a CDC 844-41 disk subsystem
J. William Atwood, Alexander MacLeod, Keh-Chiang Yu
Perform. Evaluation3