Demo Data from VLDB 2026 / reviewers in the wild / expert
Jeré A. Hassberger
dblp:98/5211
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1990
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Electronic design automation · 50% Hardware reliability and fault tolerance · 50% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › uncertainty reasoning › fuzzy systems
fuzzy logic |
0.0 | 1 | 1990 | Fault Tree Based Diagnostics Using Fuzzy Logic · IEEE Trans. Pattern Anal. Mach. Intell. 1990 |
Electronic design automation › hardware verification and test
fault diagnosis |
0.0 | 1 | 1990 | Fault Tree Based Diagnostics Using Fuzzy Logic · IEEE Trans. Pattern Anal. Mach. Intell. 1990 |
Hardware reliability and fault tolerance › reliability modeling
fault tree analysis |
0.0 | 1 | 1990 | Fault Tree Based Diagnostics Using Fuzzy Logic · IEEE Trans. Pattern Anal. Mach. Intell. 1990 |
Methods — techniques the papers use, named apart from their topics
fuzzy set theory · 0.0fuzzy relational equation · 0.0causal reasoning · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1990 | Fault Tree Based Diagnostics Using Fuzzy LogicabstractFuzzy set theory is investigated as a tool for the diagnostics of systems described by means of a fault tree. The objective is to diagnose component failures from the observation of fuzzy symptoms using the information contained in a fault tree. A two-step procedure is used to solve the problem. In this first step, causal reasoning is used to diagnose failure modes, consisting of minimal cut-sets of basic events, from the observation of triggered gates treated as symptoms. In the second step, the authors identify the particular components which have failed based on the diagnosed failure modes. To perform this second step, the solution of a fuzzy relational equation a= V-product (S/sup T/ alpha x) connecting failure mode a to basic events x is derived. With this method, the diagnostics equations can be symmetrically generated and solved in terms of the tree's basic events. The systematic nature with which a diagnosis can be generated from a fault tree lends this method to potential application of object-based programming techniques.> Piotr J. Gmytrasiewicz, Jeré A. Hassberger, John Chung-Mong Lee |
IEEE Trans. Pattern Anal. Mach. Intell. | 2 |