Demo Data from VLDB 2026 / reviewers in the wild / expert
Deepa N. Pandalai
dblp:126/4832
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1996
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Embedded and real-time systems · 72% Electronic design automation · 28% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Embedded and real-time systems
discrete event systems |
0.0 | 1 | 1996 | Condition templates: improved distributed models for automated fault monitoring of manufacturing systems · ICRA 1996 |
Electronic design automation › hardware verification and test › formal verification
timed automata |
0.0 | 1 | 1996 | Condition templates: improved distributed models for automated fault monitoring of manufacturing systems · ICRA 1996 |
Embedded and real-time systems › cyber-physical system platforms
industrial automation |
0.0 | 1 | 1996 | Condition templates: improved distributed models for automated fault monitoring of manufacturing systems · ICRA 1996 |
Embedded and real-time systems
manufacturing systems |
0.0 | 1 | 1996 | Condition templates: improved distributed models for automated fault monitoring of manufacturing systems · ICRA 1996 |
Methods — techniques the papers use, named apart from their topics
timed automata · 0.0condition templates · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1996 | Condition templates: improved distributed models for automated fault monitoring of manufacturing systemsabstractThis paper considers the problem of monitoring automated manufacturing systems with discrete input and output signals. In such systems, the timing and sequencing of signal changes (events) can be an indication of whether the process is operating correctly. We introduce condition templates as a model to represent the expected timing and sequencing relationships of these events. Condition templates can be distributed over multiple processors in the manufacturing system, allowing a distributed and robust method of fault monitoring. We present the condition templates monitoring method and develop a technique for deriving condition template models from timed automata models. Deepa N. Pandalai, Lawrence E. Holloway |
ICRA | 1 |