Demo Data from EDBT 2026 / reviewers in the wild / expert
Wes Irish
dblp:13/4308
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1987
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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 |
Distributed systems · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems › consistency models
eventual consistency |
0.0 | 1 | 1987 | Epidemic Algorithms for Replicated Database Maintenance · PODC 1987 |
Distributed systems
gossip protocols |
0.0 | 1 | 1987 | Epidemic Algorithms for Replicated Database Maintenance · PODC 1987 |
Distributed systems
replication |
0.0 | 1 | 1987 | Epidemic Algorithms for Replicated Database Maintenance · PODC 1987 |
Distributed systems › replication
replicated data management |
0.0 | 1 | 1987 | Epidemic Algorithms for Replicated Database Maintenance · PODC 1987 |
Methods — techniques the papers use, named apart from their topics
randomized algorithm · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1987 | Epidemic Algorithms for Replicated Database MaintenanceabstractWhru a dilt~lhSC is replicated at, many sites2 maintaining mutual consistrnry among t,he sites iu the fac:e of updat,es is a signitirant problem.This paper descrikrs several randomized algorit,hms for dist,rihut.ingupdates and driving t,he replicas toward consist,c>nc,y.The algorit Inns are very simple and require few guarant,ees from the underlying conllllunicat.iollsystem, yc+ they rnsutc t.hat.the off(~c~t, of ('very update is evcnt,uwlly rf+irt-ted in a11 rq1ica.s.The cost, and parformancc of t,hr algorithms arc tuned I>? c%oosing appropriat,c dist,rilMions in t,hc randoinizat,ioii step.TIN> idgoritlmls ilr(' c*los~*ly analogoIls t,o epidemics, and t,he epi-dcWliolog)-litc\ratiirc, ilitlh iii Illld~~rsti4lldill~ tlicir bc*liavior.One of tlW i$,oritlims 11&S brc>n implrmcWrd in the Clraringhousr sprv(brs of thr Xerox C'orporat~c~ Iiitcrnc4, solviiig long-standing prol>lf~lns of high traffic and tlatirl>ilsr inconsistcllcp. Alan J. Demers, Daniel H. Greene, Carl H. Hauser, Wes Irish, John Larson, Scott Shenker, Howard E. Sturgis, Daniel C. Swinehart, Douglas B. Terry |
PODC | 4 |