Demo Data from SIGIR 2026 / reviewers in the wild / expert
Richard Tibbetts
dblp:62/3468
· DBLP profile ↗
4ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 4 · 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
2 papers |
Data stream processing · 90% Query processing and optimization · 10% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data stream processing › continuous query processing
streaming SQL |
0.1 | 1 | 2008 | Towards a streaming SQL standard · Proc. VLDB Endow. 2008 |
Methods — techniques the papers use, named apart from their topics
partial order semantics · 0.1batching operator · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | What is the Population of Interest: Population Modeling for BayesDB
Richard Tibbetts |
CIDR | 1 |
| 2008 | Towards a streaming SQL standardabstractThis paper describes a unification of two different SQL extensions for streams and its associated semantics. We use the data models from Oracle and StreamBase as our examples. Oracle uses a time-based execution model while StreamBase uses a tuple-based execution model. Time-based execution provides a way to model simultaneity while tuple-based execution provides a way to react to primitive events as soon as they are seen by the system. The result is a new model that gives the user control over the granularity at which one can express simultaneity. Of course, it is possible to ignore simultaneity altogether. The proposed model captures ordering and simultaneity through partial orders on batches of tuples. The batching and the ordering are encapsulated in and can be modified by means of a powerful new operator that we call SPREAD. This paper describes the semantics of SPREAD and gives several examples of its use. Namit Jain, Shailendra Mishra, Anand Srinivasan, Johannes Gehrke, Jennifer Widom, Hari Balakrishnan, Ugur Çetintemel, Mitch Cherniack, Richard Tibbetts, Stanley B. Zdonik |
Proc. VLDB Endow. | 9 |
| 2004 | Linear Road: A Stream Data Management Benchmark
Arvind Arasu, Mitch Cherniack, Eduardo F. Galvez, David Maier 0001, Anurag Maskey, Esther Ryvkina, Michael Stonebraker, Richard Tibbetts |
VLDB | 8 |
| 2004 | Retrospective on Aurora
Hari Balakrishnan, Magdalena Balazinska, Donald Carney, Ugur Çetintemel, Mitch Cherniack, Christian Convey, Eduardo F. Galvez, Jon Salz, Michael Stonebraker, Nesime Tatbul, Richard Tibbetts, Stanley B. Zdonik |
VLDB J. | 11 |