EDBT 2026 Demo / reviewers in the wild / expert
Tal Zaccai
dblp:194/3943
· DBLP profile ↗
3ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 3 · 1 since 2021Artificial 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
2 papers |
High-performance computing · 46% Distributed systems · 46% Cloud and datacenter computing · 7% | |
| Databases, data mining, and information retrieval
1 paper |
Recommender systems · 100% | |
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › performance optimization
application acceleration |
0.9 | 1 | 2025 | Garnet: A Next-Generation Cache-Store for Accelerating Applications and Services · Proc. VLDB Endow. 2025 |
Distributed systems
distributed programming |
0.4 | 1 | 2020 | A.M.B.R.O.S.I.A: Providing Performant Virtual Resiliency for Distributed Applications · Proc. VLDB Endow. 2020 |
Distributed systems
fault tolerance |
0.4 | 1 | 2020 | A.M.B.R.O.S.I.A: Providing Performant Virtual Resiliency for Distributed Applications · Proc. VLDB Endow. 2020 |
Recommender systems › music recommendation
playlist generation |
0.3 | 1 | 2017 | Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist Generation · WSDM 2017 |
Machine learning › Probabilistic and Bayesian machine learning › hierarchical modeling
hierarchical bayesian model |
0.1 | 1 | 2017 | Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist Generation · WSDM 2017 |
Methods — techniques the papers use, named apart from their topics
bayesian hierarchical modeling · 0.6log-as-user-data · 0.4database performance optimization · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Garnet: A Next-Generation Cache-Store for Accelerating Applications and Services
Badrish Chandramouli, Vasileios Zois, Ted Hart, Tal Zaccai, Lukas M. Maas, Yoganand Rajasekaran, Darren Gehring |
Proc. VLDB Endow. | 4 |
| 2020 | A.M.B.R.O.S.I.A: Providing Performant Virtual Resiliency for Distributed ApplicationsabstractWhen writing today's distributed programs, which frequently span both devices and cloud services, programmers are faced with complex decisions and coding tasks around coping with failure, especially when these distributed components are stateful. If their application can be cast as pure data processing, they benefit from the past 40--50 years of work from the database community, which has shown how declarative database systems can completely isolate the developer from the possibility of failure in a performant manner. Unfortunately, while there have been some attempts at bringing similar functionality into the more general distributed programming space, a compelling general-purpose system must handle non-determinism, be performant, support a variety of machine types with varying resiliency goals, and be language agnostic, allowing distributed components written in different languages to communicate. This paper introduces Ambrosia, the first system to satisfy all these requirements. We coin the term "virtual resiliency", analogous to virtual memory, for the platform feature which allows failure oblivious code to run in a failure resilient manner. We also introduce novel programming language constructs for resiliently handling non-determinism. Of further interest is the effective reapplication of much database performance optimization technology to make Ambrosia more performant than many of today's non-resilient cloud solutions. Jonathan Goldstein, Ahmed S. Abdelhamid, Michael Barnett 0001, Sebastian Burckhardt, Badrish Chandramouli, Darren Gehring, Niel Lebeck, Christopher Meiklejohn, Umar Farooq Minhas, Ryan Newton, Rahee Peshawaria, Tal Zaccai, Irene Zhang |
Proc. VLDB Endow. | 12 |
| 2017 | Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist GenerationabstractThis paper describes an algorithm designed for Microsoft's Groove music service, which serves millions of users world wide. We consider the problem of automatically generating personalized music playlists based on queries containing a ``seed'' artist and the listener's user ID. Playlist generation may be informed by a number of information sources including: user specific listening patterns, domain knowledge encoded in a taxonomy, acoustic features of audio tracks, and overall popularity of tracks and artists. The importance assigned to each of these information sources may vary depending on the specific combination of user and seed~artist. Shay Ben-Elazar, Gal Lavee, Noam Koenigstein, Oren Barkan, Hilik Berezin, Ulrich Paquet, Tal Zaccai |
WSDM | 7 |