Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.

Tal Zaccai

dblp:194/3943 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
High-performance computing › performance optimization
application acceleration
0.912025
Garnet: A Next-Generation Cache-Store for Accelerating Applications and Services · Proc. VLDB Endow. 2025
Distributed systems
distributed programming
0.412020
A.M.B.R.O.S.I.A: Providing Performant Virtual Resiliency for Distributed Applications · Proc. VLDB Endow. 2020
Distributed systems
fault tolerance
0.412020
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
0.312017
Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist Generation · WSDM 2017
Machine learning › Probabilistic and Bayesian machine learning › hierarchical modeling
hierarchical bayesian model
0.112017
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
YearPublicationVenuePosition
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 Applications
abstract
When 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 Generation
abstract
This 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
WSDM7