Istvan Cseri

dblp:24/2236 · DBLP profile ↗
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5ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0004-6134-9537ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 5 · 1 since 2021

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
5 papers
Query processing and optimization · 81% Data models and query languages · 9% Data mining · 3%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 67% Cloud and datacenter computing · 33%

Topics — the 11 heaviest of 12, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization
incremental computation
0.712023
What's the Difference? Incremental Processing with Change Queries in Snowflake · Proc. ACM Manag. Data 2023
Query processing and optimization › view maintenance
incremental view maintenance
0.712023
What's the Difference? Incremental Processing with Change Queries in Snowflake · Proc. ACM Manag. Data 2023
Cloud and datacenter computing
database-as-a-service
0.112011
Adapting microsoft SQL server for cloud computing · ICDE 2011
Distributed systems › replication
primary-backup replication
0.112011
Adapting microsoft SQL server for cloud computing · ICDE 2011
Distributed systems
replication
0.112011
Adapting microsoft SQL server for cloud computing · ICDE 2011
Data models and query languages › XML query languages
XQuery
0.112005
XQuery Implementation in a Relational Database System · VLDB 2005
Data mining › predictive modeling › classification › structured classification
hierarchical labeling
0.012004
ORDPATHs: Insert-Friendly XML Node Labels · SIGMOD Conference 2004
Data models and query languages
XML data management
0.012004
ORDPATHs: Insert-Friendly XML Node Labels · SIGMOD Conference 2004
Indexing and storage engines
XML indexing
0.012004
Indexing XML Data Stored in a Relational Database · VLDB 2004
Data models and query languages › XML data management
XML labeling scheme
0.012004
ORDPATHs: Insert-Friendly XML Node Labels · SIGMOD Conference 2004
Database system architecture and tuning
relational database system
0.022005
XQuery Implementation in a Relational Database System · VLDB 2005
Indexing XML Data Stored in a Relational Database · VLDB 2004

Methods — techniques the papers use, named apart from their topics

stream objects · 0.7DML · 0.7shared-nothing partitioning · 0.2ORDPATH labeling · 0.0
YearPublicationVenuePosition
2023 What's the Difference? Incremental Processing with Change Queries in Snowflake
abstract
Incremental algorithms are the heart and soul of stream processing. Low latency results depend on the ability to react to the subset of changes in a dataset over time rather than reprocessing the entirety of a dataset as it evolves. But while the SQL language is well suited for representing streams of changes (via tables) and their application to tables over time (via DML), it entirely lacks a method to query the changes to a table or view in the first place. In this paper, we present CHANGES queries and STREAM objects, Snowflake's primitives for querying and consuming incremental changes to table objects over time. CHANGES queries and STREAMs have been in use within Snowflake for three years, and see broad adoption across our customers. We describe the semantics of these primitives, discuss the implementation challenges, present an analysis of their usage at Snowflake, and contrast with other offerings.
Tyler Akidau, Paul Barbier, Istvan Cseri, Fabian Hueske, Tyler Jones, Sasha Lionheart, Daniel Mills, Dzmitry Pauliukevich, Lukas Probst, Niklas Semmler, Dan Sotolongo, Boyuan Zhang 0004
Proc. ACM Manag. Data3
2011 Adapting microsoft SQL server for cloud computing
abstract
Cloud SQL Server is a relational database system designed to scale-out to cloud computing workloads. It uses Microsoft SQL Server as its core. To scale out, it uses a partitioned database on a shared-nothing system architecture. Transactions are constrained to execute on one partition, to avoid the need for two-phase commit. The database is replicated for high availability using a custom primary-copy replication scheme. It currently serves as the storage engine for Microsoft's Exchange Hosted Archive and SQL Azure.
Philip A. Bernstein, Istvan Cseri, Nishant Dani, Nigel Ellis, Ajay Kalhan, Gopal Kakivaya, David B. Lomet, Ramesh Manne, Lev Novik, Tomas Talius
ICDE2
2005 XQuery Implementation in a Relational Database System
Shankar Pal, Istvan Cseri, Oliver Seeliger, Michael Rys, Gideon Schaller, Dragan Tomic, Adrian Baras, Brandon Berg, Denis Churin, Eugene Kogan
VLDB2
2004 ORDPATHs: Insert-Friendly XML Node Labels
abstract
We introduce a hierarchical labeling scheme called ORDPATH that is implemented in the upcoming version of Microsoft® SQL Server™. ORDPATH labels nodes of an XML tree without requiring a schema (the most general case---a schema simplifies the problem). An example of an ORDPATH value display format is "1.5.3.9.1". A compressed binary representation of ORDPATH provides document order by simple byte-by-byte comparison and ancestry relationship equally simply. In addition, the ORDPATH scheme supports insertion of new nodes at arbitrary positions in the XML tree, their ORDPATH values "careted in" between ORDPATHs of sibling nodes, without relabeling any old nodes.
Patrick E. O'Neil, Elizabeth J. O'Neil, Shankar Pal, Istvan Cseri, Gideon Schaller, Nigel Westbury
SIGMOD Conference4
2004 Indexing XML Data Stored in a Relational Database
Shankar Pal, Istvan Cseri, Gideon Schaller, Oliver Seeliger, Leo Giakoumakis, Vasili Vasili Zolotov
VLDB2