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.

Cipri Clinciu

dblp:04/1992 · DBLP profile ↗
← Back
3ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

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

Databases, data management, data science and information retrieval · 3

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
Indexing and storage engines · 44% Query processing and optimization · 44% Database system architecture and tuning · 13%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Indexing and storage engines
column store
0.322013
Enhancements to SQL server column stores · SIGMOD Conference 2013
SQL server column store indexes · SIGMOD Conference 2011
Query processing and optimization › query execution › query operator implementation
vectorized query execution
0.212013
Enhancements to SQL server column stores · SIGMOD Conference 2013
Query processing and optimization
query optimization
0.112011
SQL server column store indexes · SIGMOD Conference 2011

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

batch processing · 0.3column store index · 0.2
YearPublicationVenuePosition
2013 Enhancements to SQL server column stores
abstract
SQL Server 2012 introduced two innovations targeted for data warehousing workloads: column store indexes and batch (vectorized) processing mode. Together they greatly improve performance of typical data warehouse queries, routinely by 10X and in some cases by a 100X or more. The main limitations of the initial version are addressed in the upcoming release. Column store indexes are updatable and can be used as the base storage for a table. The repertoire of batch mode operators has been expanded, existing operators have been improved, and query optimization has been enhanced. This paper gives an overview of SQL Server's column stores and batch processing, in particular the enhancements introduced in the upcoming release.
Per-Åke Larson, Cipri Clinciu, Campbell Fraser, Eric N. Hanson, Mostafa Mokhtar, Michal Nowakiewicz, Vassilis Papadimos, Susan Price, Srikumar Rangarajan, Remus Rusanu, Mayukh Saubhasik
SIGMOD Conference2
2011 SQL server column store indexes
abstract
The SQL Server 11 release (code named Denali) introduces a new data warehouse query acceleration feature based on a new index type called a column store index. The new index type combined with new query operators processing batches of rows greatly improves data warehouse query performance: in some cases by hundreds of times and routinely a tenfold speedup for a broad range of decision support queries. Column store indexes are fully integrated with the rest of the system, including query processing and optimization. This paper gives an overview of the design and implementation of column store indexes including enhancements to query processing and query optimization to take full advantage of the new indexes. The resulting performance improvements are illustrated by a number of example queries.
Per-Åke Larson, Cipri Clinciu, Eric N. Hanson, Artem Oks, Susan Price, Srikumar Rangarajan, Aleksandras Surna
SIGMOD Conference2
2007 Managing Query Compilation Memory Consumption to Improve DBMS Throughput
Boris Baryshnikov, Cipri Clinciu, Conor Cunningham, Leo Giakoumakis, Slava Oks, Stefano Stefani
CIDR2