SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Super-Scalar RAM-CPU Cache Compression
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Integrating compression and execution in column-oriented database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Performance tradeoffs in read-optimized databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Cooperative scans: dynamic bandwidth sharing in a DBMS
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Column-stores vs. row-stores: how different are they really?
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Query processing techniques for solid state drives
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Adaptive query processing in data stream management systems under limited memory resources
PIKM '10 Proceedings of the 3rd workshop on Ph.D. students in information and knowledge management
Dremel: interactive analysis of web-scale datasets
Proceedings of the VLDB Endowment
Dremel: interactive analysis of web-scale datasets
Communications of the ACM
Live business intelligence for the real-time enterprise
From active data management to event-based systems and more
GBASE: a scalable and general graph management system
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Trojan data layouts: right shoes for a running elephant
Proceedings of the 2nd ACM Symposium on Cloud Computing
Column-oriented query processing for row stores
Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP
Of hammers and nails: an empirical comparison of three paradigms for processing large graphs
Proceedings of the fifth ACM international conference on Web search and data mining
Processing a trillion cells per mouse click
Proceedings of the VLDB Endowment
Incrementally maintaining run-length encoded attributes in column stores
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Efficient big data processing in Hadoop MapReduce
Proceedings of the VLDB Endowment
gbase: an efficient analysis platform for large graphs
The VLDB Journal — The International Journal on Very Large Data Bases
XPath fragments on XML in columns
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Query-aware compression of join results
Proceedings of the 16th International Conference on Extending Database Technology
ADC '11 Proceedings of the Twenty-Second Australasian Database Conference - Volume 115
Audience segment expansion using distributed in-database k-means clustering
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
How to exploit the device diversity and database interaction to propose a generic cost model?
Proceedings of the 17th International Database Engineering & Applications Symposium
Q100: the architecture and design of a database processing unit
Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
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Column-oriented database systems (column-stores) have attracted a lot of attention in the past few years. Column-stores, in a nutshell, store each database table column separately, with attribute values belonging to the same column stored contiguously, compressed, and densely packed, as opposed to traditional database systems that store entire records (rows) one after the other. Reading a subset of a table's columns becomes faster, at the potential expense of excessive disk-head seeking from column to column for scattered reads or updates. After several dozens of research papers and at least a dozen of new column-store start-ups, several questions remain. Are these a new breed of systems or simply old wine in new bottles? How easily can a major row-based system achieve column-store performance? Are column-stores the answer to effortlessly support large-scale data-intensive applications? What are the new, exciting system research problems to tackle? What are the new applications that can be potentially enabled by column-stores? In this tutorial, we present an overview of column-oriented database system technology and address these and other related questions.