Integrating compression and execution in column-oriented database systems

By Daniel J. Abadi, Samuel Madden, Miguel Ferreira. SIGMOD Conference, 2006

698 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/e2a5593f587970018b9e826e5d876125b374b801

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In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Database Cracking (2007, methodology): “To handle the extra storage space required, compression is used, which is also shown to speedup query execution in column oriented databases [1, 16].”
Scalable Semantic Web Data Management Using Vertical Partitioning (2007, background): “This can lead to significant performance improvement [14].”
A common database approach for OLTP and OLAP using an in-memory column database (2009, background): “If we consider that in real world applications only 10% of the attributes of a single table are typically used in one SQL-statement (see Figure 1), that means for the column store at most 800 MB of data have to be accessed to calculate the total values [1].”
The Internet of Things: A Survey from the Data-Centric Perspective (2013, neutral): “This is closely related to the development of column-oriented databases for sensor data management [98, 2, 3].”
SW-Store: a vertically partitioned DBMS for Semantic Web data management (2009, background): “, runlength, bit-vector, or Lempel-Ziv encoding), as in C-Store [4].”
Bigtable: a distributed storage system for structured data (2006, background): “Bigtable locality groups realize similar compression and disk read performance benefits observed for other systems that organize data on disk using column-based rather than row-based storage, including C-Store [Abadi et al. 2006; Stonebraker et al. 2005] and commercial products such as Sybase IQ [Fr”
Hexastore: sextuple indexing for semantic web data management (2008, methodology): “In [5], this approach is coupled with a column-oriented DBMS [14] [15] [42] (i.e., a DBMS designed especially for the vertically partitioned case, as opposed to a row-oriented DBMS, gaining benefits of compressibility [4] and performance [7]).”
FAST: fast architecture sensitive tree search on modern CPUs and GPUs (2010, methodology): “8The original tuple data may be be independently compressed using other techniques [1] and used in conjunction with our scheme.”