DocumentCode :
646611
Title :
D4M 2.0 schema: A general purpose high performance schema for the Accumulo database
Author :
Kepner, Jeremy ; Anderson, C. ; Arcand, William ; Bestor, David ; Bergeron, Bill ; Byun, C. ; Hubbell, Matthew ; Michaleas, Peter ; Mullen, Jon ; O´Gwynn, David ; Prout, Andrew ; Reuther, A. ; Rosa, Alberto ; Yee, Charles
Author_Institution :
MIT Lincoln Lab., Lexington, MA, USA
fYear :
2013
fDate :
10-12 Sept. 2013
Firstpage :
1
Lastpage :
6
Abstract :
Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source relaxed consistency database that is widely used for government applications. Obtaining the full benefits of Accumulo requires using novel schemas. The Dynamic Distributed Dimensional Data Model (D4M)[http://www.mit.edu/~kepner/D4M] provides a uniform mathematical framework based on associative arrays that encompasses both traditional (i.e., SQL) and non-traditional databases. For non-traditional databases D4M naturally leads to a general purpose schema that can be used to fully index and rapidly query every unique string in a dataset. The D4M 2.0 Schema has been applied with little or no customization to cyber, bioinformatics, scientific citation, free text, and social media data. The D4M 2.0 Schema is simple, requires minimal parsing, and achieves the highest published Accumulo ingest rates. The benefits of the D4M 2.0 Schema are independent of the D4M interface. Any interface to Accumulo can achieve these benefits by using the D4M 2.0 Schema.
Keywords :
data models; public domain software; query processing; Accumulo ingest rates; Apache Accumulo database; D4M 2.0 schema; Web companies; associative arrays; bioinformatics; dynamic distributed dimensional data model; free text; general purpose high performance schema; high performance open source relaxed consistency database; minimal parsing; nontraditional databases; scientific citation; social media data; uniform mathematical framework; Arrays; Data models; Databases; Media; Pipelines; Sparse matrices; Twitter; Accumulo; Big Data; D4M; Hadoop; NoSQL; database schema;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
High Performance Extreme Computing Conference (HPEC), 2013 IEEE
Conference_Location :
Waltham, MA
Print_ISBN :
978-1-4799-1364-0
Type :
conf
DOI :
10.1109/HPEC.2013.6670318
Filename :
6670318
Link To Document :
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