Temporal Data Management and Processing with Column Oriented NoSQL Databases

This article introduces how temporal data can be maintained and processed by utilizing Column-oriented NoSQL databases (CoNoSQLDBs). Although each column in a CoNoSQLDB can store multiple data versions with their corresponded timestamps, its implicit temporal interval representation can cause wrong...

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Bibliographic Details
Published in:Journal of database management Vol. 26; no. 3; pp. 41 - 70
Main Authors: Hu, Yong, Dessloch, Stefan
Format: Journal Article
Language:English
Published: Hershey IGI Global 01.07.2015
Subjects:
Agglomeration
Algebra
Data management
Data processing
Data storage
Information management
Intervals
Mathematical analysis
Methods
Operators
Operators (mathematics)
Query processing
Relational algebra
Representation
Representations
Tables (data)
Time
ISSN:1063-8016, 1533-8010
Online Access:Get full text
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Summary:This article introduces how temporal data can be maintained and processed by utilizing Column-oriented NoSQL databases (CoNoSQLDBs). Although each column in a CoNoSQLDB can store multiple data versions with their corresponded timestamps, its implicit temporal interval representation can cause wrong or misleading results during temporal query processing. In consequence, the original table representation supported by CoNoSQLDBs is not suitable for storing temporal data. To maintain the temporal data in the CoNoSQLDB tables, two alternative table representations can be adopted, namely, explicit history representation (EHR) and tuple time-stamping representation (TTR) in which each tuple (data version) has an explicit temporal interval. For processing TTR, the temporal relational algebra is extended to TTRO operator model with minor modifications. For processing EHR, a novel temporal operator model called CTO is proposed. Both TTRO and CTO contain eight temporal data operators, namely, Union, Difference, Intersection, Project, Filter, Cartesian product, Theta-Join and Group by with a set of aggregation functions, such as SUM, AVG, MAX and etc. Moreover, the authors implement each temporal operator by utilizing MapReduce framework to indicate which temporal operator model is more suitable for temporal data processing in the context of CoNoSQLDBs.
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ISSN:1063-8016
1533-8010
DOI:10.4018/JDM.2015070103