Temporal Aggregation

Gamper, Johann; Böhlen, Michael H.; Jensen, Christian S.

doi:10.1007/978-0-387-39940-9_386

Cited by 12 publications

(1 citation statement)

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“…For the tuple-timestamping model, different kinds of temporal aggregations are defined, such as instantaneous temporal aggregation (Kline & Snodgrass, 1995) in which the aggregation value at time t is calculated by all the tuples which are valid at time point t, moving-window or cumulative temporal aggregation (Snodgrass, Gomez & Mckenzie, 1993) in which the aggregation value at time t is calculated by all the tuples which are valid during [t-m, t] where m denotes the length of the moving window and span temporal aggregation (Gamper, Böhlen & Jensen, 2009) in which the aggregation value indicates a period of time instead of a time point. For the attribute-timestamping model, to the author's best knowledge, no definitions of the temporal aggregations have yet been specified.…”

Section: Related Workmentioning

confidence: 99%

Temporal Data Management and Processing with Column Oriented NoSQL Databases

Deßloch

2015

Journal of Database Management

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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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Section: Related Workmentioning

confidence: 99%

Temporal Data Management and Processing with Column Oriented NoSQL Databases

Deßloch

2015

Journal of Database Management

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Efficient Computation of Parsimonious Temporal Aggregation

Mahlknecht

Dignös

Gamper

2015

Advances in Databases and Information Systems

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Temporal Data Management – An Overview

Böhlen

Dignös

Gamper

et al. 2018

Lecture Notes in Business Information Processing

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Despite the ubiquity of temporal data and considerable research on the effective and efficient processing of such data, database systems largely remain designed for processing the current state of some modeled reality. More recently, we have seen an increasing interest in the processing of temporal data that captures multiple states of reality. The SQL:2011 standard incorporates some temporal support, and commercial DBMSs have started to offer temporal functionality in a step-by-step manner, such as the representation of temporal intervals, temporal primary and foreign keys, and the support for so-called time-travel queries that enable access to past states. This tutorial gives an overview of state-of-the-art research results and technologies for storing, managing, and processing temporal data in relational database management systems. Following an introduction that offers a historical perspective, we provide an overview of basic temporal database concepts. Then we survey the state-of-the-art in temporal database research, followed by a coverage of the support for temporal data in the current SQL standard and the extent to which the temporal aspects of the standard are supported by existing systems. The tutorial ends by covering a recently proposed framework that provides comprehensive support for processing temporal data and that has been implemented in PostgreSQL.

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Temporal Aggregation

Cited by 12 publications

References 11 publications

Temporal Data Management and Processing with Column Oriented NoSQL Databases

Temporal Data Management and Processing with Column Oriented NoSQL Databases

Efficient Computation of Parsimonious Temporal Aggregation

Temporal Data Management – An Overview

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