<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, we study various ways of representing and querying fact data that are time-stamped with a time period in a data warehouse. The main focus is on how to represent the time periods that are associated with the facts in order to support convenient and efficient aggregations over time. We propose three distinct logical models that represent time periods as sets of all time points in a period (instant model), as pairs of start and end time points of a period (period model), and as atomic units that are explicitly stored in a new period dimension (period∗ model). The period dimension is enriched with information about the days of each period, thereby combining the former two models. We use four different classes of aggregation queries to analyze query formulation, query execution, and query performance over the three models. An extensive empirical evaluation on synthetic and real-world datasets and the analysis of the query execution plans reveal that the period model is the best choice in terms of runtime and space for all four query classes.</p></abstract>

In this paper, we study various ways of representing and querying fact data that are time-stamped with a time period in a data warehouse. The main focus is on how to represent the time periods that are associated with the facts in order to support convenient and efficient aggregations over time. We propose three distinct logical models that represent time periods as sets of all time points in a period (instant model), as pairs of start and end time points of a period (period model), and as atomic units that are explicitly stored in a new period dimension (period∗ model). The period dimension is enriched with information about the days of each period, thereby combining the former two models. We use four different classes of aggregation queries to analyze query formulation, query execution, and query performance over the three models. An extensive empirical evaluation on synthetic and real-world datasets and the analysis of the query execution plans reveal that the period model is the best choice in terms of runtime and space for all four query classes.

Modeling and querying facts with period timestamps in data warehouses

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Modeling and querying facts with period timestamps in data warehouses"}

In this paper, we study various ways of representing and querying fact data that are time-stamped with a time period in a data warehouse. The main focus is on...

Modeling and querying facts with period timestamps in data warehouses

Online veröffentlicht: 29. März 2019

Seitenbereich: 31 - 49

Eingereicht: 18. Apr. 2018

Akzeptiert: 27. Nov. 2018

DOI: https://doi.org/10.2478/amcs-2019-0003

Schlüsselwörter
data warehouse, time periods, logical models

© 2019 Giovanni Mahlknecht et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Modeling and querying facts with period timestamps in data warehouses

Online veröffentlicht: 29. März 2019

Seitenbereich: 31 - 49

Eingereicht: 18. Apr. 2018

Akzeptiert: 27. Nov. 2018

DOI: https://doi.org/10.2478/amcs-2019-0003

Schlüsselwörterdata warehouse, time periods, logical models

© 2019 Giovanni Mahlknecht et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
data warehouse, time periods, logical models