Handling slowly changing dimensions in data warehouses

Faisal, Sidra; Sarwar, Mansoor

doi:10.1016/j.jss.2014.03.072

Cited by 11 publications

(2 citation statements)

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“…Most of the past research on temporal data warehousing concentrates on changes in the dimension tables, often referred to as slowly changing dimensions (Kimball and Ross, 2013;Faisal and Sarwar, 2014), and on the evolution of data warehouse schemas (Blaschka et al, 1999;Wrembel and Bebel, 2007;Ahmed et al, 2014). Kimball and Ross (2013) proposed for the first time three basic techniques for representing changing attributes in the dimension tables, together with five variations thereof.…”

Section: Related Workmentioning

confidence: 99%

“…There has been extensive research on dealing with various aspects of temporal information in data warehouses, often referred to as temporal data warehousing; see the works of Golfarelli and Rizzii (2009b;2011) for an overview. Most of the past research on temporal data warehousing concentrates on changes in the dimension tables, often referred to as slowly changing dimensions (Jensen et al, 2010;Kimball and Ross, 2013;Faisal and Sarwar, 2014) and on the evolution of data warehouse schemas (Blaschka et al, 1999;Wrembel and Bebel, 2007;Ahmed et al, 2014). How to model and represent changes in the fact data has been less studied, with a few exceptions (e.g., Bliujute et al, 1998;Goller and Berger, 2015), but none of them investigates data warehouse scenarios with aggregation queries over time.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and querying facts with period timestamps in data warehouses

Mahlknecht

Dignös

Kozmina

2019

International Journal of Applied Mathematics and Computer Science

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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.

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