Temporal and Evolving Data Warehouse Design

Abstract

The data model of the classical data warehouse (formally, dimensional model) does not offer comprehensive support for temporal data management. The underlying reason is that it requires consideration of several temporal aspects, which involve various time stamps. Also, transactional systems, which serves as a data source for data warehouse, have the tendency to change themselves due to changing business requirements. The classical dimensional model is deficient in handling changes to transaction sources. This has led to the development of various schemes, including evolution of data and evolution of data model and versioning of dimensional model. These models have their own strengths and limitations, but none fully satisfies the above-stated broad range of aspects, making it difficult to compare the proposed schemes with one another. This paper analyses the schemes that satisfy such challenging aspects faced by a data warehouse and proposes taxonomy for characterizing the existing models to temporal data management in data warehouse. The paper also discusses some open challenges.

A survey on spatial, temporal, and spatio-temporal database research and an original example of relevant applications using SQL ecosystem and deep learning

“…The temporal features are typically added either by: (1) extending the existing RDBMSs or (2) creating a middle layer with the time-related functionalities without making any changes to the operational, underlying databases (Arora, 2015). Although many approaches were proposed in the literature, only some were actually implemented and materialized as prototypes or in the commercial tools (Arora, 2015;Faisal et al, 2017;Radhakrishna et al, 2015) and only VT timestamps were supportednot TT timestamps.…”

“…In the context of spatio-temporal data warehousing, more complex OLAP operations are needed, namely temporal OLAP (TOLAP) and spatial (SOLAP). For TOLAP, several time-related data warehousing designs and capabilities were introduced and expected; these include data-warehouse transaction time (DWTT) and data-warehouse load time (DWLT) in addition to ordinary VT and TT (Faisal et al, 2017). The proposed data warehouse models are supposed to handle changes made to the warehouse, categorized as schema changes and content changes.…”

“…The evolving method was able to manage the changes made to the warehouse as it evolves and can practically be done in the current commercial tools whereas the versioning method was more complicated and so was not integrated into the actual system just yet. However, in theory, the versioning method is more versatile in terms of keeping the history records while the evolving method is not able to (Faisal et al, 2017).…”

“…A survey on spatio-temporal database research is then discussed, consisting of spatio-temporal modelling, query languages, and operators; spatio-temporal data warehousing, and concept hierarchies; and spatio-temporal indexing strategies. Because a large number of applications that utilize this type of data has been constantly developedalthough not many survey papers show how current, available tools are of use or relevant to (Arora, 2015;Faisal et al, 2017;Gómez et al, 2009;Radhakrishna et al, 2015) the second part of the contributions showed our original sample applications based on spatio-temporal data using mainstay/emerging technologies.…”