Handling Bitemporal Schema Versions in Multi-temporal Environment for Data Warehouse

Gosain, Anjana; Saroha, Kriti

doi:10.1007/s13369-018-3609-0

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the last two decades, two methods namely normalized modelling (Inmon, 1991) and dimensional modelling (Kimball, 1996) are governing the implementation of DW in almost all sectors.Ofthese,dimensionalmodellinghasbeenoneofthepredominanttechniquesthattakeDW implementationtonextstage.Nevertheless,traditionaldimensionmodellingdoesnotsupportanew variablecalledtemporaldimensionwhichispopularnowadays.Furthermore,thedatasourceofa traditionalDWdoesnotprovidecompletesupportfortemporaldatawhichcontainsseveraltimedependentaspectsincludingdifferenttimestamps.Thisisduetothefactthattraditionalmethods keeponlytherecordofanyonestateofrealworldenvironmentatavalidtime(i.e.abilitytoview onlyonedataatatime).However,theworldaswellasdataformationchangeswithtimethusthereis aneedfortemporaldatabasetokeeptrackofdatachangeswithinadatabaseenvironment (Ongoma, 2014).Thus,researchersfocussedontheintroductionoftemporaldimensioninDWarchitecture (Hultgren,2012,Rönnbäcketal.,2010,Golecetal.,2017.Temporaldatamodelscanbedeveloped by extending the conceptual model of traditional DW (like UML, ER and ORM) with temporal paradigms.Temporaldimensionisasimpleandnewphenomenonthatrecordsalltime-dependent variables,maintainshistoryofdatabasesandkeepstrackofvariationssoastoplanforthefuture (Gosain & Saroha, 2019). Besides, slowly varying dimensions in certain applications have been represented as temporal variables in the fact table as well as dimension table of DW framework (Araque,2003,Phungtua-Eng&Chittayasothorn,2019.Therefore,theknowledgeacquiredfrom bothdatawarehouseandtemporaldimensionalmodellinggiverisetoanewresearchareatermedas temporaldatawarehouse(TDW).…”

Section: Introductionmentioning

confidence: 99%

UML-Based Data Warehouse Design Using Temporal Dimensional Modelling

Reddy

Chittineni²

2020

International Journal of Security and Privacy in Pervasive Computing

View full text Add to dashboard Cite

The design of a data warehouse system deals with tasks such as data source administration, ETL processing, multidimensional modelling, data mart specification, and end-user tool development. In the last decade, numerous techniques have been presented to cover all the aspects of DW. However, none of these techniques stated the recent necessities of DW like visualization, temporal dimensions, record keeping, and so on. To overcome these issues, this paper proposes a UML based DW with temporal dimensions. This framework designs time-dependent DW that allows end-users to store history of variations for long term. Besides, it authorizes to visualize the business goals of organizations in the form of attribute tree via UML, which is designed after receiving user necessities and later reconciling with temporal variables. The implementation of proposed technique is detailed with university education database for quality improvement. The proposed technique is found to be useful in terms of temporal dimension, long-term record keeping, and easy to make decision goals through attribute trees.

show abstract

Section: Introductionmentioning

confidence: 99%