On Data Representation and Use in a Temporal Relational DBMS

Abstract

Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared over the past decade. It has long been known that these proposals have adopted one of two basic approaches to the incorporation of time into the extended relational model. Recent work formally contrasted the expressive power of these two approaches, termed temporally ungrouped and temporally grouped, and demonstrated that the temporally grouped models are more expressive. IN the temporally ungrouped models, the temporal dimension is added through the addition of some number of distinguished attributes to the schema of each relation, and each tuple is "stamped" with temporal values for these attributes. By contrast, in temporally grouped models the temporal dimension is added to the types of values that serve as the domain of each ordinary attribute, and the application's schema is left intact. The recent appearance of TSQL2, a temporal extension to the SQL-92 standard based upon the temporally ungrouped paradigm, means that it is likely that commercial DBMS's will be extended to support time in this weaker way. Thus the distinction between these two approaches -and its impact on the day-to-day user of a DBMS -is of increasing relevance to the database practitioner and the database user community. In this paper we address this issue from the practical perspective of such a user. Through a series of example queries and updates, we illustrate the differences between these two approaches and demonstrate that the temporally grouped approach more adequately captures the semantics of historical data.

A Temporal JSON Data Model and Its Query Languages

“…Timewidelyexistsinvariouspracticalapplications.Typically,datageneratedbyapplications arecloselyassociatedwithtime:datamaybeavailableorcorrectatacertaintimepointoragiven timeinterval (Dyresonet al,1994).Inaddition,somehistoricaldataneedtoberecorded.Froma philosophicalpointofview,timeisubiquitous.Forexample,forBunge,astateofanythingisalistof itspropertiesataparticularmomentintime.Hence,everyapplicationwouldrequiretheconsideration oftime,explicitlyorimplicitly.Therefore,itisessentialtomanagetemporaldata (Nascimento,Sellis, &Cheng,2015;Claramunt,Schneider,&Wong,2017;Zheng,Li,&Shim,2018).Acoreissuein temporal data management is establishing a temporal data model (Nascimento, Sellis, & Cheng, 2015).Sometemporaldatamodelshavebeenproposedforrepresentingthetemporalsemanticsof data. Traditionally, temporal data management is mainly based on temporal relational databases (TanselandTin,1998;Tanselet al,1993;Chomicki,1994;Cliffordet al,1995;Clifford,Croker, & Tuzhilin, 1996). With the emergence of new application paradigms and data models, several temporaldatamodelsareproposedbeyondthetemporalrelationaldatabases.Inthecontextofthe Web,forexample,temporalXMLmodelsaredeveloped (Wang,Zhou,&Zaniolo,2004;Rizzolo andVaisman,2008;Snodgrassetal.,2008),andtemporalRDF(ResourceDescriptionFramework) modelsareproposed (Gutiérrez,Hurtado,&Vaisman,2007;WangandTansel,2019).Furthermore, todealwithBigDatawithtime (Cuzzocrea,2015),temporalNoSQLdatabasemodelsaredevised (Hu&Dessloch,2015).…”