Maintenance of Data Cubes and Summary Tables in a Warehouse

Mumick, Inderpal Singh; Quass, Dallan; Mumick, Barinderpal Singh

doi:10.7551/mitpress/4472.003.0031

Cited by 23 publications

(1 citation statement)

References 11 publications

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“…There are a number of solutions that avoid the inconsistency. They are based among others on: (1) applying algorithms that compensate for out-of-date data in MVs, e.g., (Zhuge et al, 1995;Zhuge et al, 1996;Agrawal et al, 1997;Zhuge et al, 1999), (2) maintaining versions of MVs, where one is being refreshed while the other is being read, e.g., (Quass & Widom, 1997), and (3) using additional data structures and transactions, e.g., (Colby et al, 1996;Mumick et al, 1996). Zhou et al (2007) present a condensed operator in the context of maintenance of MVs.…”

Section: Real-time Data Warehousingmentioning

confidence: 99%

On-Demand ELT Architecture for Right-Time BI

Waas¹,

Wrembel

Freudenreich

et al. 2013

International Journal of Data Warehousing and Mining

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In a typical BI infrastructure, data, extracted from operational data sources, is transformed, cleansed, and loaded into a data warehouse by a periodic ETL process, typically executed on a nightly basis, i.e., a full day’s worth of data is processed and loaded during off-hours. However, it is desirable to have fresher data for business insights at near real-time. To this end, the authors propose to leverage a data warehouse’s capability to directly import raw, unprocessed records and defer the transformation and data cleaning until needed by pending reports. At that time, the database’s own processing mechanisms can be deployed to process the data on-demand. Event-processing capabilities are seamlessly woven into our proposed architecture. Besides outlining an overall architecture, the authors also developed a roadmap for implementing a complete prototype using conventional database technology in the form of hierarchical materialized views.

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