Dynamic Method Materialization: A Framework for Optimizing Data Access Via Methods

Wrembel, Robert; Masewicz, Mariusz; Jankiewicz, Krzysztof

doi:10.1007/11827405_85

Cited by 1 publication

(1 citation statement)

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“…We can adopt a mix of techniques such as workload-based prediction of MVs refreshment and demand-driven scheduling (Thiele et al, 2009a) as well as the access prediction techniques originally developed for predicting access to materialized methods, cf. (Jezierski et al, 2003;Jezierski et al, 2004;Wrembel et al, 2006). This problem opens a space for a wide spectrum of research contributions; 5.…”

Section: Open Research Problemsmentioning

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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