Towards Stream Data Parallel Processing in Spatial Aggregating Index

Gorawski, Marcin; Malczok, Rafal

doi:10.1007/978-3-540-68111-3_23

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…In Condie et al (2010) the on-line aggregation of data was supported by means of the MapReduce technique in the Hadoop framework. Gorawski and Malczok (2007) proposed a technique for parallel processing of data streams. Gorawski and Chrószcz (2011) proposed a low-level algorithm for optimizing query processing on a data stream.…”

Section: Data Stream Processingmentioning

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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Section: Data Stream Processingmentioning

confidence: 99%