Data-Intensive Technologies for Cloud Computing

Delivering Knowledge Services in the Cloud

“…This DOI: 10.4018/jkss.2011100102 speedy data accumulation calls for a real-time knowledge derivation mechanism to keep up at least the trade-off between knowledge demand and supply on a real-time basis with in an enterprise (In today's real world, the knowledge supply should even exceed the demands for prediction and forecasting purposes in a business environment). Middleton (2010) and Mohammed et al (2010) describes this as "Data Gap" originally dubbed by LexisNexis where the knowledge demand outpaces the supply. There are various reasons that that brings this disequilibrium for knowledge feeds in knowledge-intensive processes.…”

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

“…Data-Intensive Computing is a class of parallel computing applications which use a data parallel approach to processing large volumes of data typically terabytes or petabytes in size and typically referred to as Big Data. Computing applications which devote most of their execution time to computational requirements are deemed compute-intensive and typically require small volumes of data, whereas computing applications which require large volumes of data and devote most of their processing time to I/O and manipulation of data are deemed data-intensive (Middleton, 2010). Effective solution technologies must also scale to handle the amplified data rates and simultaneously accelerate timely, effective analysis results (http://dicomputing.pnnl.…”

Active Provenance for Data-Intensive Workflows: Engaging Users and Developers

“…Scientific communities are building platforms where Data-Intensive scalable computing systems (DISC) [26] are crucial to conduct their research campaigns. Such systems often use workflows to express and combine independent processing tasks.…”