Data-centric programming language: Difference between revisions

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== Background ==

The rapid growth of the [[Internet]] and [[World Wide Web]] has led to huge amounts of information available online and the need for [[Big Data]] processing capabilities. Business and government organizations create large amounts of both structured and unstructured information which needs to be processed, analyzed, and linked.<ref>[httphttps://www.springer.com/computer/communication+networks/book/978-1-4419-6523-3/ Handbook of Cloud Computing], "Data-Intensive Technologies for Cloud Computing" by A. M. Middleton. Handbook of Cloud Computing. Springer, 2010.</ref> The storing, managing, accessing, and processing of this vast amount of data represents a fundamental need and an immense challenge in order to satisfy needs to search, analyze, mine, and visualize this data as information.<ref>"Got Data? A Guide to Data Preservation in the Information Age" by F. Berman. Communications of the ACM, Vol. 51, No. 12, 2008, pp. 50–66.</ref> Declarative, data-centric languages are increasingly addressing these problems, because focusing on the data makes these problems much simpler to express.<ref>[http://www.cccblog.org/2008/10/20/the-data-centric-gambit/ The Data Centric Gambit], by J. Hellerstein, 2008.</ref>

Computer system architectures such as [[Hadoop]] and [[HPCC]] which can support data-parallel applications are a potential solution to the terabyte and petabyte scale data processing requirements of [[data-intensive computing]].<ref>"A Design Methodology for Data-Parallel Applications" by L. S. Nyland, J. F. Prins, A. Goldberg, and P. H. Mills. Handbook of Cloud Computing. Springer, 2010.</ref><ref>"The terascale challenge" by D. Ravichandran, P. Pantel, and E. Hovy. Proceedings of the KDD Workshop on Mining for and from the Semantic Web, 2004.</ref> Clusters of commodity hardware are commonly being used to address Big Data problems.<ref>"BOOM: Data-Centric Programming in the Datacenter" by P. Alvaro, T. Condie, N. Conway, K. Elmeleegy, J. Hellerstein, and R. Sears. Electrical Engineering and Computer Sciences Department, University of California at Berkeley, Technical Report, 2009.</ref> The fundamental challenges for Big Data applications and data-intensive computing<ref>"Data-Intensive Computing in the 21st Century" by I. Gorton, P. Greenfield, A. Szalay, and R. Williams. IEEE Computer, Vol. 41, No. 4, 2008, pp. 30–32.</ref> are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The National Science Foundation has identified key issues related to data-intensive computing problems such as the programming abstractions including models, languages, and algorithms which allow a natural expression of parallel processing of data.<ref>[http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503324&org=IIS Data-Intensive Computing], NSF, 2009.</ref> Declarative, data-centric programming languages are well-suited to this class of problems.