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In many big data projects, there is no large data analysis happening, but the challenge is the extract, transform, load part of data pre-processing. [225] Big data is a buzzword and a "vague term", [226] [227] but at the same time an "obsession" [227] with entrepreneurs, consultants, scientists, and the media.
XLDB (eXtremely Large DataBases) was a yearly conference about databases, data management and analytics held from 2007 to 2019. The definition of extremely large refers to data sets that are too big in terms of volume (too much), and/or velocity (too fast), and/or variety (too many places, too many formats) to be handled using conventional solutions.
A very large database, (originally written very large data base) or VLDB, [1] is a database that contains a very large amount of data, so much that it can require specialized architectural, management, processing and maintenance methodologies.
Database scalability is the ability of a database to handle changing demands by adding/removing resources. Databases use a host of techniques to cope. [1] According to Marc Brooker: "a system is scalable in the range where marginal cost of additional workload is nearly constant."
Quality of Data (QoD) is a designation coined by L. Veiga, that specifies and describes the required Quality of Service of a distributed storage system from the Consistency point of view of its data. It can be used to support big data management frameworks, Workflow management, and HPC systems (mainly for data replication and consistency).
Large-scale macroeconometric model consists of systems of dynamic equations of the economy with the estimation of parameters using time-series data on a quarterly to yearly basis. Macroeconometric models have a supply and a demand side for estimation of these parameters. Kydland and Prescott call it the system of equations approach. [1]
The adoption and implementation of neural networks, deep learning in the field of computational economics may reduce the redundant work of data cleaning and data analytics, significantly lowering the time and cost of large scale data analytics and enabling researchers to collect, analyze data on a great scale. [12]
Computer system architectures which can support data parallel applications were promoted in the early 2000s for large-scale data processing requirements of data-intensive computing. [12] Data-parallelism applied computation independently to each data item of a set of data, which allows the degree of parallelism to be scaled with the volume of data.