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The Kimball lifecycle is a methodology for developing data warehouses, and has been developed by Ralph Kimball and a variety of colleagues. The methodology "covers a sequence of high level tasks for the effective design , development and deployment " of a data warehouse or business intelligence system. [ 1 ]
The Cross-industry standard process for data mining, known as CRISP-DM, [1] is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model. [2]
Topological data analysis (TDA) is a mathematical framework that uses tools from topology, including algebraic, differential, and geometric topology, to study the shape and structure of data. In summary, data analysis and data science are distinct yet interconnected disciplines within the broader field of data management, analysis, and mathematics.
The data is necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analytics (or customers, who will use the finished product of the analysis). [ 14 ] [ 15 ] The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
ISO/IEC/IEEE 12207 Systems and software engineering – Software life cycle processes [1] is an international standard for software lifecycle processes. First introduced in 1995, it aims to be a primary standard that defines all the processes required for developing and maintaining software systems, including the outcomes and/or activities of each process.
DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. [1]
Data-intensive computing is intended to address this need. Parallel processing approaches can be generally classified as either compute-intensive, or data-intensive. [6] [7] [8] Compute-intensive is used to describe application programs that are compute-bound. Such applications devote most of their execution time to computational requirements ...