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Big data ethics – Ethics of mass data analytics; Big data maturity model – Aspect of computer science; Big memory – A large amount of random-access memory; Data curation – Organization of collected data; Data defined storage – Marketing term for managing data by combining application, information and storage tiers
The Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications such as data cleansing and hygiene, extract, transform, load (ETL), record linking and entity resolution, large-scale ad hoc analysis of data, and creation of key data and indexes to support high-performance ...
The TDWI big data maturity model is a model in the current big data maturity area and therefore consists of a significant body of knowledge. [6] Maturity stages. The different stages of maturity in the TDWI BDMM can be summarized as follows: Stage 1: Nascent. The nascent stage as a pre–big data environment. During this stage:
Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5] It uses techniques and theories drawn from many fields within the context of mathematics , statistics, computer science , information science , and domain knowledge . [ 6 ]
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.
Japkowicz is a professor and department chair of computer science at the American University College of Arts and Sciences. [1] She researches artificial intelligence , machine learning, data mining , and big data analysis.