Data mining
[6] It also is a buzzword[7] and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence.
Often the more general terms (large scale) data analysis and analytics—or, when referring to actual methods, artificial intelligence and machine learning—are more appropriate.
These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics.
The term "data mining" was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Economic Studies in 1983.
[9][10] Lovell indicates that the practice "masquerades under a variety of aliases, ranging from "experimentation" (positive) to "fishing" or "snooping" (negative).
It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.
Several teams of researchers have published reviews of data mining process models,[19] and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.
If the learned patterns do not meet the desired standards, it is necessary to re-evaluate and change the pre-processing and data mining steps.
The premier professional body in the field is the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining (SIGKDD).
For exchanging the extracted models—in particular for use in predictive analytics—the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications.
As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications.
Notable examples of data mining can be found throughout business, medicine, science, finance, construction, and surveillance.
Safe Harbor Principles, developed between 1998 and 2000, currently effectively expose European users to privacy exploitation by U.S. companies.
[35] In the United Kingdom in particular there have been cases of corporations using data mining as a way to target certain groups of customers forcing them to pay unfairly high prices.
These groups tend to be people of lower socio-economic status who are not savvy to the ways they can be exploited in digital market places.
[36] In the United States, privacy concerns have been addressed by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act (HIPAA).
According to an article in Biotech Business Week, "'[i]n practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena,' says the AAHC.
More importantly, the rule's goal of protection through informed consent is approach a level of incomprehensibility to average individuals.
On the recommendation of the Hargreaves review, this led to the UK government to amend its copyright law in 2014 to allow content mining as a limitation and exception.
However, due to the restriction of the Information Society Directive (2001), the UK exception only allows content mining for non-commercial purposes.
[39] The European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe.
[40] The focus on the solution to this legal issue, such as licensing rather than limitations and exceptions, led to representatives of universities, researchers, libraries, civil society groups and open access publishers to leave the stakeholder dialogue in May 2013.
