“Select before You Collect”: Uses and Abuses of Profiling and Data Mining in Law and Literature

Pólemos ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 57-71
Author(s):  
Jeanne Gaakeer
Keyword(s):  
Data Mining ◽  
Information Technologies ◽  
Law And Literature

AbstractThis article addresses some of the risks involved in the uses of information technologies such as profiling and data mining by means of the German jurist-philosopher Juli Zeh’s dystopic novel Leere Herzen.

Overview of Business Intelligence through Data Mining

2014 ◽  
pp. 272-295
Author(s):  
Abdulrahman R. Alazemi ◽  
Abdulaziz R. Alazemi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Information Technologies ◽  
Customer Relationships ◽  
The Internet ◽  
Competitive Advantages ◽  
Large Databases ◽  
Hidden Knowledge ◽  
Internet Business ◽  
Future Outcomes

The advent of information technologies brought with it the availability of huge amounts of data to be utilized by enterprises. Data mining technologies are used to search vast amounts of data for vital insight regarding business. Data mining is used to acquire business intelligence and to acquire hidden knowledge in large databases or the Internet. Business intelligence can find hidden relations, predict future outcomes, and speculate and allocate resources. This uncovered knowledge helps in gaining competitive advantages, better customer relationships, and even fraud detection. In this chapter, the authors describe how data mining is used to achieve business intelligence. Furthermore, they look into some of the challenges in achieving business intelligence.

Overview of Business Intelligence through Data Mining

2016 ◽  
pp. 49-72 ◽  
Author(s):  
Abdulrahman R. Alazemi ◽  
Abdulaziz R. Alazemi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Information Technologies ◽  
Customer Relationships ◽  
The Internet ◽  
Competitive Advantages ◽  
Large Databases ◽  
Hidden Knowledge ◽  
Internet Business ◽  
Future Outcomes

The advent of information technologies brought with it the availability of huge amounts of data to be utilized by enterprises. Data mining technologies are used to search vast amounts of data for vital insight regarding business. Data mining is used to acquire business intelligence and to acquire hidden knowledge in large databases or the Internet. Business intelligence can find hidden relations, predict future outcomes, and speculate and allocate resources. This uncovered knowledge helps in gaining competitive advantages, better customer relationships, and even fraud detection. In this chapter, the authors describe how data mining is used to achieve business intelligence. Furthermore, they look into some of the challenges in achieving business intelligence.

scholarly journals Business Intelligence and competition ability of enterprise

2005 ◽  
Vol 53 (6) ◽  
pp. 85-92
Author(s):  
Vladimír Konečný ◽  
Ivana Rábová
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information And Communication Technologies ◽  
Business Intelligence ◽  
Information Technologies ◽  
Transformation Process ◽  
Strategic Decision ◽  
New Information ◽  
Information And Communication ◽  
Olap Analysis

As far as the current state of the information and communication technologies usage is concerned, the information systems of the companies cover the major part of the transaction processes and the large amount of the processes at the level of the tactical decision-making.Intensive implementation of the information technologies in many areas of the human activities cause gathering of the large amount of the data. The volume of the internal and external databases grows rapidly and the problem is to take advantage of the data they contain. But the problem is not only the growing volume of the databases but also the different and database structures. To get the new information from the large and incompatible database sources is possible but very inefficient. A manager often needs the information very fast to achieve competitive advantage and to solve problems at the level of strategic decision-making. Another problem is the fact that the databases often contain information that is hidden there and there is no way known how to get this information out of the database. In this case, the user needs at least suitable tools in order to perform experiments and to explore and identify patterns and relationships in the data.The transformation process of the data to information and to knowledge that is used in the process of decision-making is called Business Intelligence. Modern database tools offer wide support for building the data warehouse, OLAP analysis and data mining.Our contribution focuses on the application of one of the data mining techniques such as neural networks and artificial intelligence. The application of those methods will be based on the assessment of the food quality and composing of the corresponding trend indicator.

Privacy-Preserving Data Mining and the Need for Confluence of Research and Practice

2008 ◽  
pp. 2402-2420
Author(s):  
Lixin Fu ◽  
Hamid Nemati ◽  
Fereidoon Sadri
Keyword(s):  
Data Mining ◽  
Research Agenda ◽  
Information Technologies ◽  
Privacy Preserving ◽  
Theory And Practice ◽  
Clear Understanding ◽  
Sensitive Data ◽  
Privacy Preserving Data Mining ◽  
Research And Practice ◽  
Rapid Pace

Privacy-preserving data mining (PPDM) refers to data mining techniques developed to protect sensitive data while allowing useful information to be discovered from the data. In this article, we review PPDM and present a broad survey of related issues, techniques, measures, applications, and regulation guidelines. We observe that the rapid pace of change in information technologies available to sustain PPDM has created a gap between theory and practice. We posit that without a clear understanding of the practice, this gap will be widening which, ultimately, will be detrimental to the field. We conclude by proposing a comprehensive research agenda intended to bridge the gap relevant to practice and as a reference basis for the future related legislation activities.

scholarly journals Architecture of a Compact Data GRID Cluster for Teaching Modern Methods of Data Mining in the Virtual Computer Lab

2020 ◽  
Vol 226 ◽  
pp. 03004
Author(s):  
Mikhail Belov ◽  
Vladimir Korenkov ◽  
Nadezhda Tokareva ◽  
Eugenia Cheremisina
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Information Technologies ◽  
System Analysis ◽  
Nuclear Research ◽  
Data Grid ◽  
Highly Qualified ◽  
Distributed Data Storage ◽  
Computer Lab ◽  
Virtual Computer

This paper discusses the architecture of a compact Data GRID cluster for teaching new methods of Big Data analytics in the Virtual Computer Lab. Its main destination is training highly qualified IT-professionals able to solve efficiently problems of distributed data storage and processing, drawing insights, data mining, and mathematical modeling based on these data. The Virtual Computer Lab was created and successfully operated by the experts of the System Analysis and Control Department at the Dubna State University in collaboration with the Laboratory of Information Technologies (Joint Institute for Nuclear Research).

scholarly journals INFORMATION TECHNOLOGIES IN ACCOUNTING AND RELATED SECURITY RISKS

2021 ◽  
Author(s):  
Nataliia Chekh ◽  
Olena Konoplina ◽  
Yuliya Mizik
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Cloud Computing ◽  
Big Data ◽  
Information Technologies ◽  
Economies Of Scale ◽  
New Technologies ◽  
Business Environment ◽  
Wide Range ◽  
Modern Information

Today, the business environment has become extremely dynamic due to rapid changes in information technology due to competition and the desire for efficiency. Designed to meet a wide range of economic requirements, new technologies offer flexibility, economies of scale, mobility and greater accuracy. The field of accounting is subject to this new era of change. The era of the Internet of Everything (IoE) is reshaping the profession of accountant according to the current needs of organizations. Artificial intelligence and process automation take on redundant and repetitive tasks performed by professionals, creating space for more complex activities such as analysis and business consulting. The article considers the main modern information technologies and the possibilities of their use in accounting. With the use of information technology, more opportunities have opened up in the field of accounting. The purpose of the article is to analyze the use of modern information technology in accounting, to study the features of their implementation and related risks for the company. Technological determinants of the development of the organization of accounting are: the spread of mobile communications; improving the provision of Internet access services; software development; conversion of smartphones, tablets into integrated devices, their active use in the workplace of accounting staff.The main advantages of such technologies as cloud computing platforms, big data, data mining and mobile technologies are identified. The main threats associated with the use of these technologies have also been identified. It is determined that new technologies, as well as the need for real-time reporting make changes in the profession of accountant. Novice accountants may lack the knowledge to efficiently obtain and process large amounts of data. This is due to the fact that the existing curricula of accounting faculties in Ukraine are not sufficiently focused on new technologies, such as cloud computing, Big Data or data mining. As a result, it can be difficult for accountants to secure confidential information using the latest technology.

Introduction to Data Mining in Bioinformatics

2008 ◽  
pp. 93-102
Author(s):  
Hui-Huang Hsu
Keyword(s):  
Data Mining ◽  
Molecular Biology ◽  
Information Technologies ◽  
Molecular Level ◽  
Biological Data ◽  
The Core ◽  
New Knowledge

Bioinformatics uses information technologies to facilitate the discovery of new knowledge in molecular biology. Among the information technologies, data mining is the core. This chapter first introduces the concept and the process of data mining, plus its relationship with bioinformatics. Tasks and techniques of data mining are then presented. At the end, selected bioinformatics problems related to data mining are discussed. Data mining aims at uncovering knowledge from a large amount of data. In molecular biology, advanced biotechnologies enable the generation of new data in a much faster pace. Data mining can assist the biologist in finding new knowledge from piles of biological data at the molecular level. This chapter provides an overview on the topic.

Privacy-Preserving Data Mining and the Need for Confluence of Research and Practice

2011 ◽  
pp. 60-78
Author(s):  
Lixin Fu ◽  
Hamid Nemati ◽  
Fereidoon Sadri
Keyword(s):  
Data Mining ◽  
Research Agenda ◽  
Information Technologies ◽  
Privacy Preserving ◽  
Theory And Practice ◽  
Clear Understanding ◽  
Sensitive Data ◽  
Privacy Preserving Data Mining ◽  
Research And Practice ◽  
Rapid Pace

Privacy-Preserving Data Mining (PPDM) refers to data mining techniques developed to protect sensitive data while allowing useful information to be discovered from the data. In this chapter the review PPDM and present a broad survey of related issues, techniques, measures, applications, and regulation guidelines. The authors observe that the rapid pace of change in information technologies available to sustain PPDM has created a gap between theory and practice. They posit that without a clear understanding of the practice, this gap will be widening, which, ultimately will be detrimental to the field. They conclude by proposing a comprehensive research agenda intended to bridge the gap relevant to practice and as a reference basis for the future related legislation activities.

Introduction to Data Mining in Bioinformatics

2011 ◽  
pp. 1-12
Author(s):  
Hui-Huang Hsu
Keyword(s):  
Data Mining ◽  
Molecular Biology ◽  
Information Technologies ◽  
Molecular Level ◽  
Biological Data ◽  
The Core ◽  
New Knowledge

Bioinformatics uses information technologies to facilitate the discovery of new knowledge in molecular biology. Among the information technologies, data mining is the core. This chapter first introduces the concept and the process of data mining, plus its relationship with bioinformatics. Tasks and techniques of data mining are then presented. At the end, selected bioinformatics problems related to data mining are discussed. Data mining aims at uncovering knowledge from a large amount of data. In molecular biology, advanced biotechnologies enable the generation of new data in a much faster pace. Data mining can assist the biologist in finding new knowledge from piles of biological data at the molecular level. This chapter provides an overview on the topic.

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