Abstract Every interaction your company has with a customer or supplier likely generates a data trail and this data provides a wealth of information for marketers. Extracting that information and getting it into usable shape requires sophisticated data mining tools. One example of this technology is the used by police departments to identify patterns in crime. We will define, explain and discuss main aspects of data mining. Also its benefits and negative issues. What is Data? Data is defined as facts, concepts, information, or instructions in a formalized manner suitable for communication, interpretation, or processing by humans or by automatic means. Like mentioned before this processing is usually assumed to be automated and running on a computer. Data are most useful when well-presented and actually informative, data-processing systems are often referred to as information systems. Now that we know the purpose and meaning of data, we proceed to explain what data mining consist of. What is Information? Information is the result of processing, manipulating and organizing data in a way that eventually adds to the knowledge of the receiver. In other words, it is the context in which data is taken. These patterns, associations, or relationships among all this data can provide information. For example, analysis of retail point of sale transaction data can yield information on which products are selling and when. What is Knowledge? Knowledge is what is known. Like the related concepts truth, belief, and wisdom, there is no single definition of knowledge on which scholars agree, but rather numerous theories and continued debate about the nature of knowledge. Information can be converted into knowledge about historical pa... ... middle of paper ... ... increases the pressure for faster, more powerful data mining queries. This increases pressure for larger, faster systems, which are more expensive. Conclusion There are many ways that data mining can be applied to your corporate data, which will provide greater insight into your business or operations. The value that data mining provides is knowledge about patterns or events that you may not know. As data storage technology advances and information systems continue to collect and process data, a treasure is amassing that is waiting to be discovered. Are you ready to make your claim and find your riches? References Stacy Cowley, “IDG News Service”, August 2005 Linda B., Bourgue, Virginia A., Clark, Processing Data: The Survey Example, Sage Publications, Inc. (December 14, 2006) Bill Palace,” Data Mining”, June, 1996 Todd Spangler, “Teradata:
Knowledge, its source and truthfulness have been under question for a long time. People have always wondered what exactly constitutes facts and if there are any defining laws that can be attributed to all knowledge or information available in the world. Many philosophers speculated on how information can be interpreted according to its falsity or truthfulness, but have not come to definite conclusions. Edmund Gettier has provided one of the key pieces in understanding and trying to figure out what knowledge really is.
Knowledge is a fundamental component of being human. The ability to comprehend information, apply it to the future as well as understand the past, is remarkable. Without knowledge, there could be no critical thinking, empathy, or technological progress. This is an incredible ingredient of our makeup that touches every aspect of human life, and arguably the ingredient that makes us human. The great scholars and philosophers have understood this for thousands of years and have documented as such in their works. From the Biblical Genesis, which is said to represent the first humans, to popular fables, Homer’s Odyssey, and Dante’s Inferno, this message is made clear. Knowledge is the key construct that defines man
Traditional business intelligence tools are being replaced by data discovery software. The data discovery software has numerous capabilities that are dominating purchase requirements for larger distribution. A challenge remaining is the ability to meet the dual demands of enterprise IT and business users.
Knowledge is defined as information and skills one acquires through experience or education. There is; however, a certain knowledge than cannot be certain and is unjustifiable from the scientific perspective. Karen Armstrong, Robert Thurman, and Azar Nafisi wrote about this type of knowledge in their essays: “Homo Religiosus,” “Wisdom,” and “Reading Lolita in Tehran,” respectively. Each of these authors has a different view of what knowledge is exactly, how it can be achieved, and what it means to have achieved it, but each author takes on the view that the concept of knowledge should be viewed from a social stance. Armstrong refers to this uncertain knowledge as “myth,” Thurman refers to it as “wisdom,” and Nafisi refers to it as “upsilamba";
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
There are various kinds of definitions about what data mining is. The authors in [1] define data mining as “the process of extracting previously unknown information from (usually large quantities of) data, which can, in the right context, lead to knowledge”. Data mining is widely used in areas such as business analysis, bioinformatics analysis, medical analysis, etc. Data mining techniques bring us a lot of benefits. Business companies can use data mining tools to search potential customers and increase their profits; medical diagnosis can use data mining to predict potential disease. Although the term “data mining” itself is neutral and has no ethical implications, it is often related to the analysis of information associated with individuals. “The ethical dilemmas arise when data mining is executed over the data of an individual” [2]. For example, using a user’s data to do data mining and classifying the user into some group may result in a variety of ethical issues. In this paper, we deal with two kinds of ethical issues caused by data mining techniques: informational privacy issues in web-data mining and database security issues in data mining. We also look at these ethical issues in a societal level and a global level.
"Knowledge, Truth, and Meaning." Cover: Human Knowledge: Foundations and Limits. Web. 17 Feb. 2011. .
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
Knowledge is rarely considered permanent, because it is constantly changing and adapting as time passes and new discoveries are made. This title roughly translates into the question: to what extent is knowledge provisional? In other words, to what extent does knowledge exist for the present, possibly to be changed in the future? At first glance, one’s mind would immediately stray to the natural sciences, and how theories are constantly being challenged, disproven, and discarded. Because of this, one might be under the impression that knowledge is always provisional because there is always room for improvement; however, there are some cases in which this is not true. There are plenty of ideas and theories that have withstood the test of time, but on the other end of the spectrum there are many that have not. This essay will evaluate the extent to which knowledge is provisional in the areas of the human sciences and history.
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
Big data originated with web search companies that encountered problems with querying large amounts of both structured and unstructured data. With regard to its background, “big data came into being when web search companies developed ways to perform distributed computing on large data sets on computer clusters” Floyer (2014: 1). Big data then spread to enterprises due to their adoption of developing, processing and dissemination of data.
In this paper, I will analyze a portion of the general kinds or manifestations of knowledge that epistemologists have thought is vital to highlight accompanied by the thought of learning as a kind or sensation of some sorts. Knowledge appears to be something we pick up as we live; how would we pick up it, however? That will be our next request, before we inquire as to whether our obviously taking in knowledge is a trickery: might no one ever genuinely get data? Solutions for these requests could indicate finer things of data's constituents, joining the rules incorporated in knowing. They process, shade, and refine these philosophical hypothesis and theories about learning. We will pick up a feeling of what philosophers have thought knowledge is and could be, on top of why a few rationalists have thought knowledge both does not and couldn't exist.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?