Data mining driven analysis and decomposition in

Are you demotivated when your peers are discussing about data science and recent advances in big data.

Data mining driven analysis and decomposition in

The word "data" was first used to mean "transmissible and storable computer information" in The expression "data processing" was first used in However, in non-specialist, everyday writing, "data" is most commonly used in the singular, as a mass noun like "information", "sand" or "rain".

According to a common view, data is collected and analyzed; data only becomes information suitable for making decisions once it has been analyzed in some fashion.

For example, the height of Mount Everest is generally considered data. The height can be measured precisely with an altimeter and entered into a database. This data may be included in a book along with other data on Mount Everest to describe the mountain in a manner useful for those who wish to make a decision about the best method to climb it.

An understanding based on experience climbing mountains that could advise persons on the way to reach Mount Everest's peak may be seen as "knowledge". The practical climbing of Mount Everest's peak based on this knowledge made be seen as "wisdom".

In other words, wisdom refers to the practical application of a person's knowledge in those circumstances where good may result. Thus wisdom complements and completes the series "data", "information" and "knowledge" of increasingly abstract concepts. Data is often assumed to be the least abstract concept, information the next least, and knowledge the most abstract.

This view, however, has also been argued to reverse the way in which data emerges from information, and information from knowledge. Beynon-Davies uses the concept of a sign to differentiate between data and information; data is a series of symbols, while information occurs when the symbols are used to refer to something.

Since the development of computing devices and machines, these devices can also collect data.

Data mining driven analysis and decomposition in

In the s, computers are widely used in many fields to collect data and sort or process it, in disciplines ranging from marketinganalysis of social services usage by citizens to scientific research. These patterns in data are seen as information which can be used to enhance knowledge.

These patterns may be interpreted as " truth " though "truth" can be a subjective conceptand may be authorized as aesthetic and ethical criteria in some disciplines or cultures.

Events that leave behind perceivable physical or virtual remains can be traced back through data. Marks are no longer considered data once the link between the mark and observation is broken.

An analog computer represents a datum as a voltage, distance, position, or other physical quantity.

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A digital computer represents a piece of data as a sequence of symbols drawn from a fixed alphabet.It is generally accepted that role mining - that is, the discovery of roles through the automatic analysis of data from existing access control systems - must count on .

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In complex and dynamic environments where interdependencies cannot monotonously determine causality, data mining techniques may be employed in order to analyze the problem, extract key features and identify pivotal factors. Typical cases of such complexity and dynamicity are supply chain networks, where a number of involved stakeholders struggle towards their own benefit.

Content-based filtering.

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Content-based technique is a domain-dependent algorithm and it emphasizes more on the analysis of the attributes of items in order to generate predictions. Data Mining-Driven Analysis and Decomposition in Agent Supply Chain Management Networks Kyriakos C.

Data mining driven analysis and decomposition in

Chatzidimitriou1, Andreas L. Symeonidis1,2 and Pericles A. Mitkas1,2 1Department of Electrical. This session gives you a sneak peek at some of the top-scoring posters across a variety of topics through rapid-fire presentations. The featured abstracts were chosen by the Program Committee and are marked by a microphone in the online program.

Data Mining-Driven Analysis and Decomposition in Agent Supply Chain Management Networks