Lukoil Company Price Discrimination
Kostas Papunidis
MITIM 2014
Knowledge discovery
Abstract
Today vast amount of data is generated, compiled and kept in information repositories such as databases and data warehouses. Present information technology developed enough and powerful to retain any amount of data in an orderly manner.
This paper deals with data mining process, more specifically with knowledge discovery. Notwithstanding, discovering applicable patterns, tendency, principles, relationships and deviations in great amounts of data, and making significant forecasts form it, yet, remains one of the primary challenges of the information era.
Key words: Knowledge discovery, data mining, components of knowledge discovery process
Introduction
Modern business highly relies on data. Data turns to be a strategic asset for companies, which are obliged to stay vivid and competitive, similarly to scientific institutions, government, and society as the whole. The data comes from everywhere, ranging from purchases in local shop, satellite images, sensors to Internet. For instance, the Amazon collects and stores a huge amount information of their customers who visit its site; the NASA’s satellites generate terabytes of data every hour.
This data is not only in the form of traditional numbers and text, but also sounds, images, and videos. Database and data warehouse technologies; including transactional, scientific and engineering, legacy and spatial, time-series text, and object-oriented databases; computer hardware and software; and automated data collection tools are nowadays so mature and powerful that they can store any amount of data in an organized and efficient way.
Transformation, aggravation, analysis and synthesis should be done in order to discover crucial and interesting pattern. Therefore, knowledge discovery (KD) in databases is a promising solution and this paper is focused on explanation of how knowledge discovery is organized and contribute to organization.
The
MITIM 2014
Knowledge discovery
Abstract
Today vast amount of data is generated, compiled and kept in information repositories such as databases and data warehouses. Present information technology developed enough and powerful to retain any amount of data in an orderly manner.
This paper deals with data mining process, more specifically with knowledge discovery. Notwithstanding, discovering applicable patterns, tendency, principles, relationships and deviations in great amounts of data, and making significant forecasts form it, yet, remains one of the primary challenges of the information era.
Key words: Knowledge discovery, data mining, components of knowledge discovery process
Introduction
Modern business highly relies on data. Data turns to be a strategic asset for companies, which are obliged to stay vivid and competitive, similarly to scientific institutions, government, and society as the whole. The data comes from everywhere, ranging from purchases in local shop, satellite images, sensors to Internet. For instance, the Amazon collects and stores a huge amount information of their customers who visit its site; the NASA’s satellites generate terabytes of data every hour.
This data is not only in the form of traditional numbers and text, but also sounds, images, and videos. Database and data warehouse technologies; including transactional, scientific and engineering, legacy and spatial, time-series text, and object-oriented databases; computer hardware and software; and automated data collection tools are nowadays so mature and powerful that they can store any amount of data in an organized and efficient way.
Transformation, aggravation, analysis and synthesis should be done in order to discover crucial and interesting pattern. Therefore, knowledge discovery (KD) in databases is a promising solution and this paper is focused on explanation of how knowledge discovery is organized and contribute to organization.
The
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