USING METADATA TO QUERY PASSIVE DATA SOURCES

In the not too distant past, the amount of online data available to general users was relatively small. Most of the online data was maintained in organizations' database management systems and accessible only through the interfaces provided by those systems. The popularity of the Internet, in particular, has meant that there is now an abundance of online data available to users in the form of Web pages and files. This data, however, is maintained in passive data sources, that is sources that do not provide facilities to search or query their data. The data must be queried and examined using applications such as browsers and search engines. In this paper, we explore an approach to querying passive data sources based on the extraction, and subsequent exploitation, of metadata from the data sources. We describe two situations in which this approach has been used, evaluate the approach and draw some general conclusions.

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Data Mining of Personal Information

Socioeconomic and Legal Implications of Electronic Intrusion ◽

10.4018/978-1-60566-204-6.ch014 ◽

2009 ◽

pp. 230-245

Author(s):

Dionysios Politis

Keyword(s):

Data Mining ◽

Database Management ◽

Personal Information ◽

Personal Data ◽

Database Management Systems ◽

Management Systems ◽

The Internet ◽

Data Mining Techniques ◽

Open Environments

In this chapter data-mining techniques are presented that can be used to create data-profiles of individuals from anonymous data that can be found freely and abundantly in open environments, such as the Internet. Although such information takes in most cases the form of an approximation and not of a factual and solid representation of concrete personal data, nevertheless it takes advantage of the vast increase in the amount of data recorded by database management systems as well as by a number of archiving applications and repositories of multimedia files.

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DBMS and Oracle Datamining

10.20944/preprints202103.0640.v1 ◽

2021 ◽

Author(s):

John N. Bernal ◽

Johanna P. Rodriguez ◽

Jorge Portella

Keyword(s):

Data Mining ◽

Association Rules ◽

Database Management ◽

Evolutionary History ◽

Database Management Systems ◽

The Other ◽

Data Sources ◽

Management Systems ◽

Statistical Algorithms

Databases are by far the most valuable asset of companies. Since the need was seen not only to count but also to have some type of record of elements such as crops, animals, money, properties and that this record could be consulted and modified according to the situation, that is where the first database was born. , and after that, these databases cannot be disorganized, they also need to be managed and administered under established standards that facilitate their understanding and management not only by their creators but by the other people who subsequently administer them. Databases and database management systems have an interesting evolutionary history that deserves to be analyzed and this is the objective of this document, where it is sought to understand. Along with databases and their management systems, data mining or Data mining arises that in order not to extend ourselves so much, it is the job of finding common patterns in various data sources and in what way they can be used to predict situations or results of various circumstances; We also focus on the other topic that we will present, Oracle data mining, which roughly is to merge data mining with Oracle, which makes it a powerful tool for obtaining information and predicting results based on statistics.In this article we will study and analyze the ideas, concepts and basic examples that make up SGBD and Data Mining and, we will try to go deeper into this topic, the use of decision techniques such as advanced statistical algorithms. We also present a fictitious example of the application of these techniques: predicting which products can be sold based on their relationship with others. we will give a brief explanation of association rules, data mining cycle and the types of learning and the evolution that data mining has had.

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