Data Mining and Data Warehouses - SiKDD

For the 20th consecutive year, the Slovenian KDD conference (SiKDD) showcases new discoveries in the field of Data Mining, covering a broad specter of topics from Statistical Data Analysis, Data, Text and Multimedia Mining to Semantic Technologies, Link Detection and Link Analysis, Social Network Analysis and Data Warehouses.

By: marsimon / Oct. 21, 2021

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Data warehouse refers to the process of compiling and organizing data into one common database, whereas data mining refers to the process of extracting useful data from the databases. The data mining process depends on the data compiled in the data warehousing phase to recognize meaningful patterns.

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Traditional Data Mining Tools.
Traditional data mining
programs help companies establish data patterns and
trends by using a number of complex algorithms and
techniques. Some of these tools are installed on the
desktop to monitor the data and highlight trends and
others capture information residing outside a database.
The majority are available in both Windows and UNIX
versions, although some specialize in one operating
system only. In addition, while some may concentrate on
one database type, most will be able to handle any data
using online analytical processing or a similar
technology.
Dashboards. Installed in computers to monitor
information in a database, dashboards reflect data
changes and updates onscreen - often in the form of a
chart or table - enabling the user to see how the business
is performing. Historical data also can be referenced,
enabling the user to see where things have changed (e.g.,
increase in sales from the same period last year). This
functionality makes dashboards easy to use and
particularly appealing to managers who wish to have an
overview of the company's performance.
Text-mining Tools. The third type of data mining tool
sometimes is called a text-mining tool because of its
ability to mine data from different kinds of text - from
Microsoft Word and Acrobat PDF documents to simple
text files, for example. These tools scan content and
convert the selected data into a format that is compatible
with the tool's database, thus providing users with an
easy and convenient way of accessing data without the
need to open different applications. Scanned content can
be unstructured (i.e., information is scattered almost
randomly across the document, including e-mails,
Internet pages, audio and video data) or structured (i.e.,
the data's form and purpose is known, such as content
found in a database). Capturing these inputs can provide
organizations with a wealth of information that can be
mined to discover trends, concepts, and attitudes.
Techniques of Data Mining:
Regression modeling-This technique applies standard
statistics to data to prove or disprove a hypothesis. One
example of this is linear regression, in which variables
are measured against a standard or target variable path
over time. A second example is logistic regression,
where the probability of an event is predicted based on
known values in correlation with the occurrence of prior
similar events.

18 MASTANIKHAN258, April 13, 2022 at 11:08 p.m.:

Data warehousing is a method of organizing and compiling data into one database, whereas data mining deals with fetching important data from databases. Data mining attempts to depict meaningful patterns through a dependency on the data that is compiled in the data warehouse.

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