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Communications over the world wide doesnt depend on sytax or eloquence or rethoric or articulation but on the emotional context in which the message is being heard.
People can only hear you when they are moving toward you and they are not likely to when your wordss are pursuing them
Even the choices words lose their powe when they are used to overpower.
Attitudes are the real figures of speech '-Friedman

Saturday, March 7, 2015

Dataminig , Datawarehouse .A process of extracting patterns from data



Resultado de imagen para datamining images



Health care is probably the largest, at times the most expensive, business on earth.

 However, there are a lot of diseases and conditions that can be diagnosed
even before their symptoms appear.

Yes, this is possible through extensive data mining and prediction techniques
and this gives rise to an area called preventive health care.

Data mining is the science of retrieving knowledge from huge volumes of raw
and uninterpretable data. These data belong to medical records of patients,
which can be quite a lot when years of data is processed.
In such cases, efficient data mining techniques and knowledge
discovery approaches comes to a rescue.

Data mining is the process of extracting patterns from data.
Data mining is becoming an increasingly important tool
to transform this data into information
. It is commonly used in a wide range of profiling practices, such as
marketing, surveillance, fraud detection and scientific discovery.
Data Mining Techniques An Introduction to Data Mining
Data mining is the process of extracting patterns from data.
Data mining is becoming an increasingly important tool
to transform this data into information. I
t is commonly used in a wide range of profiling practices, such as marketing,
 surveillance, fraud detection and scientific discovery.

Data mining can be used to uncover patterns in data but is often
carried out only on samples of data.
 The mining process will be ineffective if the samples are not a good representation
 of the larger body of data.
Data mining cannot discover patterns that may be present in the larger body of data
if those patterns are not present in the sample being "mined". Inability to find patterns
may become a cause for some disputes between customers and service providers.
 Therefore data mining is not foolproof but may be useful if sufficiently representative
 data samples are collected. The discovery of a particular pattern in a particular
set of data does not necessarily mean that a pattern is found elsewhere in the larger data
 from which that sample was drawn.
 An important part of the process is the verification and validation
 of patterns on other samples of data.

The related terms data dredging, data fishing and data snooping refer to the use of data
mining techniques to sample sizes that are (or may be) too small for statistical inferences
 to be made about the validity of any patterns discovered (see also data-snooping bias).
Data dredging may, however, be used to develop new hypotheses,
which must then be validated with sufficiently large sample sets.

Continuous Innovation
Although data mining is a relatively new term, the technology is not.
Companies have used powerful computers to sift through volumes of
 supermarket scanner data and analyze market research reports for years.
 However, continuous innovations in computer processing power, disk storage,
 and statistical software are dramatically increasing the accuracy of analysis
 while driving down the cost.

Example: 

Data Mining Techniques
An Introduction to Data Mining
Data mining is the process of extracting patterns from data.
Data mining is becoming an increasingly important tool
 to transform this data into information.
It is commonly used in a wide range of profiling practices,
such as marketing, surveillance, fraud detection and scientific discovery.

Data mining can be used to uncover patterns in data but is often carried out
 only on samples of data.
The mining process will be ineffective if the samples are not a good representation
 of the larger body of data.
Data mining cannot discover patterns that may be present in the larger
 body of data if those patterns are not present in the sample being "mined".
 Inability to find patterns may become a cause for some disputes
between customers and service providers.
Therefore data mining is not foolproof but may be useful if sufficiently representative
 data samples are collected.
 The discovery of a particular pattern in a particular set of data does not necessarily
 mean that a pattern is found elsewhere in the larger data from which
 that sample was drawn.
 An important part of the process is the verification and validation of
 patterns on other samples of data.

The related terms data dredging, data fishing and data snooping refer to the use
 of data mining techniques to sample sizes that are (or may be) too small
 for statistical inferences to be made about the validity of any patterns discovered
(see also data-snooping bias). Data dredging may, however, be used to develop
new hypotheses, which must then be validated with sufficiently large sample sets.

Data Mining an Overview
Generally, data mining (sometimes called data or knowledge discovery)
 is the process of analyzing data from different perspectives and summarizing it
 into useful information - information that can be used to increase revenue,
 cuts costs, or both. Data mining software is one of a number of analytical tools
for analyzing data.
It allows users to analyze data from many different dimensions or angles,
categorize it, and summarize the relationships identified. Technically,
data mining is the process of finding correlations or patterns among dozens
 of fields in large relational databases.

Continuous Innovation
Although data mining is a relatively new term, the technology is not.
 Companies have used powerful computers to sift through volumes
of supermarket scanner data and analyze market research reports for years
. However, continuous innovations in computer processing power,
disk storage, and statistical software are dramatically increasing
 the accuracy of analysis while driving down the cost.

Monday, March 2, 2015

Equality before the law





Equality before the law, like universal suffrage, holds a privileged place in our political system, and to deny equality before the law delegitimizes that system. . . . when these rights are denied, the expectation that the affronted parties should continue to respect the political system . . . that they should continue to treat it as a legitimate political system--has no basis.
—David Luban, Lawye

Judges have taken control of the “right” to assert your guaranteed rights,

Corruption is the abuse of power by a public official for private gain or any organized, interdependent system in which part of the system is either not performing duties it was originally intended to, or performing them in an improper way, to the detriment of the system's original purpose. The abuse of public offices for private gain is paradigmatic of corruption.

A common belief is that corruption is a judge taking bribes. The definition exceeds this theory. Corruption describes any organized, interdependent system in which part of the system is either not performing duties it was originally intended to, or performing them in an improper way, to the detriment of the system's original purpose.


Corrupt judicial systems not only violate the basic right to equality before the law but deny procedural rights guaranteed by the  Constitution.


While corruption may facilitate criminal enterprise such as drug traffickingmoney laundering, and mail fraud.; it is not restricted to these activities. In this country, corruption is so common that it is expected when ordinary businesses or citizens interact with government officials. The end-point of political corruption is a kleptocracy, literally "rule by thieves".

A recent survey demonstrated that officers felt corruption for personal gain was a much more serious charge than engaging in corrupt behavior that appears "to benefit society at large."7 This sub cultural value system rationalizes constitutional rights violations.
Arrogance has no place in policing, and agencies that have a culture of arrogance will only foster allegations of organizational tolerance for noble cause corruption and betrayal of the public service philosophy. When officers and administrators believe that the ends justify their means, such as illegal searches, "articulation" in report writing, illegal arrests and "testilying," they corrupt their own system.
Departmental values shape professional norms and lay the foundation for the discretionary judgments necessary for effective policing. Officers, as well as police supervisors, often lose their perspectives on constitutional policing when these values are not reinforced.

Police transparency and accountability require administrators to establish internal procedures so that allegations of misconduct and cover-up will not occur. This transparency preserves the department's public image. Failing to implement a thorough and professional internal investigative system of accountability becomes very costly in litigation. Police administrators must be fair, but vigilant, in their efforts to combat noble cause corruption in order to defend their agencies against allegations of organizational tolerance for misconduct in court.

The law constantly balances interests, and policing is no different. Such a balancing incorporates protecting the rights of law-abiding citizens on one hand, and respecting the constitutional rights of alleged criminal citizens on the other.

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Alan Boyle Science editor
As MSNBC.com's science editor, Alan Boyle runs a virtual curiosity shop of the physical sciences and space exploration, plus paleontology, archaeology and other ologies that strike his fancy. Since joining MSNBC.com in 1996, Boyle has won awards from the National Academies, the American Association for the Advancement of Science, the National Association of Science Writers, the Society of Professional Journalists, the Space Frontier Foundation, the Pirelli Relativity Challenge and the CMU Cybersecurity Journalism Awards program. He is the author of "The Case for Pluto," a contributor to "A Field Guide for Science Writers," the blogger behind Cosmic Log: Bacteria can walk on 'legs' — and an occasional talking head on the MSNBC cable channel. During his 33 years of daily journalism in Cincinnati, Spokane and Seattle, he’s survived a hurricane, a volcanic eruption, a total solar eclipse and an earthquake. He has faith he'll survive the Internet as well. alanboyle@feedback.msnbc.com

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