This information was previously unknown and may be useful in any process.

Data mining or data Mining is responsible for the preparation, survey and exploration of the data to obtain information that is not visible.

Under this procedure comprise all techniques used in extracting the information to be processed.

There is a link to the industrial processes because it is useful to obtain information stored in the database of the organization.

In place where the mining or data mining is in artificial intelligence and statistical analysis.

The use of mining techniques can address the solution to the problems that may occur in the prediction, the classification and segmentation.

As to the process data Mining one can mention the following points:

• Selection of the data set

Refers to the data you want to predict, estimate or infer that the calculations used to determine or processes.

• The analysis of the properties of the data

Are histograms, scatter plots, outliers and missing data.

• Transforming the input data set

Is performed according to the previous analysis to prepare and apply the data mining technique that best suits the information to be analyzed. This process is also known as Data Preprocessing.

• The selection and application of data mining techniques

It is when the model is built to perform prediction, classification or segmentation of information.

• Knowledge extraction

Is performed by a data mining technique, obtaining a knowledge model that will represent the patterns of behavior.

You can also use various techniques to generate different models simultaneously.

• The interpretation and evaluation of data

Be made once the model has been obtained, proving that the conclusions drawn are valid and satisfactory.

Go Sky the new functionality is made available by the Sky satellite platform to allow subscribers to see their favorite programs on PCs, tablets and smartphones without having to be tied to clear canonical position between the couch and television. The flow of images arrives in this case through the Internet and can be accessed even in mobility by means of a 3G connection.

Inevitably this last detail, however, requires a thorough reflection regarding a problem at all relevant: how much traffic uses the fruition of Sky Go? According to current technologies is really conceivable watching TV without a 3G network pay heavy additional costs?

First, it should be noted that there are no official data on consumption: depends, in fact, available from banda and the device in use. For a better quality of streaming is obviously advisable to rely on fixed line or Wi-Fi networks, but Sky still provides a good quality service with a minimal amount of bandwidth to be able to make real the possibility of ubiquitous access to programs. However the group is able to provide even the most objective and detailed information about the expected use, thus providing data with which to make more precise calculations of maximum according to your subscription and 3G devices is expected to be able to use.

As explained by Sky, “To improve the signal quality (and therefore the quality of vision) increased phone traffic consumed.” However” a rough indication for a streaming fluid is consumed approximate time between 300 MB and 500 MB for tablets and between 160 MB and 300 MB for iPhone. The maximum values of consumption (and therefore the theoretical ones on which to make their own calculations) are as follows:

Remember that the quality of streaming dynamically adapts to the quality of the connection and that when not using the app does not consume Sky Go traffic. In light of the consumption figures indicated, the use of Sky Go is highly recommended in the networks to which you have access at no additional cost because few hours of vision is sufficient to exceed the threshold average offered by traditional consumer subscriptions 3G.

Another important detail is relevant to the content accessible: “Some content is not available on 3G mobile connection, landline / Wi-Fi or both” and between them shall be included football matches of the Serie A (exclusive Sky). In this case the problem is not in terms of connectivity, but rather in terms of rights to broadcast the same content online.

There are terms frequently used as synonyms for data mining. One of them called “analysis (Smart) data “, which tends to place more emphasis on techniques statistical analysis. Another term widely used, and more related to data mining, is the removal or “Knowledge Discovery in Databases or KDD, acronym in English”.

Although some authors use the terms Data Mining and KDD interchangeably, as synonyms, there are clear differences between the two. Thus most authors agree refer to the KDD as a process consisting of a set of phases, one of which is data mining. Accordingly, the data mining process is only in the implementation of a algorithm to extract data patterns and the process is called KDD includes full pre-processing, mining and post-processing of data.

The KDD according to is the extraction Automated knowledge or interesting patterns, not trivial, implicit, previously unknown potentially useful predictors of information from large databases.

Obtaining patterns and rules in the process Academician of the University of the Science computer using techniques of mining of data.

Since the implementation of a group of techniques Data Mining as clustering, the trees decision and algorithms of learning inductive, is to sort students according to academic performance and later find hidden patterns and rules that characterize, based on the relationships established between the center of origin students, educational level of parents and province home with their academic performance in first grade in college. These results may improve the process of and raise academic quality of the education at the University of the Sciences Computer (ICU).

This intends to classify the Investigation students of the University of Informatics Sciences According to their academic behavior using a set of Data Mining Techniques like clustering, decision trees and inductive learning algorithms. The main goal is to work find hidden patterns and rules that define this behavior, based on the relationship established between the scholarship level of the student’s parents and their origins with their academic grades in the first year of their career. These results help to can improve the quality of the academic process in the ICU.

The University of Informatics Sciences (UCI) account from the 2006-2007 school year with an enrollment of about 10 000 students from all provinces and municipalities, with the most various social and academic backgrounds, without, far, studies have been conducted to evaluate the influence of these factors in its further formation. Because these factors are not taken into account when make the process of attracting students new entry to college, or to give as the necessary follow-registered, which may lead in extreme conditions to Withdraw from the center. While in other cases are left to identify students with greater potential, which could form part of projects or groups of research or just arm the with faculty information convenient for them to provide attention differentiated students in sake of promoting full development of their capabilities and thus giving effect to goal University’s primary, which is to train professionals of computing better and better prepared.

All the information personal and teaching students from five years ago is digitized and held in that historic provide greater utility the of traditional reports.

This is why the University is necessary count on methods efficient and automatic to explore the great bases data processed quickly and reliably information to find patterns knowledge appropriate to solve a problem.

This is why the main objective of this work seek to determine the link between the origin and social background of students in the ICU with their academic performance by application of clustering techniques and Association Rules Mining Data.

– The information in the database is stored physically in different parts of the network.
– At each site of the network, part of the information, it is as a database itself.
– The basis of local data have their own local users, their own DBMS and programs for administration transactions, and their own administrator local data communication.
– These local databases must have a extension, which manages the functions of society necessary, the combination of these components management systems database local, is what is known as system administrator distributed database.

– This global manager allows users to access data from anywhere on the network as if it made with data from its local database, ie for the user, there should be no difference in working with local data or data from other websites.

Consequently, the distributed database is a virtual unit whose parts are physically stored in more databases “True” distinct, located in different sites.

Example of distributed database:

Consider a bank with three branches, each branch, a computer controls the terminals of the same and the system accounts. Each computer with your local system accounts in each branch is a “Site” of the BDD, the computers are connected by the network. During operations normal applications the branch terminals need only access the database from it. As only access the same local network, they are called local applications.

From the technological point of view, apparently important is the existence of some transactions that access information on more than one branch. These transactions are called global transactions or transactions distributed. The existence of global transactions is considered a characteristic that helps us discriminate between BDD and a set of database locals.

A typical transaction would be a global transfer funds from one branch to another. This application needs to update data in two different branches and ensure both the actual update sites or in none. Ensure the proper functioning of global applications is a difficult task.

Advantages of Distributed Database:

– Decentralization: In a system centralized / distributed, there is an administrator who controls entire database, however in a system there is a distributed global administrator has a policy general and delegate some tasks to administrators of each locality to establish policies locales and work efficient.
– Economy: There are two aspects to account. The first is the communication costs, if databases are widely dispersed and applications make extensive use of the data can be cheaper to split the application and done locally. The second is that costs less to create a system of small computers with the same power a single computer.
– Performance Improvement: Well, the data will be stored and used where it is generated, which will distribute the complexity of the system in different websites, optimizing the work.
– Improved reliability and availability: The failure of one or more locations or a communication link does not imply total system inoperability, even if may have duplicate data is made available total services.
– Growth: It is easier to accommodate the increase in size in a distributed system that expansion is done by adding power of processing and storage in the network by adding a new node.
– Flexibility: Allows local and remote access on a transparently.
– Availability: Duplicate data may be so that multiple people can access simultaneously efficiently. The disadvantage, system database administrator needs to worry about consistency of the same.
– Concurrency Control: The system administrator local database is responsible for managing the attendance of efficient manner.

Disadvantages of distributed database:

– The yield could be an advantage contradicted by the nature of the workload, as a node can be overwhelmed by the strategies used concurrency and failures, and local access to the data. This situation can occur when the load work requires a large number of updates concurrent on duplicate data and must be distributed.
– The reliability of distributed systems is between that, since in this database there are many taking into account factors such as: The reliability of computers, network management system based distributed data, transactions and cups error of the workload.
– The increased complexity, play against this type of systems, but often results in high expenses of construction and maintenance. This is given by the large number of hardware components, many things learn, and many applications likely to fail. By example, concurrency control and recovery failures, requires personal very such specialized and expensive.
– Processing is distributed database difficult to control, because these processes often carried out in the areas of work of the users, and even physical access is not controlled which leads to a lack of data security.

However, data mining provides a misleading appearance. Indeed, whatever the method, whoever manipulates data always produces a result. This is encouraging and it seems easy. But beyond a wide range of reasonable responses are an even greater number of responses fanciful (mismatch between the data, objectives, methodology and the various parameters). These mirages are common; they are simply to show things that do not exist instead of things that are hidden. It is therefore necessary to prune those wrong answers to choose run its course, among the reasonable results, those who best meet the objectives.

If we retain only one recommendation that it would never stop at the first result. Must be calculated, recalculate, compare, interpret, refine…

There is another common sense advice to avoid mirages: be based on robust results. This is not a spin, it’s a real board. A result is valid, so robust; it is applicable to all data sets allowed by the class of problems we wish to solve. In other words if you work on 100 and the data obtained with a method results, then it is verified that applying the same method on 100 other data collected by the same process produces a different result reasonably close to the previous .

In fact it often makes a slightly different way: it has 100 data from start to finish, but you torture data long 60 only. When we approach a pleasant solution is applied without considering the 40 other data, or all of the 100. We can then obtain a first idea of the robustness of the solution.

The most representative techniques used in data mining are

Neural networks

This is a paradigm of learning and automated processing is inspired by the way the nervous system functions in animals.

This system allows the interconnection of neurons in a network (ANN), which provides collaboration for the production of output stimuli.

Examples of neural networks include:
• The perceptron (artificial neural network type)
• The multilayer perceptron
• The self-organizing maps
The linear regression

It is one of the techniques used for the formation of relationships between data. This is a fast and effective but that in turn has shortcomings in the spaces where it can interact multidisciplinary more than 2 variables.

The decision trees

This is a prediction model used in the field of artificial intelligence from a database where diagrams are constructed logical constructs. Is a system similar to the prediction that is based on rules that serve to represent a number of conditions that occur successively in problem solving

The statistical models

This is a symbolic expression in the form of equality that is used in experimental designs and regression, with the intention of identifying the factors that modify the response variable

The clustering

Grouping consists of a series of vectors according to certain criteria which are usually at a distance. This is the arrangement of the input vectors so that they are closer to having common characteristics.

According to the lens with the completion of the analysis algorithms can be classified as supervised algorithms that predict an initially unknown data from other data that is prior knowledge. Algorithms and unsupervised, which discover patterns and trends presented data.

Not only does this technique is used, another form of see how to develop a system is the analysis either manuals of reports, guides, regulations, of dictionary of fields, etc.. The use of trees and decision tables helps to observe the conditions and actions that produce the process of creation of a new system. One aspect that is also taken into account is the flow data, Ie must examine where it came from, where they go and where data is stored.

Findings from facts:

Analysts use methods specific techniques to find facts in to gather data related to requirements. Between these include the interview, the questionnaire, review of the records (In the site where these) and observation. In general, analysts employ more than one of these techniques to be insurance of carry out a research comprehensive and accurate.

Analysts use a interview to gather information from persons or groups. It common, respondents are users of the systems existing users power of proposed system. In some cases, respondents are managers or employees who provide data to the system proposed to be affected by it. Although some analysts prefer the interview over other techniques, this is not always the best source data on the application. Since the interview requires time is necessary to use other methods to obtain the information necessary to conduct a research.

Advantages:

• Ensures uniform terms in the questions for all respondents.
• Easy to manage and evaluate.
• More objective assessment of the questions and answers part of those involved in interview.
• It takes a training limited by the interviewer.
• Obtained results interviews short.

Interviews analysts give opportunities to collect information from people who have been selected because their knowledge of the system under study. A often this method is the best source of qualitative information, other methods to collect facts are more useful for gathering quantitative data.

The interview can be classified as formal and structured. Unstructured interviews using a question-answer format that is appropriate when the analyst want to acquire general information about the system. This format encourages respondents to share their feelings, ideas and beliefs. Structured interviews standard questions used a response format open or closed. The first allows the respondent responding to questions in their own words, the other anticipated uses a set of answers.

The success of an interview depends on the skill of the interviewer and preparation for it. Analysts need to be sensitive to the difficulties that some respondents believe during the interview but also its meaning.

This technique allows analysts to gather related information from various aspects of a system group large people. The employment of standardized formats to the questions can provide more reliable data than other techniques, on the other hand, their wide distribution ensures the anonymity of the respondents, a situation that can lead to answers more honest. However, this method does not allows the analyst to observe the expressions corrections respondents. Likewise, the response may be limited as you may not have much relevance for respondents complete the questionnaire.

The high cost associated development and distribution of questionnaires demand of careful consideration analyst goal of these as well as the structure will be more useful for the study and easier to understand for respondents. It is also necessary testing with the questionnaire and, if necessary, modify them before printing and distribution.

Review record:

Various types of reports and records may provide the analyst with valuable information regarding to organizations and their operations. To the review records, the analyst examines the data seated in them about the system and users. The review may be the beginning of the study and introduction or later, this is used to compare operations current records hence may indicate that is happening.

Records include manuals policies, regulations and procedures operating standards used by most the organizations as guides. The records do not indicate the manner in which develop activities, where all the power in making decisions, or all tasks are performed.

Observation:

This method of collection of educational allows the analyst to gain information that can not be obtained by other techniques. Through the observation was obtained first-hand training about how activities are carried out, this method is more useful to see how handled the documents and have finished the processes and if follow all the steps specified.

Structured analysis:

This phase is where the analyst examines a situation independently without guidance or tools or collect technical information to describe the system. The objective of structured analysis is to organize tasks associated with the determination of requirements to obtain an accurate understanding given situation.

For structured analysis using multiple components for observation and to obtain data important for the development of the system, as can be review symbols graphics, dictionaries data description processes and procedures and rules.

Tools to document processes and decisions:

Follow procedures and decisions are important in any company. However, decisions and procedures are important to the analyst when it conducts an investigation systems within a company.

A tool is any device, object or operation used to perform a task specific. The systems analyst depends on the tools to do their job the same way as others in their daily activities. It is important knowing that tools exist, but even more knowing how to use properly. When analyzing the procedures and decisions first step is to identify conditions and actions, concepts common to all activities.

Conditions and variables of decision:

When there is a system and ask for the conditions, conditions is an example of “good” and “bad”, however, conditions change and so the analyst referred to them as variables decision.

By documenting the investigator’s decision identify both permissible as the relevant conditions that can occur in any given situation. Only be included in the study those conditions that are relevant.

Have different ways of saying the same thing can create difficulties communication for studies systems. Therefore, the analyst seeks to avoid more interpretations. You also need to organize Information collected with respect to decisions and avoid difficulties in the communication.