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Title: DBMS
Description: DBMS

Our IT help desk as become a real problem! Currently, IT issues are sent by email to individual technicians. Once an issue is received by a technician, the technician looks into a solution for the issue. One of the problems is that multiple technicians may be looking into an issue that it only takes one technician to solve. Another problem is that the reported issue might not be in the particular technicians area of expertise. Once the issue is resolved the technician moves onto the next issue. Currently no information is saved about how the issue was resolved or the date that issue was resolved. The manger of the IT department is going crazy because hes not sure what his technicians are working on, how long the issues are taking to resolve, or if there are any issues that are not be handled.

The IT manager has hired you to design a help desk system that will track all the IT issues. When an issue is created it has an open date, a description of the problem, and the employee who is reporting the issue. For each issue there is also a status flag so the manager knows which issues are open or closed. To aid the IT manager, issues are categorized as either a hardware or software issue.

Once the IT manager has reviewed an issue he can assign it to one or more technicians. Each technician has a first name, last name, address, and badge number. Each technician has a number of different areas of expertise. Each technician has obtained one or more certifications.

The technicians can update the notes and steps taken to resolve the issue. Once the issue is resolved the issue is then closed and the date the issue was closed is stored.

Our paper focuses on the need for information repositories and discovery of knowledge and thence the overview of, the so hyped, Data Warehousing and Data Mining.

Data Mining as an analytic process designed to explore data (usually large amounts of - typically business or market related - data) in search for consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data.

The ultimate goal of data mining is prediction - and predictive data mining is the most common type of data mining and one that has most direct business applications.



This stage usually starts with data preparation which may involve cleaning data, data transformations, selecting subsets of records and - in case of data sets with large numbers of variables ("fields") - performing some preliminary feature selection operations to bring the number of variables to a manageable range (depending on the statistical methods which are being considered). Then, depending on the nature of the analytic problem, this first stage of the process of data mining may involve anywhere between a simple choice of straightforward predictors for a regression model, to elaborate exploratory analyses using a wide variety of graphical and statistical methods in order to identify the most relevant variables and determine the complexity and/or the general nature of models that can be taken into account in the next stage.

This stage involves considering various models and choosing the best one based on their predictive performance (i.e., explaining the variability in question and producing stable results across samples). This may sound like a simple operation, but in fact, it sometimes involves a very elaborate process. There are a variety of techniques developed to achieve that goal - many of which are based on so-called "competitive evaluation of models," that is, applying different models to the same data set and then comparing their performance to choose the best. These techniques - which are often considered the core of predictive data mining - include: Bagging (Voting, Averaging), Boosting, Stacking (Stacked Generalizations), and

That final stage involves using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome.

The concept of bagging (voting for classification, averaging for regression-type problems with continuous dependent variables of interest) applies to the area of predictive data mining, to combine the predicted classifications (prediction) from multiple models, or from the same type of model for different learning data. It is also used to address the inherent instability of results when applying complex models to relatively small data sets. Suppose your data mining task is to build a model for predictive classification, and the dataset from which to train the model (learning data set, which contains observed classifications) is relatively small.

The concept of boosting applies to the area of predictive data mining, to generate multiple models or classifiers (for prediction or classification), and to derive weights to combine the predictions from those models into a single prediction or predicted classification.

A simple algorithm for boosting works like this: Start by applying some method (e.g., a tree classifier such as C&RT or CHAID) to the learning data, where each observation is assigned an equal weight. Compute the predicted classifications, and apply weights to the observations in the learning sample that are inversely proportional to the accuracy of the classification. In other words, assign greater weight to those observations that were difficult to classify (where the misclassification rate was high), and lower weights to those that were easy to classify (where the misclassification rate was low). boosting procedure).

Data Preparation (in Data Mining) :Data preparation and cleaning is an often neglected but extremely important step in the data mining process. The old saying "garbage-in-garbage-out" is particularly applicable to the typical data mining projects where large data sets collected via some automatic methods (e.g., via the Web) serve as the input into the analyses. Often, the method by which the data where gathered was not tightly controlled, and so the data may contain out-of-range values (e.g., Income: -100), impossible data combinations (e.g., Gender: Male, Pregnant: Yes), and the like. Analyzing data that has not been carefully screened for such problems can produce highly misleading results, in particular in predictive data mining.

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