A single mining structure can support multiple mining models that share the same domain. The following diagram illustrates the relationship of the data mining structure to the data source, and to its constituent data mining models.
Unlimited Dimensions and Aggregation Levels. Since analytic tools are designed to be used by, or at the very least, their output understood by, ordinary employees, these rules are likely to remain valid for some time to come. Current View The analytic sector of BI can be broken down into two general areas: It is important to bear in mind the distinction, although these areas are often confused.
Data analysis looks at existing data and applies statistical methods and visualization to test hypotheses about the data and discover exceptions.
Data mining seeks trends within the data, which may be used for later analysis. It is, therefore, capable of providing new insights into the Analysis of data mining, which are independent of preconceptions.
Data Analysis Data analysis is concerned with a variety of different tools and methods that have been developed to query existing data, discover exceptions, and verify hypotheses. A query is simply a question put to a database management system, which then generates a subset of data in response.
Queries can be basic e. A well-written query can exact a precise piece of information; a sloppy one may produce huge quantities of worthless or even misleading data. Queries are often written in structured query language SQLa product-independent command set developed to allow cross-platform access to relational databases.
Queries may be saved and reused to generate reports, such as monthly sales summaries, through automatic processes, or simply to assist users in finding what they need. Some products build dictionaries of queries that allow users to bypass knowledge of both database structure and SQL by presenting a drag-and-drop query-building interface.
Query results may be aggregated, sorted, or summarized in many ways.
The presentation of the data retrieved by the query is the task of the report. Presentations may encompass tabular or spreadsheet-formatted information, graphics, cross tabulations, or any combination of these forms.
A rudimentary reporting of products might simply show the results in a comprehensible fashion; more elegant output is usually advanced enough to be suitable for inclusion in a glossy annual report.
Some products can run queries on a scheduled basis and configure those queries to distribute the resulting reports to designated users through email. For example, in some organizations, IT may build a set of queries and report structures and require that employees use only the IT-created structures; in other organizations, and perhaps within other areas of the same organization, employees are permitted to define their own queries and create custom reports.
A managed report environment MRE is a type of managed query environment. It is a report design, generation, and processing environment that permits the centralized control of reporting. To users, an MRE provides an intelligent report viewer that may contain hyperlinks between relevant parts of a document or allow embedded OLE objects such as Excel spreadsheets within the report.
The most popular technology in data analysis is OLAP. OLAP servers organize data into multidimensional hierarchies, called cubes, for high-speed data analysis.
Data mining algorithms scan databases to uncover relationships or patterns. OLAP tools allow users to drill down through multiple dimensions to isolate specific data items. For example, a hypercube the multidimensional data structure may contain sales information categorized by product, region, salesperson, retail outlet, and time period, in both units and dollars.
Information can be presented in tabular or graphical format and manipulated extensively. Since the information is derived from summarized data, it is not as flexible as information obtained from an ad hoc query; most tools offer a way to drill down to the underlying raw data.
For example, PowerPlay provides the automatic launch of its sister product, Impromptu, to query the database for the records in question. ROLAP products optimize data for multi-dimensional analysis using standard relational structures.
The advantage of the MOLAP paradigm is that it can natively incorporate algebraic expressions to handle complex, matrix-based analysis. Since all organizations will require both complex analysis and analysis of large data sets, it could be necessary to develop an architecture and set of user guidelines that will enable implementation of both ROLAP and MOLAP where each is appropriate.
This provides good performance in browsing aggregate data, but slower performance in "drilling down" to further detail.Tutorials. Basic Data Mining Tutorial (SQL Server ) - This tutorial walks you through a targeted mailing scenario.
It demonstrates how to use the data mining algorithms, mining model viewers, and data mining tools that are included in Analysis Services.
The science of forensic investigation relies upon data mining algorithms, digital authentication and analysis of data, and evidence preservation through data imaging. These techniques allow law enforcement to compile data against criminals, solve crimes, and provide evidence in court.
Reality TV is the new mantra of television producers and channel executives. It is the means to increase TRP ratings and the end is always to outdo the other channels and the “similar -but-tweaked-here-and-there” shows churned out by the competition.
The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies .
Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Develop SSAS cubes from data warehouse on Multidimensional & Tabular modes with Dimensional & Data Mining Models.