Mastering SQL Server 2014 Data Mining

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Preview This Course. Learn how to use the software you already have, Excel, to perform basic data mining and analysis. Then learn about the data-mining structures and models in Excel SQL Server Analysis Services, and the new add-ins that make data mining in Excel both exceedingly powerful and incredibly easy.

Topics include: Recognize the factors involved in building an environment. Define models, induction, and prediction. Determine key concepts in Excel data-mining. Analyze key influencers. Break down how to utilize the detect categories tool. Recognize where and when to use the Shopping Basket Analysis. Skill Level Intermediate. Show More Show Less. Resume Transcript Auto-Scroll. Related Courses.

Introduction to Data Mining in SQL Server Analysis Services

Preview course. Excel Data Validation with Dennis Taylor. Search This Course Clear Search. Welcome 47s. What you should know before watching this course 3m 5s. Demonstration of the Excel data-mining tools built in this course 2m 1s.

Building an environment 6m 40s. Data-Mining Concepts. Concepts and results 4m 36s. Business problems for data mining 5m 21s.

SQL Server Database Administration Tips

Models, induction, and prediction 3m 2s. Key concepts 6m 13s. The Microsoft Data-Mining Algorithms. DQS enables you to build a knowledge base and use it to perform a variety of critical data quality tasks, including correction, enrichment, standardization, and de-duplication of your data. DQS enables you to perform data cleansing by using cloud-based reference data services provided by reference data providers. DQS also provides you with profiling that is integrated into its data-quality tasks, enabling you to analyze the integrity of your data. A high-availability solution masks the effects of a hardware or software failure and maintains the availability of applications so that the perceived downtime for users is minimized.

This section of the Developer Reference provides instructions and examples for extending the control flow and data flow of an SSIS package using the Script task and the Script component.

Mastering Sql Server 2014 Data Mining 2014

Master data management MDM describes the efforts made by an organization to discover and define non-transactional lists of data, with the goal of compiling maintainable master lists. An MDM project generally includes an evaluation and restructuring of internal business processes along with the implementation of MDM technology. The result of a successful MDM solution is reliable, centralized data that can be analyzed, resulting in better business decisions. Quick Reference The goal of monitoring databases is to assess how a server is performing.

ISBN 10: 184968894x

Effective monitoring involves taking periodic snapshots of current performance to isolate processes that are causing problems, and gathering data continuously over time to track performance trends. Ongoing evaluation of the database performance helps you minimize response times and maximize throughput, yielding optimal performance. Reference Multidimensional Expressions MDX is the query language that you use to work with and retrieve multidimensional data in Microsoft Analysis Services.

MDX utilizes expressions composed of identifiers, values, statements, functions, and operators that Analysis Services can evaluate to retrieve an object for example a set or a member , or a scalar value for example, a string or a number.

Analysis Services provides several APIs that you can use to program against an Analysis Services instance and the multidimensional databases that it makes available. This section describes the approaches available to developers who want to create custom applications using Analysis Services multidimensional solutions.

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You can use this information to choose the programming interface that best meets the requirements of a particular project. Analysis Services development projects can be based on managed or non-managed code that runs on a Windows platform, or other platforms that support HTTP access. Applies to : SQL Server This paper discusses these alternatives. I think that is it great that this chapter dives into the details of extracting data from Oracle and IBM. In the real world most data warehouses or data mining solutions do not extract all of their data from just one source SQL Server.

I really like that they also include techniques for tuning the algorithms in order improve their accuracy. This perhaps my favorite chapter because it uses real world data. No AdventureWorks insight!!! This entry was posted on January 24, at pm and is filed under Uncategorized. You can follow any responses to this entry through the RSS 2. You can leave a response , or trackback from your own site.

Mastering SQL Server Data Mining

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