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MDX with SSAS 2012 cookbook : 69 practical recipes to analyze multidimensional data stored in SSAS 2012 cubes, using high-performance MDX calculations and flexible MDX queries /

This book is written in a recipe-based style packed full of practical tips and techniques to help you analyse multidimensional data stored in SSAS 2012 cubes. If you need to master MDX queries in SSAS, then this book is for you!If you are a Microsoft SQL Server Analysis Services developer and want t...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Li, Sherry
Otros Autores: Piasevoli, Tomislav
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2013.
Temas:
Acceso en línea:Texto completo

MARC

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520 |a This book is written in a recipe-based style packed full of practical tips and techniques to help you analyse multidimensional data stored in SSAS 2012 cubes. If you need to master MDX queries in SSAS, then this book is for you!If you are a Microsoft SQL Server Analysis Services developer and want to improve your solutions using MDX, then this book is for you. This book is also an essential resource for report developers who need to access the multidimensional cubes through the MDX language. The book assumes you have some basic working knowledge of MDX and a basic understanding of dimensional. 
505 0 |a Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Elementary MDX Techniques; Introduction; Putting data on x and y axes; Skipping axes; Using a WHERE clause to filter the data returned; Optimizing MDX queries using the NonEmpty() function; Using the PROPERTIES() function to retrieve data from attribute relationships; Basic sorting and ranking; Handling division by zero errors; Setting a default member of a hierarchy in MDX script; Chapter 2: Working with Sets; Introduction; Implementing NOT IN set logic. 
505 8 |a Implementing logical OR on members from different hierarchiesImplementing logical AND on members from the same hierarchy; Iterating on a set in order to reduce it; Iterating on a set in order to create a new one; Iterating on a set using recursion; Dissecting and debugging MDX queries; Chapter 3: Working with Time; Introduction; Calculating the YTD (Year-To-Date) value; Calculating the YoY (Year-over-Year) growth (parallel periods); Calculating moving averages; Finding the last date with data; Getting values on the last date with data; Calculating today's date using the string functions. 
505 8 |a Calculating today's date using the MemberValue functionCalculating today's date using an attribute hierarchy; Calculating the difference between two dates; Calculating the difference between two times; Calculating parallel periods for multiple dates in a set; Calculating parallel periods for multiple dates in a slicer; Chapter 4: Concise Reporting; Introduction; Isolating the best N members in a set; Isolating the worst N members in a set; Identifying the best/worst members for each member of another hierarchy; Displaying few important members, others as a single row, and the total at the end. 
505 8 |a Combining two hierarchies into oneFinding the name of a child with the best/worst value; Highlighting siblings with the best/worst values; Implementing bubble-up exceptions; Chapter 5: Navigation; Introduction; Detecting a particular member in a hierarchy; Detecting the root member; Detecting members on the same branch; Finding related members in the same dimension; Finding related members in another dimension; Calculating various percentages; Calculating various averages; Calculating various ranks; Chapter 6: Business Analytics; Introduction; Forecasting using the linear regression. 
505 8 |a Forecasting using the periodic cyclesAllocating the non-allocated company expenses to departments; Analyzing fluctuation of customers; Implementing the ABC analysis; Chapter 7: When MDX is Not Enough; Introduction; Using a new attribute to separate members on a level; Using a distinct count measure to implement histograms over existing hierarchies; Using a dummy dimension to implement histograms over non-existing hierarchies; Creating a physical measure as a placeholder for MDX assignments; Using a new dimension to calculate the most frequent price. 
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