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Multidimensional Expressions (MDX) Reference SQL Server 2012 Books Online
Multidimensional Expressions (MDX) Reference SQL Server 2012 Books Online Summary: Multidimensional Expressions (MDX) is the query language that you use to work with and retrieve multidimensional data in Microsoft Analysis Services. MDX is based on the XML for Analysis (XMLA) specification, with specific extensions for SQL Server 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). Category: Reference Applies to: SQL Server 2012 Source: SQL Server Books Online (link to source content) E-book publication date: June 2012 Copyright © 2012 by Microsoft Corporation All rights reserved. No part of the contents of this book may be reproduced or transmitted in any form or by any means without the written permission of the publisher. Microsoft and the trademarks listed at http://www.microsoft.com/about/legal/en/us/IntellectualProperty/Trademarks/EN-US.aspx are trademarks of the Microsoft group of companies. All other marks are property of their respective owners. The example companies, organizations, products, domain names, email addresses, logos, people, places, and events depicted herein are fictitious. No association with any real company, organization, product, domain name, email address, logo, person, place, or event is intended or should be inferred. This book expresses the author’s views and opinions. The information contained in this book is provided without any express, statutory, or implied warranties. Neither the authors, Microsoft Corporation, nor its resellers, or distributors will be held liable for any damages caused or alleged to be caused either directly or indirectly by this book. -
Ÿþp Rovider S Ervices a Dministrator
Oracle® Hyperion Provider Services Administrator's Guide Release 12.2.1.0.0 Provider Services Administrator's Guide, 12.2.1.0.0 Copyright © 2005, 2015, Oracle and/or its affiliates. All rights reserved. Authors: EPM Information Development Team This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs, including any operating system, integrated software, any programs installed on the hardware, and/or documentation, delivered to U.S. Government end users are "commercial computer software" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, use, duplication, disclosure, modification, and adaptation of the programs, including any operating system, integrated software, any programs installed on the hardware, and/or documentation, shall be subject to license terms and license restrictions applicable to the programs. -
Sql Plus Download Mac
Sql plus download mac LINK TO DOWNLOAD oracle sql plus free download - Insight Developer for Oracle, SQLite Database, Orac, and many more programs. SQL*Plus Instant Client can be installed in two ways: Download the packages from the Oracle Technology Network (OTN). Copy the same files that are in the packages from an Oracle Database 10 g Client Administrator installation. Both the SQL*Plus and OCI packages must be from the same Oracle Database version, for example, SQL*PLus on Mac. December 1st, Goto comments Leave a comment. I would think installing SQL*Plus on the Mac would be point, click download, point click, install bam it works. Nah. It did install mostly straight forward on my old Mac. Got a new Mac and no dice. Jul 30, · Download the (free) Docker Community Edition for Mac (unless you’ve already got it installed on your system). This will enable you to run SQL Server from within a Docker container. To download, visit the Docker CE for Mac download page and click Get Docker. Find "Macintosh OSX" and download both the Basic and SQL*Plus packages from the Instant Client downloads page. Move the contents of both into a single convenient folder (I used /Applications/Application_folders/instantclient). Add the location to your PATH variable, and also set DYLD_LIBRARY_PATH to point to it; for example. That could affect running SQL*Plus from a shell script, for example. There are workarounds for the 11g instant client. The installation notes at the bottom of the download page have changed since I last did this, and it now says to hard link the library files to the user's ~/lib directory to avoid that issue. -
Data Warehousing
DMIF, University of Udine Data Warehousing Andrea Brunello [email protected] April, 2020 (slightly modified by Dario Della Monica) Outline 1 Introduction 2 Data Warehouse Fundamental Concepts 3 Data Warehouse General Architecture 4 Data Warehouse Development Approaches 5 The Multidimensional Model 6 Operations over Multidimensional Data 2/80 Andrea Brunello Data Warehousing Introduction Nowadays, most of large and medium size organizations are using information systems to implement their business processes. As time goes by, these organizations produce a lot of data related to their business, but often these data are not integrated, been stored within one or more platforms. Thus, they are hardly used for decision-making processes, though they could be a valuable aiding resource. A central repository is needed; nevertheless, traditional databases are not designed to review, manage and store historical/strategic information, but deal with ever changing operational data, to support “daily transactions”. 3/80 Andrea Brunello Data Warehousing What is Data Warehousing? Data warehousing is a technique for collecting and managing data from different sources to provide meaningful business insights. It is a blend of components and processes which allows the strategic use of data: • Electronic storage of a large amount of information which is designed for query and analysis instead of transaction processing • Process of transforming data into information and making it available to users in a timely manner to make a difference 4/80 Andrea Brunello Data Warehousing Why Data Warehousing? A 3NF-designed database for an inventory system has many tables related to each other through foreign keys. A report on monthly sales information may include many joined conditions. -
Squirrel SQL Client
Pagina: 1 di 22 Versione: 01.09 Data agg.: 08/02/2021 RM Squirrel SQL client guida rapida Autori: Marco Riva Revisori: g:\installazioni\squirrel sql client 4.1.0\squirrel sql client - guida rapida.docx Squirrel SQL client Pagina: 2 di 22 guida rapida Versione: 01.09 Data agg.: 08/02/2021 RM Sommario 1. Introduzione ........................................ 3 1.1. Fast startup .......................................... 3 2. Installazione ........................................ 4 2.1. Requisiti ............................................... 4 2.2. Download ............................................. 4 2.3. Installazione .......................................... 4 2.4. Aggiornamento ...................................... 4 3. Overview ............................................ 5 4. Configurazione ..................................... 5 4.1. Configurazione driver per DB2 for i ........... 5 4.2. Configurazione alias ............................... 7 5. Utilizzo .............................................. 12 5.1. Connessione ad un database .................. 12 5.2. Maggiori informazioni sull’area object ...... 14 5.2.1. Filtro e ricerca oggetti ..................... 14 5.2.2. Scheda content .............................. 15 5.2.3. Esportazione contenuti.................... 15 5.2.4. Modifica diretta in tabella ................ 16 5.3. Editor SQL .......................................... 17 5.3.1. Scorciatoie da tastiera .................... 17 5.3.2. Regole sintassi .............................. 17 5.3.3. Barra dei pulsanti .......................... -
Working with Mondrian and Pentaho 176
Open source business analytics William D. Back Nicholas Goodman Julian Hyde SAMPLE CHAPTER MANNING Mondrian in Action by William D. Back Nicholas Goodman and Julian Hyde Chapter 9 Copyright 2014 Manning Publications brief contents 1 ■ Beyond reporting: business analytics 1 2 ■ Mondrian: a first look 17 3 ■ Creating the data mart 36 4 ■ Multidimensional modeling: making analytics data accessible 57 5 ■ How schemas grow 86 6 ■ Securing data 115 7 ■ Maximizing Mondrian performance 133 8 ■ Dynamic security 162 9 ■ Working with Mondrian and Pentaho 176 10 ■ Developing with Mondrian 198 11 ■ Advanced analytics 227 v Working with Mondrian and Pentaho This chapter is recommended for ✓ Business analysts ✓ Data architects ✓ Enterprise architects ✓ Application developers As we pointed out in chapter 1, Mondrian is an OLAP engine. It provides a lot of power, but you need to couple it with an end-user tool to make it effective. As we’ve explored Mondrian’s various capabilities, we’ve used examples of end-user tools use to explain particular points, but we haven’t looked very deeply into any of the specific tools. In this chapter, we’ll broaden our scope and cover topics that should be of interest to all users of Mondrian. We’re going to take a look at several tools that are commonly used with Mondrian and show how they’re used. These tools are written and maintained by Pentaho, as well as several tools from other companies that work closely with Pentaho. As you’ll see, there is a rich variety of tools tai lored to specific needs: 176 Pentaho Analyzer 177 ■ Pentaho Analyzer—An Enterprise Edition plugin that provides drag-and-drop analysis as well as advanced charting. -
Microsoft SQL Server Analysis Services Multidimensional Performance and Operations Guide Thomas Kejser and Denny Lee
Microsoft SQL Server Analysis Services Multidimensional Performance and Operations Guide Thomas Kejser and Denny Lee Contributors and Technical Reviewers: Peter Adshead (UBS), T.K. Anand, KaganArca, Andrew Calvett (UBS), Brad Daniels, John Desch, Marius Dumitru, WillfriedFärber (Trivadis), Alberto Ferrari (SQLBI), Marcel Franke (pmOne), Greg Galloway (Artis Consulting), Darren Gosbell (James & Monroe), DaeSeong Han, Siva Harinath, Thomas Ivarsson (Sigma AB), Alejandro Leguizamo (SolidQ), Alexei Khalyako, Edward Melomed, AkshaiMirchandani, Sanjay Nayyar (IM Group), TomislavPiasevoli, Carl Rabeler (SolidQ), Marco Russo (SQLBI), Ashvini Sharma, Didier Simon, John Sirmon, Richard Tkachuk, Andrea Uggetti, Elizabeth Vitt, Mike Vovchik, Christopher Webb (Crossjoin Consulting), SedatYogurtcuoglu, Anne Zorner Summary: Download this book to learn about Analysis Services Multidimensional performance tuning from an operational and development perspective. This book consolidates the previously published SQL Server 2008 R2 Analysis Services Operations Guide and SQL Server 2008 R2 Analysis Services Performance Guide into a single publication that you can view on portable devices. Category: Guide Applies to: SQL Server 2005, SQL Server 2008, SQL Server 2008 R2, SQL Server 2012 Source: White paper (link to source content, link to source content) E-book publication date: May 2012 200 pages This page intentionally left blank Copyright © 2012 by Microsoft Corporation All rights reserved. No part of the contents of this book may be reproduced or transmitted in any form or by any means without the written permission of the publisher. Microsoft and the trademarks listed at http://www.microsoft.com/about/legal/en/us/IntellectualProperty/Trademarks/EN-US.aspx are trademarks of the Microsoft group of companies. All other marks are property of their respective owners. -
Microsoft Business Intelligence Interoperability with SAP
Microsoft Business Intelligence Interoperability with SAP Published: April 2009 Abstract : This white paper describes the key aspects of the Microsoft Business Intelligence (BI) solution including the Microsoft products and services that are used in the solution. The paper also describes how these components can interoperate together to deliver an effective BI solution to SAP customers. It assumes that the reader has a working knowledge of SAP ERP solutions, and an understanding of Microsoft ® products including Microsoft ® Office Excel ® 2007, Microsoft ® Office SharePoint ® Server 2007, and Microsoft ® SQL Server ® 2008. ii Contents Executive Summary....................................................................................................................1 Microsoft BI Introduction .............................................................................................................2 Making Microsoft BI People Ready.............................................................................................3 The SAP and Microsoft Alliance .................................................................................................4 Microsoft Business Intelligence for SAP.....................................................................................5 Scenario 1: Connect Directly to SAP......................................................................................5 Scenario 2: Extract Data from SAP BW .................................................................................7 SAP Business Suite -
Presto: the Definitive Guide
Presto The Definitive Guide SQL at Any Scale, on Any Storage, in Any Environment Compliments of Matt Fuller, Manfred Moser & Martin Traverso Virtual Book Tour Starburst presents Presto: The Definitive Guide Register Now! Starburst is hosting a virtual book tour series where attendees will: Meet the authors: • Meet the authors from the comfort of your own home Matt Fuller • Meet the Presto creators and participate in an Ask Me Anything (AMA) session with the book Manfred Moser authors + Presto creators • Meet special guest speakers from Martin your favorite podcasts who will Traverso moderate the AMA Register here to save your spot. Praise for Presto: The Definitive Guide This book provides a great introduction to Presto and teaches you everything you need to know to start your successful usage of Presto. —Dain Sundstrom and David Phillips, Creators of the Presto Projects and Founders of the Presto Software Foundation Presto plays a key role in enabling analysis at Pinterest. This book covers the Presto essentials, from use cases through how to run Presto at massive scale. —Ashish Kumar Singh, Tech Lead, Bigdata Query Processing Platform, Pinterest Presto has set the bar in both community-building and technical excellence for lightning- fast analytical processing on stored data in modern cloud architectures. This book is a must-read for companies looking to modernize their analytics stack. —Jay Kreps, Cocreator of Apache Kafka, Cofounder and CEO of Confluent Presto has saved us all—both in academia and industry—countless hours of work, allowing us all to avoid having to write code to manage distributed query processing. -
Copyrighted Material
02_462616 ftoc.qxd 9/2/05 4:40 PM Page vii Contents About the Authors vi Credits v Foreword xvii Chapter 1 Introduction to Data Mining 1 What Is Data Mining 2 Business Problems for Data Mining 5 Data Mining Tasks 6 Classification 6 Clustering 6 Association 7 Regression 8 Forecasting 8 Sequence Analysis 9 Deviation Analysis 10 Data Mining Techniques 11 Data Flow 11 Data Mining Project Cycle 13 COPYRIGHTEDStep 1: Data Collection MATERIAL 13 Step 2: Data Cleaning and Transformation 13 Step 3: Model Building 15 Step 4: Model Assessment 16 Step 5: Reporting 16 Step 6: Prediction (Scoring) 16 Step 7: Application Integration 17 Step 8: Model Management 17 vii 02_462616 ftoc.qxd 9/2/05 4:40 PM Page viii viii Contents Data Mining and the Current Market 17 Data Mining Market Size 17 Major Vendors and Products 18 Current Issues and Challenges 19 Data Mining Standards 20 OLE DB for DM and XML for Analysis 21 SQL/Multimedia for Data Mining 21 Java Data Mining API 23 Predictive Model Markup Language 24 Crisp-DM 28 Common Warehouse Metadata 29 New Trends in Data Mining 31 Summary 33 Chapter 2 OLE DB for Data Mining 35 Introducing OLE DB 36 Why OLE DB for Data Mining? 38 Exploring the Basic Concepts in OLE DB for Data Mining 40 Case 40 The Case Key 41 The Nested Key 41 Case Tables and Nested Tables 42 Scalar Columns and Table Columns 42 The Data Mining Model 42 Model Creation 43 Model Training 43 Model Prediction 43 DMX 43 Three Steps of Data Mining 43 Step 1: Model Creation 45 Step 2: Model Training 49 Step 3: Model Prediction 51 Prediction Functions -
Multidimensional Model Programming SQL Server 2012 Books Online
Multidimensional Model Programming SQL Server 2012 Books Online Summary: 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. 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. Category: Reference Applies to: SQL Server 2012 Source: SQL Server 2012 Books Online (link to source content) E-book publication date: January 2013 Copyright © 2012 by Microsoft Corporation All rights reserved. No part of the contents of this book may be reproduced or transmitted in any form or by any means without the written permission of the publisher. Microsoft and the trademarks listed at http://www.microsoft.com/about/legal/en/us/IntellectualProperty/Trademarks/EN-US.aspx are trademarks of the Microsoft group of companies. All other marks are property of their respective owners. The example companies, organizations, products, domain names, email addresses, logos, people, places, and events depicted herein are fictitious. No association with any real company, organization, product, domain name, email address, logo, person, place, or event is intended or should be inferred. This book expresses the author’s views and opinions. The information contained in this book is provided without any express, statutory, or implied warranties. Neither the authors, Microsoft Corporation, nor its resellers, or distributors will be held liable for any damages caused or alleged to be caused either directly or indirectly by this book. -
Predictive Analysis in Microsoft SQL Server 2012 Gain Intuitive and Comprehensive Predictive Insight
Predictive Analysis in Microsoft SQL Server 2012 Gain intuitive and comprehensive predictive insight automatically grouping similar Top Features Complete customers together. Test multiple data-mining models Inform decisions with intuitive and simultaneously with statistical scores comprehensive predictive insight Forecasting of error and accuracy and confirm available to all users. Predict sales and inventory amounts their stability with cross-validation. and learn how they are interrelated Rich and Innovative Algorithms to foresee bottlenecks and improve Build multiple, incompatible mining Benefit from many rich and performance. models within a single structure; innovative data-mining algorithms apply model analysis over filtered to support common business Data Exploration data; query against structure data to problems promptly and accurately. Analyze profitability across present complete information, all customers or compare customers enabled by enhanced mining Market Basket Analysis who prefer different brands of the structures. Discover which items tend to be same product to discover new Combine the best of both worlds by bought together to create opportunities. blending optimized near-term recommendations on-the-fly and to predictions (ARTXP) and stable long- determine how product placement Unsupervised Learning term predictions (ARIMA) with can directly contribute to your Identify previously unknown Better Time Series Support. bottom line. relationships between various elements of your business to better Discover the relationship between Churn Analysis inform your decisions. items that are frequently purchased Anticipate customers who may be together by using Shopping Basket considering canceling their service Website Analysis Analysis and generate interactive Understand how people use your forms for scoring new cases by and identify benefits that will keep them from leaving.