DOCSLIB.ORG

OLAP Cubes Ming-Nu Lee OLAP (Online Analytical Processing)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
OLAP Cubes Ming-Nu Lee OLAP (Online Analytical Processing) OLAP Cubes Ming-Nu Lee OLAP (Online Analytical Processing) Performs multidimensional analysis of business data Provides capability for complex calculations, trend analysis, and sophisticated modelling Foundation of many kinds of business applications OLAP Cube Multidimensional database Method of storing data in a multidimensional form Optimized for data warehouse and OLAP apps Structure: Data(measures) categorized by dimensions Dimension as a hierarchy, set of parent-child relationships Not exactly a “cube” Schema 1) Star Schema • Every dimension is represented with only one dimension table • Dimension table has attributes and is joined to the fact table using a foreign key • Dimension table not joined to each other • Dimension tables are not normalized • Easy to understand and provides optimal disk usage Schema 1) Snowflake Schema • Dimension tables are normalized • Uses smaller disk space • Easier to implement • Query performance reduced • Maintenance increased Dimension Members Identifies a data item’s position and description within a dimension Two types of dimension members 1) Detailed members • Lowest level of members • Has no child members • Stores values at members intersections which are stored as fact data 2) Aggregate members • Parent member that is the total or sum of child members • A level above other members Fact Data Data values described by a company’s business activities Exist at Member intersection points Aggregation of transactions integrated from a RDBMS, or result of Hierarchy or cube formula calculation Operations Slice Dice Roll-up (Drill up) Roll-down (Drill down) Pivot Slice The act of picking a rectangular subset of a cube by choosing a value for one of its dimensions Dice Similar to slice, but it allows analyst to pick specific values of multiple dimensions which produces a subcube Roll-Up (Drill up) Also known as consolidation or aggregation 1) Reducing dimensions 2) Climbing up concept hierarchy Roll-Down (Drill Down) Fragment data into smaller parts Opposite of roll-up 1) Increasing a dimension 2) Moving down the concept hierarchy Pivot Provides a different look or presentation of data by rotating the cube or axes Multidimensional Expressions (MDX) Originally developed in the late 1990s Language for expressing analytical queries Extension of SQL language A MDX expression returns a multi-dimensional result set that consist of axis data and cell data Advantages Information and calculations are consistent in an OLAP cube “What if” scenarios can be quickly created and analyzed Broad or specific terms can be easily searched for in the database Slice and dice cube data by various dimensions, measures and filters Good for analyzing time series Disadvantages Data must be organized into a star or snowflake schema which hard to implement and administer A single OLAP cube cannot have a large number of dimensions Transactional data unable to be accessed with OLAP cubes Modification of a cube requires a full update of the cube References What is OLAP (Online Analytical Processing): Cube, Operations & Types, https://www.guru99.com/online-analytical-processing.html#8 (2017). Data Cube Operations – SQL Queries, https://blogs.perficient.com/2017/08/02/data-cube-operations-sql-queries/ http://olap.com/ https://en.wikipedia.org/wiki/OLAP_cube Rouse, M. (2012). OLAP cube, https://searchdatamanagement.techtarget.com/definition/OLAP-cube Rouse, M. (2012). multidimensional expressions (MDX), https://searchsqlserver.techtarget.com/definition/multidimensional-expressions- MDX Star and SnowFlake Schema in Data Warehousing, https://www.guru99.com/star- snowflake-data-warehousing.html.
Load more

Recommended publications
  • Beyond Relational Databases
    EXPERT ANALYSIS BY MARCOS ALBE, SUPPORT ENGINEER, PERCONA Beyond Relational Databases: A Focus on Redis, MongoDB, and ClickHouse Many of us use and love relational databases… until we try and use them for purposes which aren’t their strong point. Queues, caches, catalogs, unstructured data, counters, and many other use cases, can be solved with relational databases, but are better served by alternative options. In this expert analysis, we examine the goals, pros and cons, and the good and bad use cases of the most popular alternatives on the market, and look into some modern open source implementations. Beyond Relational Databases Developers frequently choose the backend store for the applications they produce. Amidst dozens of options, buzzwords, industry preferences, and vendor offers, it’s not always easy to make the right choice… Even with a map! !# O# d# "# a# `# @R*7-# @94FA6)6 =F(*I-76#A4+)74/*2(:# ( JA$:+49>)# &-)6+16F-# (M#@E61>-#W6e6# &6EH#;)7-6<+# &6EH# J(7)(:X(78+# !"#$%&'( S-76I6)6#'4+)-:-7# A((E-N# ##@E61>-#;E678# ;)762(# .01.%2%+'.('.$%,3( @E61>-#;(F7# D((9F-#=F(*I## =(:c*-:)U@E61>-#W6e6# @F2+16F-# G*/(F-# @Q;# $%&## @R*7-## A6)6S(77-:)U@E61>-#@E-N# K4E-F4:-A%# A6)6E7(1# %49$:+49>)+# @E61>-#'*1-:-# @E61>-#;6<R6# L&H# A6)6#'68-# $%&#@:6F521+#M(7#@E61>-#;E678# .761F-#;)7-6<#LNEF(7-7# S-76I6)6#=F(*I# A6)6/7418+# @ !"#$%&'( ;H=JO# ;(\X67-#@D# M(7#J6I((E# .761F-#%49#A6)6#=F(*I# @ )*&+',"-.%/( S$%=.#;)7-6<%6+-# =F(*I-76# LF6+21+-671># ;G';)7-6<# LF6+21#[(*:I# @E61>-#;"# @E61>-#;)(7<# H618+E61-# *&'+,"#$%&'$#( .761F-#%49#A6)6#@EEF46:1-#
    [Show full text]
  • Array Databases: Concepts, Standards, Implementations
    Baumann et al. J Big Data (2021) 8:28 https://doi.org/10.1186/s40537-020-00399-2 SURVEY PAPER Open Access Array databases: concepts, standards, implementations Peter Baumann , Dimitar Misev, Vlad Merticariu and Bang Pham Huu* *Correspondence: b. Abstract phamhuu@jacobs-university. Multi-dimensional arrays (also known as raster data or gridded data) play a key role in de Large-Scale Scientifc many, if not all science and engineering domains where they typically represent spatio- Information Systems temporal sensor, image, simulation output, or statistics “datacubes”. As classic database Research Group, Jacobs technology does not support arrays adequately, such data today are maintained University, Bremen, Germany mostly in silo solutions, with architectures that tend to erode and not keep up with the increasing requirements on performance and service quality. Array Database systems attempt to close this gap by providing declarative query support for fexible ad-hoc analytics on large n-D arrays, similar to what SQL ofers on set-oriented data, XQuery on hierarchical data, and SPARQL and CIPHER on graph data. Today, Petascale Array Database installations exist, employing massive parallelism and distributed processing. Hence, questions arise about technology and standards available, usability, and overall maturity. Several papers have compared models and formalisms, and benchmarks have been undertaken as well, typically comparing two systems against each other. While each of these represent valuable research to the best of our knowledge there is no comprehensive survey combining model, query language, architecture, and practical usability, and performance aspects. The size of this comparison diferentiates our study as well with 19 systems compared, four benchmarked to an extent and depth clearly exceeding previous papers in the feld; for example, subsetting tests were designed in a way that systems cannot be tuned to specifcally these queries.
    [Show full text]
  • Benchmarking Distributed Data Warehouse Solutions for Storing Genomic Variant Information
    Research Collection Journal Article Benchmarking distributed data warehouse solutions for storing genomic variant information Author(s): Wiewiórka, Marek S.; Wysakowicz, David P.; Okoniewski, Michał J.; Gambin, Tomasz Publication Date: 2017-07-11 Permanent Link: https://doi.org/10.3929/ethz-b-000237893 Originally published in: Database 2017, http://doi.org/10.1093/database/bax049 Rights / License: Creative Commons Attribution 4.0 International This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Database, 2017, 1–16 doi: 10.1093/database/bax049 Original article Original article Benchmarking distributed data warehouse solutions for storing genomic variant information Marek S. Wiewiorka 1, Dawid P. Wysakowicz1, Michał J. Okoniewski2 and Tomasz Gambin1,3,* 1Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, Warsaw 00-665, Poland, 2Scientific IT Services, ETH Zurich, Weinbergstrasse 11, Zurich 8092, Switzerland and 3Department of Medical Genetics, Institute of Mother and Child, Kasprzaka 17a, Warsaw 01-211, Poland *Corresponding author: Tel.: þ48693175804; Fax: þ48222346091; Email: [email protected] Citation details: Wiewiorka,M.S., Wysakowicz,D.P., Okoniewski,M.J. et al. Benchmarking distributed data warehouse so- lutions for storing genomic variant information. Database (2017) Vol. 2017: article ID bax049; doi:10.1093/database/bax049 Received 15 September 2016; Revised 4 April 2017; Accepted 29 May 2017 Abstract Genomic-based personalized medicine encompasses storing, analysing and interpreting genomic variants as its central issues. At a time when thousands of patientss sequenced exomes and genomes are becoming available, there is a growing need for efficient data- base storage and querying.
    [Show full text]
  • Building an Effective Data Warehousing for Financial Sector
    Automatic Control and Information Sciences, 2017, Vol. 3, No. 1, 16-25 Available online at http://pubs.sciepub.com/acis/3/1/4 ©Science and Education Publishing DOI:10.12691/acis-3-1-4 Building an Effective Data Warehousing for Financial Sector José Ferreira1, Fernando Almeida2, José Monteiro1,* 1Higher Polytechnic Institute of Gaya, V.N.Gaia, Portugal 2Faculty of Engineering of Oporto University, INESC TEC, Porto, Portugal *Corresponding author: [email protected] Abstract This article presents the implementation process of a Data Warehouse and a multidimensional analysis of business data for a holding company in the financial sector. The goal is to create a business intelligence system that, in a simple, quick but also versatile way, allows the access to updated, aggregated, real and/or projected information, regarding bank account balances. The established system extracts and processes the operational database information which supports cash management information by using Integration Services and Analysis Services tools from Microsoft SQL Server. The end-user interface is a pivot table, properly arranged to explore the information available by the produced cube. The results have shown that the adoption of online analytical processing cubes offers better performance and provides a more automated and robust process to analyze current and provisional aggregated financial data balances compared to the current process based on static reports built from transactional databases. Keywords: data warehouse, OLAP cube, data analysis, information system, business intelligence, pivot tables Cite This Article: José Ferreira, Fernando Almeida, and José Monteiro, “Building an Effective Data Warehousing for Financial Sector.” Automatic Control and Information Sciences, vol.
    [Show full text]
  • What Is OLAP (Online Analytical Processing): Cube, Operations & Types What Is Online Analytical Processing?
    What is OLAP (Online Analytical Processing): Cube, Operations & Types What is Online Analytical Processing? OLAP is a category of software that allows users to analyze information from multiple database systems at the same time. It is a technology that enables analysts to extract and view business data from different points of view. OLAP stands for Online Analytical Processing. Analysts frequently need to group, aggregate and join data. These operations in relational databases are resource intensive. With OLAP data can be pre-calculated and pre-aggregated, making analysis faster. OLAP databases are divided into one or more cubes. The cubes are designed in such a way that creating and viewing reports become easy. OLAP cube: Ahmed Yasir Khan Page 1 of 12 At the core of the OLAP, concept is an OLAP Cube. The OLAP cube is a data structure optimized for very quick data analysis. The OLAP Cube consists of numeric facts called measures which are categorized by dimensions. OLAP Cube is also called the hypercube. Usually, data operations and analysis are performed using the simple spreadsheet, where data values are arranged in row and column format. This is ideal for two- dimensional data. However, OLAP contains multidimensional data, with data usually obtained from a different and unrelated source. Using a spreadsheet is not an optimal option. The cube can store and analyze multidimensional data in a logical and orderly manner. How does it work? A Data warehouse would extract information from multiple data sources and formats like text files, excel sheet, multimedia files, etc. The extracted data is cleaned and transformed.
    [Show full text]
  • SAS 9.1 OLAP Server: Administrator’S Guide, Please Send Them to Us on a Photocopy of This Page, Or Send Us Electronic Mail
    SAS® 9.1 OLAP Server Administrator’s Guide The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2004. SAS ® 9.1 OLAP Server: Administrator’s Guide. Cary, NC: SAS Institute Inc. SAS® 9.1 OLAP Server: Administrator’s Guide Copyright © 2004, SAS Institute Inc., Cary, NC, USA All rights reserved. Produced in the United States of America. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. U.S. Government Restricted Rights Notice. Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227–19 Commercial Computer Software-Restricted Rights (June 1987). SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513. 1st printing, January 2004 SAS Publishing provides a complete selection of books and electronic products to help customers use SAS software to its fullest potential. For more information about our e-books, e-learning products, CDs, and hard-copy books, visit the SAS Publishing Web site at support.sas.com/pubs or call 1-800-727-3228. SAS® and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are registered trademarks or trademarks
    [Show full text]
  • Improving Traveling Habits Using an OLAP Cube
    Improving traveling habits using an OLAP cube Development of a business intelligence system Marcus Hellman Marcus Hellman Spring 2016 Master Thesis in Computing Science, 30 hp Supervisor: Anders Broberg Extern Supervisor: Kim Nilsson Examiner: Henrik Bjorklund¨ Umea˚ University, Department of Computing Science Abstract The aim of this thesis is to improve the traveling habits of clients using the SpaceTime system when arranging their travels. The improvement of traveling habits refers to lowering costs and emissions generated by the travels. To do this, a business intelligence system, including an OLAP cube, were created to provide the clients with feedback on how they travel. This to make it possible to see if they are improving and how much they have saved, both in money and emissions. Since these kind of systems often are quite complex, studies on best practices and how to keep such systems agile were performed to be able to provide a system of high quality. During this project, it was found that the pre-study and design phase were just as challenging as the creation of the designed components. The result of this project was a business intelligence system, including ETL, a Data warehouse, and an OLAP cube that will be used in the SpaceTime system as well as mock-ups presenting how data from the OLAP cube could be presented in the SpaceTime web-application in the future. Acknowledgements First, I would like to thank SpaceTime and Dohi for giving me the opportunity to do this the- sis. Also, I would like to thank my supervisors Anders Broberg and Kim Nilsson for all the help during this thesis project, providing valuable feedback on my thesis report throughout the project as well as guiding me through the introduction and development of the system.
    [Show full text]
  • Constructing OLAP Cubes Based on Queries
    Constructing OLAP Cubes Based on Queries Tapio Niemi Jyrki Nummenmaa Peter Thanisch Department of Computer and Department of Computer and Department of Computer Science, Information Sciences Information Sciences University of Edinburgh FIN-33014 University of Tampere FIN-33014 University of Tampere Edinburgh, EH9 3JZ Finland Finland Scotland +358 32156595 +358 405277999 +44 7968401525 [email protected] [email protected] [email protected] ABSTRACT The design of a cube is based on knowledge of the application area and the types of queries the users are expected to pose. An On-Line Analytical Processing (OLAP) user often follows a Since constructing an OLAP cube can be a difficult task for the train of thought, posing a sequence of related queries against the end user, it is often seen as the duty of the data warehouse data warehouse. Although their details are not known in advance, administrator. This has led to researchers and vendors regarding the general form of those queries is apparent beforehand. Thus, the OLAP cube as a static storage structure for data warehouse the user can outline the relevant portion of the data posing data. This is problematic since the user often wants to make new generalised queries against a cube representing the data kinds of queries, which may also need new OLAP cubes. The warehouse. same cube is not always practical for different analysis tasks, Since existing OLAP design methods are not suitable for non- since the structure of the cube has a notable effect on efficiency professionals, we present a technique that automates cube design and ease of posing queries.
    [Show full text]
  • Understanding an OLAP Solution from Oracle
    Understanding an OLAP Solution from Oracle An Oracle White Paper April 2008 Understanding an OLAP Solution from Oracle NOTE: The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. Understanding an OLAP Solution from Oracle Page 2 Table of Contents Introduction ....................................................................................................... 4 Similarities between the Oracle Database OLAP Option and Oracle’s Hyperion Essbase ......................................................................................... 5 Commitment to Product Development .................................................... 5 Overview............................................................................................................. 5 Architectural Heritage .................................................................................. 5 Oracle’s Hyperion Essbase: Middle Tier OLAP...................................... 5 Oracle Database OLAP Option: Database-Centric OLAP.................... 6 Architecting the Appropriate Oracle OLAP Solution................................. 6 The Purpose..................................................................................................
    [Show full text]
  • Data Intensive Computing Systems
    CompSci 516 Data Intensive Computing Systems Lecture 18 NoSQL and Column Store Instructor: Sudeepa Roy CompSci 516: Data Intensive Computing Duke CS, Fall 2016 1 Systems Announcements • HW3 (last HW) has been posted on Sakai • Same problems as in HW1 but in MongoDB (NOSQL) • Due in two weeks after today’s lecture (~11/16) Duke CS, Fall 2016 CompSci 516: Data Intensive Computing Systems 2 Reading Material NOSQL: • “Scalable SQL and NoSQL Data Stores” Rick Cattell, SIGMOD Record, December 2010 (Vol. 39, No. 4) • see webpage http://cattell.net/datastores/ for updates and more pointers Column Store: • D. Abadi, P. Boncz, S. Harizopoulos, S. Idreos and S. Madden. The Design and Implementation of Modern Column-Oriented Database Systems. Foundations and Trends in Databases, vol. 5, no. 3, pp. 197–280, 2012. • See VLDB 2009 tutorial: http://nms.csail.mit.edu/~stavros/pubs/tutorial2009- column_stores.pdf Optional: • “Dynamo: Amazon’s Highly Available Key-value Store” By Giuseppe DeCandia et. al. SOSP 2007 • “Bigtable: A Distributed Storage System for Structured Data” Fay Chang et. al. OSDI 2006 Duke CS, Fall 2016 CompSci 516: Data Intensive Computing Systems 3 NoSQL Duke CS, Fall 2016 CompSci 516: Data Intensive Computing Systems 4 Duke CS, Fall 2016 CompSci 516: Data Intensive Computing Systems 5 So far -- RDBMS • Relational Data Model • Relational Database Systems (RDBMS) • RDBMSs have – a complete pre-defined fixed schema – a SQL interface – and ACID transactions Duke CS, Fall 2016 CompSci 516: Data Intensive Computing Systems 6 Today • NoSQL: ”new” database systems – not typically RDBMS – relax on some requirements, gain efficiency and scalability • New systems choose to use/not use several concepts we learnt so far – e.g.
    [Show full text]
  • Query Optimizing for On-Line Analytical Processing Adventures in the Land of Heuristics
    Aalto University School of Science Master's Programme in Computer, Communication and Information Sciences Jonas Berg Query Optimizing for On-line Analytical Processing Adventures in the land of heuristics Master's Thesis Espoo, May 22, 2017 Supervisor: Professor Eljas Soisalon-Soininen Advisors: Jarkko Miettinen M.Sc. (Tech.) Marko Nikula M.Sc. (Tech) Aalto University School of Science Master's Programme in Computer, Communication and In- ABSTRACT OF formation Sciences MASTER'S THESIS Author: Jonas Berg Title: Query Optimizing for On-line Analytical Processing { Adventures in the land of heuristics Date: May 22, 2017 Pages: vii + 71 Major: Computer Science Code: SCI3042 Supervisor: Professor Eljas Soisalon-Soininen Advisors: Jarkko Miettinen M.Sc. (Tech.) Marko Nikula M.Sc. (Tech) Newer database technologies, such as in-memory databases, have largely forgone query optimization. In this thesis, we presented a use case for query optimization for an in-memory column-store database management system used for both on- line analytical processing and on-line transaction processing. To date, the system in question has used a na¨ıve query optimizer for deciding on join order. We went through related literature on the history and evolution of database technology, focusing on query optimization. Based on this, we analyzed the current system and presented improvements for its query processing abilities. We implemented a new query optimizer and experimented with it, seeing how it performed on several queries, concluding that it is a successful improvement
    [Show full text]
  • Optimisation of Ad-Hoc Analysis of an OLAP Cube Using Sparksql
    UPTEC X 17 007 Examensarbete 30 hp September 2017 Optimisation of Ad-hoc analysis of an OLAP cube using SparkSQL Milja Aho Abstract Optimisation of Ad-hoc analysis of an OLAP cube using SparkSQL Milja Aho Teknisk- naturvetenskaplig fakultet UTH-enheten An Online Analytical Processing (OLAP) cube is a way to represent a multidimensional database. The multidimensional database often uses a star Besöksadress: schema and populates it with the data from a relational database. The purpose of Ångströmlaboratoriet Lägerhyddsvägen 1 using an OLAP cube is usually to find valuable insights in the data like trends or Hus 4, Plan 0 unexpected data and is therefore often used within Business intelligence (BI). Mondrian is a tool that handles OLAP cubes that uses the query language Postadress: MultiDimensional eXpressions (MDX) and translates it to SQL queries. Box 536 751 21 Uppsala Apache Kylin is an engine that can be used with Apache Hadoop to create and query OLAP cubes with an SQL interface. This thesis investigates whether the Telefon: engine Apache Spark running on a Hadoop cluster is suitable for analysing OLAP 018 – 471 30 03 cubes and what performance that can be expected. The Star Schema Benchmark Telefax: (SSB) has been used to provide Ad-Hoc queries and to create a large database 018 – 471 30 00 containing over 1.2 billion rows. This database was created in a cluster in the Omicron office consisting of five worker nodes and one master node. Queries were Hemsida: then sent to the database using Mondrian integrated into the BI platform Pentaho. http://www.teknat.uu.se/student Amazon Web Services (AWS) has also been used to create clusters with 3, 6 and 15 slaves to see how the performance scales.
    [Show full text]