DOCSLIB.ORG

Mondrian Technical Guide

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mondrian Technical Guide Mondrian 3.0.4 Technical Guide Developing OLAP solutions with Mondrian/JasperAnalysis March 2009 - 1 - Table of Contents License and Copyright .......................................................................................................... 5 Introduction ........................................................................................................................ 9 JasperAnalysis and Mondrian................................................................................................. 9 Mondrian and OLAP............................................................................................................ 11 Online Analytical Processing............................................................................................. 11 Conclusion ..................................................................................................................... 12 Mondrian Architecture ........................................................................................................ 13 Layers of a Mondrian system ........................................................................................... 13 API................................................................................................................................15 How to Design a Mondrian Schema...................................................................................... 17 What is a schema?.......................................................................................................... 17 Schema files................................................................................................................... 17 Logical model ................................................................................................................. 17 Cube.............................................................................................................................. 19 Measures ....................................................................................................................... 19 Dimensions, Hierarchies, Levels ....................................................................................... 20 Mapping dimensions and hierarchies onto tables ............................................................... 21 The 'all' member............................................................................................................. 22 Time dimensions............................................................................................................. 23 Order and display of levels .............................................................................................. 23 Multiple hierarchies ......................................................................................................... 24 Degenerate dimensions ................................................................................................... 25 Inline tables ................................................................................................................... 26 Member properties and formatters ................................................................................... 27 Approximate level cardinality ........................................................................................... 27 Star and snowflake schemas............................................................................................ 27 Shared dimensions.......................................................................................................... 28 Join optimization............................................................................................................. 28 Advanced logical constructs ............................................................................................. 29 Member properties.......................................................................................................... 33 Calculated members........................................................................................................ 34 Named sets.................................................................................................................... 36 Plug-ins ......................................................................................................................... 37 Member reader............................................................................................................... 40 Internationalization ......................................................................................................... 45 Aggregate tables ............................................................................................................ 47 Access-control ................................................................................................................ 48 XML elements................................................................................................................. 52 MDX Specification .............................................................................................................. 55 What is MDX?................................................................................................................. 55 What is the syntax of MDX? ............................................................................................. 55 Mondrian-specific MDX .................................................................................................... 55 Configuration Guide............................................................................................................ 58 Properties ...................................................................................................................... 58 Property list.................................................................................................................... 59 Connect strings............................................................................................................... 66 Cache management ........................................................................................................ 68 Memory management ..................................................................................................... 68 Logging ......................................................................................................................... 69 Optimizing Mondrian Performance ....................................................................................... 70 Introduction ................................................................................................................... 70 A generalized tuning process for Mondrian........................................................................ 70 - 2 - Recommendations for database tuning ............................................................................. 71 Aggregate Tables, Materialized Views and Mondrian .......................................................... 71 AggGen.......................................................................................................................... 72 Optimizing Calculations with the Expression Cache ............................................................ 72 Aggregate Tables............................................................................................................... 74 Introduction ................................................................................................................... 74 What are aggregate tables?............................................................................................. 75 A simple aggregate table................................................................................................. 76 Another aggregate table.................................................................................................. 77 Defining aggregate tables................................................................................................ 78 Building aggregate tables ................................................................................................ 79 How Mondrian recognizes Aggregate Tables...................................................................... 85 Aggregate tables and parent-child hierarchies ................................................................... 90 How Mondrian uses aggregate tables ............................................................................... 93 Tools for designing and maintaining aggregate tables ........................................................ 96 Properties that affect aggregates ..................................................................................... 97 Aggregate Table References ............................................................................................ 99 Cache Control .................................................................................................................... 99 Note for JasperAnalysis ................................................................................................... 99 Introduction ................................................................................................................... 99 How Mondrian's cache works ........................................................................................... 99 New CacheControl API ...................................................................................................100 Other cache control topics ..............................................................................................104
Load more

Recommended publications
  • Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students
    Journal of Statistics Education, Volume 19, Number 1 (2011) Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students Pedro M. Valero-Mora Universitat de València (Spain) Rubén D. Ledesma Universidad Nacional de Mar del Plata (Argentina) Journal of Statistics Education Volume 19, Number 1 (2011), www.amstat.org/publications/jse/v19n1/valero-mora.pdf Copyright © 2011 by Pedro M. Valero-Mora and Rubén D. Ledesma all rights reserved. This text may be freely shared among individuals, but it may not be republished in any medium without express written consent from the authors and advance notification of the editor. Key Words: Interactive graphics; Multivariate data; Parallel boxplots; Principal components; Cluster analysis. Abstract This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the analysis ―by hand,‖ using techniques such as pointing at, selecting and changing the colors of the points/observations. Our experience demonstrates that this approach is very useful when teaching an intermediate/advanced course on multivariate data analysis to students of Psychology, who tend to have low to moderate proficiency in Mathematics. 1. Introduction Teaching multivariate data analysis to Psychology students is an interesting challenge. On the one hand, these students generally do not have a strong grounding in Statistics or Mathematics, and they usually have a rather unenthusiastic attitude towards these subjects. On the other hand, the objectives of some multivariate techniques, such as factor analysis or cluster analysis, are 1 Journal of Statistics Education, Volume 19, Number 1 (2011) very closely tied to concepts they learn as students of Psychology; consequently, they are able to appreciate the utility of these techniques very quickly.
    [Show full text]
  • Interactive Visualization of Assessment Data: the Software Package Mondrian Preprint Nr
    U n i v e r s i t ä t A u g s b u r g Institut für Mathematik Ali Unl¨ u,¨ Anatol Sargin Interactive Visualization of Assessment Data: The Software Package Mondrian Preprint Nr. 04/2008 | 06. Februar 2008 Institut fur¨ Mathematik, Universit¨atsstraße, D-86 135 Augsburg http://www.math.uni-augsburg.de/ Impressum: Herausgeber: Institut fur¨ Mathematik Universit¨at Augsburg 86135 Augsburg http://www.math.uni-augsburg.de/forschung/preprint/ ViSdP: Ali Unl¨ u¨ Institut fur¨ Mathematik Universit¨at Augsburg 86135 Augsburg Preprint: S¨amtliche Rechte verbleiben den Autoren c 2008 [Submitted to: Applied Psychological Measurement] Running Head: Computer software review: Mondrian Interactive visualization of assessment data: The software package Mondrian Ali Ünlü and Anatol Sargin Department of Mathematics, University of Augsburg, Germany January 17, 2008 Correspondence to: Ali Ünlü Department of Mathematics University of Augsburg Universitätsstrasse 14 D-86159 Augsburg Germany Phone: +49 821 598-2236 Fax: +49 821 598-2200 Email: [email protected] Computer software review: Mondrian 2 Abstract Mondrian is state-of-the-art statistical data visualization software featuring modern interactive visualization techniques for a wide range of data types. This paper reviews the capabilities, functionality, and interactive properties of this software package. Key features of Mondrian are illustrated with data from the Programme for International Student Assessment (PISA) and for item analysis applications. Keywords: Mondrian; R; Data visualization software; Interactive graphics; Spineplot; Item analysis; Empirical Item Response Function; Distractor Analysis; Differential Item Functioning; Programme for International Student Assessment (PISA) data Computer software review: Mondrian 3 Introduction Data visualization is a vital tool in decision support.
    [Show full text]
  • The 8Th International Conference
    THE 8TH INTERNATIONAL CONFERENCE ABSTRACT BOOKLET DEPARTMENT OF BIOSTATISTICS ABSTRACT BOOKLET The abstracts contained in this document were reviewed and accepted by the useR! 2012 program committee for presentation at the conference. The abstracts appear in the order that the presen- tations were given, beginning with tutorial abstracts, followed by invited and contributed abstracts. The index at the end of this document may be used to navigate by presenting author name. Reproducible Research with R,LATEX, & Sweave Theresa A Scott, MS1?, Frank E Harrell, Jr, PhD1? 1. Vanderbilt University School of Medicine, Department of Biostatistics ?Contact author: [email protected] Keywords: reproducible research, literate programming, statistical reports During this half-day tutorial, we will first introduce the concept and importance of reproducible research. We will then cover how we can use R,LATEX, and Sweave to automatically generate statistical reports to ensure reproducible research. Each software component will be introduced: R, the free interactive program- ming language and environment used to perform the desired statistical analysis (including the generation of graphics); LATEX, the typesetting system used to produce the written portion of the statistical report; and Sweave, the flexible framework used to embed the R code into a LaTeX document, to compile the R code, and to insert the desired output into the generated statistical report. The steps to generate a reproducible statistical report from scratch using the three software components will then be presented using a detailed example. The ability to regenerate the report when the data or analysis changes and to automatically update the output will also be demonstrated.
    [Show full text]
  • Mondrian Is a General Purpose Statistical Data-Visualization System
    – this page as pdf – About Mondrian is a general purpose statistical data-visualization system. It features outstanding interactive visualization techniques for data of almost any kind, and has particular strengths, compared to other tools, for working with Categorical Data, Geographical Data and LARGE Data. All plots in Mondrian are fully linked, and offer many interactions and queries. Any case selected in a plot in Mondrian is highlighted in all other plots. Currently implemented plots comprise Histograms, Boxplots y by x, Scatterplots, Barcharts, Mosaicplots, Missing Value Plots, Parallel Coordinates/Boxplots, SPLOMs and Maps. Mondrian works with data in standard tab-delimited or comma-separated ASCII files and can load data from R workspaces. There is basic support for working directly on data in Databases (please email for further info). Mondrian is written in JAVA and is distributed as a native application (wrapper) for MacOS X and Windows. Linux users need to start the jar-file. The latest version can be downloaded here. If you have any questions or comments, please email [email protected]. Bugs may be submitted to the bug-tracker as well as per email. News: (08/29/13) New nightly build version 1.5b, fixing performance problems in very large maps and previewing the new universal importer. (07/31/13) Posted some sample demo videos created by Antony Unwin. Once we get some more videos, there will be a video section on this page! (10/03/12) Added new maps for France to the Map Library - on departmant level (96 departments), and - on regional level (22 regions) (01/11/11) New release: Version 1.2 The scatterplotsmoother now includes "principle curves", which are one of the nonlinear generalizations of principal components.
    [Show full text]
  • Mondrian Is a General Purpose Statistical Data-Visualization System
    – this page as pdf – About Mondrian is a general purpose statistical data-visualization system. It features outstanding interactive visualization techniques for data of almost any kind, and has particular strengths, compared to other tools, for working with Categorical Data, Geographical Data and LARGE Data. All plots in Mondrian are fully linked, and offer many interactions and queries. Any case selected in a plot in Mondrian is highlighted in all other plots. Currently implemented plots comprise Histograms, Boxplots y by x, Scatterplots, Barcharts, Mosaicplots, Missing Value Plots, Parallel Coordinates/Boxplots, SPLOMs and Maps. Mondrian works with data in standard tab-delimited or comma-separated ASCII files and can load data from R workspaces. There is basic support for working directly on data in Databases (please email for further info). Mondrian is written in JAVA and is distributed as a native application (wrapper) for MacOS X and Windows. Linux users need to start the jar-file. The latest version can be downloaded here. If you have any questions or comments, please email [email protected]. Bugs may be submitted to the bug-tracker as well as per email. News: (01/11/11) New release: Version 1.2 The scatterplotsmoother now includes "principle curves", which are one of the nonlinear generalizations of principal components. All smoothers can be plotted for subgroups, which have a color assigned, "smoother by colors". The color scheme has been refined once again, to make use of colors as efficiently as possible. The use of alpha-transparency is now consistent between scatterplots and parallel coordinate plots. New Transformations: columnwise minimum and maximum.
    [Show full text]
  • Graphics for Data Analysis Interactive and Dynamic with R and Ggobi.Pdf
    Use R! Series Editors: Robert Gentleman Kurt Hornik Giovanni Parmigiani Use R! Albert: Bayesian Computation with R Cook/Swayne:Interactive and Dynamic Graphics for Data Analysis Paradis: Analysis of Phylogenetics and Evolution with R Pfaff:Analysis of Integrated and Cointegrated Time Series with R Dianne Cook Deborah F. Swayne Interactive and Dynamic Graphics for Data Analysis With R and GGobi With Contributions by Andreas Buja, Duncan Temple Lang, Heike Hofmann, Hadley Wickham, and Michael Lawrence Dianne Cook Deborah F. Swayne Department of Statistics AT & T Labs - Research Iowa State University Shannon Laboratory 325 Snedecor Hall 180 Park Avenue Ames, IA 50011-1210 Florham Park, NJ 07932-1049 [email protected] [email protected] Series Editors: Robert Gentleman Kurt Hornik Program in Computational Biology Department für Statistik und Mathematik Division of Public Health Sciences Wirtschaftsuniversität Wien Augasse 2-6 Fred Hutchinson Cancer Research Center A-1090 Wien 1100 Fairview Ave. N, M2-B876 Austria Seattle, Washington 981029-1024 USA Giovanni Parmigiani The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University 550 North Broadway Baltimore, MD 21205-2011 USA ISBN 978-0-387-71761-6 e-ISBN 978-0-387-71762-3 Library of Congress Control Number: 2007925720 © 2007 Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden.
    [Show full text]