Automation of MultiDimensional DB Design (poster)
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This document presents the Auto-MDB framework for automated multidimensional database design via schema transformation. The framework aims to address common questions developers have around defining OLAP cubes from business queries. It provides simple rules for transforming business queries into OLAP cubes by defining measures, fact tables, and dimensions. The document demonstrates applying the framework to transform Query 8 from the TPC-H benchmark into an OLAP cube. It aims to automate multidimensional database design to produce a full-featured solution and evaluates the approach using the TPC-H*d benchmark, a truly OLAP variant of TPC-H translated into the MDX query language.
Automation of MultiDimensional DB Design (poster)
- 1. AUTO-MDB: A FRAMEWORK FOR AUTOMATED MULTIDIMENSIONAL DATABASE DESIGN VIA SCHEMA TRANSFORMATION ALFREDO CUZZOCREA ICAR-CNR & UNIV. OF CALABRIA. ITALY RIM MOUSSA, HEJER AKAICHI LATICE LAB. UNIV. OF TUNIS & ESTI UNIV. OF CARTHAGE . TUNISIA Motivations Questions of Developpers of BI Solutions 1. Advantages of On-Line Analytical Processing: Presentation -visual OLAP, user interaction Ease of Maintenance -data is stored as is viewed, Performance -aggregated data calculus, 2. BI market is booming, according to research from market watchers, such as Pringle & Company and Gartner, the market for BI platforms will remain one of the fastest growing software markets in most regions 3. MDB Design milestone is often neglected, OLAP cubes are defined in a haphazard way without worrying about performance and maintenance cost. Auto-MDB Framework Simple Rules for turning business queries into OLAP Cubes Measures definition Fact Table definition Dimensions definition Turning Business Query Q8 of TPC-H benchmark into an OLAP Cube How to define cubes? will there be a single cube or multiple cubes? Which optimizations are the most suitable for running the workload? Data fragmentation & parallel OLAP ? Derived data (aggregate tables, indexes, derived attributes, data synopsis) ? Project Goal Full-featured solution for multidimensional database design TPC-H*d Benchmark TPC-H*d Benchmark Truly OLAP variant of TPC-H benchmark –the most prominent decision support system benchmark TPC-H SQL workload translated into MDX (MultiDimensional eXpressions) The workload is composed of 23 MDX statements for OLAP cubes and 23 MDX statements for OLAP business queries. Screenshots of C8 and Q8 Pivot Tables and corresponding MDX Statements Future Work References Advanced Virtual Cube Design Application to TPC-DS benchmark TH 19 Investigate more derived data strategies, such as data synopsis calculus E. F. Codd, S. B. Codd, and C. T. Salley. Providing OLAP to user-analysts: An IT mandate. 1993. Alfredo Cuzzocrea and Rim Moussa: Multidimensional Database Design via Schema Transformation: Turning TPC-H into the TPC-H*d Multidimensional Benchmark. COMAD, 2013. Further Information https://sites.google.com/site/rimmoussa/auto_multidimensional_dbs rim.moussa@esti.rnu.tn ACM CONFERENCE ON MANAGEMENT OF DATA COMAD@AHMEDABAD.INDIA 2013