Automation of MultiDimensional DB Design (poster)
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Project Goal Full-featured solution for multidimensional database design 19 TH ACM CONFERENCE ON MANAGEMENT OF D ATA COMAD@AHMEDABAD .INDIA 2013 Future Work References Further Information 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. https://sites.google.com/site/rimmoussa/auto_multidimensional_dbs [email protected] A UTO-MDB: A FRAMEWORK FOR A UTOMATED MULTIDIMENSIONAL D ATABASE DESIGN VIA SCHEMA TRANSFORMATION ALFREDO CUZZOCREA ICAR-CNR & UNIV . OF CALABRIA. ITALY RIM MOUSSA, HEJER AKAICHI LA TICE LAB. UNIV . OF TUNIS & ESTI UNIV . OF CARTHAGE . TUNISIA TPC-H*d Benchmark 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 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. 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) ? 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 Application to TPC-DS benchmark Advanced Virtual Cube Design Investigate more derived data strategies, such as data synopsis calculus
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Transcript of Automation of MultiDimensional DB Design (poster)
Project Goal Full-featured solution for multidimensional database design
19TH ACM CONFERENCE ON MANAGEMENT OF DATA [email protected] 2013
Future Work
References
Further Information
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.
https://sites.google.com/site/rimmoussa/auto_multidimensional_dbs
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
TPC-H*d BenchmarkAuto-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
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.
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) ?
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
Application to TPC-DS benchmark
Advanced Virtual Cube Design
Investigate more derived data strategies, such as data synopsis calculus
