1. Parallel OLAP Andrew Rau-Chaplin Faculty of Computer Science Dalhousie University Joint Work with F. Dehne T. Eavis S. Hambrusch

2. Decision Support Systems A time-oriented analysis of scientific or organizational data Information Processing Online Analytical Processing (OLAP) Data Minning

3. Data Warehousing for Decision Support Operational data collected into DW DW used to support multi- dimensional views Views form the basis of OLAP processing Our focus: the OLAP server

4. Data Cube Generation Proposed by Gray et al in 1995 Can be generated from a relational DB but… A B C The cuboid ABC (or CAB) ABC AB ACBC AC B ALL 12 18 83 21 34 38 50 21

5. Core OLAP Operations Five fundamental OLAP operations: roll- up, drill-down, slice, dice, and pivot Range Queries

6. The Challenge Design and build a parallel ROLAP system Full cube generation Partial cube generation Indexing and query resolution For High dimensionality: 10 – 30 D Large input data sizes: Gigabytes Large output data sizes: Terabytes Implications Parallel + external memory Shared disk + Shared nothing

7. The Architectural Model Shared Disk A set of P processors connected via an interconnection fabric standard-sized local memory concurrent access to a shared disk array Shared Nothing A set of p processors connected via and interconnection fabric Standard size local memory Independent local disk(s) Algorithm Design CGM (Coarse Grained Multicomputer) Communication Fabric p1p1 p2p2 p3p3 p4p4 pnpn p1p1 p2p2 p3p3 p4p4 pnpn

8. Coarse Grained Multicomputer A set of P processors Arbitrary communication topology or shared memory m memory per processor, m >>p Communication round consists of an h-relation in which all proc. send and receive O(m) data Communication Fabric

9. MOLAP vs. ROLAP ModelYearColourSales Chevy1990Red5 Chevy1990Blue87 Ford1990Green64 Ford1990Blue99 Ford1991Red8 Ford1991Blue7

10. Existing Parallel Results Goil & Choudhary MOLAP Approach Parallelize the generation of each cuboid Challenge > 2d comm. rounds

11. Parallelizing the Data Cube Generating Data Cubes (Shared Disk) Generating Data Cubes (Shared Nothing) Generating Partial Data Cubes Parallel Multi-dimensional Indexing Conclusions and Future Work