Syllabus. Syllabus. Motivation Decision Support. Syllabus
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1 Presentation: Sophia Discussion: Tianyu Metadata Requirements and Conclusion 3 4 Decision Support Decision Making: Everyday, Everywhere Decision Support System: a class of computerized information systems that support decision making activities. What are needed to support decision making? Data Analysis Tools Heteroguous Large Scale User Friendly Good Query Throughput Data Warehouse OLAP tools 5 6 1
2 --data warehouse A A data warehouse is a Subject-oriented Integrated Time-variant Non-volatile Day-to to-day, transaction Usually from one source Within a short period of time Frequent updates RDMS OLAP VS. OLTP Manager: I need to analyze the curve of sales of our company within the past 10 years. Let s try to find something interesting! Casher: One computer was sold and I got $1000. I need to update the database. That s what I do everyday! OLAP OLTP collection of data in support of management s decision making process. 7 8 OLTP OLAP Users Clerk, IT professional Knowledge worker Function Day to day operations Decision support DB Design Application-oriented Subject-oriented Data Current, up-to-date detailed. Usage repetitive Ad-hoc Historical, summarized, multidimensional, Access Read/write Lots of scans Unit of work Short, simple transaction Complex query # rec accessed tens Millions # users thousands Hundreds DB size 100 MB-GB 100 GB-TB Metric Transaction throughput Query throughput 9 10 Why not RDBM & OLTP? Decision Maker VS. Daily User Decision maker I I just need read special data! design Cut I need concurrency historical access Alright, I ll build data!!! Large control Scale Where and and are my own system! Multidimensional recovery they?! mechanism!!! Data I don t No way. have It them, will Are you kidding!!! and downgrade I Go don t away! want the to It s so important!!! performance. have them! Daily User
3 Typical OLAP architecture Back-end Tools Front-end Tools Multidimensional Numerical Measure Dimension -- Attribute Sales Front-end operations Pivot Rollup Drilldown Slice and dice Product City Data Database Design Methodology Star Schema Fact Table (dimensions) Foreign Key
4 Star Schema Example Database Design Methodology Problem of Star Schema Un-normalized (redundancy) No attribute hierarchy Ex: (Vancouver, BC, Canada, North America) (Victoria, BC, Canada, North America) Normalize dimension tables Snowflake Schema Example Snowflake Schema More Problems: saving of space is negligible in comparison to the typical magnitude of the fact table. Efficiency Warehouse Servers Indices Answer queries using indices and views Optimization of Complex queries Parallel Processing Server Architecture: Specialized SQL Servers ROLAP MOLAP SQL Extensions: next paper Indices Bit map One bit for each record Bitmap Compression Joint Indices
5 Joins of the fact table with a subset of dimension tables, with the aggregation of one or more measures. Example: Total Sale of each product in each State Fact table Product What to Materialize? Workload, cost of update, storage A greedy algorithm State Use of Views Selection, Rollup Generator(V: view Q: query): Same dimensions Selection clause of Q implies that of V Group by of V is a subset of Q Minimal Generator M : A set of generators (could be more than one) Other generators generate some member of it Query: Total Sales of clothing in Washington Minimal Generators: Total sales by each state for each product Total sales by each city for each category Other Generators: Total sales by each city for each product Metadata Requirements and Conclusion Metadata requirements Administrative metadata Information necessary for setting up and using a warehouse Business metadata Business terms; ownership; charging policy Operational metadata Information collected during the operation of the warehouse Metadata requirements Store and manage all the metadata associated with the warehouse
6 Conclusion Data Warehousing is really hard Data Cleansing Physical Design Management Visualization There re a lot of opportunities waiting for you guys~ 31 6
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