Bitmap Index-Based Sampling Methods
Discussion: Incvisage
Paper itself
…
•
What did you think?
–
Writing
–
Technical Depth
–
Experiments
–
Evaluation
–
Value of Contribution
When do I prefer an OLA approach
over IncVisage? Other way around?
When do I prefer an OLA approach
over IncVisage? Other way around?
•
Want to not lose any information
•
I want to see the best possible current
estimate of a given value
•
Key trends, big picture
•
Make decisions with guarantees
Sampling Method
•
Sampling method described is a bitmap index-
based sampler.
–
What is a bitmap index?
–
Pros/cons of bitmap index for sampling?
Sampling Method
•
Sampling method described is a bitmap index-
based sampler.
–
What is a bitmap index?
–
Pros/cons of bitmap index for sampling?
Sampling Method
•
Sampling method described is a bitmap index-
based sampler.
–
What is a bitmap index?
–
Pros/cons of bitmap index for sampling?
•
Con: Only supports sampling on categorical predicates easily,
not range-based predicates.
•
Con: Knowledge of relevant attributes in advance
•
Con: Storage may be large
•
Con: Random I/O for retrieving samples
•
Pro: Arbitrary predicate combinations supported
•
Pro: No workload necessary
•
Pro: Bitmaps can be stored pretty compactly
Bitmap Sampling vs. OLAP vs.
Sample/Seek (or BlinkDB)
•
Pros/Cons of each?
Bitmap Sampling vs. Data Cubes vs.
Sample/Seek (or BlinkDB)
•
Pros/Cons of each?
–
BlinkDB or Sample/Seek works well for reasonably
well-defined query templates, but not actual
queries
–
Materalization or Data cubes works well for well-
defined queries
–
Bitmap Sampling works well for arbitrary queries,
but can be slow
Generalizations
•
Would this approach work for bar charts? Why
or why not?
Dive into the world of bitmap index-based sampling methods through discussions on IncVisage, OLA approaches, and more. Explore the pros and cons, learn about bitmap indexes, and compare with OLAP and other techniques for data processing and analysis.
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Presentation Transcript
Paper itself What did you think? Writing Technical Depth Experiments Evaluation Value of Contribution
When do I prefer an OLA approach over IncVisage? Other way around?
When do I prefer an OLA approach over IncVisage? Other way around? Want to not lose any information I want to see the best possible current estimate of a given value Key trends, big picture Make decisions with guarantees
Sampling Method Sampling method described is a bitmap index- based sampler. What is a bitmap index? Pros/cons of bitmap index for sampling?
Sampling Method Sampling method described is a bitmap index- based sampler. What is a bitmap index? Pros/cons of bitmap index for sampling?
Sampling Method Sampling method described is a bitmap index- based sampler. What is a bitmap index? Pros/cons of bitmap index for sampling? Con: Only supports sampling on categorical predicates easily, not range-based predicates. Con: Knowledge of relevant attributes in advance Con: Storage may be large Con: Random I/O for retrieving samples Pro: Arbitrary predicate combinations supported Pro: No workload necessary Pro: Bitmaps can be stored pretty compactly
Bitmap Sampling vs. OLAP vs. Sample/Seek (or BlinkDB) Pros/Cons of each?
Bitmap Sampling vs. Data Cubes vs. Sample/Seek (or BlinkDB) Pros/Cons of each? BlinkDB or Sample/Seek works well for reasonably well-defined query templates, but not actual queries Materalization or Data cubes works well for well- defined queries Bitmap Sampling works well for arbitrary queries, but can be slow
Generalizations Would this approach work for bar charts? Why or why not?
