Bottom-Up Computation of Sparse and Iceberg CUBEs

Jul 31, 2014

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Bottom-Up Computation of Sparse and Iceberg CUBEs . Jianlin Feng School of Software SUN YAT-SEN UNIVERSITY May. 10, 2010. The Paper. K. Beyer, R. Ramakrishnan. Bottom-Up Computation of Sparse and Iceberg CUBEs. ACM SIGMOD, 1999. The Iceberg-CUBE Problem. SELECT A, B, C, COUNT(*) FROM R

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Bottom-Up Computation of Sparse and Iceberg CUBEs

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Bottom-Up Computation of Sparse and Iceberg CUBEs Jianlin Feng School of Software SUN YAT-SEN UNIVERSITY May. 10, 2010
The Paper • K. Beyer, R. Ramakrishnan. Bottom-Up Computation of Sparse and Iceberg CUBEs. ACM SIGMOD, 1999.
The Iceberg-CUBE Problem SELECT A, B, C, COUNT(*) FROM R CUBE BY A, B, C HAVING COUNT(*) >=N
Cuboid Lattice
Sample Processing Tree
BUC Processing Tree
BUC: Sorting and Partitioning
The BUC Algorithm

Bottom-up parsing

Bottom-up parsing. Goal of parser : build a derivation Top-down parser : build a derivation by working from the start symbol towards the input. Builds parse tree from root to leaves Builds leftmost derivation

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Bottom Up Production

Bottom Up Production. Montana McCabe. Bottom Up Production. What is it? Bottom up production can be defined as chemical self assembly. Example: Tree The alternative is top down production. Example: Building . www.astwood-herefordshire.co.uk/wildlife/chequers_tree_full.jpg

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Computation on meshes, sparse matrices, and graphs

Computation on meshes, sparse matrices, and graphs. Some slides are from David Culler, Jim Demmel, Bob Lucas, Horst Simon, Kathy Yelick, et al., UCB CS267. Parallelizing Stencil Computations. Parallelism is simple Grid is a regular data structure

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BOTTOM-UP PARSING

BOTTOM-UP PARSING. Sathu Hareesh Babu 11CS10039 G-8. Bottom-Up Parsing. Bottom-Up Parser : Constructs a parse tree for an input string beginning at the leaves(the bottom) and working up towards the root(the top)

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Distributed Computation of a Sparse Matrix Vector Product

Distributed Computation of a Sparse Matrix Vector Product. Lecture 18 MA/CS 471 Fall 2003. So Far. We have so far discussed: We discovered (by constructing the iteration for the error vector) that the convergence rate was determined by the spectral radius of:

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Disjunctive Bottom Set and Their Computation

Disjunctive Bottom Set and Their Computation. Dr Wenjin Lu & Dr Ross King Department of Computer Science, The University of Wales, Aberystwyth {wwl,rdk}@aber.ac.uk. Background and Motivation Disjunctive Bottom Set Computing the Disjunctive Bottom Set Conclusions and Future Work.

271 views • 13 slides

Bottom-Up Parsing

Bottom-Up Parsing. “Shift-Reduce” Parsing Reduce a string to the start symbol of the grammar. At every step a particular substring is matched (in left-to-right fashion) to the right side of some production and then it is substituted by the non-terminal in the left hand side of the production.

791 views • 25 slides

Bottom up parsing

Bottom up parsing. General idea LR(0) SLR LR(1) LALR To best exploit JavaCUP, should understand the theoretical basis (LR parsing);. Top-down vs Bottom-up. Bottom-up more powerful than top-down; Can process more powerful grammar than LL, will explain later.

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Bottom-Up Parser

Bottom-Up Parser. Reference. Andrew W. Appel, Modern Compiler Implementation in Java, 2 nd edition, Cambridge Press, 2002. Section 3.2 and 3.3. Weakness of LL(k) parsing. They must predict which production to use, having seen only the first k tokens of the right-hand side. LR(k) Parsing.

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Bottom Up Parsing

Bottom Up Parsing. Parsing Techniques. Top-down parsers (LL(1), recursive descent) Start at the root of the parse tree from the start symbol and grow toward leaves (similar to a derivation) Pick a production and try to match the input Bad “pick”  may need to backtrack

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Bottom-Up Concurrency

Don’t Start with Dekker’s Algorithm: Top-Down Introduction of Concurrency Michael L. Scott Multicore Programming Education Workshop 8 March 2009. Bottom-Up Concurrency. AKA Concurrency for Wizards Usually taught in the OS course Dekker’s algorithm Peterson’s algorithm

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Bottom-up Parsing

Bottom-up Parsing. A general style of bottom-up syntax analysis, known as shift-reduce parsing. Two types of bottom-up parsing: Operator-Precedence parsing LR parsing (Shift – Reduce Parser). Bottom Up Parsing. “Shift-Reduce” Parsing Reduce a string to the start symbol of the grammar.

644 views • 14 slides

Distributed Computation of a Sparse Matrix Vector Product

Distributed Computation of a Sparse Matrix Vector Product. Lecture 18 MA/CS 471 Fall 2003. Recall. We wish to solve Ax=b for some sparse A In each of the three iterative schemes we need to evaluate a matrix vector product of the form: Qv for some sparse matrix Q .

343 views • 15 slides

Why Bottom-Up ?

Tuning Molecule-mediated Spin Coupling in Bottom-up Fabricated Vanadium-TCNE Nanostructures Daniel Wegner Institute of Physics and Center for Nanotechnology (CeNTech) University of M ü nster Germany. Why Bottom-Up ?. Challenges Low temperatures Leads Molecule orientation Deposition.

446 views • 21 slides

Parsing Bottom Up

Parsing Bottom Up. CMPS 450 J. Moloney. Bottom Up Parsing. Parse tree build up from leaves We will examine “LR” parsers – L eft-to-right, R ightmost derivation. Rightmost Parsers Use a stack. Read input from left to right. Create a rightmost derivation.

228 views • 6 slides

Disjunctive Bottom Set and Their Computation

139 views • 13 slides

Bottom-up parsing

Bottom-up parsing. Goal of parser : build a derivation top-down parser : build a derivation by working from the start symbol towards the input. builds parse tree from root to leaves builds leftmost derivation

119 views • 9 slides

Computation on meshes, sparse matrices, and graphs

293 views • 28 slides

Lecture 13 Sparse matrix computation

Lecture 13 Sparse matrix computation. Matlab function: spdiags. Download spdiags01.m And spdiags02.m. n=5; e = ones(n,1); A = spdiags([e -2*e 3*e], -1:1, n, n) full(A) >> spdiags01 A = (1,1) -2 (2,1) 1 (1,2) 3 (2,2) -2 (3,2) 1

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Aggregate Function Computation and Iceberg Querying in Vertical Databases

Aggregate Function Computation and Iceberg Querying in Vertical Databases. Yue (Jenny) Cui Advisor: Dr. William Perrizo Master Thesis Oral Defense Department of Computer Science North Dakota State University. Outline. Introduction Review of Aggregate Functions Review of Iceberg Queries

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