Fast and Scalable Relational Division on Fast and Scalable - - PowerPoint PPT Presentation

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Fast and Scalable Relational Division on Fast and Scalable - - PowerPoint PPT Presentation

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Fast and Scalable Relational Division on Fast and Scalable Relational Division on Database Systems Database Systems Andr S. Gonzaga , Robson L. F. Cordeiro 1. Introduction 2. Contributions 3. Background a. Genetic Data b. Diviso

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Fast and Scalable Relational Division on Database Systems

André S. Gonzaga, Robson L. F. Cordeiro

Fast and Scalable Relational Division on Database Systems

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1. Introduction 2. Contributions 3. Background a. Genetic Data b. Divisão Relacional 4. Related Work 5. Proposed Algorithms a. Index-Divison b. Division Data Generator 6. Experiments a. Synthetic Data b. Case Study 7. Conclusion

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Relational Division allows simple representations

  • f queries involving the concept of “for all”

INTRODUCTION

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  • 1. To select candidates having all

the skills for a given job

INTRODUCTION

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  • 2. To select the diseases that have all

the given symptoms

INTRODUCTION

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  • 2. To select the animals that have all

the desired genetic conditions

INTRODUCTION

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  • 1. Relational Algebra:

INTRODUCTION

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  • 1. Relational Algebra:
  • 2. RDBMS / SQL:
  • a. Does not have an explicit operator for it.
  • b. There are several possible implementations in SQL.
  • c. Most of the time the relational division is used indirectly.

INTRODUCTION

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1. Introduction 2. Contributions 3. Background a. Genetic Data b. Divisão Relacional 4. Related Work 5. Proposed Algorithms a. Index-Divison b. Division Data Generator 6. Experiments a. Synthetic Data b. Case Study 7. Conclusion

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1. Evaluate the division implementations in RDBMS in different cases of use. CONTRIBUTIONS

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1. Evaluate the division implementations in RDBMS in different cases of use. 2. Investigate which aspects of the data affect the execution time of each implementation. CONTRIBUTIONS

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1. Evaluate the division implementations in RDBMS in different cases of use. 2. Investigate which aspects of the data affect the execution time of each implementation. 3. Propose a new algorithm to solve the relational division queries. CONTRIBUTIONS

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1. Evaluate the division implementations in RDBMS in different cases of use. 2. Investigate which aspects of the data affect the execution time of each implementation. 3. Propose a new algorithm to solve the relational division queries. 4. Perform a case study to select genetic data using the relational division. CONTRIBUTIONS

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1. Introduction 2. Contributions 3. Background a. Genetic Data b. Divisão Relacional 4. Related Work 5. Proposed Algorithms a. Index-Divison b. Division Data Generator 6. Experiments a. Synthetic Data b. Case Study 7. Conclusion

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SNP - Single Nucleotide Polymorphism BACKGROUND | Genetic Data

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SNP - Single Nucleotide Polymorphism

  • Variations among the individuals in genome wherein the

least frequent allele has an abundance of 1% or greater BACKGROUND | Genetic Data

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SNP - Single Nucleotide Polymorphism

  • Variations among the individuals in genome wherein the

least frequent allele has an abundance of 1% or greater

  • Some SNPs are reported to be highly related to diseases or

development of specific traits of the individual. BACKGROUND | Genetic Data

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SNP - Single Nucleotide Polymorphism

  • Variations among the individuals in genome wherein the

least frequent allele has an abundance of 1% or greater

  • Some SNPs are reported to be highly related to diseases or

development of specific traits of the individual.

  • Represents about 90% of all genetic variations
  • f the individuals.

BACKGROUND | Genetic Data

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SNP - Single Nucleotide Polymorphism BACKGROUND | Genetic Data

SNP

Codified as: Position along the chromosome Alleles: 11, 12 , 21, 22

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SNP - Single Nucleotide Polymorphism BACKGROUND | Genetic Data Codified as: Position along the chromosome Alleles: 11, 12 , 21, 22

Genetic data of the Individual

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1. Introduction 2. Contributions 3. Background a. Genetic Data b. Divisão Relacional 4. Related Work 5. Proposed Algorithms a. Index-Divison b. Division Data Generator 6. Experiments a. Synthetic Data b. Case Study 7. Conclusion

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  • It is the only, directly, algebraic correspondent to the

Universal Quantification (∀) from the Relational Calculus. BACKGROUND | Relational Division

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BACKGROUND | Relational Division

  • It is the only, directly, algebraic correspondent to the

Universal Quantification (∀) from the Relational Calculus. The division operation is a derived operator.

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BACKGROUND | Relational Division

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BACKGROUND | Relational Division

DIVIDEND

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BACKGROUND | Relational Division

DIVIDEND

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BACKGROUND | Relational Division

DIVIDEND

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BACKGROUND | Relational Division

DIVIDEND

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BACKGROUND | Relational Division

DIVISOR

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BACKGROUND | Relational Division

DIVISOR

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BACKGROUND | Relational Division

DIVISOR

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BACKGROUND | Relational Division

QUOTIENT

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RELATED WORK

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RELATED WORK

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1. Introduction 2. Contributions 3. Background a. Genetic Data b. Divisão Relacional 4. Related Work 5. Proposed Algorithms a. Index-Divison b. Division Data Generator 6. Experiments a. Synthetic Data b. Case Study 7. Conclusion

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  • We developed a new algorithm for the division operation

PROPOSED ALGORITHMS

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PROPOSED ALGORITHMS | Index-Division Valid groups: {1, 2, 3}

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Valid groups: {1, 2, 3} PROPOSED ALGORITHMS | Index-Division

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Valid groups: {1, 2, 3} PROPOSED ALGORITHMS | Index-Division

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Valid groups: {1, 2, 3} PROPOSED ALGORITHMS | Index-Division

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Valid groups: {1, 2, 3} PROPOSED ALGORITHMS | Index-Division

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Valid groups: {1, 2, 3} PROPOSED ALGORITHMS | Index-Division

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1. Introduction 2. Contributions 3. Background a. Genetic Data b. Divisão Relacional 4. Related Work 5. Proposed Algorithms a. Index-Divison b. Division Data Generator 6. Experiments a. Synthetic Data b. Case Study 7. Conclusion

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PROPOSED ALGORITHMS | Data Generator 1. Cardinality, the number of tuples in the relations of dividend R1 and of divisor R2;

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PROPOSED ALGORITHMS | Data Generator 1. Cardinality, the number of tuples in the relations of dividend R1 and of divisor R2; 2. Number of individuals, the number of groups of tuples representing the individuals to be evaluated in the operation;

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PROPOSED ALGORITHMS | Data Generator 1. Cardinality, the number of tuples in the relations of dividend R1 and of divisor R2; 2. Number of individuals, the number of groups of tuples representing the individuals to be evaluated in the operation; 3. Correlation, the percentage of individuals, from the total, which satisfy all the requirements on R2 thus being part of the result;

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PROPOSED ALGORITHMS | Data Generator 1. Cardinality, the number of tuples in the relations of dividend R1 and of divisor R2; 2. Number of individuals, the number of groups of tuples representing the individuals to be evaluated in the operation; 3. Correlation, the percentage of individuals, from the total, which satisfy all the requirements on R2 thus being part of the result; 4. Variability, the differences in size between individuals, adjusting the number of tuples on each group.

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1. Introduction 2. Contributions 3. Background a. Genetic Data b. Divisão Relacional 4. Related Work 5. Proposed Algorithms a. Index-Divison b. Division Data Generator 6. Experiments a. Synthetic Data b. Case Study 7. Conclusion

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EXPERIMENTS 1. Synthetic data: R1 : [ 100.000, 1.000.000 ] R2: [ 1, 1.000 ] Correlation: 0% to 100% Variability: 0% to 100%

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EXPERIMENTS 1. Synthetic data: R1 : [ 100.000, 1.000.000 ] R2: [ 1, 1.000 ] Correlation: 0% to 100% Variability: 0% to 100% 2. Genetic Data: 4,100 Animals 10,000 SNPs > 40,000,000 tuples!

http://qtl-mas-2012.kassiopeagroup.com/en/index.php

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EXPERIMENTS

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1. Introduction 2. Contributions 3. Background a. Genetic Data b. Divisão Relacional 4. Related Work 5. Proposed Algorithms a. Index-Divison b. Division Data Generator 6. Experiments a. Synthetic Data b. Case Study 7. Conclusion

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CONCLUSION We consider that a possible implementation of the Index Division inside the core of the DBMS could achieve the best performance on relational division queries.

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Thanks for your attention!

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REFERENCES