evaluation techniques for rule based deduction in dynamic
play

Evaluation Techniques for Rule-Based Deduction in Dynamic Databases - PowerPoint PPT Presentation

Jan 11, 2023 •296 likes •459 views

www.imdea.org Evaluation Techniques for Rule-Based Deduction in Dynamic Databases Joaqun Arias IMDEA Software Institute madrid institute for advanced studies in software development technologies www.imdea.org Joaqun Arias Herrero

  1. www.imdea.org Evaluation Techniques for Rule-Based Deduction in Dynamic Databases Joaquín Arias IMDEA Software Institute madrid institute for advanced studies in software development technologies

  2. www.imdea.org Joaquín Arias Herrero http://www.software.imdea.org/~joaquin.arias 2011-2014 BSc Computer Science (UPM) Thesis “Memory management in logic programming with tabling” 2014-2015 MSc Software and Systems (UPM) Thesis ‘‘Towards a Generic Interface to Integrate CLP and Tabled Execution (Modular TCLP)” 2013-today Research at the IMDEA Software Institute Advisor: Manuel Carro Liñares PhD topic: “Evaluation Techniques for Rule-Based Deduction in Dynamic Database” madrid institute for advanced studies in software development technologies

  3. www.imdea.org Big Data Volume Variety Velocity Veracity madrid institute for advanced studies in software development technologies

  4. www.imdea.org Big Data vs Stream/Dynamic Data Volume Variety Velocity Veracity madrid institute for advanced studies in software development technologies

  5. www.imdea.org Big Data vs Stream/Dynamic Data Dynamic Data are generated by: Real-time sensors System events Volume Social media messages ... Variety Velocity Veracity madrid institute for advanced studies in software development technologies

  6. www.imdea.org Big Data vs Stream/Dynamic Data Dynamic Data are generated by: Real-time sensors System events Volume Social media messages ... Variety Velocity They could be analyzed and used to: Optimize retailers’ stock Optimize supply chain or delivery route Veracity Detect fraudulent transactions Detect cyber attacks Optimize traffic flows ... madrid institute for advanced studies in software development technologies

  7. www.imdea.org Example: Rail ticket retail madrid institute for advanced studies in software development technologies

  8. www.imdea.org Example: Rail ticket retail Rule If there is great activity in the social media regarding a concert, reduce the price-offers and increase the frequencies of trains with the city where the concert will take place. madrid institute for advanced studies in software development technologies

  9. www.imdea.org Example: Rail ticket retail Rule If there is great activity in the social media regarding a concert, reduce the price-offers and increase the frequencies of trains with the city where the concert will take place. Event Great activity is detected in Malaga regarding a concert in Alicante. madrid institute for advanced studies in software development technologies

  10. www.imdea.org Example: Rail ticket retail Rule If there is great activity in the social media regarding a concert, reduce the price-offers and increase the frequencies of trains with the city where the concert will take place. Event Great activity is detected in Malaga regarding a concert in Alicante. Action Modify the offers between Malaga and Alicante. madrid institute for advanced studies in software development technologies

  11. www.imdea.org Example: Rail ticket retail Rule If there is great activity in the social media regarding a concert, reduce the price-offers and increase the frequencies of trains with the city where the concert will take place. Event Great activity is detected in Malaga regarding a concert in Alicante. Action Modify the offers between Malaga and Alicante. Event The concert is postponed one week / bad weather forecast. madrid institute for advanced studies in software development technologies

  12. www.imdea.org Example: Rail ticket retail Rule If there is great activity in the social media regarding a concert, reduce the price-offers and increase the frequencies of trains with the city where the concert will take place. Event Great activity is detected in Malaga regarding a concert in Alicante. Action Modify the offers between Malaga and Alicante. Event The concert is postponed one week / bad weather forecast. Action Update the offers (in real time) and report the event. madrid institute for advanced studies in software development technologies

  13. www.imdea.org Our approach: The application of TCLP , an interface that integrate tabled execution with constraints solvers in a logic programming environment (Prolog) to the evaluation of Dynamic Databases with Rule-Based deduction. Advantages: Expressiveness Will be easier to express complex rules and non-trivial reasoning. Readable Constraint logic programming is closer to the know- ledge representation that e.g. map/reduce functions. Non-monotonicity Revision of previous inferences rules due to modified data (by insertion, modification or deletion). Less computation Automatic reuse of inferences brought by tabling. Only one language Rules and actions can be written using the same for- malism. madrid institute for advanced studies in software development technologies

  14. www.imdea.org Our approach: The application of TCLP , an interface that integrate tabled execution with constraints solvers in a logic programming environment (Prolog) to the evaluation of Dynamic Databases with Rule-Based deduction. Challenges: Design new constraint solvers Design and integrate in TCLP a solver to reduce the required accesses to databases. Re-engineer the tabling engine To avoid using obsolete data / results. Optimize TCLP To minimize the recomputation due to modified data by using previously tabled results. madrid institute for advanced studies in software development technologies

  15. www.imdea.org Thanks Value madrid institute for advanced studies in software development technologies

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Dagstuhl-Seminar on Rule Markup Techniques 7th of February, 2002 XL: A rule-based query and

Dagstuhl-Seminar on Rule Markup Techniques 7th of February, 2002 XL: A rule-based query and

Dagstuhl-Seminar on Rule Markup Techniques 7th of February, 2002 XL: A rule-based query and transformation language for XML and SSD Fran cois Bry and Sebastian Schaffert Ludwig-Maximilians-Universit at M unchen

393 views • 35 slides
Forward vs. backward proof Consider the following Natural Deduction rule: Rule can be read in two

Forward vs. backward proof Consider the following Natural Deduction rule: Rule can be read in two

Forward vs. backward proof Consider the following Natural Deduction rule: Rule can be read in two ways: Backwards: to prove suffjces to prove Forwards: if holds then holds With apply -style proofs we are reasoning

387 views • 10 slides
Data Mining Classification: Alternative Techniques Lecture Notes for Chapter 4 Rule-Based

Data Mining Classification: Alternative Techniques Lecture Notes for Chapter 4 Rule-Based

Data Mining Classification: Alternative Techniques Lecture Notes for Chapter 4 Rule-Based Introduction to Data Mining , 2 nd Edition by Tan, Steinbach, Karpatne, Kumar 1 Rule-Based Classifier Classify records by using a collection of

440 views • 14 slides
Natural Deduction and Rule Induction Dr. Liam OConnor University of Edinburgh LFCS UNSW, Term

Natural Deduction and Rule Induction Dr. Liam OConnor University of Edinburgh LFCS UNSW, Term

Natural Deduction Rule Induction Ambiguity Simultaneous Induction Natural Deduction and Rule Induction Dr. Liam OConnor University of Edinburgh LFCS UNSW, Term 3 2020 1 Natural Deduction Rule Induction Ambiguity Simultaneous Induction

610 views • 59 slides
Developing and managing dynamic business collaborations A rule based approach for modeling and

Developing and managing dynamic business collaborations A rule based approach for modeling and

Developing and managing dynamic business collaborations A rule based approach for modeling and verifying business collaboration systems Bart Orrins Department of Information Management Tilburg University ... And everything after

543 views • 33 slides
Rule-based Processing of Dynamic Linked Data Tutorial at the European Semantic Web Conference

Rule-based Processing of Dynamic Linked Data Tutorial at the European Semantic Web Conference

Rule-based Processing of Dynamic Linked Data Tutorial at the European Semantic Web Conference (ESWC) 2017 Andreas Harth and Tobias Kfer INSTITUTE AIFB, CHAIR FOR WEB SCIENCE KIT Die Forschungsuniversitt in der Helmholtz-Gemeinschaft

1.44k views • 110 slides
Path-Based Query Computation by Automated Deduction Peter Baumgartner AG 2

Path-Based Query Computation by Automated Deduction Peter Baumgartner AG 2

Path-Based Query Computation by Automated Deduction Peter Baumgartner AG 2 www.mpi-sb.mpg.de/~baumgart/ P. Baumgartner Path-Based Query Computation by Automated Deduction Page 1 Background Deduction Systems First-Order Classical and

256 views • 10 slides
Dependability Evaluation Techniques for Dependability Evaluation The dependability evaluation of

Dependability Evaluation Techniques for Dependability Evaluation The dependability evaluation of

Dependability Evaluation Techniques for Dependability Evaluation The dependability evaluation of a system can be carried out either: experimentally (heuristic) : a system prototype is built and empirical statistical data are used to evaluate

470 views • 25 slides
A Decision Tree-based Approach to Dynamic Pointcut Evaluation Robert Dyer and Hridesh Rajan

A Decision Tree-based Approach to Dynamic Pointcut Evaluation Robert Dyer and Hridesh Rajan

A Decision Tree-based Approach to Dynamic Pointcut Evaluation Robert Dyer and Hridesh Rajan Department of Computer Science Iowa State University { rdyer,hridesh } @cs.iastate.edu October 19, 2008 Overview Problem Our Approach Evaluation

629 views • 47 slides
Knowledge Based Systems: Knowledge Based Systems: Logic and Deduction Logic and Deduction

Knowledge Based Systems: Knowledge Based Systems: Logic and Deduction Logic and Deduction

Knowledge Based Systems: Knowledge Based Systems: Logic and Deduction Logic and Deduction Course: CS40002 Course: CS40002 Instructor: Dr. Pallab Dasgupta Pallab Dasgupta Instructor: Dr. Department of Computer Science & Engineering

519 views • 16 slides
Computation and Deduction Lecture 15: Natural Deduction March 4, 1997 1. Natural Deduction 2.

Computation and Deduction Lecture 15: Natural Deduction March 4, 1997 1. Natural Deduction 2.

Computation and Deduction Lecture 15: Natural Deduction March 4, 1997 1. Natural Deduction 2. Local Reduction 3. Congruences 15.1 Predicate Logic i : type. % individuals o : type. % formulas %name i T S %name o A B C and : o -> o

214 views • 9 slides
Natural Deduction Akim Demaille akim@lrde.epita.fr EPITA cole Pour lInformatique et les

Natural Deduction Akim Demaille akim@lrde.epita.fr EPITA cole Pour lInformatique et les

Natural Deduction Akim Demaille akim@lrde.epita.fr EPITA cole Pour lInformatique et les Techniques Avances June 10, 2016 Define logic [fuc, ] A. Demaille Natural Deduction 2 / 49 Natural Deduction 1 Logical Formalisms

1.16k views • 80 slides
Natural Deduction for Classical Propositional Logic Valentin Goranko DTU Informatics September

Natural Deduction for Classical Propositional Logic Valentin Goranko DTU Informatics September

Natural Deduction for Classical Propositional Logic Valentin Goranko DTU Informatics September 2010 1 Natural Deduction ND: System for structured deduction from a set of assumptions, based on rules, specific

142 views • 13 slides
Automatic acquisition of Named Entities for Rule-Based Machine Translation Antonio Toral , Andy

Automatic acquisition of Named Entities for Rule-Based Machine Translation Antonio Toral , Andy

Introduction MINELex Methodology Evaluation Conclusions Automatic acquisition of Named Entities for Rule-Based Machine Translation Antonio Toral , Andy Way DCU 2 nd International Workshop on Free/Open-Source Rule-Based Machine Translation

944 views • 69 slides
Evaluation of Techniques to Detect Wrong Interaction Based Trace Links Paul Hbner and Barbara

Evaluation of Techniques to Detect Wrong Interaction Based Trace Links Paul Hbner and Barbara

Evaluation of Techniques to Detect Wrong Interaction Based Trace Links Paul Hbner and Barbara Paech Institute for Computer Science Heidelberg University Motivation: Why Do We Need Another Trace Link Creation Approach? Our application

398 views • 27 slides
Optimizing a Rule Engine using Adaptive Programming Techniques Changhai Ke Chief Architect, ILOG

Optimizing a Rule Engine using Adaptive Programming Techniques Changhai Ke Chief Architect, ILOG

SEAMS Workshop May 2006, Shanghai Optimizing a Rule Engine using Adaptive Programming Techniques Changhai Ke Chief Architect, ILOG http://www.ilog.com Agenda Rule engines, rule interpreters Implementation & challenges 1.

427 views • 13 slides
Technology Folklore Martin Thompson & Dave Farley http://code.google.com/p/disruptor/

Technology Folklore Martin Thompson & Dave Farley http://code.google.com/p/disruptor/

Technology Folklore Martin Thompson & Dave Farley http://code.google.com/p/disruptor/ http://www.davefarley.net http://mechanical-sympathy.blogspot.com/ Who are we? Disruptor Sample Folklore: Queues, an efficient way to exchange data

228 views • 21 slides
Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal

Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal

Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set Tianze Shi* Liang Huang Lillian Lee* * Cornell Oregon State University University Short Version Transition-based

1.27k views • 66 slides
Logic as a Tool Chapter 2: Deductive Reasoning in Propositional Logic 2.1 Logical Deductive

Logic as a Tool Chapter 2: Deductive Reasoning in Propositional Logic 2.1 Logical Deductive

Logic as a Tool Chapter 2: Deductive Reasoning in Propositional Logic 2.1 Logical Deductive Systems: an overview Valentin Goranko Stockholm University October 2016 Goranko Deductive systems Goranko Deductive systems Logical consequence:

1.16k views • 28 slides
Systems for Integrated Computation and Deduction The C ALCULEMUS IHP Training Network

Systems for Integrated Computation and Deduction The C ALCULEMUS IHP Training Network

Systems for Integrated Computation and Deduction The C ALCULEMUS IHP Training Network HPRN-CT-2000-00102 Christoph Benzmller and Jrg Siekmann Saarland University and German Research Centre for AI (DFKI) c Benzmller, C ALCULEMUS

247 views • 6 slides
Parsing as Deduction Joseph K uhner March 24, 2007 Joseph K uhner Parsing as Deduction

Parsing as Deduction Joseph K uhner March 24, 2007 Joseph K uhner Parsing as Deduction

Outline Parsing Deduction System Parsing of CFG - Example CYK Tree Adjoining Grammars Parsing Deduction for Tree Adjoining Grammars (TAG) Agenda-Chart Deduction Procedure Parsing as Deduction Joseph K uhner March 24, 2007 Joseph K

536 views • 34 slides
Planning and Satisfiability Conclusion References Jussi Rintanen SAT-SMT School, Trento, June

Planning and Satisfiability Conclusion References Jussi Rintanen SAT-SMT School, Trento, June

Planning Introduction SAT QBF, SSAT, SMT Planning and Satisfiability Conclusion References Jussi Rintanen SAT-SMT School, Trento, June 2012 Reduction of poly-time NDTMs to SAT Cook [Coo71] Planning Introduction Early works Significance

1.72k views • 124 slides
Combining Deduction and Algebraic Constraints for Hybrid System Analysis Andr e Platzer

Combining Deduction and Algebraic Constraints for Hybrid System Analysis Andr e Platzer

Combining Deduction and Algebraic Constraints for Hybrid System Analysis Andr e Platzer University of Oldenburg, Department of Computing Science, Germany Verify07 at CADE07 Andr e Platzer (University of Oldenburg) Combining

975 views • 59 slides
Deep Reinforcement Learning Philipp Koehn 21 April 2020 Philipp Koehn Artificial Intelligence:

Deep Reinforcement Learning Philipp Koehn 21 April 2020 Philipp Koehn Artificial Intelligence:

Deep Reinforcement Learning Philipp Koehn 21 April 2020 Philipp Koehn Artificial Intelligence: Deep Reinforcement Learning 21 April 2020 Reinforcement Learning 1 Sequence of actions moves in chess driving controls in car

815 views • 63 slides

More recommend