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BigData Applications from Graph Analytics to Machine Learning by Aggregates in Recursion
- Source :
- Electronic Proceedings in Theoretical Computer Science, Vol 306, Iss Proc. ICLP 2019, Pp 273-279 (2019)
- Publication Year :
- 2019
- Publisher :
- Open Publishing Association, 2019.
-
Abstract
- In the past, the semantic issues raised by the non-monotonic nature of aggregates often prevented their use in the recursive statements of logic programs and deductive databases. However, the recently introduced notion of Pre-mappability (PreM) has shown that, in key applications of interest, aggregates can be used in recursion to optimize the perfect-model semantics of aggregate-stratified programs. Therefore we can preserve the declarative formal semantics of such programs while achieving a highly efficient operational semantics that is conducive to scalable implementations on parallel and distributed platforms. In this paper, we show that with PreM, a wide spectrum of classical algorithms of practical interest, ranging from graph analytics and dynamic programming based optimization problems to data mining and machine learning applications can be concisely expressed in declarative languages by using aggregates in recursion. Our examples are also used to show that PreM can be checked using simple techniques and templatized verification strategies. A wide range of advanced BigData applications can now be expressed declaratively in logic-based languages, including Datalog, Prolog, and even SQL, while enabling their execution with superior performance and scalability.<br />In Proceedings ICLP 2019, arXiv:1909.07646. Paper presented at the 35th International Conference on Logic Programming (ICLP 2019), Las Cruces, New Mexico, USA, 20-25 September 2019, 7 pages (short paper - applications track)
- Subjects :
- FOS: Computer and information sciences
Computer Science - Logic in Computer Science
Computer Science - Machine Learning
SQL
Computer science
Semantics (computer science)
Formal semantics (linguistics)
Machine learning
computer.software_genre
lcsh:QA75.5-76.95
Operational semantics
Machine Learning (cs.LG)
Datalog
Prolog
Computer Science - Databases
computer.programming_language
Recursion
business.industry
lcsh:Mathematics
Databases (cs.DB)
lcsh:QA1-939
Logic in Computer Science (cs.LO)
Scalability
lcsh:Electronic computers. Computer science
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 20752180
- Volume :
- 306
- Database :
- OpenAIRE
- Journal :
- Electronic Proceedings in Theoretical Computer Science
- Accession number :
- edsair.doi.dedup.....af9b190b63549f5e7d9fb93e9c1e05be