Scaling-up reasoning and advanced analytics on BigData
| Autor: | Mohan Yang, Tyson Condie, Ariyam Das, Matteo Interlandi, Carlo Zaniolo, Alexander Shkapsky |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Logic in Computer Science Horn clause Computer science Relational database 0102 computer and information sciences 02 engineering and technology computer.software_genre 01 natural sciences Theoretical Computer Science Datalog Prolog Computer Science - Databases Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Logic programming computer.programming_language Computer Science - Programming Languages Programming language business.industry Deductive database Databases (cs.DB) Logic in Computer Science (cs.LO) Computational Theory and Mathematics 010201 computation theory & mathematics Hardware and Architecture Analytics Scalability 020201 artificial intelligence & image processing business computer Software Programming Languages (cs.PL) |
| Zdroj: | Theory and Practice of Logic Programming. 18:806-845 |
| ISSN: | 1475-3081 1471-0684 |
| DOI: | 10.1017/s1471068418000418 |
| Popis: | BigDatalog is an extension of Datalog that achieves performance and scalability on both Apache Spark and multicore systems to the point that its graph analytics outperform those written in GraphX. Looking back, we see how this realizes the ambitious goal pursued by deductive database researchers beginning forty years ago: this is the goal of combining the rigor and power of logic in expressing queries and reasoning with the performance and scalability by which relational databases managed Big Data. This goal led to Datalog which is based on Horn Clauses like Prolog but employs implementation techniques, such as Semi-naive Fixpoint and Magic Sets, that extend the bottom-up computation model of relational systems, and thus obtain the performance and scalability that relational systems had achieved, as far back as the 80s, using data-parallelization on shared-nothing architectures. But this goal proved difficult to achieve because of major issues at (i) the language level and (ii) at the system level. The paper describes how (i) was addressed by simple rules under which the fixpoint semantics extends to programs using count, sum and extrema in recursion, and (ii) was tamed by parallel compilation techniques that achieve scalability on multicore systems and Apache Spark. This paper is under consideration for acceptance in Theory and Practice of Logic Programming (TPLP). Under consideration in Theory and Practice of Logic Programming (TPLP) |
| Databáze: | OpenAIRE |
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