Design and Implementation of the LogicBlox System

Aref, Mostafa; Cate, Balder ten; Green, Todd; Kimelfeld, Benny; Olteanu, Dan; Pašalić, Enes; Veldhuizen, Todd L.; Washburn, Geoffrey

doi:10.1145/2723372.2742796

Cited by 189 publications

(181 citation statements)

References 31 publications

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“…In particular, SPARQL 1.1 and openCypher have conjunctive regular path queries as their core constructs. They are also expressible in modern Dataloglike query languages [3] and in SQL:1999. As discussed in the Introduction, gMark supports the output of query workloads in all these concrete query language syntaxes.…”

Section: Related Workmentioning

confidence: 99%

“…The database systems (and their supported query languages) that we consider in our study are 3 : For the sake of fairness, we used default configurations for each system i.e., without special purpose optimizations.…”

Section: Design Of Experimentsmentioning

confidence: 99%

“…Native graph databases such as Neo4j [1] and Sparksee [2] propose their own declarative data model and query language, with particular attention to query optimization, and space and performance. In contrast to this trend of specialized systems, general-purpose systems such as LogicBlox [3] rely on declarative solutions that can cover a broader range of use cases. Furthermore, knowledge representation systems such as Virtuoso [4] implement the standard RDF graph data model and SPARQL query language to handle complex navigational and recursive queries on large-scale Semantic Web data.…”

Section: Introductionmentioning

confidence: 99%

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gMark: Schema-Driven Generation of Graphs and Queries

Bagan

Bonifati

Ciucanu

et al. 2017

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

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Abstract-Massive graph data sets are pervasive in contemporary application domains. Hence, graph database systems are becoming increasingly important. In the experimental study of these systems, it is vital that the research community has shared solutions for the generation of database instances and query workloads having predictable and controllable properties. In this paper, we present the design and engineering principles of gMark, a domain-and query language-independent graph instance and query workload generator. A core contribution of gMark is its ability to target and control the diversity of properties of both the generated instances and the generated workloads coupled to these instances. Further novelties include support for regular path queries, a fundamental graph query paradigm, and schema-driven selectivity estimation of queries, a key feature in controlling workload chokepoints. We illustrate the flexibility and practical usability of gMark by showcasing the framework's capabilities in generating high quality graphs and workloads, and its ability to encode user-defined schemas across a variety of application domains.

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Section: Related Workmentioning

confidence: 99%

Section: Design Of Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

gMark: Schema-Driven Generation of Graphs and Queries

Bagan

Bonifati

Ciucanu

et al. 2017

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

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show abstract

“…Because of this problem, using RDFox incrementally did not improve running times in our case study, suggesting a need for native support for n-ary predicates in RDFox. LogicBlox is a programming platform [2] for combining transactions with analytics in enterprise application areas including web-based retail planning and insurance. It uses a typed, Datalog-based custom language LogiQL and has a comprehensive development framework.…”

Section: Tools and Performancementioning

confidence: 99%

Efficient verification of railway infrastructure designs against standard regulations

Luteberget

Johansen

2017

Form Methods Syst Des

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In designing safety-critical infrastructures s.a. railway systems, engineers often have to deal with complex and large-scale designs. Formal methods can play an important role in helping automate various tasks. For railway designs formal methods have mainly been used to verify the safety of so-called interlockings through model checking, which deals with state change and rather complex properties, usually incurring considerable computational burden (e.g., the state-space explosion problem). In contrast, we focus on static infrastructure models, and are interested in checking requirements coming from design guidelines and regulations, as usually given by railway authorities or safety certification bodies. Our goal is to automate the tedious manual work that railway engineers do when ensuring compliance with regulations, through using software that is fast enough to do verification on-the-fly, thus being able to be included in the railway design tools, much like a compiler in an IDE. In consequence, this paper describes the integration into the railway design process of formal methods for automatically extracting railway models from the CAD railway designs and for describing relevant technical regulations and expert knowledge as properties to be checked on the models. We employ a variant of Datalog and use the standardized "railway markup language" railML as basis and exchange format for the formalization. We developed a prototype tool and integrated it in industrial railway CAD software, developed under the name RailCOMPLETE . This on-the-fly verification tool is a help for the engineer while doing the designs, and is not a replacement to other more heavy-weight software like for doing interlocking verification or capacity analysis. Our tool, through the export into railML, can be easily integrated with these other tools. We apply our tool chain in a Norwegian railway project, the upgrade of the Arna railway station.

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“…Systems implement either top-down evaluation, e.g., XSB (by default) (Swift and Warren 2012) and other Prolog systems that include tabling, such as YAP (Santos Costa et al 2012), Ciao Prolog (Hermenegildo et al 2012), BProlog (Zhou 2012) and as a library in SWI Prolog (Weilemaker et al 2017), or bottom-up evaluation, e.g., LDL++ (Arni et al 2003), Coral (Ramakrishnan et al 1994) and LogicBlox (Aref et al 2015), among many others. In this paper we consider the properties and advantages of each strategy and propose that they be integrated in a single system, with each having a distinct procedural interpretation.…”

Section: Introductionmentioning

confidence: 99%

Top-down and Bottom-up Evaluation Procedurally Integrated

Warren

2018

EasyChair Preprints

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This paper describes how the Logic Programming System XSB combines top-down and bottomup computation through the mechanisms of variant tabling and subsumptive tabling with abstraction, respectively.It is well known that top-down evaluation of logical rules in Prolog has a procedural interpretation as recursive procedure invocation (Kowalski 1986). Tabling adds the intuition of short-circuiting redundant computations (Warren 1992). This paper shows how to introduce into tabled logic program evaluation a bottom-up component, whose procedural intuition is the initialization of a data structure, in which a relation is initially computed and filled, on first demand, and then used throughout the remainder of a larger computation for efficient lookup. This allows many Prolog programs to be expressed fully declaratively, programs which formerly required procedural features, such as assert, to be made efficient.

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Design and Implementation of the LogicBlox System

Cited by 189 publications

References 31 publications

gMark: Schema-Driven Generation of Graphs and Queries

gMark: Schema-Driven Generation of Graphs and Queries

Efficient verification of railway infrastructure designs against standard regulations

Top-down and Bottom-up Evaluation Procedurally Integrated

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