Mapping ORM to Datalog: An Overview

Halpin, Terry; Curland, Matthew; Stirewalt, Kurt; Viswanath, Navin; McGill, Matthew J.; Beck, S. M.

doi:10.1007/978-3-642-16961-8_72

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…For example, there is a direct mapping between LogiQL and conceptual modeling languages like ORM [22]. It is also straightforward to support function-and array-based language syntax since this highly normalized schema does not require naming of relation variables (i.e., table columns).…”

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confidence: 99%

Design and Implementation of the LogicBlox System

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Cate

Green

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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The LogicBlox system aims to reduce the complexity of software development for modern applications which enhance and automate decision-making and enable their users to evolve their capabilities via a "self-service" model. Our perspective in this area is informed by over twenty years of experience building dozens of missioncritical enterprise applications that are in use by hundreds of large enterprises across industries such as retail, telecommunications, banking, and government. We designed and built LogicBlox to be the system we wished we had when developing those applications.In this paper, we discuss the design considerations behind the LogicBlox system and give an overview of its implementation, highlighting innovative aspects. These include: LogiQL, a unified and declarative language based on Datalog; the use of purely functional data structures; novel join processing strategies; advanced incremental maintenance and live programming facilities; a novel concurrency control scheme; and built-in support for prescriptive and predictive analytics.

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confidence: 99%

Design and Implementation of the LogicBlox System

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Cate

Green

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

187

175

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“…Nevertheless, in all these scenarios the goal is to use OCL for code-generation purposes as part of a data validation component. Similar generative approaches exist also for other pairs of query and target languages [34]. Once generated, there is no link between the code and the models and therefore it cannot be used to speed up the model queries.…”

Section: Related Workmentioning

confidence: 99%

Mogwaï: A framework to handle complex queries on large models

Daniel

Sunyé

Cabot

2016

2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)

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While Model Driven Engineering is gaining more industrial interest, scalability issues when managing large models have become a major problem in current modeling frameworks. Scalable model persistence has been achieved by using NoSQL backends for model storage, but existing modeling framework APIs have not evolved accordingly, limiting NoSQL query performance benefits. In this paper we present the Mogwaï, a scalable and efficient model query framework based on a direct translation of OCL queries to Gremlin, a query language supported by several NoSQL databases. Generated Gremlin expressions are computed inside the database itself, bypassing limitations of existing framework APIs and improving overall performance, as confirmed by our experimental results showing an improvement of execution time up to a factor of 20 and a reduction of the memory overhead up to a factor of 75 for large models.

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“…Further research and development work is encouraged to extend support for modeling reference schemes in not only the various languages mentioned in this article but also extended versions of datalog (Abiteboul, Hull, & Vianu, 1995) such as LogiQL (Halpin & Rugaber, 2014), for which basic mapping from ORM has already been implemented (Halpin et al, 2010), and to provide automated transformation of models between them.…”

Section: Acknowledgmentmentioning

confidence: 99%

Modeling of Linguistic Reference Schemes

Halpin

2015

International Journal of Information System Modeling and Design

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When using natural language, people typically refer to individual things by using proper names or definite descriptions. Data modeling languages differ considerably in their support for such linguistic reference schemes. Understanding these differences is important for modeling reference schemes within such languages and for transforming models from one language to another. This article provides a comparative review of reference scheme modeling within the Unified Modeling Language (version 2.5), the Barker dialect of Entity Relationship modeling, Object-Role Modeling (version 2), relational database modeling, and the Web Ontology Language (version 2.0). The author identifies which kinds of reference schemes can be captured within these languages as well as those reference schemes that cannot be. The author's analysis covers simple reference schemes, compound reference schemes, disjunctive reference and context-dependent reference schemes.

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Mapping ORM to Datalog: An Overview

Cited by 12 publications

References 16 publications

Design and Implementation of the LogicBlox System

Design and Implementation of the LogicBlox System

Mogwaï: A framework to handle complex queries on large models

Modeling of Linguistic Reference Schemes

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