MOOGLE: A Model Search Engine

Abstract.Pattern detection serves different purposes in managing large collections of process models, ranging from syntax checking to compliance validation. This paper presents a runtime analysis of four graph-theoretical algorithms for (frequent) pattern detection. We apply these algorithms to large collections of process and data models to demonstrate that, despite their theoretical intractability, they are able to return results within (milli-) seconds. We discuss the relative performance of these algorithms and their applicability in practice.

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“…Also, metamodel-based search engines for models from software engineering, such as ER diagrams, are subject to research [18].…”

Section: Related Workmentioning

confidence: 99%

A Runtime Analysis of Graph-Theoretical Algorithms to Detect Patterns in Process Model Collections

Becker¹,

Breuker²,

Delfmann³

et al. 2013

Business Process Management Workshops

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“…Moogle [29] addresses these drawbacks by using metamodel information during the entire searching process. The metamodel is used to create more efficient indexes and to support more complex queries, which may refer to metamodel elements.…”

Section: Introductionmentioning

confidence: 99%

MOOGLE: a metamodel-based model search engine

Lucrédio

Fortes²,

Whittle

2010

Softw Syst Model

Self Cite

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Models are becoming increasingly important in the software development process. As a consequence, the number of models being used is increasing, and so is the need for efficient mechanisms to search them. Various existing search engines could be used for this purpose, but they lack features to properly search models, mainly because they are strongly focused on text-based search. This paper presents Moogle, a model search engine that uses metamodeling information to create richer search indexes and to allow more complex queries to be performed. The paper also presents the results of an evaluation of Moogle, which showed that the metamodel information improves the accuracy of the search. Communicated by Prof. Krzysztof Czarnecki.This work was developed at the

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“…For instance, Moogle [22] is a model search approach that creates an index to retrieve full models from a repository, where our solution aims to improve performances of queries at the model level. IncQuery [4] is also not considered as a direct competitor because it does not provide a prefetch mechanism.…”

Section: Emf-based Toolmentioning

confidence: 99%

Advanced prefetching and caching of models with PrefetchML

2018

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Caching and prefetching techniques have been used for decades in database engines and file systems to improve the performance of I/O intensive application. A prefetching algorithm typically benefits from the system's latencies by loading into main memory elements that will be needed in the future, speeding-up data access. While these solutions can bring a significant improvement in terms of execution time, prefetching rules are often defined at the data-level, making them hard to understand, maintain, and optimize. In addition, low-level prefetching and caching components are difficult to align with scalable model persistence frameworks because they are unaware of potential optimizations relying on the analysis of metamodel-level information, and are less present in NoSQL databases, a common solution to store large models. To overcome this situation we propose PrefetchML, a framework that executes prefetching and caching strategies over models. Our solution embeds a DSL to configure precisely the prefetching rules to follow, and a monitoring component providing insights on how the prefetching execution is working to help designers optimize his performance plans. Our experiments show that PrefetchML is a suitable solution to improve query execution time on top of scalable model persistence frameworks. Tool support is fully available online as an open-source Eclipse plugin.

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MOOGLE: A Model Search Engine

Cited by 39 publications

References 13 publications

A Runtime Analysis of Graph-Theoretical Algorithms to Detect Patterns in Process Model Collections

A Runtime Analysis of Graph-Theoretical Algorithms to Detect Patterns in Process Model Collections

MOOGLE: a metamodel-based model search engine

Advanced prefetching and caching of models with PrefetchML

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