Loop invariants

Furia, Carlo A.; Meyer, Bertrand; Velder, Sergey

doi:10.1145/2506375

Cited by 39 publications

(9 citation statements)

References 51 publications

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“…In this subsection, we prove the correctness of the proposed merging algorithms of the nested-loop join by applying the loop invariant technique [42] again. The loop invariant technique consists of three parts: (1) initialization, (2) maintenance, and (3) termination.…”

Section: ) Correctness Of the Nested-loop Join Approachmentioning

confidence: 97%

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MusQ: A Multi-Store Query System for IoT Data Using a Datalog-Like Language

et al. 2020

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The growing number of connected Internet of Things (IoT) devices has increased the necessity for processing IoT data from multiple heterogeneous data stores. IoT data integration is a challenging problem owing to the heterogeneity of data stores in terms of their query language, data models, and schemas. In this paper, we propose a multi-store query system for IoT data called MusQ, where users can formulate join operation queries for heterogeneous data sources. To reconcile the heterogeneity between source schemas of IoT data stores, we extract a global schema from local source schemas semi-automatically by applying schema-matching and schema-mapping steps. In order to minimize the burden on the user to understand the finer details of various query languages, we define a unified query language called the multi-store query language (MQL), which follows a subset of the Datalog grammar. Thus, users can easily retrieve IoT data from multiple heterogeneous sources with MQL queries. As the three MQL query-processing join algorithms are based on a mediator-wrapper approach, MusQ performs efficient data integration over significant volumes of IoT data from multiple stores. We conduct extensive experiments to evaluate the performance of the MusQ system using a synthetic and large real IoT data set for three different types of data stores (RDBMS, NoSQL, and HDFS). The experimental results demonstrate that MusQ is suitable, scalable, and efficient query processing for multiple heterogeneous IoT data stores. Those advantages of MusQ are important in several areas that involve complex IoT systems, such as smart city, healthcare, and energy management. INDEX TERMS Data management and analytics, Internet of Things, multi-store system, query processing, schema integration. FIGURE 2. Grammar of MQL queries.

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Section: ) Correctness Of the Nested-loop Join Approachmentioning

confidence: 97%

“…To prove the correctness of Algorithm 1, we use the loop invariant technique [42]. This approach examines the correctness of the algorithm in three loop stages: (1) initialization, (2) maintenance, and (3) termination.…”

mentioning

confidence: 99%

MusQ: A Multi-Store Query System for IoT Data Using a Datalog-Like Language

et al. 2020

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“…In fact, lessons from the verification practice suggest that providing loop invariants is much harder compared to other specification elements such as method pre/post-conditions. However, many researchers have recently explored techniques for inferring loop invariants automatically or with little human help [47], which might be of interest for the symbolic execution community for a more efficient handling of loops. These approaches normally target inductive predicates, which are closed under the state transition relation (i.e., they make no reference to past behavior).…”

Section: Invariantsmentioning

confidence: 99%

A Survey of Symbolic Execution Techniques

et al. 2018

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Many security and software testing applications require checking whether certain properties of a program hold for any possible usage scenario. For instance, a tool for identifying software vulnerabilities may need to rule out the existence of any backdoor to bypass a program's authentication. One approach would be to test the program using different, possibly random inputs. As the backdoor may only be hit for very specific program workloads, automated exploration of the space of possible inputs is of the essence. Symbolic execution provides an elegant solution to the problem, by systematically exploring many possible execution paths at the same time without necessarily requiring concrete inputs. Rather than taking on fully specified input values, the technique abstractly represents them as symbols, resorting to constraint solvers to construct actual instances that would cause property violations. Symbolic execution has been incubated in dozens of tools developed over the last four decades, leading to major practical breakthroughs in a number of prominent software reliability applications. The goal of this survey is to provide an overview of the main ideas, challenges, and solutions developed in the area, distilling them for a broad audience.If you are considering citing this survey, we would appreciate if you could use the following BibTeX entry: http://goo.gl/Hf5Fvc 0:2 R. Baldoni et al.

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“…-P and Q are assertions, conditions on the program variables used in C. They will be written using standard mathematical notation together with logical operators. We can use functions and predicates to express high-level properties based on a domain theory [39] covering specifics of the application area.…”

Section: Correctnessmentioning

confidence: 99%

Knowledge redundancy approach to reduce size in association rules

Vera

Ortiz

Molina

et al. 2020

IJCAI

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Association Rules Mining is one of the most studied and widely applied fields in Data Mining. However, the discovered models usually result in a very large set of rules; so the analysis capability, from the user point of view, is diminishing. Hence, it is difficult to use the found model in order to assist in the decisionmaking process. The previous handicap is hightened in the presence of redundant rules in the final set. In this work, a new definition of redundancy in association rules is proposed, based on user prior knowledge. A post-processing method is developed to eliminate this kind of redundancy, using association rules known by the user. Our proposal allows finding more compact models of association rules to ease its use in the decision-making process. The developed experiments have shown reduction levels that exceed 90 percent of all generated rules, using prior knowledge always below ten percent. So, our method improves the efficiency of association rules mining and the exploitation of discovered association rules.Povzetek: Opisan je sistem za zmanjševanje števila in dolžine pravil s pomočjo analize redundantnosti za metode asociativnega učenja.

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Loop invariants

Cited by 39 publications

References 51 publications

MusQ: A Multi-Store Query System for IoT Data Using a Datalog-Like Language

MusQ: A Multi-Store Query System for IoT Data Using a Datalog-Like Language

A Survey of Symbolic Execution Techniques

Knowledge redundancy approach to reduce size in association rules

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