Towards Data Quality Runtime Verification

Bičevskis, Jānis; Bičevska, Zane; Nikiforova, Anastasija; Odītis, Ivo

doi:10.15439/2019f168

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…These properties can be related to the system's control ŕow, data ŕow, or other aspects of its behavior. Runtime veriőcation can also be applied to check constraints concerning stored data such as data range and data type constraints [24], data consistency constraints, data transformation or manipulation constraints [13], data security constraints [12], and real-time data constraints. One approach to checking constraints over stored data is to use a runtime monitor [3,12] to observe the execution of the system and check for violations of the constraints.…”

Section: Hardware-assisted Techniquesmentioning

confidence: 99%

Practical runtime verification of cross-organizational smart contracts

Kangogo,

Péter,

Klenik

et al. 2024

Preprint

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Smart contracts in blockchains often implement critical functionality; consequently, their development time software fault detection, removal, and avoidance have been the target of intensive research. However, the applicability of runtime dependability mechanisms to smart contracts has not been explored systematically, mainly due to the transaction pricing models of public blockchains. In this paper, we propose a novel and practical runtime verification approach for Hyperledger Fabric, the leading cross-organizational blockchain platform, as an error detection facility for fault tolerance mechanisms; building on the proposition that, in contrast to the public setting, cross-organizational blockchains have ample computational headroom for their systematic use. We introduce a smart contract entity framework architecture with reusable domain models and encapsulated runtime verification. We describe Hypernate, our open-source implementation of the approach for Java smart contracts, which uses the Java Modelling Language for requirement specification and integrates the OpenJML verification tool. We demonstrate and evaluate the approach on a Fabric implementation of the TPC-C benchmark.

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Section: Hardware-assisted Techniquesmentioning

confidence: 99%

Practical runtime verification of cross-organizational smart contracts

Kangogo,

Péter,

Klenik

et al. 2024

Preprint

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“…Teixeira et al [34] studied the properties of a runtime verificationspecified API and wrote a minimalist specification language. Bicevskis et al [35] discuss data quality checking during the execution of a business process by using runtime verification. Lee et al [36] developed an effective model-checking method for IoT system operation verification.…”

Section: Runtime Verificationmentioning

confidence: 99%

Research on Drone Fault Detection Based on Failure Mode Databases

Hou

Song

et al. 2023

Drones

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Drones are widely used in a number of key fields and are having a profound impact on all walks of life. Working out how to improve drone safety through fault detection is key to ensuring the smooth execution of tasks. At present, most research focuses on fault detection at the component level as it is not possible to locate faults quickly from the global system state of a UAV. Moreover, most methods are offline detection methods, which cannot achieve real-time monitoring of UAV faults. To remedy this, this paper proposes a fault detection method based on a fault mode database and runtime verification. Firstly, a large body of historical fault information is analyzed to generate a summary of fault modes, including fault modes at the system level. The key safety properties of UAVs during operation are further studied in terms of system-level fault modes. Next, a monitor generation algorithm and code instrumentation framework are designed to monitor whether a certain safety attribute is violated during the operation of a UAV in real time. The experimental results show that the fault detection method proposed in this paper can detect abnormal situations in a timely and accurate manner.

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“…To conduct this survey the authors defined a hypothesis that "there is a direct relation between business outcomes and data quality factors". To know the impact of data quality in the business process, a runtime data quality verification mechanism has been discussed [42]. This mechanism checks data quality in two aspects that the business process completed well with the provided data and whether the completed process affect the stored data negatively.…”

Section: Background and Related Workmentioning

confidence: 99%

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

Jarwar

Chong²

2020

Applied Sciences

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Due to the convergence of advanced technologies such as the Internet of Things, Artificial Intelligence, and Big Data, a healthcare platform accumulates data in a huge quantity from several heterogeneous sources. The adequate usage of this data may increase the impact of and improve the healthcare service quality; however, the quality of the data may be questionable. Assessing the quality of the data for the task in hand may reduce the associated risks, and increase the confidence of the data usability. To overcome the aforementioned challenges, this paper presents the web objects based contextual data quality assessment model with enhanced classification metric parameters. A semantic ontology of virtual objects, composite virtual objects, and services is also proposed for the parameterization of contextual data quality assessment of web objects data. The novelty of this article is the provision of contextual data quality assessment mechanisms at the data acquisition, assessment, and service level for the web objects enabled semantic data applications. To evaluate the proposed data quality assessment mechanism, web objects enabled affective stress and teens’ mood care semantic data applications are designed, and a deep data quality learning model is developed. The findings of the proposed approach reveal that, once a data quality assessment model is trained on web objects enabled healthcare semantic data, it could be used to classify the incoming data quality in various contextual data quality metric parameters. Moreover, the data quality assessment mechanism presented in this paper can be used to other application domains by incorporating data quality analysis requirements ontology.

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Towards Data Quality Runtime Verification

Cited by 5 publications

References 11 publications

Practical runtime verification of cross-organizational smart contracts

Practical runtime verification of cross-organizational smart contracts

Research on Drone Fault Detection Based on Failure Mode Databases

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

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