Knowledge Representation in Smart Rules Engine

Kargin, Anatolii; Petrenko, Tetyana

doi:10.1109/aiact.2019.8847831

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Another related piece of research is [ 13 ], where the authors proposed a rule engine which allowed flexible rule strategies. In terms of smart rules engines, in [ 14 , 15 ], the authors presented a solution based on fuzzy logic for processing information. The research presented in the current paper uses the rule engine from [ 16 ], mainly due to its simplicity.…”

Section: Related Workmentioning

confidence: 99%

Parallel Processing of Sensor Data in a Distributed Rules Engine Environment through Clustering and Data Flow Reconfiguration

Alexandrescu

2023

Sensors

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An emerging reality is the development of smart buildings and cities, which improve residents’ comfort. These environments employ multiple sensor networks, whose data must be acquired and processed in real time by multiple rule engines, which trigger events that enable specific actuators. The problem is how to handle those data in a scalable manner by using multiple processing instances to maximize the system throughput. This paper considers the types of sensors that are used in these scenarios and proposes a model for abstracting the information flow as a weighted dependency graph. Two parallel computing methods are then proposed for obtaining an efficient data flow: a variation of the parallel k-means clustering algorithm and a custom genetic algorithm. Simulation results show that the two proposed flow reconfiguration algorithms reduce the rule processing times and provide an efficient solution for increasing the scalability of the considered environment. Another aspect being discussed is using an open-source cloud solution to manage the system and how to use the two algorithms to increase efficiency. These methods allow for a seamless increase in the number of sensors in the environment by making smart use of the available resources.

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Section: Related Workmentioning

confidence: 99%

Parallel Processing of Sensor Data in a Distributed Rules Engine Environment through Clustering and Data Flow Reconfiguration

Alexandrescu

2023

Sensors

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“…For reacting to the variety of the IoT data, rules-based approaches can be an efficient mechanism that provides autonomous and dynamic service scenarios to handle the events [ 2 , 3 , 4 ]. The rule operation in IoT architecture is a computational model that requires functionalities including deployment and managing rules, real-time event processing, and action assignment [ 5 , 6 , 7 ]. Using the functionalities, the collected sensing data as events to be evaluated based on registered rules and actions are assigned to deliver the command to the actuators for updating the environment.…”

Section: Introductionmentioning

confidence: 99%

Distributed Rule-Enabled Interworking Architecture Based on the Transparent Rule Proxy in Heterogeneous IoT Networks

Jin

Kim

2023

Sensors

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Rule-enabled Internet of Things (IoT) systems operate autonomous and dynamic service scenarios through real-time events and actions based on deployed rules. For handling the increasing events and actions in the IoT networks, the computational ability can be distributed and deployed to the edge of networks. However, operating a consistent rule to provide the same service scenario in heterogeneous IoT networks is difficult because of the difference in the protocols and rule models. In this paper, we propose a transparent rule deployment approach based on the rule translator by integrating the interworking proxy to IoT platforms for operating consistent service scenarios in heterogeneous IoT networks. The rule-enabled IoT architecture is proposed to provide functional blocks in the layers of the client, rule service, IoT service, and device. Additionally, the interworking proxy is used for translating and transferring rules between IoT platforms in different IoT networks. Based on the interactions between the IoT platforms, the same service scenarios are operated in the IoT environment. Moreover, the integrated interworking proxy enables the heterogeneity of IoT frameworks in the IoT platform. Therefore, rules are deployed on IoT platforms transparently, and consistent rules are operated in heterogeneous IoT networks without considering the underlying IoT frameworks.

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“…Jong et al [159] have proposed temporal knowledge representation and reasoning technology. Considering heterogeneous data, Kargin et al [160] have proposed an intelligent rule engine model which realized the cognitive functions of data generalization and abstraction, while Ebrahimipour et al [161] have proposed a knowledge representation method based on ontology. This method can address the problems related to noise data, data arrangement, and ambiguous technical vocabulary in text maintenance records.…”

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confidence: 99%

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

Dou

Sun

Wu³

et al. 2021

IJGI

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In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems.

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Knowledge Representation in Smart Rules Engine

Cited by 5 publications

References 9 publications

Parallel Processing of Sensor Data in a Distributed Rules Engine Environment through Clustering and Data Flow Reconfiguration

Parallel Processing of Sensor Data in a Distributed Rules Engine Environment through Clustering and Data Flow Reconfiguration

Distributed Rule-Enabled Interworking Architecture Based on the Transparent Rule Proxy in Heterogeneous IoT Networks

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

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