Association Rules Mining for Predictive Analytics in IoT Cloud System

Kireev, Vasiliy S.; Guseva, Anna I.; Bochkaryov, Pyotr V.; Kuznetsov, Ilya; Filippov, Stanislav A.

doi:10.1007/978-3-319-99316-4_14

Cited by 14 publications

(12 citation statements)

References 12 publications

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“…The pieces of evidence exposed by the literature 4,5,12,13,16,17 show that it is possible to extract interesting information from a device's change patterns instead of usage patterns. This slight difference reduces the amount of data that must be stored and analyzed, which satisfies an essential requirement in constrained environments.…”

Section: Related Workmentioning

confidence: 99%

“…This slight difference reduces the amount of data that must be stored and analyzed, which satisfies an essential requirement in constrained environments. Also, the clustering data process, presented by the literature, 4,5,10,11,16 certainly increases the computational cost due to the pre-processing but optimizes the association analysis reducing the number of candidates. The approaches presented by McArthur et al 14 and Wang et al 15 expose more compelling evidence by extracting relevant correlations in a small but dense data set.…”

Section: Related Workmentioning

confidence: 99%

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DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices

Alencar

Barreto

Fernandes

et al. 2020

International Journal of Distributed Sensor Networks

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In the context of smart home, it is very important to identify usage patterns of Internet of things (IoT) devices. Finding these patterns and using them for decision-making can provide ease, comfort, practicality, and autonomy when executing daily activities. Performing knowledge extraction in a decentralized approach is a computational challenge considering the tight storage and processing constraints of IoT devices, unlike deep learning, which demands a massive amount of data, memory, and processing capability. This article describes a method for mining implicit correlations among the actions of IoT devices through embedded associative analysis. Based on support, confidence, and lift metrics, our proposed method identifies the most relevant correlations between a pair of actions of different IoT devices and suggests the integration between them through hypertext transfer protocol requests. We have compared our proposed method with a centralized method. Experimental results show that the most relevant rules for both methods are the same in 99.75% of cases. Moreover, our proposed method was able to identify relevant correlations that were not identified by the centralized one. Thus, we show that associative analysis of IoT device state change is efficient to provide an intelligent and highly integrated IoT platform while avoiding the single point of failure problem.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices

Alencar

Barreto

Fernandes

et al. 2020

International Journal of Distributed Sensor Networks

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“…However, the work lacks extensive performance analysis. In [82], Kireev et al proposed an association rule based predictive algorithm to predict the need for repair of different units (e.g., heating, air conditioning) in IoT based smart homes. The proposed model has two steps.…”

Section: B Association Rule Based Bps Mining Techniquesmentioning

confidence: 99%

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

et al. 2020

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The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. INDEX TERMS Association rules, behavioral patterns, data mining, frequent pattern, Internet of Things, knowledge discovery, wireless sensor networks.

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“…A análise associativaé a descoberta de regras que exibem as condições atributo-valor que geralmente ocorrem juntas em um determinado conjunto de dados (dataset) [1], [4]- [6]. Este dataseté formado por transações que são compostas por múltiplos itens independentes e não repetidos.…”

Section: Análise Associativaunclassified

“…A pesquisa de [6] resultou em um preditor, baseado nos padrão de estados dos dispositivos, das probabilidade de uma falha no sistema por meio do algoritmo de FP-Growth.…”

Section: Trabalhos Relacionadosunclassified

Extração Descentralizada de Regras de Associação em Bases de Dados Embarcadas de Dispositivos Internet das Coisas

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Anais Estendidos Do X Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2020)

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Embora extração de conhecimento de bases de dados seja geralmente realizada em arquiteturas centralizadas, sua execução em cenário descentralizado é um desafio computacional importante, especialmente quando realizadas no contexto da Internet das Coisas onde há as rigorosas restrições de armazenamento e processamento nos dispositivos. Para contornar tais restrições, este artigo apresenta um método para minerar correlações implícitas entre as ações disponíveis nos dispositivos IoT através da análise associativa embarcada. Baseada nas variações das métricas support, lift e confidence, o método proposto identifica as correlações mais relevantes entre pares de ações de diferentes dispositivos e sugere-as ao usuário como forma de integração através de requisições HTTP. Os experimentos mostraram que, em média, as regras mais relevantes foram as mesmas em ambas arquiteturas para 99.75% dos casos. Além disso, o método proposto foi capaz de identificar correlações relevantes que não foram identificadas pela arquitetura centralizada, reforçando assim o entendimento positivo de que a análise do padrão de ações dos dispositivos é uma abordagem eficiente para prover um ambiente IoT altamente integrado e inteligente apesar das restrições existentes em cenários descentralizados.

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Association Rules Mining for Predictive Analytics in IoT Cloud System

Cited by 14 publications

References 12 publications

DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices

DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

Extração Descentralizada de Regras de Associação em Bases de Dados Embarcadas de Dispositivos Internet das Coisas

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