PACT-ART: Enrichment, Data Mining, and Complex Event Processing in the Internet of Cultural Things

Mousheimish, Raef; Taher, Yéhia; Zeitouni, Karine; Dubus, Michel

doi:10.1109/sitis.2016.80

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…Nevertheless, both studies are very theoretical, and they fail to propose a particular architecture to combine CEP and PA. Taher and Zeitouni et al [40] describe a framework named autoCEP, which is used to build CEP patterns in a predictive way by using time series; again, they do not present a full architecture which integrates CEP with PA. The same authors have extended their previous work by combining the autoCEP and the Butterfly frameworks to build a system that allows us to make early predictions of security violations during the transport of art works [41]. While predictions are made uniquely during the CEP process in that work, in our proposal predictions take place before this process, so these predictions feed the events stream income in the CEP engine, therefore enriching the decision-making process in real time.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

et al. 2019

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The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study.

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Section: Related Workmentioning

confidence: 99%

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

et al. 2019

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“…We find applications that integrate IoT, CEP and (ED)BPM in several research area and business sectors such as Health-care, logistics, manufacturing, banking, smart cities/homes, cultural heritage, agriculture, etc. ( [45], [46]). Hence the interest to propose an architecture that integrates all these technologies side by side, in order to achieve a proactive event/instance management.…”

Section: Event-driven Business Process Managementmentioning

confidence: 99%

IoDEP: Towards an IoT-Data Analysis and Event Processing Architecture for Business Process Incident Management

Ismaili-Alaoui

Bäına

Benali

2022

IJACSA

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IoT is becoming a hot spot area of technological innovations and economic development promises for many industries and services. This new paradigm shift affects all the enterprise architecture layers from infrastructure to business. Business Process Management (BPM) is a field among others that is affected by this new technology. To assist data and events explosion resulting, among others, from IoT, data analytic processes combined with event processing techniques, examine large data sets to uncover hidden patterns, unknown correlations between collected events, either at a very technical level (incident/anomaly detection, predictive maintenance) or at business level (customer preferences, market trends, revenue opportunities) to provide improved operational efficiency, better customer service and competitive advantages over rival organizations. In order to capitalize the business value of data and events generated by IoT sensors, IoT, Data Analytics and BPM need to meet in the middle. In this paper, we propose an end-to-end IoT-BPM integration architecture (IoDEP: IoT-Data-Event-Process) for a proactive business process incident management. A case study is presented and the obtained results from our experimentations demonstrate the benefit of our approach and allowed us to confirm the efficiency of our assumptions.

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“…Complex Event Processing (CEP) is a technique that is used in the domain of CH information aggregation and correlation to identify potentially dangerous or critical situations that may suggest the need for monitoring, surveying or warning for disaster prevention and risk assessment [53]. A well-investigated technique for fusing audio information is that of exploiting Sematic Web technologies, such as ontologies and semantic rules.…”

Section: Related Workmentioning

confidence: 99%

Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding

Kasnesis

Tatlas

Mitilineos

et al. 2019

Sensors

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Cultural heritage sites, apart from being the tangible link to a country’s history and culture, actively contribute to the national economy, offering a foundation upon which cultural tourism can develop. This importance at the cultural and economic level, advocates for the need for preservation of cultural heritage sites for the future generations. To this end, advanced monitoring systems harnessing the power of sensors are deployed near the sites to collect data which can fuel systems and processes aimed at protection and preservation. In this paper we present the use of acoustic sensors for safeguarding cultural sites located in rural or urban areas, based on a novel data flow framework. We developed and deployed Wireless Acoustic Sensors Networks that record audio signals, which are transferred to a modular cloud platform to be processed using an efficient deep learning algorithm (f1-score: 0.838) to identify audio sources of interest for each site, taking into account the materials the assets are made of. The extracted information is presented exploiting the designed STORM Audio Signal ontology and then fused with spatiotemporal information using semantic rules. The results of this work give valuable insight to the cultural experts and are publicly available using the Linked Open Data format.

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PACT-ART: Enrichment, Data Mining, and Complex Event Processing in the Internet of Cultural Things

Cited by 6 publications

References 17 publications

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

IoDEP: Towards an IoT-Data Analysis and Event Processing Architecture for Business Process Incident Management

Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding

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