Software Tools for Conducting Real-Time Information Processing and Visualization in Industry: An Up-to-Date Review

Sousa, Regina; Miranda, Rui; Moreira, Ailton; Alves, Carlos; Lori, Nicolás F.; Machado, José Manuel

doi:10.3390/app11114800

Cited by 15 publications

(6 citation statements)

References 21 publications

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“…3. Basic Apache Kafka architecture [33] the incorporation of new classes and the refinement of existing ones without the need for retraining [12], [13], [30].…”

Section: Evolving Neuro-fuzzy Systemsmentioning

confidence: 99%

“…Apache Kafka is an open-source distributed streaming platform designed for high-throughput, fault-tolerance, and scalable real-time data stream processing. It is used for building real-time data pipelines and streaming applications providing a distributed publish-subscribe messaging system where producers write data to topics, and consumers read data from the topics [33].…”

Section: Apache Kafka For Stream Processing Applicationsmentioning

confidence: 99%

“…In the context of IoMT, Apache Kafka enhances connected health and real time ingestion of medical data, automated diagnosis and big data analytics [33], [34].…”

Section: Apache Kafka For Stream Processing Applicationsmentioning

confidence: 99%

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Evolving Neuro-Fuzzy Systems for Real-Time Data Processing in Internet of Medical Things

Nyirenda,

Chilipa

2024

Preprint

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Evolving neuro-fuzzy systems (ENFS) have shown great promise in analysis of streaming data, integrating artificial neural networks and fuzzy logic to create self-learning, data-driven models suitable for online (real-time) applications. In healthcare, ENFS have the potential to transform the Internet of Medical Things (IoMT) by enabling real-time data processing, decision support, and timely emergency response. In this study six evolving fuzzy min-max neural network models have been implemented using Python and Kafka, and evaluated on the MIT-BIH Arrhythmia dataset comprising 109,446 samples corresponding to shapes of heartbeats for the normal case and the cases affected by different arrhythmias and myocardial infarction. To achieve online continual learning, the prequential test-then-train method has been used to sequentially classify the data instances from scratch and update the system structure along with the arrival of new data samples. The Enhanced Fuzzy Min-Max Neural Network (EFMMNN) achieved the highest accuracy of 98.11%. Future work will explore deploying these evolving fuzzy systems in real-world healthcare settings using edge-fog computing.

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“…3. Basic Apache Kafka architecture [33] the incorporation of new classes and the refinement of existing ones without the need for retraining [12], [13], [30].…”

Section: Evolving Neuro-fuzzy Systemsmentioning

confidence: 99%

Section: Apache Kafka For Stream Processing Applicationsmentioning

confidence: 99%

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Evolving Neuro-Fuzzy Systems for Real-Time Data Processing in Internet of Medical Things

Nyirenda,

Chilipa

2024

Preprint

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“…Thus, the REST protocol is more suitable in the case of Online Roadshow data exchange. The requirement of the CEP paradigm allows for information from the event to flow through IT architecture, makes the data available for processing in real-time prior to storage [46][47][48]. This provides data transformation or data pre-processing to increase the efficiency of message processing.…”

Section: Bandwidth Consumptionmentioning

confidence: 99%

A New Big Data Processing Framework for the Online Roadshow

Leow

Ong

2023

BDCC

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The Online Roadshow, a new type of web application, is a digital marketing approach that aims to maximize contactless business engagement. It leverages web computing to conduct interactive game sessions via the internet. As a result, massive amounts of personal data are generated during the engagement process between the audience and the Online Roadshow (e.g., gameplay data and clickstream information). The high volume of data collected is valuable for more effective market segmentation in strategic business planning through data-driven processes such as web personalization and trend evaluation. However, the data storage and processing techniques used in conventional data analytic approaches are typically overloaded in such a computing environment. Hence, this paper proposed a new big data processing framework to improve the processing, handling, and storing of these large amounts of data. The proposed framework aims to provide a better dual-mode solution for processing the generated data for the Online Roadshow engagement process in both historical and real-time scenarios. Multiple functional modules, such as the Application Controller, the Message Broker, the Data Processing Module, and the Data Storage Module, were reformulated to provide a more efficient solution that matches the new needs of the Online Roadshow data analytics procedures. Some tests were conducted to compare the performance of the proposed frameworks against existing similar frameworks and verify the performance of the proposed framework in fulfilling the data processing requirements of the Online Roadshow. The experimental results evidenced multiple advantages of the proposed framework for Online Roadshow compared to similar existing big data processing frameworks.

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“…These games are based on a paradigm shift from entertainment-focused gaming to skill development, knowledge acquisition, and societal awareness [46]. At the same time, the integration of artificial intelligence (AI) into UIs [47,48] empowers systems to understand user intent; process information efficiently; and offer personalized, anticipatory digital experiences [49].…”

Section: Introduction and Orientationmentioning

confidence: 99%

A Review of Immersive Technologies, Knowledge Representation, and AI for Human-Centered Digital Experiences

Partarakis,

Zabulis

2024

Electronics

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The evolution of digital technologies has resulted in the emergence of diverse interaction technologies. In this paper, we conducted a review of seven domains under a human-centric approach user interface design, human-centered web-based information systems, semantic knowledge representation, X-reality applications, human motion and 3D digitization, serious games, and AI. In this review, we studied these domains concerning their impact on the way we interact with digital interfaces, process information, and engage in immersive experiences. As such, we highlighted the shifts in design paradigms, user-centered principles, and the rise of web-based information systems. The results of such shifts are materialized in modern immersive technologies, semantic knowledge representation, serious games, and the facilitation of artificial intelligence for interactions. Through this exploration, we aimed to assist our understanding of the challenges that lie ahead. The seamless integration of technologies, ethical considerations, accessibility, education for technological literacy, interoperability, user trust, environmental sustainability, and regulatory frameworks are becoming significant. These challenges present opportunities for the future to enrich human experiences while addressing societal needs. This paper lays the groundwork for thoughtful and innovative approaches to the challenges that will define the future of human–computer interaction and information technologies.

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Software Tools for Conducting Real-Time Information Processing and Visualization in Industry: An Up-to-Date Review

Cited by 15 publications

References 21 publications

Evolving Neuro-Fuzzy Systems for Real-Time Data Processing in Internet of Medical Things

Evolving Neuro-Fuzzy Systems for Real-Time Data Processing in Internet of Medical Things

A New Big Data Processing Framework for the Online Roadshow

A Review of Immersive Technologies, Knowledge Representation, and AI for Human-Centered Digital Experiences

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