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Future Internet
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Intelligent Computing and Information Processing
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Intelligent Computing and Information Processing

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (15 May 2026) | Viewed by 1371

Editor

Dr. Andreas Mladenow

E-Mail Website
Guest Editor
Faculty of Business, Economics and Statistics, University of Vienna, 1090 Vienna, Austria
Interests: information and communication technology; e-learning; information technology; cloud computing; information system management

Special Issue Information

Dear Colleagues,

The concepts and technologies covered in “Intelligent Computing and Information Processing” pertain to the application of computer-aided techniques for the analysis, processing, and interpretation of complex data sets. The application of artificial intelligence, knowledge-based systems, automated decision-making processes, and semantic data processing has profound effects on organizational innovation, digital business models, process optimization, and decision support. Considering this, this issue encourages scholars to submit actual data, novel ideas, or conceptual models that integrate organizational, technological, transdisciplinary, and economic viewpoints. Submissions may present novel methods, frameworks, or empirical studies highlighting how intelligent computing enhances strategic insights, decision quality, information workflows, and operational efficiency, while addressing contextual and organizational challenges.

Topics of interest include, but are not limited to, the following:

  • Human–AI Interaction and Cognitive Interfaces;
  • AI-Based Decision Support Systems;
  • Process Mining and Intelligent Workflow Automation;
  • Edge and Cloud-Based Intelligent Computing Architectures;
  • Semantic Information Processing and Knowledge Graphs.

Dr. Andreas Mladenow
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • process automation
  • intelligent systems
  • information processing
  • artificial intelligence
  • e-learning
  • knowledge representation
  • semantic technologies
  • business intelligence
  • human–computer interaction
  • edge and cloud computing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

35 pages, 4344 KB  
Article
From Opaque Streams to Explainable Systems: Semantic MQTT Integration at the Edge
by Niklas Doerner and Maria Maleshkova
Future Internet 2026, 18(7), 334; https://doi.org/10.3390/fi18070334 - 24 Jun 2026
Viewed by 395
Abstract
Industrial systems increasingly rely on MQTT-based message streaming to enable automated, data-driven production processes at the network edge. While semantic models such as the SSN/SOSA ontology enable machine-interpretable descriptions of observations and actuations, an explicit model of message transport is rarely considered. Consequently, [...] Read more.
Industrial systems increasingly rely on MQTT-based message streaming to enable automated, data-driven production processes at the network edge. While semantic models such as the SSN/SOSA ontology enable machine-interpretable descriptions of observations and actuations, an explicit model of message transport is rarely considered. Consequently, MQTT-based communication remains opaque, particularly regarding information processing, hindering the semantic analysis of application-specific topic structures and the behavior of transport protocols. To close this gap, this work introduces the revised MQTT4SSN ontology as a key contribution, extending existing semantic models with protocol-aware representations of MQTT entities, control packets, and transport-level interactions. MQTT4SSN enables end-to-end semantic traceability, from sensor observations and actuator controls to the underlying message transmission within distributed systems. Building on this contribution, the MQTT2RDF integration framework incorporates MQTT4SSN as its core to capture live MQTT traffic and represent both payload meaning and transport-level provenance within an RDF knowledge graph. This work presents a novel approach for representing edge computing and information processing over MQTT, addressing two key challenges. First, the framework supports semantic interpretation of topic hierarchies and provides configurable mappings between MQTT topics, payload structures, and observation or actuation semantics. This approach facilitates the setup of edge computing systems and enables context-aware subscription management and structured data formatting, thereby improving interoperability between heterogeneous deployments. Second, transport-level provenance analytics provide a semantic basis for query-based detection, classification support, and diagnostic analysis of malformed or incomplete MQTT communication. The approach provides explainable, traceable information processing through transport provenance, which is essential for safety-critical industrial environments. The contributions are validated through an industrial use case from a production environment, demonstrating its applicability for system monitoring, troubleshooting, and semantic analytics of MQTT-based infrastructures. Full article
(This article belongs to the Special Issue Intelligent Computing and Information Processing)
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33 pages, 5966 KB  
Article
A Unified Representation Learning Framework for Structure-Aware Predictive Business Process Monitoring via Knowledge Graph-Enhanced Multi-Task Learning
by Ding Pan, Yawen Chen, Yan Li and Yunpeng Ma
Future Internet 2026, 18(5), 263; https://doi.org/10.3390/fi18050263 - 16 May 2026
Viewed by 337
Abstract
Predictive business process monitoring (PBPM) plays an important role in intelligent workflow management by enabling organizations to anticipate future process behavior and support operational decisions. However, many existing approaches represent execution traces primarily as linear prefixes, thereby limiting their capacity to explicitly capture [...] Read more.
Predictive business process monitoring (PBPM) plays an important role in intelligent workflow management by enabling organizations to anticipate future process behavior and support operational decisions. However, many existing approaches represent execution traces primarily as linear prefixes, thereby limiting their capacity to explicitly capture the control-flow semantics of non-sequential processes. To address this limitation, this paper proposes KG-MTPM, a knowledge-graph-enhanced multi-task framework that integrates process-model-level structural knowledge with prefix-level runtime dynamics in a unified predictive architecture. In particular, control-flow relations are organized as a process knowledge graph so that non-linear execution dependencies can be explicitly represented during prediction. Based on the integrated representation, the model jointly predicts the next-activity, next-activity time, and remaining-time of an ongoing case. Experiments on three real-world event log datasets demonstrate that KG-MTPM achieves the best overall performance among the evaluated baselines, with a marked advantage in time-related prediction tasks. Relative to the best-performing baseline, KG-MTPM improves next-activity prediction accuracy from 0.84 to 0.85, while reducing the mean absolute error (MAE) of next-activity time prediction from 0.81 to 0.25 and that of remaining-time prediction from 0.98 to 0.47. Ablation results confirm the contributions of both the structure-aware representation and the multi-task learning scheme. Overall, the findings suggest that explicit modeling of process structure is beneficial for predictive monitoring in business processes with complex execution behavior. Full article
(This article belongs to the Special Issue Intelligent Computing and Information Processing)
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