IOTEL: A Tool for Generating IoT-enriched Object-Centric Event Logs

• URL: http://arxiv.org/abs/2603.07906v1
• Date: Mon, 09 Mar 2026 02:59:22 GMT
• Title: IOTEL: A Tool for Generating IoT-enriched Object-Centric Event Logs
• Abstract summary: IOTEL is a tool for generating IoT-enriched object-centric event logs.<n>It enables structured IoT data integration compatible with existing process mining tools.<n>It support practitioners and researchers in analysing IoT-enhanced business processes.
• Score: 7.039340314230959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integrating Internet of Things (IoT) data with business process event logs is crucial for analysing IoT-enhanced processes, yet remains challenging due to differences in abstraction levels and the separation of data sources. Simply incorporating raw IoT data increases the size and complexity of the resulting log, often requiring additional processing before process analysis can be performed. While tools for generating IoT-enriched event logs exist, they either rely on specialised schemas or focus on extracting event logs from sensor data, offering limited support for integrating process-relevant IoT data into existing event logs. To address this gap, we present IOTEL, a tool for systematically generating IoT-enriched object-centric event logs (OCEL). By building on the OCEL schema, IOTEL enables structured IoT data integration compatible with existing process mining tools. It support practitioners and researchers in analysing IoT-enhanced business processes, as demonstrated in a real-world scenario. A video demonstrating the tool is available online.

Related papers

• An object-centric core metamodel for IoT-enhanced event logs [1.092202156339801]
  We present a core model synthesizing the most important features of existing data models.<n>A prototypical Python implementation is used to evaluate the model against various use cases.
  arXiv  Detail & Related papers  (2025-06-26T14:19:44Z)
• Agentic Search Engine for Real-Time IoT Data [1.9275428660922078]
  The Internet of Things (IoT) has enabled diverse devices to communicate over the Internet, yet the fragmentation of IoT systems limits seamless data sharing and coordinated management.<n>This paper presents the IoT Agentic Search Engine (IoT-ASE), a real-time search engine tailored for IoT environments.
  arXiv  Detail & Related papers  (2025-03-15T20:46:17Z)
• LLM-based event abstraction and integration for IoT-sourced logs [2.6811507121199325]
  In this paper, we shed light on the potential of leveraging Large Language Models (LLMs) in event abstraction and integration. Our approach aims to create event records from raw sensor readings and merge the logs from multiple IoT sources into a single event log. We demonstrate the capabilities of LLMs in event abstraction considering a case study for IoT application in elderly care and longitudinal health monitoring.
  arXiv  Detail & Related papers  (2024-09-05T12:38:13Z)
• From Internet of Things Data to Business Processes: Challenges and a Framework [2.9799866120078935]
  The IoT and Business Process Management (BPM) communities co-exist in many shared application domains, such as manufacturing and healthcare. This work proposes a framework to perform a set of structured steps to convert low-level IoT sensor data into higher-level process events.
  arXiv  Detail & Related papers  (2024-05-14T12:07:07Z)
• IoTScent: Enhancing Forensic Capabilities in Internet of Things Gateways [45.44831696628473]
  This paper presents IoTScent, an open-source forensic tool that enables IoT gateways and Home Automation platforms to perform IoT traffic capture and analysis. IoTScent is specifically designed to operate over IEEE5.4-based traffic, which is the basis for many IoT-specific protocols such as Zigbee, 6LoWPAN and Thread. This work provides a comprehensive description of the IoTScent tool, including a practical use case that demonstrates the use of the tool to perform device identification from Zigbee traffic.
  arXiv  Detail & Related papers  (2023-10-05T09:10:05Z)
• Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges [55.82853124625841]
  Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
  arXiv  Detail & Related papers  (2023-09-14T05:43:36Z)
• The Internet of Senses: Building on Semantic Communications and Edge Intelligence [67.75406096878321]
  The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human receptors' We elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms may satisfy the requirements of IoS use cases.
  arXiv  Detail & Related papers  (2022-12-21T03:37:38Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT [51.68933585002123]
  We investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks. In this paper, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework. In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
  arXiv  Detail & Related papers  (2021-10-28T08:14:57Z)
• Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks [96.24723959137218]
  We study an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network.
  arXiv  Detail & Related papers  (2020-11-25T12:51:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.