4.7 Article

Oliot EPCIS: Engineering a web information system complying with EPC Information Services standard towards the Internet of Things

Journal

COMPUTERS IN INDUSTRY
Volume 94, Issue -, Pages 82-97

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.compind.2017.10.004

Keywords

Oliot EPCIS; EPCIS; GS1; Supply chain management; The Internet of Things; IoT

Funding

  1. Institute for Information & communications Technology Promotion (IITP) grant - Korea government (MSIP) [2015-0-00215]
  2. International Research & Development Program of the National Research Foundation of Korea (NRF) - Ministry of Science, ICT & Future Planning of Korea [2016K1A3A7A03952054]
  3. MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program [IITP-2017-2013-0-00877]

Ask authors/readers for more resources

A global standard, Electronic Product Code Information Services (EPCIS), standardizes ways to capture and share crucial moments in a lifecycle of physical objects. EPCIS has the possibility of becoming a viable standard for a web information system of the Internet of Things (IoT) because EPCIS is a de facto standard for Radio Frequency IDentification (RFID) technology and be flexible enough to manage various sensor data in IoT environments. However, an EPCIS system should become efficient and scalable enough to deal with billions of physical objects. In the paper, we share our three-year experience developing an open source, Oliot EPCIS. The experience in several research projects leads to the continuously refined architecture as well as our extra features for efficiency and scalability. According to the experiments, Oliot EPCIS is more efficient and scalable than existing open source solutions. Also, our discussion of the results shows a user the appropriate way to use Oliot EPCIS in one's own application domain. (C) 2017 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Computer Science, Artificial Intelligence

ChronoGraph: Enabling Temporal Graph Traversals for Efficient Information Diffusion Analysis over Time

Jaewook Byun, Sungpil Woo, Daeyoung Kim

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2020)

Article Computer Science, Artificial Intelligence

Object traceability graph: Applying temporal graph traversals for efficient object traceability

Jaewook Byun, Daeyoung Kim

EXPERT SYSTEMS WITH APPLICATIONS (2020)

Article Nutrition & Dietetics

Estimating Dietary Intake from Grocery Shopping Data-A Comparative Validation of Relevant Indicators in Switzerland

Jing Wu, Klaus Fuchs, Jie Lian, Mirella Lindsay Haldimann, Tanja Schneider, Simon Mayer, Jaewook Byun, Roland Gassmann, Christine Brombach, Elgar Fleisch

Summary: In light of the increasing prevalence of diet-related chronic diseases globally, there is an urgent need for scalable and non-invasive dietary monitoring techniques. Digital receipts from loyalty cards can serve as objective and traceable markers for individual food choice behavior, without requiring manual logging of meal items.

NUTRIENTS (2022)

Article Computer Science, Artificial Intelligence

Enabling Time-Centric Computation for Efficient Temporal Graph Traversals From Multiple Sources

Jaewook Byun

Summary: This paper proposes a novel time-centric computation approach for efficient all-pairs temporal graph traversals. The approach simplifies program logic and reduces the burden of coding, and is expected to enhance performance by reusing intermediate results. The effectiveness of the proposed approach is evaluated through experiments and discussions on handling ever-evolving real-world temporal networks.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)

Article Computer Science, Software Engineering

Oliot EPCIS: An open-source EPCIS 2.0 system for supply chain transparency

Jaehyun Ahn, Haifa Gaza, Juhyeok Lee, Hyeongchan Kim, Jaewook Byun

Summary: The international standard GS1 EPCIS has brought data transparency to supply chains and logistics in the era of the Internet of Things by officially supporting sensor data and Semantic Web in its major release of v2.0 in July 2022. Oliot EPCIS is an open-source Web information system that aims to fully comply with the data format and service interface requirements of the standard. This paper presents the challenges faced by a highly scalable EPCIS system and how the proposed system addresses these challenges. We share quantitative and qualitative evaluations compared to existing open sources.

SOFTWAREX (2023)

Proceedings Paper Computer Science, Information Systems

ChronoGraph: Enabling temporal graph traversals for efficient information diffusion analysis over time

Jaewook Byun, Sungpil Woo, Daeyoung Kim

2020 IEEE 36TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2020) (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Secure-EPCIS : Addressing Security Issues in EPCIS for IoT applications

Sungpil Woo, Jaehee Ha, Jaewook Byun, Kiwoong Kwon, Yalew Tolcha, Daeyoun Kang, Hoang Minh Nguyen, Myungchul Kim, Daeyoung Kim

2017 13TH IEEE WORLD CONGRESS ON SERVICES (SERVICES) (2017)

Article Computer Science, Interdisciplinary Applications

Efficient and privacy-enhanced object traceability based on unified and linked EPCIS events

Jaewook Byun, Sungpil Woo, Daeyoung Kim

COMPUTERS IN INDUSTRY (2017)

Proceedings Paper Computer Science, Hardware & Architecture

The Patient-centric Mobile Healthcare System enhancing Sensor Connectivity and Data Interoperability

Jongseok Choi, Minkeun Ha, Janggwan Im, Jaewook Byun, Kiwoong Kwon, Wondeuk Yoon, Dongsoo Kim, Sehyeon Heo, Daeyoung Kim

2015 INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN INTERNET OF THINGS (RIOT) (2015)

Proceedings Paper Computer Science, Theory & Methods

Oliot EPCIS: New EPC Information Service and Challenges towards the Internet of Things

Jaewook Byun, Daeyoung Kim

2015 IEEE INTERNATIONAL CONFERENCE ON RFID (RFID) (2015)

Proceedings Paper Computer Science, Theory & Methods

Lilliput: Ontology-based platform for IoT Social Networks Towards socialized people, objects, and places

Jaewook Byun, Seong Hoon Kim, Daeyoung Kim

2014 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2014) (2014)

Article Computer Science, Interdisciplinary Applications

A human-centric system combining smartwatch and LiDAR data to assess the risk of musculoskeletal disorders and improve ergonomics of Industry 5.0 manufacturing workers

Francesco Pistolesi, Michele Baldassini, Beatrice Lazzerini

Summary: More than one in four workers worldwide suffer from back pain, resulting in the loss of 264 million work days annually. In the U.S., it costs $50 billion in healthcare expenses each year, rising up to $100 billion when accounting for decreased productivity and lost wages. The impending Industry 5.0 revolution emphasizes worker well-being and their rights, such as privacy, autonomy, and human dignity. This paper proposes a privacy-preserving artificial intelligence system that monitors the posture of assembly line workers. The system accurately assesses upper-body and lower-body postures while respecting privacy, enabling the detection of harmful posture habits and reducing the likelihood of musculoskeletal disorders.

COMPUTERS IN INDUSTRY (2024)

Article Computer Science, Interdisciplinary Applications

Smart PSS modelling language for value offer prototyping: A design case study in the field of heating appliance offers

Xavier Boucher, Camilo Murillo Coba, Damien Lamy

Summary: This paper explores the new business strategies of digital servitization and smart PSS delivery, and develops conceptual prototypes of smart PSS value offers for early stages of the design process. It presents the development and experimentation of a modelling language and toolkit, and applies it to the design of a smart PSS in the field of heating appliances.

COMPUTERS IN INDUSTRY (2024)

Article Computer Science, Interdisciplinary Applications

A methodological and theoretical framework for implementing explainable artificial intelligence (XAI) in business applications

Dieudonne Tchuente, Jerry Lonlac, Bernard Kamsu-Foguem

Summary: Artificial Intelligence (AI) is becoming increasingly important in various sectors of society. However, the black box nature of most AI techniques such as Machine Learning (ML) hinders their practical application. This has led to the emergence of Explainable artificial intelligence (XAI), which aims to provide AI-based decision-making processes and outcomes that are easily understood, interpreted, and justified by humans. While there has been a significant amount of research on XAI, there is currently a lack of studies on its practical applications. To address this research gap, this article proposes a comprehensive review of the business applications of XAI and a six-step framework to improve its implementation and adoption by practitioners.

COMPUTERS IN INDUSTRY (2024)

Article Computer Science, Interdisciplinary Applications

Deep reinforcement learning for continuous wood drying production line control

Francois-Alexandre Tremblay, Audrey Durand, Michael Morin, Philippe Marier, Jonathan Gaudreault

Summary: Continuous high-frequency wood drying, integrated with a traditional wood finishing line, improves the value of lumber by correcting moisture content piece by piece. Using reinforcement learning for continuous drying operation policies outperforms current industry methods and remains robust to sudden disturbances.

COMPUTERS IN INDUSTRY (2024)

Article Computer Science, Interdisciplinary Applications

Semantic knowledge-driven A-GASeq: A dynamic graph learning approach for assembly sequence optimization

Luyao Xia, Jianfeng Lu, Yuqian Lu, Wentao Gao, Yuhang Fan, Yuhao Xu, Hao Zhang

Summary: Efficient assembly sequence planning is crucial for enhancing production efficiency, ensuring product quality, and meeting market demands. This study proposes a dynamic graph learning algorithm called assembly-oriented graph attention sequence (A-GASeq), which optimizes the assembly graph structure to guide the search for optimal assembly sequences. The algorithm demonstrates superiority and broad utility in real-world scenarios.

COMPUTERS IN INDUSTRY (2024)

Article Computer Science, Interdisciplinary Applications

Fundamental requirements of a machine learning operations platform for industrial metal additive manufacturing

Mutahar Safdar, Padma Polash Paul, Guy Lamouche, Gentry Wood, Max Zimmermann, Florian Hannesen, Christophe Bescond, Priti Wanjara, Yaoyao Fiona Zhao

Summary: Metal-based additive manufacturing can achieve fully dense metallic components, and the application of machine learning in this field has been growing rapidly. However, there is a lack of framework to manage these machine learning models and guidance on the fundamental requirements for a cross-disciplinary platform to support process-based machine learning models in industrial metal AM.

COMPUTERS IN INDUSTRY (2024)