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A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".
Deadline for manuscript submissions: 20 September 2021.
Special Issue Editor
Interests: knowledge engineering; open data; data science
Special Issues and Collections in MDPI journals
Special Issue Information
Dear Colleagues,
Open data are considered the lifeline of artificial intelligence. Wikipedia is a source of important data for text analysis in the field of artificial intelligence, and ImageNet recognizes it as key data for image analysis based on deep learning. High-quality data are essential for implementing artificial intelligence. Governments and enterprises around the world provide large-scale open data to citizens and encourage free use. Recently, open data on COVID-19 have continuously been increasing, and efforts to analyze data and find new alternatives using artificial intelligence technology are also actively underway.
The purpose of this issue is to share research on open data and artificial intelligence technology and to explore new challenges. The main topics include an introduction to various policies, technologies, and standards for activating the use of open data, and applications by using open data and artificial intelligence technologies.
Dr. Haklae Kim
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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.
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-blind 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 1400 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
- Challenges, barriers, and drivers of AI and open data
- Open data standardization and quality
- Open data policy
- The use of AI technologies and open data
- Open datasets for artificial intelligence
- Open-data-driven services and applications (IoT, healthcare, governments, COVID-19)
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Authors: Dongjum Kim, Hyunji Kim, Chaeun Song, Jiwoo Yang, and Haklae Kim
Abstract: The Korean government continues to disclose public data and is pursuing various policies for industrial use. However, although the government continues to release public data, the use of data is still limited. There are various issues in terms of data, such as low level of data quality and standards for data interlinking, and improvement of the process of identifying and opening data demand in a timely manner is also a topic to discuss. On the other hand, diagnosing whether public data that is already open is used is essential for establishing improvement directions. This paper evaluates data utilization by dividing the datasets on the public data portal into local governments and data classification. Data utilization is measured based on the number of inquiries and downloads of public data portals. This study diagnoses the use of public data provided by local governments, and proposes a plan to activate data utilization based on the diagnosis result.
Title: Classification of PGHD time series using time alignment measurement
Author: Taehong Kim
Email: [email protected]
Abstract: In recent years, using publicly available personal health records such as personal medical records and medication records provided by the government, the basis for creating health records for an individual's lifetime is being met. In this study, indirect personal health information, such as personal lifestyle, living environment, and family history, and personal medical records are clustered through common influence factors such as season and temperature, and through this, individual health records are classified. In order to compare and cluster various living environments and health events of different cycles, time series information of individuals is matched and patterns are normalized through Dynamic Time Warping technology. This study is expected to be used as a basis for securing the basis for the study of the constitution of modern precision medicine and oriental medicine.
Title: Toward Real World Evidence Based Clinical care using PGHD Platform : Focused on Clinical Validation and Usability Test
Author: Taehong Kim
Email: [email protected]
Abstract: After the global pandemic, the application of Untact-based 4th Industrial Revolution technology is being promoted in various fields of society, and interest in the effectiveness of the ICT technology-based personal health management system is also emerging in the medical field. However, the clinical application of the ICT medical platform requires not only the advancement of technology, but also the verification of the effectiveness of clinical application, the usefulness of the presented information, and the convenience of stakeholders. The Personal Health Data Platform is a patient-centered personal health information management platform that supports the management of personal health information based on personal smartphones, web, and chatbots, and linking electronic medical records to hospitals. In this study, as a clinical implementation study to directly utilize the PGHD platform for treatment, the results of the three-way usefulness evaluation of 60 doctors, patients, and 360 healthy people are synthesized, and the ICT platform is utilized with the usefulness of patient information. A clinical applicability assessment is performed to determine whether an auxiliary care service is applicable to actual care. The derived results are expected to be used as a data analysis-based research for transition of Korean traditional medicine based on participatory medicine.
Title: Development of a knowledge graph-based user movement path tracking system using COVID-19 Open Data
Authors: Yang Hyejin, and Jangwon Gim
Emails: [email protected], [email protected]
Abstract: In the pandemic of Corona 19, epidemiological surveillance is essential to predict the spread of the Corona 19 virus. In particular, the rapid identification and tracking of the confirmed patient's movements play an important role in preventing the nth infection spread. However, the confirmed patient's path is private information about a specific individual and is limitedly shared with citizens. Therefore, after the epidemiological surveillance, it takes a certain amount of time for ordinary citizens to grasp the confirmed patient's path of movement and check whether there is a point of contact with the person's path. Therefore, in this paper, we propose a system that can track the confirmed patient's path and check the information of the confirmed patient. Through knowledge graph-based modeling of corona 19 confirmed cases, it is possible to connect the confirmed case information and identify individual confirmed cases' movement path. Therefore, the proposed system supports the quick and accurate investigations of epidemiological investigation personnel. It is also possible to support quick response by allowing ordinary citizens to check whether their own travel path and the travel path of the confirmed patients have an intersection. As a result, the proposed system is expected to play a role in protecting private information and support the convenience and rapidity of epidemiological investigations, thereby contributing to corona 19 quarantine activities in the future.
Title: Development of Portable Atmospheric Environment Measurement System based on SOSA/SSN Ontology
Authors: Sukhoon Lee, Jangwon Gim
Emails: [email protected], [email protected]
Abstract: In the Internet of Things environment, most observational data from sensors are stored and managed in a relational database as simple values, but it is difficult to detect the relationship between systems, sensors, and data. This paper builds a data model based on SOSA and SSN ontologies for a portable atmospheric environment measurement system which is our previous research. As a result, it is possible to explicitly express information about data structure and the relationships in system and sensor, properties, and observation regions.
Title: The Knowledge Model for Dataset Navigation
Authors: Yun-Young Hwang, Jin-Hee Yuk, and Sumi Shin
Emails: [email protected], [email protected], [email protected]
Abstract: This paper define and express the network relationship between knowledge that allows cross-reference navigation of knowledge as an ontology. We present a methodology for constructing ontology-based knowledge navigation that allows cross-reference navigation between knowledge and related concepts. In order to verify the validity of the presented methodology, we applied the methodology of this study to implement ontology-based knowledge navigation for actual disaster-related processes in operation.
Title: Development of Knowledge Graph for Disaster Data Management related Flooding Disaster using Open Data
Authors: Jiseong Son, Ji-sun Kang, Hyoung-seop Shim, Chul-Su Lim
Emails: [email protected], [email protected], [email protected], [email protected]
Abstract: Despite the development of various technologies and systems using artificial intelligence to solve the problem of disaster, it is still difficult challenges. Data is the foundation to solve diverse disaster problems using AI, big data analysis, and so on. Therefore, we need to focus on their various data. Disaster data depend on domain by type of disaster. In addition, it include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there is several issue that the source and format of each data are different because various data are collected by different organizations. Also, the vocabularies used for each domain are inconsistent. In this paper, we propose a knowledge graph for resolve heterogeneity among various disaster data and provide interoperability among domains. Among disasters domain, we describe the knowledge graph for flooding disaster using Korean open data sets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist in solving and managing disaster problems.