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CDEC: Cross-disciplinary Data Exchange and Collaboration
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CDEC: Cross-disciplinary Data Exchange and Collaboration

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 23640

Special Issue Editors

Dr. Teruaki Hayashi

E-Mail Website
Guest Editor
School of Engineering, The University of Tokyo, Tokyo, Japan
Interests: market of data; design of data; creativity; information retrieval; knowledge structuring
Prof. Dr. Yukio Ohsawa

E-Mail Website
Guest Editor
School of Engineering, The University of Tokyo, Tokyo 113-8654, Japan
Interests: chance discovery; market of data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to recent social movement of big data and artificial intelligence, the importance of data and data mining has been increasing. In the background of these expectations, there are externalizations of interdisciplinary issues. Many papers about data mining have been published, and the processes of analyzing data have been shared widely. However, there are not many studies targeting the process of Cross-disciplinary Data Exchange and Collaboration (CDEC) using knowledge acquired through data mining. Because CDEC includes various activities of different stakeholders, it is difficult to evaluate the patterns or the processes quantitatively. CDEC includes the practical fields which analytically performs the topics using data, the challenging solutions against social issues, and cross-disciplinary data collaboration and its processes. CDEC targets, not only cleanly formatted single data, but also heterogeneous data that affect human behaviors, thoughts, and intentions in different domains. We also focus on the discussion to obtain tacit knowledge of data mining through analysis and synthesis. In addition to these research fields, the cognitive approach for observing the processes of knowledge discovery and data exchange is also included in our focus.

The 1st International Workshop on CDEC will be held in Singapore on 17 November 2018, in conjunction with IEEE-ICDM 2018. We believe that this Special Issue provides a chance of reaching even broader audiences to the authors of CDEC 2018, who are therefore invited to submit extended versions of their papers to the Special Issue "Cross-disciplinary Data Exchange and Collaboration" of the journal Information by MDPI. However, authors interested in extending their workshop papers must be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% copy/paste from the proceedings paper. Each manuscript will be blind reviewed by MDPI academic editors.

Dr. Teruaki Hayashi
Prof. Yukio Ohsawa
Guest Editors

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 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. Information 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 1600 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.

Published Papers (7 papers)

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Editorial

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3 pages, 145 KiB  
Editorial
Editorial for the Special Issue on “CDEC: Cross-Disciplinary Data Exchange and Collaboration”
by Teruaki Hayashi and Yukio Ohsawa
Information 2020, 11(8), 392; https://0-doi-org.brum.beds.ac.uk/10.3390/info11080392 - 10 Aug 2020
Viewed by 1726
Abstract
Due to recent developments in big data and artificial intelligence (AI), the importance of data and data mining is increasing [...] Full article
(This article belongs to the Special Issue CDEC: Cross-disciplinary Data Exchange and Collaboration)

Research

Jump to: Editorial

13 pages, 448 KiB  
Article
Feature Extraction of Laser Machining Data by Using Deep Multi-Task Learning
by Quexuan Zhang, Zexuan Wang, Bin Wang, Yukio Ohsawa and Teruaki Hayashi
Information 2020, 11(8), 378; https://0-doi-org.brum.beds.ac.uk/10.3390/info11080378 - 27 Jul 2020
Cited by 11 | Viewed by 3345
Abstract
Laser machining has been widely used for materials processing, while the inherent complex physical process is rather difficult to be modeled and computed with analytical formulations. Through attending a workshop on discovering the value of laser machining data, we are profoundly motivated by [...] Read more.
Laser machining has been widely used for materials processing, while the inherent complex physical process is rather difficult to be modeled and computed with analytical formulations. Through attending a workshop on discovering the value of laser machining data, we are profoundly motivated by the recent work by Tani et al., who proposed in situ monitoring of laser processing assisted by neural networks. In this paper, we propose an application of deep learning in extracting representative features from laser processing images with a multi-task loss that consists of cross-entropy loss and logarithmic smooth L1 loss. In the experiment, AlexNet with multi-task learning proves to be better than deeper models. This framework of deep feature extraction also has tremendous potential to solve more laser machining problems in the future. Full article
(This article belongs to the Special Issue CDEC: Cross-disciplinary Data Exchange and Collaboration)
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15 pages, 1403 KiB  
Article
Topic Jerk Detector: Detection of Tweet Bursts Related to the Fukushima Daiichi Nuclear Disaster
by Hiroshi Nagaya, Teruaki Hayashi, Hiroyuki A. Torii and Yukio Ohsawa
Information 2020, 11(7), 368; https://0-doi-org.brum.beds.ac.uk/10.3390/info11070368 - 21 Jul 2020
Cited by 2 | Viewed by 2744
Abstract
In recent disaster situations, social media platforms, such as Twitter, played a major role in information sharing and widespread communication. These situations require efficient information sharing; therefore, it is important to understand the trends in popular topics and the underlying dynamics of information [...] Read more.
In recent disaster situations, social media platforms, such as Twitter, played a major role in information sharing and widespread communication. These situations require efficient information sharing; therefore, it is important to understand the trends in popular topics and the underlying dynamics of information flow on social media better. Developing new methods to help us in these situations, and testing their effectiveness so that they can be used in future disasters is an important research problem. In this study, we proposed a new model, “topic jerk detector.” This model is ideal for identifying topic bursts. The main advantage of this method is that it is better fitted to sudden bursts, and accurately detects the timing of the bursts of topics compared to the existing method, topic dynamics. Our model helps capture important topics that have rapidly risen to the top of the agenda in respect of time in the study of specific social issues. It is also useful to track the transition of topics more effectively and to monitor tweets related to specific events, such as disasters. We attempted three experiments that verified its effectiveness. First, we presented a case study applied to the tweet dataset related to the Fukushima disaster to show the outcomes of the proposed method. Next, we performed a comparison experiment with the existing method. We showed that the proposed method is better fitted to sudden burst accurately detects the timing of the bursts of the topic. Finally, we received expert feedback on the validity of the results and the practicality of the methodology. Full article
(This article belongs to the Special Issue CDEC: Cross-disciplinary Data Exchange and Collaboration)
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11 pages, 3446 KiB  
Article
Standardization Procedure for Data Exchange
by Yoshiaki Fukami
Information 2020, 11(6), 339; https://0-doi-org.brum.beds.ac.uk/10.3390/info11060339 - 25 Jun 2020
Cited by 2 | Viewed by 2938
Abstract
Common specification of data promotes data exchange among many and unspecified individuals and organizations. However, standardization itself tends to discourage innovation that can create new uses of data. To overcome this dilemma of innovation and standardization, this paper analyzes and proposes hypotheses regarding [...] Read more.
Common specification of data promotes data exchange among many and unspecified individuals and organizations. However, standardization itself tends to discourage innovation that can create new uses of data. To overcome this dilemma of innovation and standardization, this paper analyzes and proposes hypotheses regarding the process through which the World Wide Web Consortium (W3C) has realized innovations such as web applications by updating the standard. I hypothesize the following changes in standardization process management at the W3C as key factors supporting innovation through standardization among stakeholders with conflicting interests: (1) defining the scope of the specifications to be developed according to functions instead of technical structures; (2) design of a development management policy based on feedback from implementations, referred to as an “implementation-oriented policy”; (3) inclusion of diversified stakeholders in open standardization processes that facilitate consensus formation and the diffusion of developed standards; and (4) adopting a royalty-free to encourage third-party developers to implement proposed specifications and advance update of proposals. This single case analysis leads to the development and diffusion of common technological data specifications, which are the driving factors for innovation utilizing big data generated by exchanging data of various origins. Full article
(This article belongs to the Special Issue CDEC: Cross-disciplinary Data Exchange and Collaboration)
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13 pages, 3280 KiB  
Article
How Will Sense of Values and Preference Change during Art Appreciation?
by Akinori Abe, Kotaro Fukushima and Reina Kawada
Information 2020, 11(6), 328; https://0-doi-org.brum.beds.ac.uk/10.3390/info11060328 - 18 Jun 2020
Cited by 13 | Viewed by 3287
Abstract
We have conducted several experiments where various types of information offering strategies were performed. We obtained interesting phenomena from the results. The participants seemed to be able to gradually understand the artwork by offering information of the artwork. Of course, for an abstract [...] Read more.
We have conducted several experiments where various types of information offering strategies were performed. We obtained interesting phenomena from the results. The participants seemed to be able to gradually understand the artwork by offering information of the artwork. Of course, for an abstract art, the information of the artworks functions better understanding of artworks. Even for a representational painting, the quality and quantity of understanding was gradually changing. Thus, the information of art sometimes influences the art appreciation. In this paper, we will discuss how the value and preference of art will change according to offered information? In addition, we will discuss determining which factor (information) will change the viewers’ value and preference of art in the art appreciation. For that, we conducted two experiments, where information of the artwork was offered randomly (each person may obtain different information for the artswork). Additionally, for all the artworks, the information was offered in the same manner (all persons will obtain the same information for the artworks). The information involved title, painting materials, techniques, production year, name of artist, price, background, and theme of the artworks. Full article
(This article belongs to the Special Issue CDEC: Cross-disciplinary Data Exchange and Collaboration)
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21 pages, 1425 KiB  
Article
Forecasting Net Income Estimate and Stock Price Using Text Mining from Economic Reports
by Masahiro Suzuki, Hiroki Sakaji, Kiyoshi Izumi, Hiroyasu Matsushima and Yasushi Ishikawa
Information 2020, 11(6), 292; https://0-doi-org.brum.beds.ac.uk/10.3390/info11060292 - 30 May 2020
Cited by 9 | Viewed by 4595
Abstract
This paper proposes and analyzes a methodology of forecasting movements of the analysts’ net income estimates and those of stock prices. We achieve this by applying natural language processing and neural networks in the context of analyst reports. In the pre-experiment, we applied [...] Read more.
This paper proposes and analyzes a methodology of forecasting movements of the analysts’ net income estimates and those of stock prices. We achieve this by applying natural language processing and neural networks in the context of analyst reports. In the pre-experiment, we applied our method to extract opinion sentences from the analyst report while classifying the remaining parts as non-opinion sentences. Then, we performed two additional experiments. First, we employed our proposed method for forecasting the movements of analysts’ net income estimates by inputting the opinion and non-opinion sentences into separate neural networks. Besides the reports, we inputted the trend of the net income estimate to the networks. Second, we employed our proposed method for forecasting the movements of stock prices. Consequently, we found differences between security firms, which depend on whether analysts’ net income estimates tend to be forecasted by opinions or facts in the context of analyst reports. Furthermore, the trend of the net income estimate was found to be effective for the forecast as well as an analyst report. However, in experiments of forecasting movements of stock prices, the difference between opinion sentences and non-opinion sentences was not effective. Full article
(This article belongs to the Special Issue CDEC: Cross-disciplinary Data Exchange and Collaboration)
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12 pages, 2313 KiB  
Article
TEEDA: An Interactive Platform for Matching Data Providers and Users in the Data Marketplace
by Teruaki Hayashi and Yukio Ohsawa
Information 2020, 11(4), 218; https://0-doi-org.brum.beds.ac.uk/10.3390/info11040218 - 16 Apr 2020
Cited by 13 | Viewed by 4215
Abstract
Improvements in Web platforms for data exchange and trading are creating more opportunities for users to obtain data from data providers of different domains. However, the current data exchange platforms are limited to unilateral information provision from data providers to users. In contrast, [...] Read more.
Improvements in Web platforms for data exchange and trading are creating more opportunities for users to obtain data from data providers of different domains. However, the current data exchange platforms are limited to unilateral information provision from data providers to users. In contrast, there are insufficient means for data providers to learn what kinds of data users desire and for what purposes. In this paper, we propose and discuss the description items for sharing users’ calls for data as data requests in the data marketplace. We also discuss structural differences in data requests and providable data using variables, as well as possibilities of data matching. In the study, we developed an interactive platform, named “treasuring every encounter of data affairs” (TEEDA), to facilitate matching and interactions between data providers and users. The basic features of TEEDA are described in this paper. From experiments, we found the same distributions of the frequency of variables but different distributions of the number of variables in each piece of data, which are important factors to consider in the discussion of data matching in the data marketplace. Full article
(This article belongs to the Special Issue CDEC: Cross-disciplinary Data Exchange and Collaboration)
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