Special Issue "MoDAT: Designing the Market of Data"

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 January 2019)

Special Issue Editor

Guest Editor
Prof. Yukio Ohsawa

School of Engineering, The University of Tokyo, Tokyo, Japan
Website | E-Mail
Interests: chance discovery; market of data

Special Issue Information

Dear Colleagues,

The 6th International Workshop on the Market of Data (MoDAT2018 http://www.panda.sys.t.u-tokyo.ac.jp/MoDAT/) will be held on 17 November, 2018, in Singapore. This workshop deals with how to create and design a market where data are reasonably dealt with, i.e., sold, opened free, or shared after negotiations. Relevant areas include, but are not limited to:

Data/Text mining and visualization

  • Visualization of links among data, representing the possibility to combine them and to discuss use scenarios of data
  • Visualization of links and distances among data, representing their similarities
  • Mining data or text to find important events and attributes, in order to compute the links and distances
  • Extracting causalities, for externalizing links among data

Knowledge representation

  • Construction of dictionaries of variables, for reasonably linking data
  • Representing the hierarchical structure of relevance among concepts and variables, used in the thoughts of analysts and users

Methods for creative communication and argumentation

  • Data-based communication for evaluating the value of an event, i.e., chance discovery, and data which may include such an event
  • Visual interface for triggering meaningful thoughts of stakeholders

We initiated the MoDAT workshop in 2013 (http://www.panda.sys.t.u-tokyo.ac.jp/MoDAT/program2013.html#program), in ICDM2013. In MoDAT workshops so far, participants discussed aspects of data links, data/text/web mining, creative communication, etc. With reference of these past workshop outcomes, we should make the Market of Data use of more effective and efficient shared mechanism to function moving forward, i.e., not only within a certain area of interest, but also cross data domains, cross functions and cross applications to yield new opportunities integrating different applications.

This Special Issue intends to contain a selection of carefully revised and extended best papers of the past MoDAT workshops. The conference papers should be cited and noted on the first page of the paper; authors are asked to disclose that it is a conference paper in their cover letter and include a statement on what has been changed compared to the original conference paper. Each submission to this journal issue should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases. However, submissions new to conferences or workshops so far are also encouraged and welcome.

All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Information and collected together on this Special Issue website.

We also call for anyone interested in this topic and MoDAT.

Prof. Yukio Ohsawa
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. 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 1000 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 (5 papers)

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Research

Open AccessArticle Predict Electric Power Demand with Extended Goal Graph and Heterogeneous Mixture Modeling
Information 2019, 10(4), 134; https://doi.org/10.3390/info10040134
Received: 29 January 2019 / Revised: 4 April 2019 / Accepted: 8 April 2019 / Published: 10 April 2019
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Abstract
In this study, methods for predicting energy demand on hourly consumption data are established for realizing an energy management system for buildings. The methods consist of an energy prediction algorithm that automatically separates the datasets to partitions (gate) and creates a linear regression [...] Read more.
In this study, methods for predicting energy demand on hourly consumption data are established for realizing an energy management system for buildings. The methods consist of an energy prediction algorithm that automatically separates the datasets to partitions (gate) and creates a linear regression model (local expert) for each partition on the heterogeneous mixture modeling, and an extended goal graph that extracts candidates of variables both for data partitioning and for linear regression for the energy prediction algorithm. These methods were implemented as tools and applied to create the energy prediction model on two years' hourly consumption data for a building. We validated the methods by comparing accuracies with those of different machine learning algorithms applied to the same datasets. Full article
(This article belongs to the Special Issue MoDAT: Designing the Market of Data)
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Open AccessArticle Tangled String for Multi-Timescale Explanation of Changes in Stock Market
Information 2019, 10(3), 118; https://doi.org/10.3390/info10030118
Received: 26 January 2019 / Revised: 8 March 2019 / Accepted: 13 March 2019 / Published: 22 March 2019
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Abstract
This work addresses the question of explaining changes in the desired timescales of the stock market. Tangled string is a sequence visualization tool wherein a sequence is compared to a string and trends in the sequence are compared to the appearance of tangled [...] Read more.
This work addresses the question of explaining changes in the desired timescales of the stock market. Tangled string is a sequence visualization tool wherein a sequence is compared to a string and trends in the sequence are compared to the appearance of tangled pills and wires bridging the pills in the string. Here, the tangled string is extended and applied to detecting stocks that trigger changes and explaining trend changes in the market. Sequential data for 11 years from the First Section of the Tokyo Stock Exchange regarding top-10 stocks with weekly increase rates are visualized using the tangled string. It was found that the change points obtained by the tangled string coincided well with changes in the average prices of listed stocks, and changes in the price of each stock are visualized on the string. Thus, changes in stock prices, which vary across a mixture of different timescales, could be explained in the time scale corresponding to interest in stock analysis. The tangled string was created using a data-driven innovation platform called Innovators Marketplace on Data Jackets, and is extended to satisfy data users here, so this study verifies the contribution of data market to data-driven innovation. Full article
(This article belongs to the Special Issue MoDAT: Designing the Market of Data)
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Open AccessArticle Estimating Spatiotemporal Information from Behavioral Sensing Data of Wheelchair Users by Machine Learning Technologies
Information 2019, 10(3), 114; https://doi.org/10.3390/info10030114
Received: 30 January 2019 / Revised: 1 March 2019 / Accepted: 4 March 2019 / Published: 15 March 2019
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Abstract
Recent expansion of intelligent gadgets, such as smartphones and smart watches, familiarizes humans with sensing their activities. We have been developing a road accessibility evaluation system inspired by human sensing technologies. This paper introduces our methodology to estimate road accessibility from the three-axis [...] Read more.
Recent expansion of intelligent gadgets, such as smartphones and smart watches, familiarizes humans with sensing their activities. We have been developing a road accessibility evaluation system inspired by human sensing technologies. This paper introduces our methodology to estimate road accessibility from the three-axis acceleration data obtained by a smart phone attached on a wheelchair seat, such as environmental factors, e.g., curbs and gaps, which directly influence wheelchair bodies, and human factors, e.g., wheelchair users’ feelings of tiredness and strain. Our goal is to realize a system that provides the road accessibility visualization services to users by online/offline pattern matching using impersonal models, while gradually learning to improve service accuracy using new data provided by users. As the first step, this paper evaluates features acquired by the DCNN (deep convolutional neural network), which learns the state of the road surface from the data in supervised machine learning techniques. The evaluated results show that the features can capture the difference of the road surface condition in more detail than the label attached by us and are effective as the means for quantitatively expressing the road surface condition. This paper developed and evaluated a prototype system that estimated types of ground surfaces focusing on knowledge extraction and visualization. Full article
(This article belongs to the Special Issue MoDAT: Designing the Market of Data)
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Open AccessArticle Matrix-Based Method for Inferring Elements in Data Attributes Using a Vector Space Model
Information 2019, 10(3), 107; https://doi.org/10.3390/info10030107
Received: 31 January 2019 / Revised: 1 March 2019 / Accepted: 2 March 2019 / Published: 8 March 2019
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Abstract
This article addresses the task of inferring elements in the attributes of data. Extracting data related to our interests is a challenging task. Although data on the web can be accessed through free text queries, it is difficult to obtain results that accurately [...] Read more.
This article addresses the task of inferring elements in the attributes of data. Extracting data related to our interests is a challenging task. Although data on the web can be accessed through free text queries, it is difficult to obtain results that accurately correspond to user intentions because users might not express their objects of interest using exact terms (variables, outlines of data, etc.) found in the data. In other words, users do not always have sufficient knowledge of the data to formulate an effective query. Hence, we propose a method that enables the type, format, and variable elements to be inferred as attributes of data when a natural language summary of the data is provided as a free text query. To evaluate the proposed method, we used the Data Jacket’s datasets whose metadata is written in natural language. The experimental results indicate that our method outperforms those obtained from string matching and word embedding. Applications based on this study can support users who wish to retrieve or acquire new data. Full article
(This article belongs to the Special Issue MoDAT: Designing the Market of Data)
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Open AccessArticle Related Stocks Selection with Data Collaboration Using Text Mining
Information 2019, 10(3), 102; https://doi.org/10.3390/info10030102
Received: 23 January 2019 / Revised: 17 February 2019 / Accepted: 4 March 2019 / Published: 7 March 2019
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Abstract
We propose an extended scheme for selecting related stocks for themed mutual funds. This scheme was designed to support fund managers who are building themed mutual funds. In our preliminary experiments, building a themed mutual fund was found to be quite difficult. Our [...] Read more.
We propose an extended scheme for selecting related stocks for themed mutual funds. This scheme was designed to support fund managers who are building themed mutual funds. In our preliminary experiments, building a themed mutual fund was found to be quite difficult. Our scheme is a type of natural language processing method and based on words extracted according to their similarity to a theme using word2vec and our unique similarity based on co-occurrence in company information. We used data including investor relations and official websites as company information data. We also conducted several other experiments, including hyperparameter tuning, in our scheme. The scheme achieved a 172% higher F1 score and 21% higher accuracy than a standard method. Our research also showed the possibility that official websites are not necessary for our scheme, contrary to our preliminary experiments for assessing data collaboration. Full article
(This article belongs to the Special Issue MoDAT: Designing the Market of Data)
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