研究者業績

Kiyoki Yasushi

  (清木 康)

Profile Information

Affiliation
Professor (Distinguished professor, Keio University), Data Science, Musashino University
Degree
工学(慶應義塾)
工学(Keio University)

Researcher number
10169956
J-GLOBAL ID
200901089282037598
researchmap Member ID
5000069496

External link

Papers

 164
  • Xing Chen, Yasushi Kiyoki
    Frontiers in Artificial Intelligence and Applications, Jan 16, 2024  
    This paper aims to analyze the phenomenon of inapplicability of experience, which means that sometimes we make mistakes when we use our past experience to solve current problems. We propose a knowledge model based on the concept of “dark-matter”, which is a term used to describe the time-related data that is hidden from our observation. We use a two-dimensional matrix to represent both time-related and non-time-related data, and we call it space. We also introduce the concept of parallel spaces, which are composed of several spaces that can explain different situations and outcomes. We use case studies to illustrate how knowledge is generated and expressed using “dark-matter” and parallel spaces. We also reveal the reason for the inapplicability of experience and suggest some solutions. The contribution of this paper is that we provide a new perspective and a new model to understand and process knowledge based on “dark-matter” and parallel spaces.
  • Yasushi Kiyoki, Asako Uraki, Shiori Sasaki, Yukio Chen
    Frontiers in Artificial Intelligence and Applications, Jan 16, 2024  
    “Semantic space creation” and “distance-computing” are basic functions to realize semantic computing for environmental phenomena memorization, retrieval, analysis, integration and visualization. We have introduced “SPA-based (Sensing, Processing and Actuation) Multi-dimensional Semantic Computing Method” for realizing a global environmental system, “5-Dimensional World Map System”. This method is important to design new environmental systems with Cyber-Physical Space-integration to detect environmental phenomena occurring in a physical-space (real space). This method maps those phenomena to a multi-dimensional semantic-space, performs semantic computing, and actuates the semantic-computing results to the physical space with visualizations for expressing environmental phenomena, causalities and influences. As an actual system of this method, currently, the 5D World Map System is globally utilized as a Global Environmental Semantic Computing System, in SDG14, United-Nations-ESCAP: (https://sdghelpdesk.unescap.org/toolboxes). This paper presents a semantic computing method, focusing on “Time-series-Analytical Semantic-Space Creation and Semantic Distance Computing on 5D World Map System” for realizing global environmental analysis in time-series. This paper also presents the time-series analysis of actual environmental changes on 5D World Map System. The first analysis is on the depth of earthquakes Earthquake with time-series semantic computing on 5D World Map System, which occurred around the world during the period from Aug. 23rd to Aug. 28th, 2014, and Jan 7th to Jan. 13th, 2023. The second is the experimental analysis of the time-series difference extraction on glacier melting phenomena in Mont Blanc, Alps, during the period from 2013 to 2022, and Puncak Jaya (Jayawijaya Mountains), Papua, during the period from 1991 to 2020 as important environmental changes.
  • Yasuhiro Hayashi, Yasushi Kiyoki, Yoshinori Harada, Kazuko Makino, Seigo Kaneoya
    Frontiers in Artificial Intelligence and Applications, Jan 16, 2024  
    This paper proposes a spatio-temporal and categorical correlation computing method for induction and deduction analysis. This method is a data analytics method to reveal spatial, temporal, and categorical relationships between two heterogeneous sets in past events by correlation calculation, thereby finding insights to build new connections between the sets in the future. The most significant feature of this method is that it allows inductive and deductive data analysis by applying context vectors to compute the relationship between the sets whose elements are time, space, and category. Inductive analysis corresponds to data mining, which composes a context vector as a hypothesis to extract meaningful relationships from trends and patterns of past events. Deductive analysis searches past events similar to a context vector’s temporal, spatial, and categorical conditions. Spatio-temporal information about the events and information such as frequency, scale, and category are used as parameters for correlation computing. In this method, a multi-dimensional vector space that consists of time, space, and category dimensions is dynamically created, and the data of each set expressed as vectors is mapped onto the space. The similarity degree of the computing shows the strength of relationships between the two sets. This context vector is also mapped onto the space and is calculated distances between the context vector and other vectors of the sets. This paper shows the details of this method and implementation method and assumed applications in commerce activities.
  • Yasushi Kiyoki, Koji Murakami, Shiori Sasaki, Asako Uraki
    Frontiers in Artificial Intelligence and Applications, Jan 14, 2022  
    Semantic space creation and computing are essentially significant to realize semantic interpretations of situations and symptoms in human-health. We have presented a semantic space creation and computing method for domain-specific research areas. This method realizes semantic space creation with domain-oriented knowledge and databases. This paper presents a semantic space creation and computing method for “Human-Health Database” with the implementation process for “Human-Health-Analytical Semantic Computing”. This paper also presents a new knowledge base creation method for personal health data for preventive care and potential risk inspection with global and geographical mapping and visualization in 5-Dimensional World Map System. This method focuses on the analysis of personal health and potential-risk inspection and realizes a set of semantic computing functions for semantic interpretations of situations and symptoms in human-health. This method is applied to “Human-Health-Analytical Semantic Computing” to realize world-wide evaluation for (1) multi-parameterized personal health data, such as various biomarkers, clinical physical parameters, lifestyle parameters, other clinical/physiological or human health factors, etc., for health monitoring, and (2) time-series multi-parameterized health data in the national/regional level for global analysis of potential cause of disease. This Human-Health-Analytical Semantic Computing method realizes a new multidimensional data analysis and knowledge sharing for a global-level health monitoring and disease analysis. The computational results are able to be visualized in the time-series difference of the values in each place, the difference between the values of multiple places in a focused area, and the time-series differences between the values of multiple places to detect and predict a potential-risk of diseases.
  • Koichiro Kawashima, Yasuhiro Hayashi, Yasushi Kiyoki, Tetsuya Mita
    Frontiers in Artificial Intelligence and Applications, Jan 14, 2022  
    Dynamic routing with combinations of mobility and activity is expected as new methodology for supporting sensitivity to various contexts for traveling. It is important to realize the dynamics by integrating “mobility and activity” in physical and cyber spaces. This paper presents a mobility and activity integration system for making routing plans from an original point to a destination with a scenario as “sensitivity to context” on the route. The “sensitivity to context” expresses reactions to the intentions and situations of a moving user. This system applies semantic computing to find out the appropriate mobility and activity, that dynamically calculates semantic associations between user’s intentions and mobility services. This system makes a moving plan reflecting “sensitivity to context” created by query creation operators for synthesizing and expressing “everyday intention” and “mobility situation.” This system has a distance calculation function for “feature value vectors” expressing the means of mobility and the features of facility spots, and outputs some expected means of moving towards the destination with activities on the route.

Misc.

 34

Books and Other Publications

 5

Presentations

 213

Teaching Experience

 34

Works

 4
  • 2001 - Present Others
    概要: 宇宙開発事業団における高度情報化、知識ベース化のための方式、システムおよびデータベースの構成方法についての検討を行ない、その指針を提示する。
  • 2000 - Present Others
    概要: データベースと感性情報についての最近の研究動向、および、我々の研究について、講演を行った。(〜平成13年まで)
  • 2000 - Present Others
    概要: マルチデータベースシステム、知識管理方式についての最近の研究(〜平成13年まで)
  • 1996 - Present Others
    概要: 日立製作所が開発したミドルウェアシステムOpenTP1に関する研究会の座長として、最近の分散システム、ネットワークシステムの最近の研究動向に関する講演、および、パネル討論の進行を行った。(〜平成12年まで)

Industrial Property Rights

 6