This paper proposes a “Zero-Plastic Model” that focuses on the complete elimination and systematic replacement of plastic products. The model represents the deviation from the “reduce, reuse, recycle” framework, emphasizing prevention over treatment and introducing innovative technological solutions for plastic alternatives. The paper illustrates the limitations of existing recycling systems. The paper presents three aspects, including (1) the systematic reduction of plastic dependency through frameworks and behavioral interventions, (2) the development and implementation of sustainable replacement materials, and (3) the integration of technologies to facilitate this transition. The proposed “Zero Plastic Model” establishes sustainability frameworks while introducing a novel approach focused on complete plastic elimination rather than recycling. The framework integrates circular economy principles and draws inspiration from pre-plastic era solutions to achieve zero residual plastic in the system. The research contributes to the growing body of knowledge on sustainable practices and offers a practical framework for organizations and communities seeking to eliminate their plastic dependency.

In this study, we propose a method to accomplish two tasks: transforming accumulated data from corporate production activities into knowledge, and recommending necessary information in a timely manner according to corporate activity policies and information users. This method extracts documents containing key phrases from large-scale language resources by leveraging stored text and numeric data. It then generates information expressing intent that encourages the information user’s decision making or some kind of action. Specifically, we propose a method that produces knowledge from past information and makes it reusable. By acquiring new information that serves as a signal for situational changes, the method supports interactive communication and recommends information that aids users in making personalized decisions within corporate activities. Specifically, while showing examples of the application of specialized knowledge domains in a specific customer segmentation within corporate activities, this method is centered on language and other numeric information generated within corporate economic activities that are recorded on databases. To express information that either represents the relationship between customers and companies or prompts decision-making or behavioral changes, domain-specific knowledge is built from various knowledge information processing tasks within economic activities. Then, through information acquisition that detects signals based on situational awareness, this method indicates that each text can be generalized into semantic representations as personalized information for users.

The Paris Agreement of 2015 has prompted countries to accelerate their efforts to become carbon neutrality efforts, which meant reducing CO2 emissions to virtually zero. Limiting global warming to less than 1.5°C by 2050 rely on technologies that remove CO2 from the atmosphere faster than humans release it. This implies that CO2 will be removed at a rate of 1-30 gigaton per year by 2050. Carbon Capture Storage / Sequestration (CCS) and Utilization (CCU) are concepts and technologies that collect emitted CO2 store it permanently underground, or recycle it as energy or chemicals for use in manufacturing and other economic activities. CCS and CCU have been discussed globally, but have not reached local and practical levels. Currently planned large-scale CCS requires significant government investment and new technological developments for capture, transport, and storage / sequestration, therefore implementation is expected to start in the second half of 2030 towards the 2050 goal. The need to start acting now where possible rather than waiting for the distant future, makes it important to implement CCS and CCU on a small scale and build towards future scale-up as an immediate solution. This study proposes a support method and system to help companies that emit large amounts of CO2 such as power plants, cement, petrochemicals, and steel industries, to decide how to treat their CO2 emissions in the context of decarbonization. In this study, a Simple, Measurable, Attainable, Relative, and Time-Bound (SMART) decision support method and Direct Air Capture Location and Cost Simulator (DLCS) system were developed to provide a solution to the Negative Emission 5W1H “What, Who, Which, When, Why, and How” from the perspective of a company that emits CO2. A prototype model with parameter settings was proposed based on knowledge gained from practical experience. The functionality of the SMART method and DLCS system was confirmed by applying sample data from the actual data of the ‘Tokyo Region’ as a Proof of Concept (PoC). In this PoC, characteristics of direct air capture which is a critical technology for negative emissions, were verified. The core of the SMART and DLCS model entails combinatorial optimization, distance calculation, cost estimation, and market projection including constraint solution.

This paper proposes a spatio-temporal and categorical data mining method for commercial transaction data. This method searches for elements from a set using three search methods: specific, concept, and pattern, which represent the level of abstraction of search conditions, and spatio-temporal information and category information, which correspond to domain knowledge. This method aims to obtain knowledge of combinations of events with substantial physical, temporal, and categorical correlations between two data sets based on the amount of correlation and frequent occurrence patterns. In other words, it is to realize the mechanism in the database by which humans try to obtain knowledge through memory recall about the location, trends, timing, and frequency of events. This method uses aggregation functions to extract spatio-temporal and categorical features of elements in two different sets contained in a single set. Furthermore, this method performs Numerization, which converts the features from linguistic to numerical and Linguization, which converts the features from numerical and linguistic formats. The set elements are represented as vector data consisting of spatio-temporal and categorical features by numerical and linguistic formats. Numerical and linguistic formats are used for specific and conceptual searches, while linguistic formats are used for pattern searches. This method uses a dynamic vector creation function at search to dynamically map only those set elements that satisfy the search conditions into a semantic orthogonal space with time, space, and category dimensions. This method calculates correlation computation by calculating the distance between elements for each feature, normalizing and integrating their scores. Additionally, this method extracts as correlation rules combinations of events with substantial physical, temporal, and category correlations by calculating support and confidence levels based on the frequency of occurrence of the elements. Namely, Apriori algorithm is contained in the calculation for correlation rules. In this paper, we present the details of the proposed method and its implementation as an application in business commerce.

One of the essential computations in ocean environmental study is semantic computing for interpreting and analyzing the changes of various situations (coral area, ocean water, places of ocean livings, sea level, etc.). It is important to realize global ocean-environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way from a viewpoint of global environments. It is also significant to memorize those situations and compute ocean-environment change in various aspects and contexts, in order to discover what are happening in the nature of ocean. We have various aspects and contexts in ocean-environmental changes in our planet, and it is essential to realize a new analyzer for computing differences in those situations for finding actual aspects and contexts existing in oceans. We have proposed a new method for Differential Computing in our Multi-dimensional World map in [3,4]. We utilize a multi-dimensional semantic computing model, and a multi-dimensional space with an adaptive-axes selection mechanism to implement semantic computing [1,2]. Computing ocean-environmental changes in multi-aspects and contexts using semantic computing, important factors that change natural ocean-environment are highlighted. Semantic computing is an important and promising approach to semantic analysis for various environmental phenomena and changes in real world. This paper presents a new concept of “Coral-Health-Level Analysis Semantic-Space for Ocean-environment” for realizing global Ocean-environmental analysis. This space and computing method are based on environmental-database creation with coral-health-level-analysis sensors for analyzing and interpreting environmental phenomena and changes occurring in the oceans. This paper focuses on coral-health-level in South-East Asian Ocean area, as an experimental study for creating “Coral-Health-Level Analysis Semantic-Space for Ocean-environment”. We have created a semantic-space for coral-health-level analysis in the South-East Asian Ocean area with 24-dimensional axes (coral-health-level parameters). As the first step, this space is applied to South-East Asian Ocean area, and it is expandable to multiple spots in Ocean-areas to analyze and compare their coral-health-level situations in the global scope for Ocean-environmental analysis. In this paper, we also apply 5-Dimensional World Map System for semantic computing to analysis in South-East Asian Ocean area, as an international collaborative platform for Ocean-environment analysis with spatio-temporal and semantic dimensions.

In this paper, we proposed an implementation method of a system according to ‘tri-knowledge base with personal context vectors model’ that proposed in previous study. The system aims not only a single set of contexts and knowledge base, but also create snapshot and stored it as the memories of the changes in timeseries. Organisational contexts vectors were added to the system. As well as the personal context vectors to absorb personal preferences, the organisational context vectors can absorb organisational preference without changing the knowledgebase. Experiments were conducted to verify the functionality of memorising the changes in timeseries and the effects of the organisational context vector.

Achieving the 2030 and 2050 Paris Agreement targets to improve the global environment and address climate change is critical despite the costs and time requirements to implement possible measures. However, current measures are primarily undertaken from a global macro perspective, and lack established means to examine them from micro, realistic, and practical perspectives. CO2 direct air capture (DAC) is an innovative negative CO2 emission technology in its early commercial stages that can help control and mitigate climate change in the long term. Despite technological advances in the past decade, there are still misconceptions about the current and long-term costs of DAC, and energy, water, and area demands. This paper presents a knowledge-based indication method with a prototype system for a DAC location and cost simulator to support early-stage decisions from a micro-local perspective and promote the use of the applicable and scalable DAC technology. Additionally, this study presents a method for determining optimal locations, estimating project costs, and prioritizing projects for early-stage feasibility assessments, policy, and business decisions on DAC investments to accelerate its deployment. The main feature of this approach is to provide a data model and unified cost index ($/CO2 Ton) to calculate the optimal location and cost projection of DAC implementation based on location characteristics and a quantified industry knowledge base considering various cost-carbon intensity constraints—such as reservoirs, infrastructure, low-carbon electricity, heat, transportation, and atmospheric conditions—that affect the suitability of specific locations.

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.

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.

“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.

In this paper, we proposed a new workplace data model and its calculation method. The method was designed to calculate appropriate workplace according to the intents (activities) and situations of a worker. The data model was designed as a semantic space with three knowledge bases: ‘Activity-affecting’, ‘Place-determining’, and ‘Activity and Place’. Experiments were conducted to show the different results depending on activities and the contexts of the workplace and presented the feasibility of the proposed data model and calculation method.

This paper presents an important application of 5D World Map System with a Risk-Resilience calculation and visualization method using time-series multilayered data for disasters and environmental change analysis to make appropriate and urgent solutions to global and local environmental phenomena in terms of short and long-term changes. This method enables the calculation of the current risk and resilience of a target region or city for disasters and rapid environmental changes, based on the analysis of past time-series changes of natural and socioeconomic factors’ distribution. This method calculates the total risk and resilience to disaster as a total aggregate value that reflects the amount of change in each variable in the past, by transforming multidimensional and heterogeneous variables into a form that allows comparative and arithmetic operations through geographical normalization and projection. As an implementation and experiments, we apply our method to assessing the role of forests in urban disaster resilience as an example, by analyzing time-series changes in vegetation and forest distribution and their relationships in urban areas. Specifically, using GIS, satellite data, demographic data, urban infrastructure data, and disaster data, we analyze the relationship among urban disaster occurrence and 1) population density, 2) urban infrastructure development, and 3) forest distribution and calculate “urban-forest-disaster risk/resilience”.

The important process of time series analysis for public health data is to determine target data as a semantic discrete value, according to a context from continuous phenomenon around our circumstance. Typically, each field of experts has their own fields’ specific and practical knowledge to specify an appropriate target part of data which contains the key features of their intended context in each analysis. Those are often implicit, thus not defined as systematically and quantitatively. In this paper, we present a context-based time series analysis and prediction method for public health data. The most essential point of our approach is to express a basis of time series context as the combination of the following 5 elements (1: granularity setting on time axis, 2: feature extraction method, 3: time-window setting, 4: differential computing function, and 5: pivot setting) to determine target data as semantic discrete values, according to the time series context of analysis for public health data. One of the main features of our method is to create different results by switching time series contexts. The method realizes 1) introducing a new normalization (context expression) method to fix a target reference data for time series analysis and prediction according to a context, and 2) presenting a process to generate semantic discrete values reflecting the 5 elements. And the significant features of the proposing method are 1) our context definition realizes the closed world of the semantic differential computing on time axis from the viewpoint of database system, and 2) the 5 elements enable to explicit and quantify experts’ semantic viewpoint of specifying a certain reference data according to a context for each analysis and prediction. As our experiment, we have realized analysis and prediction by applying actual public health data. The results of the experiments show the prediction feasibility of our method in the field of public health data, effectiveness to generate results for discussion regarding switching context, and applicability to express time series context of an expert knowledge for analysis and prediction as combination of the 5 elements to make the knowledge explicit and quantitative expression.

This paper describes about project “Data Sensorium” launched at the Asia AI Institute of Musashino University. Data Sensoriumis a conceptual framework of systems providing physical experience of content stored in database. Spatial immersive display is a key technology of Data Sensorium. This paper introduces prototype implementation of the concept and its application to environmental and architectural dataset.

It is significant to detect, estimate and predict “Human-health situations” and “a spread of transmitted disease” with past and current information of health-related phenomena. Temporal-transition and differential computing realizes semantic interpretations for situation changes in two phenomena with “temporal-length” in “specific situation”. The “temporal-length” in “specific situation” is used to compare two phenomena in multiple contexts in semantics. We present a new Temporal-transition Differential Computing Model for detecting, estimating and predicting “Human-health situations” and “a spread of transmitted disease.” This model defines “temporal-transition data structure” for expressing past and current information of health-related phenomena with temporal-axis, and two processes for Human-Health Semantic Space Creation and Semantic Computing with dimensional control mechanism.

The “SPA-based 5D World Map System” realizes Cyber-Physical-Space integration to detect changes of environmental phenomena with real data resources in a physical-space (real space), map them to the cyber-space to make knowledge bases and analytical computing, and actuate the computed results to the real space with visualization for expressing the causalities and influences. This paper presents an important application of 5D World Map System, adding “AI-Sensing” functions for “Global Environment-Analysis” to make appropriate and urgent solutions to global and local environmental phenomena in terms of short and long-term changes. Focusing on the ocean plastic garbage issues, this paper describes the methodology of AI-Sensing, the preliminary models and experiments on the accuracy of AI-Sensing and the substantiative experiments on the feasibility and effectiveness of AI-Sensing with real local data. In addition, the example outputs of the integration of AI-Sensing algorithm and SPA-based 5D World Map System and the future direction of a collaborative project for ocean plastic-garbage reduction are introduced.

It is highlighted for machine learning models implementing functions based on data training without program coding. Artificial neural network is one of the efficient machine learning models. Different from the other machine learning models like artificial neural network, we have presented semantic computing models which represent “meaning” of machine learning results. In our model, semantic spaces are created based on training data sets. Data calculations are performed on the space. Data are mapped to semantic spaces and presented as points in semantic spaces. The mapped positions of data represent the “meaning” of data. In this paper, we first present our new discovery in the formation of semantic spaces. We use the word “matter” to represent features of semantic spaces which are related to the non-temporal data. As the same time, we use the word “dark-matter” to represent the features of semantic spaces which are temporally changed. We use the word “energy” to represent matrixes which are used in the semantic computations to generate output data. We reveal that the “dark-matter” is the spatiotemporal matrix and present a mechanism of “memory” for implementing the semantic computation. The most important contribution of this paper is that we developed a new mechanism for implementing machine learning with “knowledge” in the “memory.” In the paper, we use case studies to illustrate the concepts and the mechanism. At the beginning, we present an example on creating a semantic space from a “chaotic state” to an “ordered state.” After that, we use examples to illustrate the mechanism of the “memory” and the semantic computation. The space expansion and the space division are also illustrated by examples.

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.

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.

Global warming and climate change affect not only all living things but also many non-living things. Furthermore, those phenomena caused extreme disasters that become impossible to ignore. Coral bleaching is a phenomenon to show ocean warming due to climate change. This paper presents the analysis and visualization of the coral health levels database by using 5D World Map System. Coral health levels are analyzed using a coral-knowledge image that includes coral with a coral health chart. We use image processing and color semantic distance to interpret coral health levels. We have implemented an actual space integration system to access environmental information resources with coral health levels and image analysis that the results have been shown on the 5D World Map System. As for the experiment study, the study areas of coral health levels analysis are located in the ocean close to Thailand’s islands as Ko Ha (Five Island), Ko Bon, Ko Hin Ngam, Ko Tarutao, Ko Thalu, and Ko Samaesarn.

The authors have developed the Geo CPS platform, which incorporates the advantages of cyber-physical systems, geographic information systems, and tangible user interfaces, and provides a platform to connect the three components for interactive sensing, processing, and actuation in smart city development. It can be also applied for education in environmental and disaster management, as a tool for technical training, or as a testbed for business solutions.

The most important mechanism of the computer is that various functions are implemented based on programs stored in it. Programs are developed by program languages implementing functions of models. One of the efficient methods to construct a model is to construct it by semantic computation models. Using semantic computation models, we can construct a model in a semantic space. In this paper, we present a mechanism to execute models presented by the semantic spaces. We have presented a mechanism to implement combinational and sequential logic computations based on the semantic space model. The combinational and sequential logic computations are the basic functions in computer systems. However, we still need a control mechanism like that in computers. In this paper, we present a control mechanism based on the semantic space model and some of execution examples. The most important contribution of this paper is that we first present a concept for control and program based on the semantic space model. In order to demonstrate the efficiency of the proposed mechanism, we performed a demonstration experiment. In the experiment, an agent is constructed for unmanned ground vehicle control with the control mechanism. A video camera is used to determine the position of the vehicle and obstacles on the road. The control signals, including “turn left,” “turn right,” “go ahead” and “stop” outputted from the agent are used to demonstrate the efficiency of the mechanism.

Semantic computing is essentially significant for realizing the semantic interpretation of natural and social phenomena and analyzes the changes of various environmental situations. The 5D World Map (5DWM) System [4,6,8] has introduced the concept of “SPA (Sensing, Processing and Analytical Actuation Functions)” for global environmental system integrations [1–4], as a global environmental knowledge sharing, analysis and integration system. Environmental knowledge base creation with 5D World Map is realized for sharing, analyzing and visualizing various information resources to the map which can facilitate global phenomena-observations and knowledge discoveries with multi-dimensional axis control mechanisms. The 5DWM is globally utilized as a Global Environmental Semantic Computing System, in SDGs 9, 11, 14, United-Nations-ESCAP: (https://sdghelpdesk.unescap.org/toolboxes) for observing and analyzing disaster, natural phenomena, ocean-water situations with local and global multimedia data resources. This paper proposes a new semantic computing method as an important approach to semantic analysis for various environmental phenomena and changes in a real world. This method realizes “Self-Contained-Knowledge-Base-Image” & “Contextual-Semantic-Interpretation” as a new concept of “Coral-Health-level Analysis in Semantic-Space for Ocean-environment” for global ocean-environmental analysis [8,9,12,18]. This computing method is applied to automatic database creation with coral-health-level analysis sensors for interpreting environmental phenomena and changes occurring in the oceans in the world. We have focused on an experimental study for creating “Coral-Health-level Analysis Semantic-Space for Ocean-environment” [8,9,12,18]. This method realizes new semantic interpretation for coral health-level with “coral-images and coral-health-level knowledge-chart”.

While individuals benefit from the goods and services provided by companies that enrich their lives and that have adapted to a dynamic environment that is always changing, these companies pay a high communication cost to access opportunities to provide these goods and services and to seek a better understanding of individual customers’ changing needs. Although vast amounts of information can be obtained, databases and machine learning are playing an increasingly important role in extracting meaning from this information, turning it into meaningful information assets that consider circumstances and contexts, and individualizing the economy of information. I propose an implementation method for providing information to enrich the profiles of individual customers by consolidating different data, calculating the individual customers’ needs through the relationships between customers and products, evaluating the change in relationships between individual customers and products over time, and providing goods and services to suit different intervals of change to factors such as lifestyle and living environment. As there are different factors involved in estimating the incidence of needs, and different frequencies and rates at which they occur, based on the special characteristics of products, different data are required to estimate such needs. By profiling individuals over the long term, it is possible to build an information provision environment that is conducive to companies’ customer acquisition.

Cross-cultural religious tourism is computational to promote cross-cultural communication and understanding according to impression distance. Our motivation to implement semantic search with an emotion-oriented context into the proposed system is to realize global tourism recommendations expressed in different cultures. The objectives of this paper are (1) to find the religious places by using the tourist’s emotional distance, (2) to find similar religious places not only in the same culture but also in the different cultures with the tourist’s emotional distance calculations. Experimental results demonstrate the feasibility and applicability of this method.

Mental health, an essential factor for maintaining a high quality of life, is determined by one’s nutritional, physical, and psychological situations. Since mental health is influenced by multiple factors, a multidisciplinary approach is effective. Due to the complexity of this mechanism, most non-specialists have little knowledge and access to the related information. There are multiple factors that influence one’s mental health, such as nutrition, physical activities, daily habits, and personal cognitive characteristics. Because of this complexity, it can be hard for non-specialists to find and implement appropriate methods for improving their mental health. This paper presents the 2-Phase Correlation Computing method for interpreting the characteristics of each emotion/mental state, nutrients, exercises, life habits with a vector space. The vector space reflects the roles of neurotransmitters. The 2-Phase Correlation Computing extracts the information expected to be most relevant to the user’s request. In this method, expert knowledge, characteristics of emotions, and mental states are defined in the “Requests” Matrix, and each stimulus into “Nutrients”, “Exercises”, and “Life Habits” Matrixes. “Nutrients”, “Exercises”, and “Life Habits” are expressed and computed to as “Stimuli”. In short, this method introduces logos to the chaotic world of decision making in mental health.

This paper presents a new knowledge base creation method for personal/collective health data with knowledge of preemptive care and potential risk inspection with a global and geographical mapping and visualization functions of 5D World Map System. The final goal of this research project is a realization of a system to analyze the personal health/bio data and potential-risk inspection data and provide a set of appropriate coping strategies and alert with semantic computing technologies. The main feature of 5D World Map System is to provide a platform of collaborative work for users to perform a global analysis for sensing data in a physical space along with the related multimedia data in a cyber space, on a single view of time-series maps based on the spatiotemporal and semantic correlation calculations. In this application, the concrete target data for world-wide evaluation is (1) multi-parameter personal health/bio data such as blood pressure, blood glucose, BMI, uric acid level etc. and daily habit data such as food, smoking, drinking etc., for a health monitoring and (2) time-series multi-parameter collective health/bio data in the national/regional level for global analysis of potential cause of disease. This application realizes a new multidimensional data analysis and knowledge sharing for both a personal and global level health monitoring and disease analysis. The results are able to be analyzed by the time-series difference of the value of each spot, the differences between the values of multiple places in a focused area, and the time-series differences between the values of multiple locations to detect and predict a potential-risk of diseases.

In recent years, with the development of information technology, many cyber-physical systems, in which real space and the information space are linked for data acquisition and analysis, have been constructed. The purpose of constructing a cyber-physical system is to solve and improve social and environmental problems. An important target is the railway space, which aims to provide safe and stable transportation services as part of the social infrastructure. In this paper, we propose a new data model, the “Context Cube Semantic Network”, for the railway space and a metric method that employs an integrated scale based on heterogeneous correlations of purpose, sensibility, and distance for the railway space. Furthermore, we constructed a station guidance system that implements the proposed method and evaluates subjects at the station. As a result, we clarified the effectiveness and applicability of the system.

In this study, we proposed data-mining based bell-curve analogical hydrographs analysis with lag time vertical axes and bankfull discharge horizontal axes to make flood susceptibility prediction. We utilized flood data reports, hourly/daily rainfall data and daily water discharge of Hulu Langat district, Selangor Malaysia from the year 2013–2016 to do flood susceptibility. We implement data mining concept by sorting the database, followed by plotting hydrograph to identify flood patterns and establish relationships to predict flood trends. This method is an intersection between the knowledge field of hydrology and mathematical modeling. When an outlier from the graph is detected, the knowledge from hydrology can be applied to understand the reason behind the appearance of outliers. Besides, the knowledge of mathematical modeling is necessary to assist us in predicting flood susceptibility. The purpose of this study is to predict the flood susceptibility which is vital to prepare the users/public well prepared for smooth and efficient evacuation. In 4 years context, our flood depth predictions are nearly 100% accurate. Factor influencing the lag time and steepness of rising limb are related to land use and topographical features. Implications of the results and future research directions are also presented.

This paper presents a comparison of prediction methods for a water quality index (WQI) that is used for classification of water quality in rivers or canals. In this work, we consider the water quality index of two canals namely Phadung Krung Kasem Canal and Saen Saep Canal, Bangkok, Thailand as a case study. We compare results from M5P, M5Rules, REPTree with results from multilayer perceptron. The models employ five input variables including dissolved oxygen (DO), biological oxygen demand (BOD), ammonia nitrogen (NH3-N), Fecal Coliform bacteria (FCB) and Total Coliform bacteria (TCB) which were measured in the canals. The data in this research had been collected from Bangkok Metropolitan Authority, Thailand from 1 January 2007 to 31 November 2017. The total number of data is 2,000 records. The 10-fold cross validation method is used for evaluation of prediction models. It allows to determine the most effective method. Our experimental results show that the REPTree method yielded the highest accuracy to predict water quality index compared to other methods proposed in this paper.

Artificial intelligence systems require logic calculation to give true or false judgment. However, artificial intelligence systems cannot be simply implemented by basic Boolean logic calculation. Deep artificial neural networks implemented by multiple matrix calculation is one of the efficient methods to construct artificial intelligence systems. We have presented semantic computing models in which input data are mapped in to a semantic space and presented as points in semantic spaces. From the point view of our semantic computing model, the multiple matrix calculation like artificial neural networks is a data mapping operation. That is, input data are mapped into a semantic space by the multiple matrix calculation. In our method, the true or false logic judgement is transmitted into calculating Euclidean distances of those points in the semantic spaces. In order to apply the semantic computation model for developing artificial intelligence systems, it is important to understand the mechanism between the logic calculation and semantic space and the deep-learning mechanism. In this paper, we present logic calculation implemented by the multiple matrix calculation which is the basic calculation method to implement the artificial intelligence system. The most important contribution of this paper is that we first present the mechanism for implementing logic calculation with semantic space model and machine learning. In the paper, we use three example cases to illustrate the mechanism. We first present an example case on implementing combination logic calculations based on linear space mapping. After that, we present an example case where the semantic space is constructed based on principal component analysis. The third example case is on sequential logic operations. The concept of semantic space, subspace selection and learning mechanism utilized in the example cases are also illustrated.

In tourism, finding the optimal place and route is necessary and essential according to emotional aspect and physical distance. Our proposed novel distance system which is the physical and emotional distance offers the physical and emotional solution to overcome the optimal selection problem. We demonstrate a route planning system that responds to the user's emotional and locational context using the novel distance. This distance can find the optimal place and path to match the user's emotion and location. And we develop a tour-semantic space, which is a semantic space that characterises tourist places, and we measure the novel distance as a relation of user context. Tour-Semantic Space is created using colour information of tourist place photos, genre, words of review and location information as metadata of tourist place. And we were mapping words and place data representing emotions to the tour-semantic space Thereby realising semantic search and integration according to the user's context. The feature of this research is that tourist place recommendation and path search can be performed according to a user's context by taking physical distance and emotional distance into consideration. For example, a tourist place is recommended to the user even when the physical distance between place and the user is long because the emotional distance between the place's characteristic and the user's felling is short. Our experimental results suggest retrieving a place and creating a plan according to the user's context, which consists of emotional aspects and physical location. We aim to develop a system that mimics a person's behaviour, such as people selecting a tourist place themselves considering emotional aspects and physical distances.

The significant computation in global environmental analysis is "context-oriented semantic computing" to interpret the meanings of natural phenomena occurring in the nature. Our semantic computing method realizes the semantic interpretation of natural phenomena and analyzes the changes of various environmental situations. It is important to realize global environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way with a large amount of information resources in global environments. Semantic computations contribute to make "appropriate and urgent solutions" to the changes of environmental situations. It is also significant to memorize those situations and compute environment changes in various aspects and contexts, in order to discover what are happening in the nature of our planet. We have various (almost infinite) aspects and contexts in environmental changes, and it is essential to realize a new analyzer for computing the meanings of those situations and making solutions for discovering actual aspects and contexts. We propose a new method for semantic computing in our Multi-dimensional World map. We utilize a multi-dimensional computing model, the Mathematical Model of Meaning (MMM) [1–3], and a multi-dimensional space with an adaptive axis adjustment mechanism. In semantic computing for environmental changes in multi-aspects and contexts, we present important functional pillars for analyzing natural environment situations. We also present a method to analyze and visualize the highlighted pillars using our Multi-dimensional World Map (5-Dimensional World Map) System. We introduce the concept of "SPA (Sensing, Processing and Analytical Actuation Functions)" for realizing a global environmental system, to apply it to Multi-dimensional World Map System. This concept is essential to design environmental systems with Physical-Cyber integration to detect environmental phenomena in a physical-space (real space), map them to cyber-space to make analytical and semantic computing, and actuate the analytically computed results to the real space with visualization for expressing environmental phenomena, causalities and influences. This system currently realizes the integration and semantic-analysis for KEIO-MDBL-UN-ESCAP Joint system for global ocean-water analysis with image databases. We have implemented an actual space integration system for accessing environmental information resources and image analysis.

Humankind faces a most crucial mission; we must endeavour, on a global scale, to restore and improve our natural and social environments. This is a big challenge for global information systems development and for their modelling. In this paper, we discuss on different aspects of conceptual modelling in global environmental context. The paper is the summary of the panel session “The Future of Conceptual Modelling” in the 29th International Conference on Information Modelling and Knowledge Bases.

Improved computing environments performing large-scale data processing and high-speed computational processing facilitate the delivery of new algorithms to businesses while considering cost efficiency for small-scale investments. Implementing the proposed method more as a criterion for feasibility and economic rationality in specific problem areas rather than as an approach to generic issues, we aim to develop technologies of practical use in the real world. Recently, it has become possible for customers to monitor their buying behavior through smart devices, and with the improvement of computing performance, it has become possible to improve the accuracy of prediction and recommendation cycles through active online learning. This study proposes a method for dynamically recommending products that are highly likely to be selected by the user by combining the user's reaction with reuse of knowledge and real-time online learning to cyclically repeat feedback that is more specific to the user.We propose a method to sense streaming data by utilizing a user's behavior, intervening a user's behavioral change through interactions, such as recommendations, and evaluating the userâĂŹs buying intention and interest in each product. Using the evaluation results for recommendations helps achieve positive feedback and effectively support the selection of more exciting or different products. We propose a recommendation method specific to individual customers based on past transaction data, where changes can be monitored in real-time by reusing the knowledge acquired in advance through batch processing of knowledge discovery and data mining and processing the stream data in real-time online. We will present the implementation of our proposed method targeting the database system and machine learning algorithm.

The detection of deforestation by remote sensing technologies has been one of the most important research issues in forest monitoring over the last decades. However, only identifying the area of change is usually not sufficient to understand how critical the effects are on the environment including increased CO2 emissions, loss of biodiversity, and soil degradation. To interpret the causes of the detected forest loss and the full impacts upon an ecosystem, additional expert knowledge is required. Traditionally the environmental standard classifies the measurement value, as called parameter value, from the environmental sensor into several condition categories to presenting meaningful quantitative measures of environmental results and establishing whether or not the problem of environmental exists. There are several traditional calculations to measure the interpretation of environmental phenomena such as numerical approach as represented, e.g., by pattern matching that is supported by classical Boolean logic rule. However, in the Boolean logic rule, the truth interpretation values of parameters may only be the truth values, true and false in a category. This paper demonstrates the type of logical approach that has huge potential to assign the interpretation of environmental phenomena in where the truth value may fall in the range between completely true and completely false.

This paper presents a 5D World Map System's application for disaster-resilience monitoring as "Environmental AI System" of each player's implementation of United Nation's SDG 9 and DGS 11 from global-level to regional-level, country-level, sub-regional-level and city-level. In Asia-Pacific, disaster risk is outpacing disaster resilience. The gap between risk and resilience-building is growing in those countries with the least capacity to prepare for and respond to disasters. Using the Sensing-Processing-Actuation (SPA) functions of 5D World Map System, a disaster risk analysis can be conducted in multiple contexts, including regional, national, and sub-national. At the regional, national and subnational levels, the analysis will focus on identifying disaster risk hotspots through incorporating existing multi-hazard disaster risk and socio-economic risk information. The system will further be used to assess future risks through integration of global climate scenarios downscaled to the region as well as countries. This paper presents the design of two new actuation functions of 5D World Map System: (1) Short-term warning with prediction and push alert and (2) Long-term warning with context-dependent multidimensional visualization, and examines the applicability of these functions by indicating that (1) will support both resident and those who are working at the operational level by being customized to disaster risk analysis for each target region/country/area, and (2) assist both policy-makers and sectoral ministries in target countries to use the analysis for evidence-based policy formulation, planning and investment towards building disaster-resilient society.

In order to realize an artificial intelligent system, a basic mechanism should be provided for expressing and processing the semantic. We have presented semantic computing models in which original data are mapped in to a semantic space and presented as points in semantic spaces. That is, we presented a method to process semantic information by calculating Euclidean distances of those points in the semantic spaces. In our continuous studies, we note that different mapping matrixes are required to map the original data in to the semantic space when this model is applied in different application areas. Therefore, it is an important research topic to develop methods to create the mapping matrixes applied in different areas. Many research works are presented on applying the model in the areas of semantic information retrieving, semantic information classifying, semantic information extracting, and semantic information analyzing on reason and results, etc. In these works, the mapping matrixes are created based on the analyzations in the application areas with human knowledge. In this paper, we present a new method to perform the semantic mapping through deep-learning computation. The most important feature of our method is that we implement semantic mapping through training data sets rather than the mapping matrix which is created based on the analyzations of human being. We first discuss five basic operations, the semantic space creation, semantic mapping, semantic mapping matrix, semantic space expansion and contraction. After that, we present our method. In order to present correlations of the semantic information correctly in Euclidean distances, the axes of a semantic space must be orthogonal to each other. Therefore, we also discuss how to implement semantic orthogonal mapping. We believe that our study will open new application areas on semantic computing and deep-learning.

This paper describes a new method of data retrieval from free text documents in medical domain. Proposed approach creates the document summary and highlights most important keywords in the text. To achieve this result we process the document natural language text and build a descriptor as an internal representation of the document. This descriptor is a graph with concepts, relations between them, and concept points as a metric of relevance. By means of points in the descriptor the approach performs ambiguity resolution, selects most relevant concepts to display in the summary, and votes for keywords highlighting in the text. Besides the direct representation of identified information in the summary, this work proposes a way to provide extended summary by using additional knowledge about relations between medications, procedures, diseases and anatomy. The described approach helps to speed up analysis and decision making processes by means of providing aggregated summary for a document and highlighting most meaningful parts of the document's text. Experiment results demonstrate that automatic summary generation and keywords highlighting can be successfully performed by the proposed approach to achieve meaningful and highly relevant results.

Metadata space creation for semantic associative search is an essential process for semantic searching for news article. We apply semantic computing methodology to realize an Environmental-News-Article Search System with Context-Oriented Interpretations. This paper presents a method for creating an environmental-news-article metadata space for applying semantic computing to environmental news-resources. Our creation process for realizing an environmental-news-article semantic space is (1)select news article regarding environment from news-resources, (2)extract news article that have keyword-tag “Global Warming” or “Warming” and set them as news article of “Global Warming,” (3)obtain all keyword-tag that news article of “Global Warming” have, (4)Count appearance frequency of each keyword-tag in news article of “Global Warming” and determine frequency thresholds of keyword-tag for composing metadata space, (5)create the environmental-news-article metadata space using the keyword-tag creamed off. We compared between several experiments with changed search phenomenon or search context for semantic associative search, and we made certain that our Environmental-News-Article Search System extracts the metadata space properly depending on the context and retrieves phenomena and documents semantically close to the search phenomenon or the search context. Our evaluation shows the effectiveness of the method for creating an environmental-news-article metadata space for applying semantic computing to environmental news-resources.

In this paper, we present information providing a method to realize semantic representation of train service situations by a combination of sign-logo images. Through this method, multiple images having visual meanings such as pictograms, icons and sign-logos are combined to represent images to express train service situations in the railway space based on the knowledge of the railway company such as transfer ways in accidents. Features of this method are as follows: (1) To express events occurred in railway space by combining multiple images as visual communication based on “5W1H” that corresponds with human communication and information understanding. (2) To dynamically calculate the correlation between an event and meaning of the combined sign-logo images. (3) To be associated with the event of a railway space including an attractive tour guide to foreign visitors by combining sign-logo images. We have developed an experimental system as a digital signage and have applied the method in order to realize a sign-based guide at all train stations for foreign visitors in Japan. The effectiveness and feasibility of this method are shown in this paper.

A new generation robot for communication is expected to have various abilities related to changing the expression of emotion. There are many factors related to human sensitivities that affect emotional expression, and their relationships are complicated. Moreover, emotional expression should change according to the purpose of the application. In order to realize programmable emotional expression, we proposed a method using global evaluation for the heterogeneous sensing elements in a Kansei space for expression. The important feature of our system was that it realized an emotion-expressing robot with sentence-unit-based emotion analysis. Various emotional expressions were generated from the results of evaluation. We conducted verification experiments and obtained good evaluations of emotional expression changes and the ease of making settings.

The amount of chlorophyll in a plant can indicate leaf N concentration, as chlorophyll is a major component of nitrogen. Identifying chlorophyll levels in plants, therefore, leads to appropriate nitrogen fertilizer recommendations for the plants and to their fertilization at the proper time, optimizing efficiency of agricultural production and helping to preserve the environment by reducing excess use of nitrogen fertilizer. Measuring leaf N concentration in a high accuracy laboratory is expensive and time consuming, whereas a SPAD chlorophyll handheld meter can be carried conveniently in the field and can assess rapidly. However, the handheld meter's capacity is limited, especially in large areas, where samples must be taken to represent an area. The use of vegetation indices calculated from UAV imagery and correlated with SPAD values enables better estimations of N leaf concentrations over large areas. The result showed that relationship between NDVI and SPAD value (chlorophyll contents) of waxy corn (Zea mays L. var. ceratina) at V6 and R1 stage in R2were 0.594 and 0.632, respectively. The results of this study are beneficial for knowing the amount of chlorophyll in plants, which can result in accurate nitrogen fertilizer requirements and better administration of management zones in precision agriculture.

Semantic computing integration with deep-learning realizes a new artificial brain-memory system. We have presented a concept of “MMM: Semantic Computing System” for analyzing and interpreting environmental phenomena and changes occurring in the oceans and rivers in the world. We also introduce the concept of “SPA (Sensing, Processing and Analytical Actuation Functions)” for realizing a global environmental system, to apply it to Multi-dimensional World Map (5-Dimensional World Map) System. This concept is effective and advantageous to design environmental systems with Physical-Cyber integration to detect environmental phenomena as real data resources in a physical-space (real space), map them to cyber-space to make analytical and semantic computing, and actuate the analytically computed results to the real space with visualization for expressing environmental phenomena, causalities and influences. This paper presents integration and semantic-analysis methods for KEIO-MDBL-UN-ESCAP Joint system for global ocean-water analysis with Coral-Image Analysis in two environmental-semantic spaces with water-quality and image databases. We have implemented an actual space integration system for accessing environmental information resources with water-quality and image analysis. We clarify the feasibility and effectiveness of our method and system by showing several experimental results for environmental medical document data Environmental-semantic space integration realizes deep analysis environmental phenomena and situations. The essential computation in environmental study is context-dependent-differential computation to analyze the changes of various situations (air, water, CO2, places of livings, sea level, coral area, etc.). It is important to realize global environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way with a large amount of information resources in terms of global environments. In the design of environment-analysis systems, one of the most important issues is how to integrate several environmental aspects and analyze environmental data resources with semantic interpretations. In this paper, we present an environmental-semantic computing system. Our environmental-semantic computing system realizes integration and semantic-search among environmental-semantic spaces with waterquality and image databases.

We herein present a method that dynamically generates the curricula specialized to the learning circumstances of individual learners, given prior learning goals and learning objects. A generated curriculum encourages the selection of learning behaviors according to the learning objects that may be feasibly acquired within a limited timeframe. Our method evaluates the circumstances of an individual learner; by dynamically selecting feasible learning objects based on the individual's learning behaviors and their past records, it finds the best learning tasks within the constraints of time, circumstances, and activities. Using prior known rules and strengths of causal/dependency relations between learning items, our method enables, from individual test results, the discovery of the learning objects that are important, and how they should be ordered, on an individual basis. This enables an effective support in choosing the most appropriate learning behaviors, tailored to the individual learner. It also enables the selection of effective learning behaviors by examining the behavior records of other individuals, treating the influence of their prior learning behaviors on subsequent learning behaviors as experience quotients and using them by converting them into expected scores for the individual's learning behaviors. Accordingly, we evaluate whether the learning behaviors selected by the individual are indeed learning tasks that would correspond to anticipated learning results, conduct prior assessment of the influence that the results of this intervention would have upon the learning circumstances and thus prioritize more effective learning behaviors. When implemented, our method assesses the changes in the individual's learning circumstances based on their learning behaviors on a timeline and subsequently adjusts the recommended behaviors. The method can provide effective support for individual learners: along with effective feedback on learning task selection in response to the individual's circumstances, it dynamically generates an individualized curriculum by measuring the relationships between the individual's learning circumstances and learning items. We herein present a method for dynamically generating the curricula in response to an individual's learning circumstances through measuring the causal/dependency relations between learning items, thus enabling the calculation of the relationship between an individual's past learning record, and the learning behaviors and learning objects available to be chosen by the individual. We investigate its efficacy and achievability through empirical testing by using actual data.

This paper presents a multi-parameterized water quality prediction method with differential computing among sampling sites at Bangkok City, Thailand. Here, two canals were selected for case study and nine parameters were chosen for water quality prediction, they are Temperature, pH, DO, BOD, COD, NH3-N, NO2-N, NO3-N, and TP. The data obtained from 2007 to November 2017. The differential computing is chosen to predict the parameters along sampling sites. The results are indicated the predictive values of temperature and pH are entirely accurate than another parameter because the error values are low values and both parameters are slightly changed from the past up to present. Therefore, the differential computing possibly uses to predict some water quality parameters which they are quite stable conditions.

Deforestation is a major problem in ecosystem degradation and one of the main sources of carbon emission to the atmosphere. The use of multi-temporal satellite remote sensing has proven to be effective means to monitor forest conditions on global scale. The effectiveness of the utilization of remotely sensed images will depend on the analysis model and parameter selection procedure to provide information that meets the requirements of deforestation monitoring. Here we demonstrate the ability of semantic computing for analyzing satellite images that applied to interpreting tropical deforestation. The typical semantic computing works for interpreting numerical or textual data. It still remains challenging for utilizing remote sensing images for environmental monitoring in semantic computing since the data is presented in interval value. Therefore, we proposed interval matrix decomposition for automatically generate semantic projection that address uncertain value in environmental parameters. In this study, independent dimensions of Landsat Thematic Mapper imagery (Landsat-8) and The Phased Array Type L-band SAR-2 (PALSAR-2) were combined to create an integrated interpretation of environmental condition for a study area in the deforestation zone of global tropical forest. We proposed essential semantic interval-dimensions derived from heterogonous satellite images, combination of L-Band SAR and optical, namely: HV gamma-naught, red, green, blue, NIR, SWIR channel; and combinational features: soil temperature, soil moisture, temporal change density, temporal change velocity, shape and texture. Afterward, semantic computing is employed as analysis model to explain significant knowledge of deforestation activity. The experimental result shows that integrated independent dimensions from both the optical and SAR domains, has potential for presenting for aspect-based deforestation assessment and to enable the design of robust forest monitoring systems.

As global warming is worsening recently, coral bleaching is also happening more frequently because corals are sensitive to temperature change. Consequently, the need to study and monitor coral reefs is rapidly increasing in order to better understand the impact of environmental change on ocean ecology. There are two major approaches to monitoring coral reef. First, remote sensing, by using satellite imagery and/or aerial photography coral can be studied in large quantity in a very short period of time. The downside of this approach is that images obtained by satellite normally do not have enough spatial resolution and its images are not able to record very deep underwater objects. The second approach is using a diver to perform a detailed investigation of coral reef, which gives a lot more detail, but this approach is very time-consuming and expensive. Our aim is to use a small, commercially available camera as a tool to automatically monitor coral in real-time with the least possible human intervention. The main purpose of this research is to demonstrate that a consumer camera, with a little modification, can be used to detect coral bleaching. The modification was done so that the camera can record images in near-infrared range, which is important for coral detection.

This paper presents an application of 5D World Map System with Context-Diversity-Responsive Semantic Associative Search for a large database of environmental news articles. By applying 5D World Map System to data describing social and natural phenomena, a new news-article subscription environment is realized by the dynamic combination of various semantic, temporal and spatial “context”. The new subscription environment enables to create, accumulate and visualize a series of analyzed results of semantic relations among phenomena as an interpreted social story. The system realizes the extraction of correlative and causal relations between phenomena which are potentially included in news articles. A semantic associative search method is applied to this system for realizing the concept that “semantics” of words, documents, and events/phenomena vary according to the “context”. The main feature of this system is to create various context-dependent patterns of social stories according to user's viewpoints and the diversity of context in phenomena dynamically. This system also provides an environment for analyzing the time-series change and spatial expansion of social and natural phenomena on a time-series multi-geographical space. In this paper, we show a prototype system applied for vast ten years of news-articles and several experiments about “global warming” to clarify the feasibility of the system.

The global environmental analysis system is a new platform to analyze environmental multimedia data that acquired from nature resources. This study aims to realize and interpret coral reefs phenomena and changes occurring that happening in global scale by utilizing Acropora coral as a bioindicator or natural sensing. This paper presents a new environmental-semantic computing system of multispectral imagery for automated coral health monitoring and analysis to realize and recognize coral condition in actual situation. Multispectral semantic-image space for coral monitoring and analysis can be utilized for ocean environment monitoring and assessment by measuring coral reef health which highly beneficial to the current ocean pollution problem. Our method applies semantic distance calculation to measure similarity between multispectral image data and context images including three coral conditions (healthy, bleaching, and dead). In our experiments, we applied the SPA function which is an effective concept to design environmental systems with Physical-Cyber integration. This paper presents case study of Acropora coral monitoring and assessment at Man-nai Island, Rayong province, Thailand. Therefore, an additional objective of this research is to apply the Artificial Intelligence (AI) and Environmental monitoring system for combatting the ocean pollution problem by transferring the knowledges and technology from computer science fields to fundamental marine science research.

New information-provision focusing on individual passengers is expected in a railway environment. Our method realizes multiple semantic spaces, which are selected according to passenger's contexts. By using correlation metrics for the causal interaction of different semantic spaces, this method anticipates the passenger's needs and generates a ranking of services and facilities. Experimental study confirms that the ranking of passenger requirements for services and facilities would change appropriately in response to the causal interaction of the semantic space. The experimental results show the feasibility and applicability of this method.

This paper presents a global sharing analysis and visualization of water quality analysis by 5D World Map (5DWM) system. The data resources in this research collected from Sichang Island, Chonburi province, Thailand during 1990 to 2002 and 2010 to 2017. Six parameters of water quality were selected during 1990 to 2002 i.e. chlorophyll a, ammonia, nitrite, nitrate, phosphate and. The total locations sites were 21 stations which situated around Sichang Island. Otherwise, ten parameters were selected during 2010 to 2016 i.e. temperature, salinity, dissolved oxygen (DO), pH, ammonia, nitrite, nitrate, phosphate, silicate, and alkalinity. On the other hand, eight parameters were selected in February and July 2017 i.e. temperature, pH, oxidation reduction potential (ORP), conductivity, turbidity, dissolved oxygen (DO), TDS (Total Dissolved Solids), and salinity. All parameter of water quality was added and display by 5DWorld Map in order to visualize and sharing the water quality. Our results showed that 5DWorld Map can apply to environmental analysis and semantic computing. We apply the dynamic evaluation and mapping functions of multiple views of temporal-spatial metrics, and integrate the results of semantic evaluation to analyze environmental multimedia information resources. 5D World Map System for world-wide viewing for Global Environmental Analysis for water quality around Sichang Island, Thailand was reported in this study.

This research deals with the monitoring techniques using normal and multi-spectrum cameras to detect coral healthiness. The Acropora Corals were investigated in laboratory experiment. Parameter of investigation is Ammonia contamination from 0.01 ppm to 10.0 ppm. Under these conditions, the images of coral were captured and analyzed. An Entropy and coral indices of the obtained images were proposed. The important finding in this study is the feature extraction of coral healthiness: (1) event detection, (2) % degradation and (3) relationship between coral index and entropy. For event detection, the trends of water quality parameters such as temperature and salinity was considered. The obtained entropy values gave % degradation of coral. It was also found that GRCI index related with the relevant entropy values of coral images.

Environmental-semantic space integration is a promising approach to realize deep analysis environmental phenomena and situations. The essential computation in environmental study is context-dependent-differential computation to analyze the changes of various situations (air, water, CO2, places of livings, sea level, coral area, etc.). It is important to realize global environmental computing methodology for analyzing difference and diversity of nature and livings in a context dependent way with a large amount of information resources in terms of global environments. In the design of environment-analysis systems, one of the most important issues is how to integrate several environmental aspects and analyze environmental data resources with semantic interpretations. In this paper, we present an environmental-semantic computing system. Our environmental-semantic computing system realizes integration and semantic-search among environmental-semantic spaces with water-quality and image databases. We have already presented a concept of “Semantic Computing System” for analyzing and interpreting environmental phenomena and changes occurring in the oceans and rivers in the world. We also introduce the concept of “SPA (Sensing, Processing and Analytical Actuation Functions)” for realizing a global environmental system, to apply it to Multi-dimensional World Map (5-Dimensional World Map) System. This concept is effective and advantageous to design environmental systems with Physical-Cyber integration to detect environmental phenomena as real data resources in a physical-space (real space), map them to cyber-space to make analytical and semantic computing, and actuate the analytically computed results to the real space with visualization for expressing environmental phenomena, causalities and influences. This paper presents integration and semantic-analysis methods for two environmental-semantic spaces with water-quality and image databases. We have implemented an actual space integration system for accessing environmental information resources with water-quality and image analysis. We clarify the feasibility and effectiveness of our method and system by showing several experimental results for environmental medical document databases.

This paper presents an alum dosage prediction in coagulation process by using Weka Data Mining Software. The data in this research had been collected from Dongmarkkaiy Water Treatment Plant (DWTP), Vientiane capital, Laos PDR from 1st January 2008 to 31st October 2016. The total number of collected data were 2,891 records. In this research, we compared the results from multilayer perceptron (MLP), M5Rules, M5P, and REPTree method by using the root mean square error (RMSE) and mean absolute error (MAE) value. Three input independent variables, i.e. turbidity, pH, and alkalinity were used. The dependent variable was alum added for the coagulation process. Our experimental results indicated that the MLP method yielded the highest precision method in order to predict the alum dosage.

Deforestation is still a major nature phenomenon in our society. For assessing deforestation effect, satellites remote sensing provides a fundamental data for observation. While new remote-sensing technologies are able to represent high-resolution forest mapping, the application is still limited only for detecting and mapping the deforestation area. In this paper, we proposed a new method for retrieve the information contained on Satellite Multispectral images in order to interpreting deforestation effect in the context of soil degradation. We proposed an idea to interpret reflected “substances (material)” of bare soil in deforested area in spectrum domain into human language. The objectives of this paper are to (1) recognize the deforestation activity automatically. (2) Identify deforestation causes and examines the deforestation effect based on deforestation causes. (3) Scrutinize deforestation effects on soil degradation. (4) Representing nature knowledge of deforestation effect by performing calculation for semantic retrieval, to bring the clear comprehensible knowledge even for people who are not familiar with forestry. Semantic retrieval formed by understanding queries and showing queries result based on semantic calculation. As for experimental study, Riau Tropical Forest has been selected as the study area, where the multispectral data was acquired by using Landsat 8 Satellite between 2013 and 2014; Where forest fire and logging activities are reported, and detected.

Three species of corals at Sichang Island i.e. Acropora sp. Goniopora sp. and Pavona sp. were subjected to a stress test with low salinity and normal salinity at concentration 10, 20 and 30 psu, respectively. Under water photograph and eye observation of coral activity were recorded at 12, 24 and 48 hours. The entropy or surface roughness and percent polyp activity were analyzed with comparison to eye observation of coral activity. The experiment was carried out under continuously water temperature and underwater light intensity controlled. The results indicated that “Healthy” entropy values for Acropora sp. are 1.57–1.62 and for Goniopora sp. are 4.26–4.46. In contrast, for Pavona sp., short polyp coral, there was no “Healthy” entropy value resulted from any photographic assessment in this study. The “Healthy” value of Acropora sp. evaluated from percent active polyp was more than 52.4. For conclusion, The Entropy and percent active polyp values were suitable tools for Acropora sp. and Pavona sp., a short polyp coral's healthy identification. Whereas the eye observation of coral activity both percent polyp extend and percent polyp long was suitable for Goniopora sp., a long polyp coral.

This paper presents a new analysis method and the functions for multi-dimensional sensing data, including multi-parameter sensor data and series of sensing images, for a collaborative knowledge creation system called 5D World Map System, and the applications in the field of multidisciplinary environmental researches. The main feature of 5D World Map System is to provide a platform of collaborative work for users to perform a global analysis for sensing data in a physical space along with the related multimedia data in a cyber space, on a single view of time-series maps based on the spatiotemporal and semantic correlation calculations. The concrete target data of the proposed new method and functions for world-wide evaluation is (1) multi-parameter sensor data such as water-quality, air-quality, soil-quality etc., and (2) multispectral and natural-color image data taken by moving cameras such as UAV/car-mounted cameras or mobile phones for environmental monitoring. The proposed world-wide evaluation functions enable multiple remote-users to acquire real-time sensing data from multiple sites around the world, perform analytical visualizations of the acquired sensing data by a selected world environmental standard to discover the incidental phenomena, and provide the analysed results to related users' terminal equipment automatically. These new functions realize a new multidimensional data analysis and knowledge sharing for a collaborative environment. Especially, in the world-wide evaluation function, applying the concept of “semantic computing” to determining the environmental-quality levels of multiple places around the world. The results are able to be analysed by the time-series difference of the value of each place, the differences between the values of multiple places in a focused area, and the time-series differences between the values of multiple places, and calculated as a “world ranking”, to detect and predict an environmental irregularity and incident. In our world-wide evaluation method, we define the environmental impacts as “semantics” of environmental condition. The originality of our method is in (1) an interpreter to convert the numerical environmental quality-level to the qualitative impacts/meanings by the sentence or a set of words that even non-specialists or ordinary people are able to understand, and (2) a visualizer to realize a global comparison and “world-ranking” with a semantic computing for targeting the multi-parameter sensing values of multiple sites around the world.

The rapid temporal and spatial changes of human population and technology development have resulted in the expansion of agriculture, industrial activity, and deforestation. Those massive expansions affect water resources, especially river. Since the river is the main resource of water in human life, it is crucial to analyze the river water-quality in order to detect the water contamination. In this paper, we present an automatic system for water-quality analysis using several databases and different contexts in dynamic sub-space selection contexts. This system obtains information resources by transforming the sensor-value information to language information. This system aims to monitor, analyze and evaluate the Global Water-quality by using Semantic-ordering functions both in single and multiple parameters. Semantic-ordering is used for spatial-dynamics environmental changes in multiple contexts (aquatic life, agricultural, drinking, fish, industrial usage, and irrigation context). As for the experimental study, four places have been selected as study areas; (1) Hawaii (USA), (2) Pori, (Finland), (3) Riga (Latvia), and (4) Vientiane (Laos). The data resource was acquired from March to September 2016. The result shows that this system is able to analyze and identify the ordering of the different water-quality on different places in the global point of view level and to present the global-scale ranking of water quality.

The global environmental analysis system is a new platform to analyze environmental multimedia data that acquired from nature resources. This system aims to realize and interpret environmental phenomena and changes occurring that happening in world wide scope. Semantic computing is important and promising approach to multispectral semantic-image analysis for various environmental aspects and contexts in physical world. In the previous study, we proposed a new system of agricultural monitoring and analysis based on semantic computing concept that it realizes the interpretation of agricultural health condition as human-level interpretation. In this paper, we propose a new analytical method for agriculture global comparisons to realize and recognize crop condition with several places in global scale. Multispectral semantic-image space for agricultural analysis can be utilized for global crop health monitoring by comparing crop conditions among different places. Our method applies semantic distance calculation to measure similarity among multispectral image data to realize the crop health condition as a ranking. According to our new proposed analytical method, we demonstrate a prototype implementation in the case of rye farm in Latvia and Finland. This prototype implementation shows an analysis in the case that image data have same crop type and conditions.

In this paper a new method of data retrieval from free text documents in medical domain is proposed. Presented approach gives the document summary and highlights important keywords in the text to support further analysis of multiple medical documents. The document is processed with natural language processing techniques to find medical keywords and assign them to concepts in the medical ontology. These concepts contribute to higher levels in the hierarchy and build the document descriptor as a graph with concepts in the nodes and corresponding relevance points. The descriptor is used to generate the summary in a form of tree. Finally, we highlight the most important keywords in the original text. Presented experiments demonstrate the proposed approach, which successfully summarizes and highlights meaningful medical information.

Google Earth with high-resolution imagery basically takes months to process new images before online updates. It is considered as a time consuming and slow process especially for post-disaster application. In this study, we aim to develop a fast and accurate method of updating maps by detecting local differences occurred over different time series; where only region with differences will be updated. In our system, aerial imageries from Massachusetts's building open datasets are used as training datasets; meanwhile Saitama district datasets are used as input images. Semantic segmentation is then applied to input images to get predicted map patches of building. Semantic segmentation is a pixel-wise classification of images by implementing convolutional neural network technique. Convolutional neural network technique is implemented due to being not only efficient in learning highly discriminative image features such as buildings, but also partially robust to incomplete and poorly registered target maps. Next, in order to understand overall changes occurred in an area, both semantic segmented images from the same scene are undergone change detection method. Lastly, difference extraction method is implemented to specify the category of building changes. The results reveal that our proposed method is able to overcome current time-consuming map updating problem. Hence map updating will be cheaper, faster and more effective especially post-disaster application, by leaving unchanged region and only updating changed region.

Music plays an important role in the human's life. It is not only a set of sounds – music evokes emotions subjectively perceived by listeners. The growing amount of audio data wakes up a need for content-based searching. Traditionally, tunes information has been retrieved based on a reference information, for example, the title of a tune, the name of an artist, the genre and so on. When users would like to try to find music pieces in a specific mood such standard reference information of the tunes is not sufficiently effective. We need new methods and approaches to realize emotion-based search and tune content analysis. This paper proposes a new music-tune analysis approach to realize automatic emotion recognition by means of essential musical features. The innovativeness of this research is that it uses new musical features for tune's analysis, which are based on human's perception of the music. Most important distinction of the proposed approach is that it includes broader range of tunes genres, which is very significant for music emotion recognition system. Emotion description on continuous plane instead of categories results in more supported adjectives for emotion description which is also a great advantage.

Human-microbiome-relations extraction is important for analyzing the effects on human gut microbiome from the difference of human attributes such as country, sex, age and so on. Human gut microbiome, a set of bacteria, provides various pathological and biological impacts on a hosting human body system. This paper presents a new analytical method for data resources that are difficult to understand such as human gut microbiome, by extracting the unknown relations with other adjunct metadata (e.g. human attributes data) with context-dependent clustering and semantic analysis. This method realizes the significant bacterial components acquisition for categorizing human attributes. The most important feature of our method is to analyze the unknown relations of human-microbiome with or without a correlation between a human attribute and bacteria that is found by related studies in bacteriology. With this method, an analyst is able to grasp the overview of bacteria data clustered by several clustering algorithms (k-means clustering / hierarchical clustering) using bacteria data selected by human attributes as a set of context. In addition, even without an association between a human attribute and bacteria as heuristic knowledge, an analyst is able to extract human-microbiome-relations focusing on a number of bacteria selected from all bacteria combinations by one-way analysis of variance (ANOVA) and our original criteria called the “degree of separation” of clustering. This paper also presents an experimental study about human-microbiome-relations extraction and the experimental results that show the feasibility and effectiveness of this method.

In the design of multimedia data mining systems, one of the most important issues is how to search and analyze media data, according to contexts. We have introduced a semantic associative search method based on our “Mathematical Model of Meaning (MMM) [1, 2, 3]”. This model is applied to compute semantic correlations between keywords, images, music and documents dynamically in a context-dependent way.

Dengue fever is a communicable disease that attacks more than 120 countries in the world during 50 years. Therefore, it is to make sense to say that collaboration among the countries, especially neighborhood countries, is one important key to combat the dengue. Currently, except a serological collaboration, collaboration in dengue is sporadic and temporal. This paper addresses the initiative to build vector-control strategy collaborative among Surabaya (Indonesia), Kuala Lumpur (Malaysia), and Bangkok (Thailand). Deriving the global policy from World Health Organization (WHO), we build the system that (1) extracting global feature from the local feature, (2) selecting the significant features, to determine ranking of importance of a feature, by weighting a feature, and (3) matching the pattern of data to the suitable strategy by measuring the similarity. We built the system from the real data of the Surabaya, Kuala Lumpur and Bangkok in 2012. We verified reliability of the system by comparing the data with the actual action in January 2012 The result shows that the system is system feasible to be implemented, however we still need more preparation to implement the system.

The multi-dimensional analysis is a promising approach to a new interpreting of environments by ground of the value-information and language-information on intellectual activities in various environment meanings to society. This paper presents a new analysis-system with semantic computing for environments in water-quality areas by integrating the fundamental important parameters of water-quality for creating the new meaning to society. The multi-water-parameter-analysis in a multi-dimensional space is important for current research issues in some water-quality research fields, which are based on the values and meanings of each parameter for obtaining the meaningful words in the category of agriculture, aquatic life, fish, drinking, industrial and irrigation. The multi-dimensional semantic space is significantly utilized for various interpretations related to the water-quality.

Multispectral image becomes widely used for environmental analysis to detect an object or phenomena that human eyes cannot capture. One of the main type of images acquired by remote sensing such as satellite or aircraft for earth observation. This paper presents a multispectral analysis for aerial images that captured by dual cameras (visible and infrared camera), which are mounted on an unmanned autonomous vehicle (UAV) or Drone. In our experiments, four spectral bands (three visible and one infrared band) were imaged, processed and analyzed to detect agricultural area and measure the health of vegetation. To interpret environmental phenomena and realize an environmental analysis, this study applies semantic analysis by creating a multispectral semantic image space, combined with three numerical indicators (the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI) and the soil adjusted vegetation index (SAVI)) that can be used to analyze plant health, photosynthetic activity and detect environmental object to determine an agricultural area. This paper also proposed the concept of multi-spectrum semantic-image space for agricultural monitoring by defining the correlation meaning from multi-dimensional parameters which related to agricultural analysis to realize and explain agriculture conditions. This paper presents the experimental study on a rice field, a cornfield, a salt farm and a coconut farm in Thailand.

Humankind faces a most crucial mission; we must endeavour, on a global scale, to restore and improve our natural and social environments. In this environmental study, we will use context-dependent differential computation to analyse changes in various factors (temperatures, colours, level of CO2, habitats, sea levels, coral areas, etc.). In this paper, we will discuss a global environmental computing methodology for analysing the diversity of nature and animals, using a large amount of information on global environments.

Semantic computing is an important and promising approach to semantic analysis for various environmental phenomena and changes in real world. This paper presents a new semantic computing method with multi-spectrum images for analyzing and interpreting environmental phenomena and changes occurring in the physical world.

This paper presents real-time sensing, processing and actuating functions of a collaborative knowledge sharing system called 5D World Map System, and the applications in the field of multidisciplinary environmental research and education. The first objective is to integrate the analysis of sensing data into a knowledge sharing system with multimedia, based on the framework of Sensing-Processing-Actuation of Cyber-Physical Systems. The proposed real-time sensing, processing and actuating functions enable multiple remote-users to acquire real-time sensing data from multiple sites around the world, perform analytical visualizations of the acquired sensing data by the selected calculation methods on the system to discover the incidental phenomena, and provide the analysed results to related users' terminal equipment automatically. The second objective of the research is to realize a new multidimensional data analysis and knowledge sharing system for a collaborative environment by applying the concept of “differential computing” to the analysis of sensing data. Especially, in the processing function, the time-series difference of the value of each sensor, the differences between the values of multiple sensors in a selected area, and the time-series differences between the values of multiple sensors, are calculated, to detect an environmental incident and estimate the possibility of occurrence of the same kind of incident in the neighboring sites within the same area. By using 5D World Map System integrated with these functions, the users are able to perform a global analysis on the environmental sensing data along with the related multimedia data on a single view of time-series maps, based on the spatiotemporal and semantic correlation calculations.

Dengue fever is the fastest spreading communicable disease in the world. Spreading of virus is driven by increasing number of human moving. In many dengue-endemic countries, problem in dengue spreading is predicting infected area and determine perfect strategy to prevent the disease. Predicting infected area spot relates with pattern of human moving, while strategy to prevent is depend on vulnerability of area. In this paper we proposed an adaptive spreading model of area-disease based on human movement. This method combines an area-based mathematical model with discrete life-cycle of virus. The proposed method includes (1) state-space model of routine movement cycle, (2) algorithm of spreading, (3) prediction of the next infection area by graph relation, and (4) vulnerability value of suspected area. There are two important features in this method: real-time prediction of infected area and flexibility to adapt in the different situation. To perform the simulation we utilize real data of infected people in Surabaya in January 2011.The result shows that this method is suitable for near future prediction and easy to compensate time-varying changing. However, the accuracy needs to be improved.

A multi-database environment is commonly important for creating new values by integrating heterogeneous data resources. We have designed a realtime association computing system for interactive information exchange among multi-databases. The metadatabase system organized to measure a relationship of interactive data and define a feedback control output to a system. In this paper, we present the applicability of this method to a multidatabase on railway information. We show several experimental results which have been obtained by associative computing for two different databases as a multidatabase environment. By those results, we clarify the effectiveness of the associative computing method in the actual multi-databases.

This paper presents the analysis and visualization of river-water quality in 25 rivers in Thailand by using 5D World Map system. Water pollution is analyzed by using Water Quality Index (WQI) and Metal Index (MI) which focus on Ping, Nan and Chao Phraya River (the important rivers of Thai). The WQI indicator was used to evaluate water quality by conductivity, NO3-N, NO2-N, NH3-N, Cd, Cr, Mn, Ni, Pb, Zn and As. The MI indicator was used to estimate concentration of metal in the river. The results on 5D World Map System show that several actual values assigned to water-quality parameters are shown in snapshots. The results of Water Quality Index (WQI) show the WQI levels 32.697 at Chaophraya river (Bangkok, 2004) and 38.534 at Ping river (Nakhonsawan, 2014) for Irrigation and Aquatic life respectively, and can be classified into categories of quality-levels for Irrigation and Aquatic life. The results of Metal Index (MI) show that the MI level reaches 92.902 at Ping River (Nakhonsawan, 2014) and 1803.303 at Ping River (Nakhonsawan, 2014) for Irrigation and Aquatic life respectively.

This paper describes a web-based search application used for locating restaurants in a multidimensional content space via interactive space visualization. The primary goal of our research is to reduce the cognitive complexity of query selection, as well as to help the user avoid one of the common pitfalls of traditional search mechanisms: retrieving too many or too few results. Our method approaches this problem by continuously staying one step ahead of the user, constructing a graphical output to summarize how further query adjustments will impact subsequent search results. In actively precomputing queries ahead of the user we help them avoid the trial-and-error search process often associated with trying to find something in an unfamiliar, opaque database. We combine our visual search method with a profile-based knowledge system which helps users find restaurants accessed by people with similar interests.

We design and implement a music-tune analysis system to realize automatic emotion identification and prediction based on acoustic signal data. To compute physical elements of music pieces we define three significant tunes parameters. These are: repeated parts or repetitions inside a tune, thumbnail of a music piece, and homogeneity pattern of a tune. They are significant, because they are related to how people perceive music pieces. By means of these three parameters we can express the essential features of emotional-aspects of each piece. Our system consists of music-tune features database and computational mechanism for comparison between different tunes. Based on Hevner's emotions adjectives groups we created a new way of emotion presentation on emotion's plane with two axes: activity and happiness. That makes it possible to determine perceived emotions of listening to a tune and calculate adjacent emotions on a plane. Finally, we performed a set of experiments on western classical and popular music pieces, which presented that our proposed approach reached 72% precision ratio and show a positive trend of system's efficiency when database size is increasing.

This paper presents an explorative cultural-image analyzer and its application to comparative analyses of cultural arts and crafts. The goal of this system is to provide a new image-exploration environment that reflects the diversity of humans' sense of color and the breadth of cultural human knowledge by detecting and visualizing characteristic historical color-trends within cultural-image data sets. The primary components of this system are the two explorative analysis methods with feature estimation and evaluation of culture-dependent colors: (a) image-group exploration and (b) color exploration. The system visualizes the distinct differences among image groups aggregated by the attributes such as author, era, region, etc. and provides notable images for users through the image-group exploration method. In addition, the system visualizes the subtle differences of colors in images and provides a key to analyze cultural art works by the color-exploration method, with a zooming function for color distributions and cultural-color name estimation. By utilizing the existing annotations and attributes that are available for most images, the system analyzes the differences of colors among image-groups defined by statistical analysis and visualizes the representations of each image-group on an overview map. This system enables a user to analyze the characteristics of a collection of cultural art works by browsing the representative images of each image-group, exploring the specified culture-dependent colors with high accuracy, and observing subtle differences of colors among image-groups according to culture-dependent color names at a glance.

Multimedia retrieval task is faced with increasingly large datasets and variously changing preferences of users in every query. We realize that the high dimensional representation of physical data which previously challenges search algorithms now brings chances to cope with dynamic contexts. In this paper, we introduce a method of building a large-scale video frame retrieval environment with a fast search algorithm that handles user's dynamic contexts of querying by imagination and controlling response time. The search algorithm quickly finds an initial candidate, which has highest-match possibility, and then iteratively traverses along feature indexes to find other neighbor candidates until the input time bound is elapsed. The experimental studies based on the video frame retrieval system show the feasibility and effectiveness of our proposed search algorithm that can return results in a fraction of a second with a high success rate and small deviation to the expected ones. Moreover, its potential is clear that it can scale to large dataset while preserving its search performance.

Currently, floods are not only occurred in the outskirts of the river course, but also in the urban area, especially in the big city. The main problem of urban flood is the fact that it occurs in highly populated areas. It is a global phenomenon that causes widespread devastation, economic damages and loss of human lives. All the strategies basically are good for long term mitigation but not appropriate for solving the real problems when a disaster happens, because it is static and not real time. To overcome, two main points should be developed: socio-cultural knowledge on floods and flood prevention infrastructure development. Both are correlated to build the settlement of flood problem. Therefore, it is essential to build an integrated system combining Cyber-Physical-Human. The proposed system includes (1) physical layer that consist of sensors rainfall and river water levels and satellite sensors, (2) abstract layer consist of flood modelling (3) interaction with human. Mitigation system based Cyber - Physical - Human will be very useful for agencies related to flood control and as a decision making tool for the government and society at large. Surabaya is chosen as study area.

Dengue fever is the fastest spreading communicable disease in the world. The virus has been increasing its geographic reach, partly due to increased urbanization and partly due to climate change. From the viewpoint of human movement, population density the most suspected factor in spreading, but some results nowadays show that the relationship between population density and dengue fever is unclear. This paper presents a new approach in measuring human involvement by modelling contagious places. The proposed system includes (1) statistical analysis about correlations between contagious places and Dengue fever cases, (2) a multi-layer weighting method for determining the weight of each cell (3) a ranking and classification method for places' human-mingling, and (4) building a risk-map of contagious places as a control method in Dengue spreading. The result is new dimension to measure vulnerability of land use concerning with pattern of human moving. Our new approach is advantageous for effecting monitoring in change of public facilities, in comparison to the approach based on the population density, It is more useful in urban planning due to priority in evacuation, and control dengue based on places that attract people.

To maintain and uplift the motivation for doing exercise which is tend to be too simple in rehabilitation and ergotherapy, doing exercise with music is one of the good solutions. In this paper, we confirm that doing exercise with music is fun. Next, we design five types of musical features which relate to exercises. These features are evaluated by doing five types of exercises with listening to the tunes. Based on the results obtained from the experiments, we found many new things, for example, tempo has the adjustment limitation based on how well the user knows the tune. In this paper, we report the latest results from the experiments and explain the way to progress of this research.

This paper describes a method for discovering URLs with contextually relevant deep-topics, and then propagating such information to collaborating users lacking such information. When a user is knowledgeable about a subject, their reasons for frequently browsing a URL extend beyond the fact that it is merely related to said subject. This paper's method includes an algorithm for discovering the surface-topic of a URL, and the underlying deep-topic that a user is truly interested in with respect to a given URL. The deep-topic extraction process works by using URLs linked together through a user's behavioral browsing patterns in order to discover the surface or group-topic of surrounding URLs, and then subtracting those topics to discover hidden deeper topics. This paper describes the three parts of the method: Information Extraction, Propagation, and Verification & Integration, which together form a method with high levels of parallelism due to its distributed and independent nature. This paper also discusses concrete usage-scenarios for the included method, and data structures which would support the implementation of this paper's method.

Visual information such as pictorial symbols, icons and images capture our imagination. In our paper, we discuss icons and images in the context of cross-cultural communication. The authors present their own viewpoints to the subject. We discuss about communication in the multi-cultural world and analyze icons in cross-cultural context. Two professional application domains for icons will be presented. A Kansei-based cross-cultural multimedia computing system and a cross-cultural image communication system are described. Icons are a good means for communication within a certain application domain and in a certain context.

Numerous stock market analysis methods have been proposed from simple moving average to the use of artificial intelligence such as neural networks and Bayesian networks. In this paper, we introduce a new concept and a methodology that enable predictability of asset price movement in the market by way of inference from the past data. We use schema to describe an economic instance, and a set of schema in time series to describe the flow of economic instances in the past. Within the schema, we introduce a concept of velocity and momentum to effectively characterize the dynamic nature of the market. We compare the current and the past instances to identify resemblance and take inference as a predictive capability of future asset price movement.

In the natural environment research field, computer systems are widely utilized. The majority of computer systems are used to store observed data. Super computers are used to simulate environment changes, for example, the global climate change. Prior research has shown that computer systems make it possible to store and access huge amount of observed data based on database manage systems. The simulation accuracy of nature environment changes is also improved accompanied by the progress of computer technology. In this work, we propose a new method to discover what are happening in the nature of our planet utilizing differential computing in our Multi-dimensional World Map. We have various (almost infinite) aspects and contexts in environmental changes in our planet, and it is essential to realize a new analyzer for computing differences in those situations for discovering actual aspects and contexts existing in the nature of our planet. By using Differential Computing, important factors that change natural environment are highlighted. Furthermore, the highlighted factors are visualized by using our Multi-dimensional World Map, which makes it possible to view the nature environment changes in the view of history, geographic, etc.

This paper describes a method for directly encoding and sharing the unique knowledge and perspective of Website-usage and relationships that each person using the Web holds. Knowledge sharing is achieved by creating a Real-time Dynamic Collaborative Space which combines a user's, their friends', and the public's combined knowledge in a new space. This new space provides a context in which the abstract properties of a user's knowledge is expressed through an intuitive visualization which uses real-world-space and behavior knowledge-sharing analogues for allowing the user to understand their Personal Web Context in relation to other people's. This new space and visualization is dynamic, reacting in real-time to a user's and their friends' changing knowledge of the Web. This paper describes the new space, the modeling of Web knowledge, and a discussion of implementation strategies and an in-progress prototype.

This paper presents a context-based multi-dimensional corporate analysis method that evaluates companies based on user-specified contextual settings. The contextual settings are translated and decomposed into distinct spaces, finance, technology, and brand, each of which consists of a subspace containing multiple parameters. The contextual settings determine the relevance of each of such parameters in evaluating companies by assigning appropriate weight to the parameter. The important feature of this corporate analysis method is that it allows the user to analyze companies seamlessly only with the contextual settings without the knowledge of multi-dimensional decomposition.

Our imagination-based query creation method is a new approach to image-recall functions reflecting color-based imaginations in human brains. This method dynamically represents user's imaginations and creates queries for image retrieval. The important aim of this method is to express user's abstract intentions in a computable objects and to recall those imaginations from image databases with color-analytical image retrieval. The main features of this method are: (1) a hierarchical model for color indexing to express image contexts, (2) color zooming for context-dependent color histogram generation by utilizing color names as contextual words, (3) integrating several histograms by using five histogram-combining operations and dynamic semantic weighting, and (4) threshold controlling for semantic correlation. This method exploits color information to retrieve intended images expressing a user's imagination. This paper also represents a “Database for Imagination Expression” (DBIE), which stores, shares and reuses created queries. Additionally, several qualitative experiments are also represented to examine the effectiveness and the feasibility of our method.

This paper includes five contributions on the topic of multimedia information systems for social, cross-cultural and environmental computing. Approaches, models, and methods for Web Semantization, Cross-cultural Image Computing, Information Modelling and Data Mining, Mobile Information Systems for Ubiquitous Society, and Multimedia Systems for Cross-cultural and Environmental Computing are introduced and discussed. This paper is based on the contributions presented in the panel discussion of EJC 2012 Conference.

Data analysis based on spatial and temporal relationships leads to new knowledge discovery in multi-database environments. As various and almost infinite relationships are potentially existing among heterogeneous databases, it is important to realize an objective-based dynamic data analysis environment with appropriate data collection from selected databases.

In our paper we discuss advanced viewpoints to “Kansei” and cross-cultural computing, to context modelling, context-sensitive information systems, and cross-cultural software engineering. Culture is embodied in how people interact with other individuals and with their environment. It is a way of life formed under specific historical, natural and social conditions. Culture can be considered as one example of context. A computational method, a computer system, or an application is context-sensitive if it includes context-based functions and if it uses context to provide relevant information and services to the user, where relevancy depends on the user's impressions and the situation at hand. Because of globalization, cultural competence together with context sensitive information systems and their development has become an important dimension for success in today's international business and research.

Pictorial symbols capture our imagination. A visual vocabulary - that anyone from any culture, any country, and in any context of life can understand - is a very interesting research challenge. The collections of visual symbols usually are context-specific, and include symbols and signs for example at airports, in hotels, in traffic and in maps. Our ongoing research work is related to pictorial symbols in our living environments. These symbols symbolize our living environments and guide us in different situations. Here we introduce our idea of intelligent icons and their functions. We also give some examples of their applications. We review spatial relations as an interesting approach to (a) icon recognition and (b) to illustrate the situation-specific relation between the user and the icon in question. The design principles and the basic model of the system architecture of our I-Icon system are described. We also present an example of intelligent icon implementation.

This paper points out that achievements in the field of multimedia analysis and retrieval represent an important opportunity for improvement of recommender system mechanisms. Online shopping systems use various recommender systems; however a study of different approaches has shown that they do not exploit the potential of information carried by multimedia product data for product recommendations. We demonstrate how this can be accomplished by a personalized recommender system framework that is based on a method of analysis of colour features of entity images. Colour-features are based on image colour histograms, psychological properties of colours and a learning mechanism. We have developed a service-oriented framework for a personalized recommender system that is based on incorporation of this method into a highly interactive business process model. The framework is designed in a generic way and can be applied to an arbitrary domain. It is based on service-oriented architecture in order to promote its flexibility and reuse, which is important when applying it to existing recommender system environments. An experimental study was performed for the domain of travel agency. The framework provides several important advantages, such as automatic creation of entity image meta-data which is based on colour-based image analysis and extraction of their semantic properties, user-interaction based learning, dynamic selection and presentation ordering of entity images, and feedback for creation of base image entity sets.

This paper proposes a cross-cultural computing system that deals with multilingual analysis. This system focuses on a cultural aspect comparison that is based on linguistic basic elements. The most important task of our system is to realize a cross-cultural computation in the framework of correlation computation by using vectorized numeric data that express cultural aspects in some concepts and objects with regard to speech sounds.

Geosocial networking services allow users to create,use, and share information and to communicate with other people regarding geographical locations and time. Geosocial networking services generate large amounts of spatiotemporal contents that mainly include information about personal interests, activities, or real-life experiences. This paper discusses a new geovisualization tool named StickViz that helps retrieve k-neighbors by considering the spatial, temporal, and thematic interests of users over geosocial networking services.In particular, we define phenomena of interest for combining user interests with respect to location, time, and topic and propose a phenomenon-based k-neighbor query to find people having similar interests in spatial, temporal, and thematical dimensions. Moreover, we use a three-dimensional (two spatial dimensions and one temporal dimension) space so that the queries and spatiotemporal contents can be represented in the same space. Finally, we present a prototype of StickViz to navigate spatiotemporal contents and search k-neighbors on the basis of the predefined phenomena of interest through three-dimensional visual interfaces.

This paper presents an image search system with an emotion-oriented context recognition mechanism. Our motivation implementing an emotional context is to express user's impressions for retrieval process in the image search system. This emotional context recognizes the most important features by connecting the user's impressions to the image queries. The Mathematical Model of Meaning (MMM: [2], [4] and [5]) is applied for recognizing a series of emotional contexts for retrieving the most highly correlated impressions to the context. These impressions are then projected to a color impression metric to obtain the most significant colors for subspace feature selection. After applying subspace feature selection, the system then clusters the subspace color features of the image dataset using our proposed Pillar-Kmeans algorithm.

This paper presents a combined-image query creation method for expressing user's intentions by combining multiple digital images for image retrieval. This method uses image databases provided for query-creation and performs several set-operators to express user's imagination by combining user's imaginary images and real scenes. The user's intentions are expressed by the operation of subspace projection in the image feature space. This method makes it possible to create an imaginary image as the combined-image query for expressing user's intentions by combining several images and operators in the query creation process. The important feature of this method is to use shape and color features for expressing imaginations by extending our previously proposed method. This paper shows several experimental results to clarify the feasibility and effectiveness of our method.

Context is a multi-dimensional concept. It is hard to define context generally for computer science. Which information is considered as context, which is not? Why are the certain context elements relevant for a certain case, but irrelevant for another? How to explain this to computers? Can computers learn these issues as humans do? In our paper we present different viewpoints to the concept of context and to context modelling starting from requirements engineering and ending up to multi-disciplinary education. Based on context related literature research and discussions in our paper, we can summarize that a complete and comprehensive definition and model of context is difficult to achieve and may not even be appropriate at all. However we can conclude that there is a common understanding that context always relates to an entity, context is used to solve a problem, context depends on the domain of use, context depends on time and context is evolutionary.

With the advance of ubiquitous computing and mobile environments, we have begun to continuously monitor changes in real-world condition and environment through wireless sensor networks. Opportunities also exist for people to create information related to the world around them by using mobile phones equipped with sensing devices, and share that information online with others. In this paper, we propose a novel approach for the interconnection of earth observation data and spatiotemporal web contents on the basis of spatiotemporal and thematic relationships. In particular, we use the concept of moving phenomena of interests to link between measurement sensing data and people-centric contents on the basis of spatiotemporal proximity and thematic relevance. This paper also shows a simple application that automatically generates semantic tags with respect to natural geographic phenomena, such as typhoons, climate changes, and air pollution, on the basis of our interconnection approach. We are able to easily understand qualitative meanings with respect to a certain phenomenon expressed by quantitative numeric conditions.

Various knowledge resources are spread to a world-wide scope. Unfortunately, most of them are community-based and never thought to be used among different communities. That makes it difficult to gain “connection merits” in a web-scale information space. This paper presents a three-layered system architecture for computing dynamic associations of events to related knowledge resources. The important feature of our system is to realize dynamic interconnection among heterogeneous knowledge resources by event-driven and event-centric computing with resolvers for uncertainties existing among those resources. This system navigates various associated data including heterogeneous data-types and fields depending on user's purpose and standpoint. It also leads to effective use for the sensor data because the sensor data can be interconnected with those knowledge resources. This paper also represents application to the space weather sensor data.

It is becoming important to realize a cross-cultural communication environment among societies with different cultures. Images are effective media for exchanging cultural characteristics across cultures. This paper presents a culture-dependent color-emotion model for cross-culture oriented image retrieval system that realizes color-emotion spaces to search images with human emotion aspects. Many image retrieval systems have been featured by color analysis, but culture-dependent aspects of images are not considered intensively. Our system creates color impression spaces based on Ekman's 17 basic emotions. For the first step, the culture-dependent color impression space is created by using cultural-features. We apply automatic clustering using our previous method “Valley Tracing” in order to generate dynamic representative colors. This system automatically creates a set of culture-dependent color impression metadata.

Clustering performance of the K-means greatly relies upon the correctness of the initial centroids. Usually the initial centroids for the K-means clustering are determined randomly so that the determined centroids may reach the nearest local minima, not the global optimum. This paper proposes a new approach to optimizing the designation of initial centroids for K-means clustering. This approach is inspired by the thought process of determining a set of pillars' locations in order to make a stable house or building. We consider the pillars' placement which should be located as far as possible from each other to withstand against the pressure distribution of a roof, as identical to the number of centroids amongst the data distribution. Therefore, our proposed approach in this paper designates positions of initial centroids by using the farthest accumulated distance between them. First, the accumulated distance metric between all data points and their grand mean is created. The first initial centroid which has maximum accumulated distance metric is selected from the data points. The next initial centroids are designated by modifying the accumulated distance metric between each data point and all previous initial centroids, and then, a data point which has the maximum distance is selected as a new initial centroid. This iterative process is needed so that all the initial centroids are designated. This approach also has a mechanism to avoid outlier data being chosen as the initial centroids. The experimental results show effectiveness of the proposed algorithm for improving the clustering results of K-means clustering.

The application area of image retrieval systems has widely spread to the WWW and multimedia database environments. This paper presents an image search system with analytical functions for combining shape, structure, and color features. The system pre-processes an image segmentation from hybrid color systems of HSL and CIELAB. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system extracts three features of an image which are color, shape and structure. We apply the 3D Color Vector Quantization for the color feature extraction. The shape properties of an image which include eccentricity, area, equivalent diameter, and convex area, are analyzed for extracting the shape feature. The image structure is identified by applying 2D Forward Mirror-Extended Curvelet Transform. Another distinctive idea introduced in this paper is a new distance metric which represents a semantic similarity. This paper has evaluations of the system using 1000 JPEG images from COREL image collections. The experimental results clarify the feasibility and effectiveness of the proposed system to improve accuracy for image retrieval.

Virtual communities rely primarily on ICT to connect their members to work together, and to share knowledge and practices. The importance of virtual collaborative work is increasing not only because of its economical and environmental benefits, but also due to its flexibility for establishing dynamically new cross-organizational and cross-cultural innovative teams. Virtual collaborative spaces should support their joint activities. In order to design and realize such spaces, an understanding of the tasks to be carried out by the virtual community is necessary, as well as an understanding of the related processes, contexts, and knowledge. In our paper, we introduce a reference model of a Cross-Cultural Cyber Space (CCS) for context-based knowledge creation and sharing between the members of the cross-cultural collaborative community. We also describe the prototype implementation of the CCS, a 3D cross-cultural art museum system.

In this paper, we propose a real-time and collaborative hyper-media system that introduces database-enhanced collaborative models and multimedia processing models for creating improvised performances in the Opera of Meaning. The system provides a main story media and a corresponding shared canvas that is shared among Internet-wide user communities. Our shared canvas mechanisms make it possible to describe and share users' ideas and impressions about the main story media. The key technology of this system is a timeline-dependent and script-driven live performance engine, which provides users with ECA rules to express and characterize the users' ideas and impressions by using existing multimedia data such as video files and image files. The system provides directors and participants of improvised performance with a set of database operators for controlling and contributing to the performance. The system motivates users to contribute to the performance by exploiting users' own media libraries and existing web services. We have implemented the prototype system which is applicable to the existing video and image files on the Web.

This paper presents an interconnection method for heterogeneous knowledge bases depending on user's interests as a context. Various knowledge bases have been created in each field by using collaborative working environments such as Wiki. One of the important issues is how to interconnect these knowledge bases and represent the relationships between various concepts in heterogeneous fields. An event affects various aspects of an area, field, or community. In order to understand an arbitrary event or concept, it is necessary to find various relationships over heterogeneous fields. Generally, the relationships over heterogeneous fields strongly depend on contexts and situations. It is important to realize the dynamic interconnection of knowledge bases depending on contexts and situations. Therefore, we design an interrelation management function (IMF) that defines the operator for interconnection data. In this paper, we propose a framework for a context-dependent dynamic interconnection method by using the interrelation management function.

This paper presents a design and implementation for the “4D World Map System,” a knowledge representation system which enables semantic, temporal and spatial analysis of documents, and integrates and visualizes the analyzed results as a 4-dimentional dynamic historical atlas (4D World Map Set). The main feature of this system is to create various context-dependent patterns of historical/cultural stories according to a user's viewpoints dynamically. This system generates multiple views of semantic and temporal-spatial relationships among documents of the humanities and social sciences. This system organizes the relationships among documents into various historical/cultural stories by a user's viewpoints. A semantic associative search method is applied to this system for realizing the concept that “semantics” of words, documents, and events vary according to the “context”. Semantically-evaluated and analyzed document data are also mapped dynamically onto a time-series multi-geographical space. This system provides high visibility of semantic correlations between documents in time series variation with geographic information. In this paper, we also show several experiments by using news articles and International Relations documents to clarify the feasibility of the system.

With the advance of the Web technologies, people can more easily access the geographic information and generate various types of user contents including geo-information on the Web. Consequently, geo-web and geo-communities have been infrastructures to share and connect information on the Web for many years, and people start to describe a specific phenomenon on places by own representation methods such as text, images, videos, etc. In this demonstration, we introduce a new type of location-based services, called Sticker, which can search and aggregate the relevant user generated contents/media to moving phenomena such as hurricanes, flooding, and global warming. In particular, the Sticker navigates user-generated contents with three dimensional view of space-time(2D+1D) and allows users to retrieve related information with the moving phenomena in a spatiotemporal domain as well as interesting keywords.

Recently artists are exploring ways for incorporating large amounts of information and networking as part of their medium. One of the main challenges in applying information technology to film and opera is in relating different types of media to the meaning of story narrative. Opera of Meaning is a new format for distributed, collaborative and interactive viewing where the association of different media elements is done dynamically by semantic and impression search that is performed by the public during the performance in context of a main story. This opens new research questions in database modeling and semantic technology related to story meaning, media auto-tagging, automatic editing and mixing, user interaction, social networking and more. We plan to offer this format to artists, producers and the public, opening a new venue for social creation and experiencing of impression and meaning in digital media.

This paper presents a multiple knowledge spaces creation process for domain oriented semantic associative search. We have presented a generation method of semantic associative search spaces for domain-specific research areas. This process enables database managers and experts of the specific domain to collaborate in constructing semantic search spaces with domain-oriented knowledge. Domain-specific knowledge space creation is essentially important to obtain appropriate knowledge from expert-level document repositories. This paper presents the implementation process for knowledge space creation and the software environment for supporting collaboration work between database managers and domain experts.

This paper presents a dynamic image-query creation and metadata extraction method with semantic correlation computation between color-combinations and impressions of multiple image data. The main features of our method are (1) to create an image-query which reflects user's intention dynamically according to the color-based combinations of images with common features selected by a user as context, (2) to extract appropriate impression by each image collection which cannot be easily extracted from a single image, (3) to provide users an image retrieval environment reflecting historical and cultural semantics and impression of color especially for cultural properties, and (4) to enable an image retrieval environment for the collection of images by time, culture, author e.t.c.. The queries are created by the combination of multiple image sets and operations, which are intersection, accumulation, average, difference of color elements of sample images. First, a set of multiple images with common features is set as sample data for a query creation. Second, color histograms are extracted from the image sets for creating feature vector of a query. Third, the correlations between an image-query vector and target image vectors are calculated on a space which represents the relationship between color and the impression according to historical and cultural semantics of color. This image-query creation method representing impression of color makes it possible to expand the range of image retrieval for a large number of image data of cultural property in digital archives, such as electronic library and electronic museum, automatically.

This paper presents an implementation method of a meta-level knowledge base system for analyzing personal career opportunities by connecting occupational and educational databases. We have designed and implemented several functions to analyze information on personal career development, on the meta-level database system for connecting occupational and educational databases. This method is used to create dynamic relationships among heterogeneous databases on occupations, educational contents, social and educational issues and personal career information. In this method, several functions are defined for analyzing relationships among heterogeneous databases. (1) Educational contents represent, for instance, lectures in educational institutes, (2) occupations represent descriptions of concrete occupations, (3) social and educational issues represent academic industrial fields, and (4) personal career information represents histories, objectives and interests of an individual user's career. These databases are connected and analyzed in a dynamic way, according to users' contexts and situations. By using our method, individual career development becomes to be effectively supported when discovering personal career opportunities and designing personalized career development plans.

This paper proposes a general framework of for a system with a semantic browsing and visualization interface called Knowledge Communication, Collaboration and Creation Browser (KC3 Browser) integrates multimedia contests and web services on the grid networks, and makes a semantic mash-up called knowledge workspace (k-workspace) with various visual gadgets according to user's contexts (e.g. their interests, purpose and computational environments).

In the current World Wide Web (WWW) environment, it is important to realize intelligent and effective information acquisition mechanisms. We propose a semantic associative search method based on our mathematical model of meaning to realize intelligent and effective information acquisition. Our method provides a dynamic context recognition mechanism for information acquisition according to user's queries given as contexts. We integrate two application systems of our semantic associative search method to realize an intelligent and effective information acquisition environment for WWW information resources. We apply our method to information retrieval on WWW information resources, which are identified by Uniform Resource Locators (URLs). This application system makes it possible to dynamically obtain semantically related information resources on WWW, according to user's queries given as contexts. Furthermore, we have applied our method to queries and information resources described in multiple languages. We integrate those applications for supporting WWW information acquisition.

A parallel processing scheme and software architecture of SMASH, a parallel processing system for supporting a wide variety of database applications is presented. The main feature of this system is that functional programming concepts are applied to define new database operations and data types and to exploit parallelism inherent in an arbitrary set of database operations. A primitive set (SMASH primitive set) of the software architecture is presented that defines an abstract machine interface between high-level database languages and general-purpose hardware systems for parallel processing. The primitive set is used to implement functional computation systems for executing arbitrary database operations in parallel. A previously proposed stream-oriented parallel processing scheme for relational database operations is extended to support more complex database operations which deal with complex data structures. Several experimental results of parallel processing for database operations are shown to clarify feasibility of the proposed architecture.>