Jangwon Gim

Research Advising System Based on Prescriptive Analytics

As the amount of data increases enormously, business analytics such as descriptive, predictive, and prescriptive analytics is one of the most important topics for better decision making especially for CTO or CIO in corporate. Prescriptive analytics shows fundamental difference with descriptive analytics and predictive analytics in that it requires high-value alternative actions or decisions to achieve a given goal. However, only a few studies have been introduced since it is a emerging technology. Thus, this study aims to trigger research on this technical area by implementing a prescriptive analytics system and by verifying it in the point of usability and usefulness. The system, InSciTe Advisory, is focused on improving research performance and is based on 5W1H questions to build actionable strategies to achieve a given goal. The comparison evaluation of the system with Elsevier SciVal showed a rate of 118.8% in usefulness and reliability.

Prescriptive Analytics System for Scholar Research Performance Enhancement

We introduce a prescriptive analytics system, InSciTe Advisory, to provide researchers with advice for their future research direction and strategy. It consists of two main parts: descriptive analytics and prescriptive analytics. Descriptive analytics provides results from research activity history as well as the research power index for the designated researcher. Prescriptive analytics suggests a group of role model researchers to the researcher, as well as methods to adopt their best practices. The prescription for the researcher is provided according to 5W1H questions and their corresponding answers. All of the analytical results and their explanations related to the given researcher are automatically generated and saved to a report. This researcher-centric prescriptive analytics framework is expected to be a useful tool to understand the designated researcher from the perspective of prescriptive and descriptive analytics. We evaluated user satisfaction results for InSciTe Advisory and Elsvier Scival by five test users. The result of the evaluation demonstrated that user satisfaction of InSciTe Advisory is 126.5% higher than Scival.

Application for Temporal Analysis of Scientific Technology Information

In recent, business intelligence becomes one of important issues due to various analyses on technology trends. Especially, understanding the relations and influences between technologies is core property for the high-performed analysis. To do this, a few works have utilized ontologies constructed automatically but still have many errors and it causes difficulty while interpreting technology trends. Therefore this paper introduces an application which visualizes relationships and influences between technologies according to time series. Our application provides clues for intuitive observations of relationship change between technologies.

An RDB to RDF Mapping System Considering Semantic Relations of RDB Components

For the expansion of the Semantic Web, studies in converting the data stored in the relational database into the ontology are actively in process. Such studies mainly use an RDB to RDF mapping model, the model to map relational database components to RDF components. However, pre-proposed mapping models have got different expression modes and these damage the accessibility and reusability of the users. As a consequence, the necessity of the standardized mapping language was raised and the W3C suggested the R2RML as the standard mapping language for the RDB to RDF model. The R2RML has a characteristic that converts only the relational database schema data to RDF. For the same reasons above, the ontology about the relation data between table name and column name of the relational database cannot be added. In this paper, we propose an RDB to RDF mapping system considering semantic relations of RDB components in order to solve the above issue. The proposed system generates the mapping data by adding the RDFS attribute data into the schema data defined by the R2RML in the relational database. This mapping data converts the data stored in the relational database into RDF which includes the RDFS attribute data. In this paper, we implement the proposed system as a Java-based prototype, perform the experiment which converts the data stored in the relational database into RDF for the comparison evaluation purpose and compare the results against D2RQ, RDBToOnto and Morph. The proposed system expresses semantic relations which has richer converted ontology than any other studies and shows the best performance in data conversion time.

Domain Terminology Collection for Semantic Interpretation of Sensor Network Data

Many studies have investigated the management of data delivered over sensor networks and attempted to standardize their relations. Sensor data come from numerous tangible and intangible sources, and existing work has focused on the integration and management of the sensor data itself. The data should be interpreted according to the sensor environment and related objects, even though the data type, and even the value, is exactly the same. This means that the sensor data should have semantic connections with all objects, and so a knowledge base that covers all domains should be constructed. In this paper, we suggest a method of domain terminology collection based on Wikipedia category information in order to prepare seed data for such knowledge bases. However, Wikipedia has two weaknesses, namely, loops and unreasonable generalizations in the category structure. To overcome these weaknesses, we utilize a horizontal bootstrapping method for category searches and domain-term collection. Both the category-article and article-link relations defined in Wikipedia are employed as terminology indicators, and we use a new measure to calculate the similarity between categories. By evaluating various aspects of the proposed approach, we show that it outperforms the baseline method, having wider coverage and higher precision. The collected domain terminologies can assist the construction of domain knowledge bases for the semantic interpretation of sensor data.

A Study of Prescriptive Analysis Framework for Human Care Services Based On CKAN Cloud

A number of sensor devices are widely distributed and used today owing to the accelerated development of IoT technology. In particular, this technological advancement has allowed users to carry IoT devices with more convenience and efficiency. Based on the IoT sensor data, studies are being actively carried out to recognize the current situation or to analyze and predict future events. However, research for existing smart healthcare services is focused on analyzing users’ behavior from single sensor data and is also focused on analyzing and diagnosing the current situation of the users. Therefore, a method for effectively managing and integrating a large amount of IoT sensor data has become necessary, and a framework considering data interoperability has become necessary. In addition, an analysis framework is needed not only to provide the analysis of the users’ environment and situation from the integrated data, but also to provide guide information to predict future events and to take appropriate action by users. In this paper, we propose a prescriptive analysis framework using a 5W1H method based on CKAN cloud. Through the CKAN cloud environment, IoT sensor data stored in individual CKANs can be integrated based on common concepts. As a result, it is possible to generate an integrated knowledge graph considering interoperability of data, and the underlying data is used as the base data for prescriptive analysis. In addition, the proposed prescriptive analysis framework can diagnose the situation of the users through analysis of user environment information and supports users’ decision making by recommending the possible behavior according to the coming situation of the users. We have verified the applicability of the 5W1H prescriptive analysis framework based on the use case of collecting and analyzing data obtained from various IoT sensors.

XOnto-Apriori: An Effective Association Rule Mining Algorithm for Personalized Recommendation Systems

This paper proposes an extended association rule mining algorithm to improve the accuracy of the Onto-Apriori algorithm which is one of the association rule mining algorithm. The Onto-Apriori algorithm considered the usage of ontology in the association rule mining algorithms. However it does not reflect relationships between items which might have similarity between them. Therefore generated rules from the algorithm are not sufficient to find association between items. To solve the problem, we propose an algorithm, XOnto-Apriori, that can generates association rules and can suggest more valuable candidate item sets applying ontology reasoning function. Experimental results show our proposed approach. The XOnto-Apriori algorithm shows more effective results.

Canonical Sensor Ontology Builder Based on ISO/IEC 11179 for Sensor Network Environments: A Standardized Approach

The advancement of sensor technology has led to an explosive increase in sensors. It causes semantic heterogeneity problems, and much research has focused on sensor ontology building to solve the problems. However, there are still remaining several issues, and one of the most critical issues is about a method for progressive and dynamic concepts management and reuse of sensor ontology. This paper proposes an ontology generation system based on ISO/IEC 11179–MDR (metadata registry). The proposed system is referred to as the Canonical Sensor Ontology Builder (CaSOB) and can create ontologies by reusing the common concepts registered in a canonical sensor ontology concept registry, an MDR. This paper defines a mapping model and processes to create ontology with the concepts registered in an MDR. Our proposal provides many advantages such as high standardization, consistent concept usage, and easy semantic exchange. Therefore, CaSOB facilitates the high quality sensor ontology creation and reduces the costs of sensor ontology integration and system development.

Intelligent Research Performance Appraisal Model Based on Internal/Environmental Evaluation Features

Analysis, prediction, and recommendation of information about experts are very important tasks for future research planning and strategy establishment. However, it takes much time and efforts even for precise analysis of experts because we need to analyze huge and diverse heterogeneous information. There are several application and tools for supporting analysis about researchers, but they provide fragmentary analysis result based on simple evaluation criteria. Therefore, in this paper, we suggest new researcher evaluation model based on diverse performance evaluation features. By using the suggested model, we can analyze and compare researchers in various perspectives. In addition, we can rank researchers and recommend outstanding collaborator in a specified research field.

A Data Visualization System for Considering Relationships Among Scientific Data

With the recent explosive increase in the amount of web-based scientific data in big data environments, various researcher support systems have been developed to help discover desired scientific data and search insights. Scientific and researcher-related data are also applied to social networking services, thus promoting inter-researcher networking. However, much time and effort is put into big data mining to extract information customized to researchers’ specific needs. Moreover, systems that facilitate information extraction by schematizing various inter-data relationships are absent. In this paper, we propose a system that facilitates relevant information extraction from scientific data and provides intuitive data visualization. Such data visualization allows efficient relationship expression between scientific data (relationships between researchers, acronyms and technical terms, and synonyms of a technology name), and provides an author disambiguation interface for authors with the same name. As a result, researchers can extract relevant information from big data with scientific data, and obtain significant information based on cleansed and disambiguated data.