Tae-Eung Sung

Detection and Analysis of Trend Topics for Global Scientific Literature Using Feature Selection Based on Gini-Index

As the volume and diversity of scientific resources grows, trend detection and analysis have become much more important issues. A variety of trend detection, characterization, evaluation and visualization methodologies have been introduced for various application domains. In this paper, we consider detection of temporal trends for topics using feature selection and extraction of additional information from the subtopics of them, for the Global Trends Briefing (GTB) dataset. Thus, we propose a novel trend detection method using feature selection based on the Improved Gini-Index (I-GI) algorithm, which can obtain representative features for given topics. Second, with those features, we extract subtopics for the topics and visualize temporal/emerging/upward/ downward trends with them. Third, utilizing the relations among the subtopics, we obtain relevant documents and seed sentences that co-occur with the upper features for the topic. In addition, we can extract information to forecast future trends relevant to the issues: for example, financial market or emerging technology. In the experimental results, we could obtain good representative features, more specific trends for the topics, and additional useful information.

Technological innovation strategy: how do technology life cycles change by technological area

ABSTRACT Discussions on the patterns of technological innovation have significant implications in terms of the efficient distribution of national R&D resources and the establishment of corporate managerial strategies. This study is focused on calculating and analysing technology cycle time (TCT) by technological area based on patent data that can be used as easily accessible objective indicators for the purpose of modelling the patterns of technological innovation by period and technological area. The main technological areas handled by the study include medical science, vehicles, metallurgy, and computing. Of the patent data registered with the United States Patent and Trademark Office from 1990 to 2014, International Patent Classification sub-class codes representing each technological area were selected to collect patent data, and to generate TCT statistics every five years. The TCT statistics generated is interpreted as the technological life cycle, to be used in modelling technological innovation patterns individualised by technology and period.

A network analysis of strategic alliance drivers in ICT open ecosystem: with focus on mobile, cloud computing, and multimedia

As the phenomenon of convergence in the field of information and communication technology (ICT) and services is accelerated with the development of science and technology, the ecosystem of the ICT industry is gradually evolving into an open ecosystem. This study analyzes the strategic alliance mechanism in the ICT industry, taking the view that active strategic alliances should be pursued for co-evolution between the companies in an open ecosystem. Based on previous studies and expert opinions, the objectives of a strategic alliance are classified into main evaluation categories such as strategy-oriented, cost-oriented, resource-oriented and learning-oriented, and the detailed subcategories are presented. Through an analysis of the ecosystem of the ICT industry, multimedia contents (production, distribution), platform (cloud computing, mobile cloud computing), network (wired network, wireless network), device (home electronics, mobile) are identified as the agents to be analyzed. By applying the analytic network process (ANP) based on the above, the priority of the objectives of strategic alliances in the ICT industry is determined by the agent. This study is expected to promote the development of a business model and co-opetition between the companies to create sustainable competitive advantages in the future open ecosystem, and to contribute to the creation of corporate and national profits.

Database Valuation: How to Determine the Economic Lifetime and Contribution of DB Assets?

Regardless of the increasing interest in practical uses of technology valuation, there exists no valuation model for measuring appropriate values of database assets. Cost approach which involves the calculation of input cost such as software database can be utilized for calculating the establishing expenses of databases, but we need to propose an optimal valuation model to properly reflect future business profitability and standardize how to estimate and apply to primary variables associated. In this study, we look into how to determine the economic life expectancy of database assets based on durable period of 5 years and the contribution of database assets to the entire business value created through the business model therein. Since there has been no valuation model to date to lead to an objective market value of database assets, it is expected that database valuation framework proposed herein would be significantly useful when various data-driven business models are involved.

Models of Database Assets Valuation and their Life-cycle Determination

Although the methodology and models to assess the economic value of technology assets such as patents are being presented in various ways, there does not exist a structured assessment model which enables to objectively assess a database property`s value, and thus there is a need to enhance the application feasibility of practical purposes such as licensing of DB assets, commercialization transfer, security, etc., through the establishment of the valuation model and the life-cycle decision logic. In this study, during the valuation process of DB assets, the size of customer demand group expected and the amount of demand, the size and importance of data sets, the approximate degree of database` contribution to the sales performance of a company, the life-cycle of database assets, etc. will be analyzed whether they are appropriate as input variables or not. As for most of DB assets, due to irregular updates there are hardly cases their life-cycle expires, and thus software package`s persisting period, ie. 5 years, is often considered the standard. We herein propose the life-cycle estimation logic and valuation models of DB assets based on the concept of half life for DB usage frequency under the condition that DB assets` value decays and there occurs no data update over time.

Affect-Based Retrieval of Landscape Images Using Probabilistic Affective Model

We consider the problem of ranking the web image search using human affects. For this, a Probabilistic Affective Model (PAM) is presented for predicting the affects from color compositions (CCs) of images, then the retrieval system is developed using them. The PAM first segments an image into seed regions, then extracts CCs among seed regions and their neighbors, finally infer the numerical ratings of certain affects by comparing the extracted CCs with pre-defined human-devised color triplets. The performance of the proposed system has been studied at an online demonstration site where 52 users search 16,276 landscape images using affects, then the results demonstrated its effectiveness in affect-based image annotation and retrieval.