Jong-Yi Hong

Bio: Jong-Yi Hong is an academic researcher from Pohang University of Science and Technology. The author has contributed to research in topics: Knowledge sharing & Service (business). The author has an hindex of 7, co-authored 13 publications receiving 974 citations. Previous affiliations of Jong-Yi Hong include Andong National University & Kyungnam University.

Context-aware systems

Abstract: Nowadays, numerous journals and conferences have published articles related to context-aware systems, indicating many researchers' interest. Therefore, the goal of this paper is to review the works that were published in journals, suggest a new classification framework of context-aware systems, and explore each feature of classification framework. This paper is based on a literature review of context-aware systems from 2000 to 2007 using a keyword index and article title search. The classification framework is developed based on the architecture of context-aware systems, which consists of the following five layers: concept and research layer, network layer, middleware layer, application layer and user infrastructure layer. The articles are categorized based on the classification framework. This paper allows researchers to extract several lessons learned that are important for the implementation of context-aware systems.

Context-aware system for proactive personalized service based on context history

Abstract: Predicting the preferences of users and providing the personalized services or products based on their preferences are the important issues. However, the research considering users' preferences on context-aware computing is a relatively insufficient research field. Hence, this paper aims to propose an agent-based framework for providing the personalized services using context history on context-aware computing. Based on the proposed framework, we implement a prototype system to show the feasibility of the framework. Previous researches require that the users input their preference manually, but this research provides the personalized services extracting the relationship between users' profile and services under the same context automatically.

A diagnosis framework for identifying the current knowledge sharing activity status in a community of practice

Abstract: The concept of Communities of Practice (CoPs) has been highlighted as an effective method for knowledge sharing in Knowledge Management (KM) and strategically utilized by many organizations. Therefore, the need to diagnose knowledge sharing activities in CoPs has increased. Previous research on CoP strategies has generally suggested broad guidelines without diagnosing the current knowledge sharing status of individual CoPs. Furthermore, diagnosis methodologies are not connected to strategic direction and require too much time and effort to conduct regularly. The purpose of this paper is to develop a sustainable diagnosis framework for identifying knowledge sharing activities in CoPs using Social Network Analysis (SNA) and to suggest strategies for individual CoPs based on the proposed diagnosis framework. Finally, we apply the proposed diagnosis framework to an industry case.

A maturity model based CoP evaluation framework: A case study of strategic CoPs in a Korean company

Abstract: Knowledge management is becoming increasingly important as organizations realize that continuous competitive advantage depends on effective management of their vast and various knowledge assets. In order to improve knowledge management, CoPs (Communities of Practice) are used as powerful tools. By operating CoPs internally and externally, they have made improvements like increased core competency, induced innovation learning, enhanced working efficiency and promoted responsiveness. In order to run CoPs successfully, the evaluation of CoP is also crucial. CoP operators need to understand the current position systemically and determine the currently practiced activities. In order to do this, they need visible and quantifiable metrics for measurement. After deciding the current status of CoP, new objectives and strategies for their improvement are provided. In this paper, we intend to assess the condition of CoPs indirectly, not to measure the efficiency of CoP directly. This research provides a holistic, systematic, and comprehensive framework with maturity stages for navigating CoPs, and provides a road map for moving from immature, inconsistent activities to mature, disciplined approaches aligned to strategic business imperatives.

A feasibility test model for new telecom service development using MCDM method: A case study of video telephone service in Korea

Abstract: The telecommunications industry is undergoing dramatic changes fueled by rapid technical development and regulatory changes. Especially, in Korea, the penetration rate of mobile service is almost 90%; therefore, there is not enough room for a new customer. Furthermore, today's new services have been influenced by more factors in decision making before launching. It has been the main issue for decision makers whether a new service will be successful or not. However, a few methods have been introduced to evaluate the feasibility of a new service. And very few papers have introduced to the evaluatation of comparative advantages among competitive services, but such methods mainly depend on subjective opinion of experts. The proposed model considers the multidimensional factors influencing telecom service evaluation based on the ANP's advantages, which helps service providers in a telecom field to select a new service with the view of their strategies. Also, the BSC perspectives are adapted in the model to generate evaluation measures. Additionally, a new video phone service in Korea is evaluated based on a proposed model.

Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model

Abstract: The purpose of this study is to propose a unified model integrating the technology acceptance model (TAM), task fit technology (TTF) model, MOOCs features and social motivation to investigate continuance intention to use MOOCs. A sample of 252 participants in China that have already used MOOCs took part in this study. Structural equation modeling implemented via partial least squares (PLS) is conducted to test the research hypotheses. The results show that research framework for integrating the TAM for the adoption and TTF model for utility provides a more comprehensive understanding of the behaviors related to this context: (1) perceived usefulness and attitude are critical to the continuance intention to use MOOCs; (2) perceived usefulness is a significant mediator of the effects of perceived ease of use, task-technology fit, reputation, social recognition and social influence on continuance intention; (3) perceived ease of use, task-technology fit, reputation, social recognition and social influence are found to play important roles in predicting continuance intention; (4) individual-technology fit, task-technology fit, and openness affect the perceived ease of use; (5) unexpectedly, perceived ease of use and social influence have no significant effect on attitude, and individual-technology and openness do not affect perceived usefulness.

Heterogeneity in Mobile Cloud Computing: Taxonomy and Open Challenges

Abstract: The unabated flurry of research activities to augment various mobile devices by leveraging heterogeneous cloud resources has created a new research domain called Mobile Cloud Computing (MCC). In the core of such a non-uniform environment, facilitating interoperability, portability, and integration among heterogeneous platforms is nontrivial. Building such facilitators in MCC requires investigations to understand heterogeneity and its challenges over the roots. Although there are many research studies in mobile computing and cloud computing, convergence of these two areas grants further academic efforts towards flourishing MCC. In this paper, we define MCC, explain its major challenges, discuss heterogeneity in convergent computing (i.e. mobile computing and cloud computing) and networking (wired and wireless networks), and divide it into two dimensions, namely vertical and horizontal. Heterogeneity roots are analyzed and taxonomized as hardware, platform, feature, API, and network. Multidimensional heterogeneity in MCC results in application and code fragmentation problems that impede development of cross-platform mobile applications which is mathematically described. The impacts of heterogeneity in MCC are investigated, related opportunities and challenges are identified, and predominant heterogeneity handling approaches like virtualization, middleware, and service oriented architecture (SOA) are discussed. We outline open issues that help in identifying new research directions in MCC.

Data Mining for Internet of Things: A Survey

Abstract: It sounds like mission impossible to connect everything on the Earth together via Internet, but Internet of Things (IoT) will dramatically change our life in the foreseeable future, by making many "impossibles" possible. To many, the massive data generated or captured by IoT are considered having highly useful and valuable information. Data mining will no doubt play a critical role in making this kind of system smart enough to provide more convenient services and environments. This paper begins with a discussion of the IoT. Then, a brief review of the features of "data from IoT" and "data mining for IoT' is given. Finally, changes, potentials, open issues, and future trends of this field are addressed.

The Smartphone Psychology Manifesto

Abstract: By 2025, when most of today's psychology undergraduates will be in their mid-30s, more than 5 billion people on our planet will be using ultra-broadband, sensor-rich smartphones far beyond the abilities of today's iPhones, Androids, and Blackberries. Although smartphones were not designed for psychological research, they can collect vast amounts of ecologically valid data, easily and quickly, from large global samples. If participants download the right "psych apps," smartphones can record where they are, what they are doing, and what they can see and hear and can run interactive surveys, tests, and experiments through touch screens and wireless connections to nearby screens, headsets, biosensors, and other peripherals. This article reviews previous behavioral research using mobile electronic devices, outlines what smartphones can do now and will be able to do in the near future, explains how a smartphone study could work practically given current technology (e.g., in studying ovulatory cycle effects on women's sexuality), discusses some limitations and challenges of smartphone research, and compares smartphones to other research methods. Smartphone research will require new skills in app development and data analysis and will raise tough new ethical issues, but smartphones could transform psychology even more profoundly than PCs and brain imaging did.

Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

Abstract: Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data,” understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context,” or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context-aware computing,” and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.