Ilkyu Ha

Meta-validation of UML structural diagrams and behavioral diagrams with consistency rules

The UML is a widely accepted standard in object-oriented modeling. As the UML is semantically rich, we can widely describe the system that will be developed, but we cannot guarantee the consistency of the designed model. Therefore, it is important to minimize the error from verifying user model in early stage. Here, we propose an useful model for verifying the consistency of UML structural diagrams and behavioral diagrams using OCL-shaped verification rules and meta-metamodels. Firstly, we devise meta-metamodel of the structural diagram and behavioral diagram, we derive verifying rules from each meta-metamodel for verifying the consistency, and then formally specify the rules with OCL for automatic verification. Finally, we verify the usefulness of the rule with a case study.

Technologies and research trends in wireless body area networks for healthcare: a systematic literature review

Owing to the increasing interest in the integration of the medical technology and the information and communications technology, research on wireless body area networks (WBANs), which apply a sensor network to the human body, is being actively conducted. Existing sensor network technology has the potential to be used in a WBAN; however, it has some limitations. In particular, a WBAN has a very different network environment compared to a sensor network that uses free space, because wireless sensors in a BAN transmit through parts of the human body. Therefore, research on WBANs involves a variety of research areas that differ slightly from those of conventional sensor networks and take into account the characteristics of the human body. This study investigates the environmental characteristics of a WBAN that differ from conventional sensor networks and examines the areas that have been studied in academia to realize more efficient WBANs. From studies published since 2001, when the concept of the WBAN was introduced, research trends in WBANs were investigated using the systematic literature review (SLR) technique. The investigation in this study includes the classification of research and research fields in line with research content. Further, survey results are presented and the outlook for further study is summarized.

Cross Checking Rules to Improve Consistency between UML Static Diagram and Dynamic Diagram

There are many well-formedness rules of each UML element in UML specification[1], but there are not any rules that check the consistency among UML diagrams. Therefore, in this paper, we propose several checking rules to improve the consistency among UML diagrams, especially between UML static diagram and dynamic diagram. So we make explicit some requirements on consistency of UML diagrams that are buried in the original well-formedness rules of UML specification and derive some checking rules. Finally, we examine the usefulness of the derived rules through a case study.

MapReduce functions to analyze sentiment information from social big data

Opinion mining, which extracts meaningful opinion information from large amounts of social multimedia data, has recently arisen as a research area. In particular, opinion mining has been used to understand the true meaning and intent of social networking site users. It requires efficient techniques to collect a large amount of social multimedia data and extract meaningful information from them. Therefore, in this paper, we propose a method to extract sentiment information from various types of unstructured social media text data from social networks by using a parallel Hadoop Distributed File System (HDFS) to save social multimedia data and using MapReduce functions for sentiment analysis. The proposed method has stably performed data gathering and data loading and maintained stable load balancing of memory and CPU resources during data processing by the HDFS system. The proposed MapReduce functions have effectively performed sentiment analysis in the experiments. Finally, the sentiment analysis results of the proposed system are very close to those of manual processes.

The Research Trends and the Effectiveness of Smart Learning

The introduction of useful microblogging services like Twitter derives significant changes in various areas of society. The smart tools such as mobile devices and microblogging services have led to great changes in the traditional way of education. It is possible to access good quality educational contents and methods in a more convenient way. Although a lot of smart tools have been applied for educational application, there are only limited researches that demonstrate the educational effectiveness of smart tools through experiment considerations. In this study, research documents about smart learning from 2007 have been treated based on 5 major searching sites; we investigate the research trends of smart learning of education. The opinion of the researchers about the educational use of smart tools is analyzed. Then, through substantial experiment, we are acquainted that the smart tools have a positive effect on the educational environment. As a practical application, the Twitter microblogging service is used for educational communications in 2 classes of a university during a semester. As a result, we can find that smart tools contribute to increase the efficiency of education, but learners and teachers need to pay considerable efforts on the use of smart tools to achieve the purpose.

Analysis of Twitter research trends based on SLR

With the increasing interest in micro blogging services, extensive research has focused on Twitter for a variety of purposes. For effective research on Twitter, an investigation of academic research papers related with Twitter will be required in order to analyze research trends. However, it is not easy to review the vast data. Therefore, we extract the representative literature related to Twitter and investigate the trends in Twitter research by using the systematic literature review (SLR) method. The five most popular resource sites are selected to collect Twitter-related literature and the 106 papers selected based on SLR are analyzed to study interesting trends in Twitter research. To classify the research area, the selected papers are divided into five main research categories according to research topics. In addition, other valuable results such as the inclination of the authors are also understood.

Influence of Twitter Activity on College Classes

With the rapid adoption of smartphones, Twitter has become a major mobile SNS. In spite of the increasing use of Twitter services by students and instructors in colleges, very little empirical studies that concern the impact of social media on the student learning environment have been published. In this paper, a Twitter utilization experiment is conducted for college classes to understand the effects on college student engagement and grades. The participants of experimental study comprise students of 2 college classes during one semester and only voluntary students attend this Twitter-based experiment. Network of Twitter using pattern such as community building, collaboration relationship and information sharing among students are analyzed. Some results about the relationships of seat positions, following patterns and grades among students have been studied. Through understanding of the students netwoking pattern, we find that the possibility of Twitter as a mobile educational tool.

Even energy consumption and backside routing: An improved routing protocol for effective data transmission in wireless body area networks

One of the core technologies of wireless body area networks (WBANs) is the routing technology. For effective routing in WBANs, various network operations such as lifetime extension and energy efficiency are required to be considered. However, the characteristics of the body serve as the most important management elements for stable operation and for guaranteeing effective WBAN management. Therefore, in this study, a routing protocol, even energy consumption and backside routing, is proposed and designed for stable operation of WBANs; it considers even energy consumption for the lifetime extension of the network and the path loss of the node located at the back of the body. For this study, the Mobile-ATTEMPT (M-ATTEMPT) as a recent routing technology is analysed, and an improved algorithm is proposed. Finally, the excellence of the proposed protocol is proved through a variety of experiments. The results show a significant improvement than previous routing technologies in terms of network lifetime, throughput and residual energy.

An Optimal Data Gathering Method for Mobile Sinks in WSNs

In Wireless Sensor Networks (WSN), energy is one of the most important resources since each node collects, processes and transmits data to its base station. Most of the traditional works in WSNs are consisted of static nodes and one base station. Recently, some mobile data gathering methods are proposed to prolong the operation time of sensor networks. One or more mobile collectors are used to gather sensed data from sensor nodes at short transmission ranges. This paper presents to find optimal visiting points and data gathering path for a mobile sink within clusters. With defining an optimal clustering and data gathering path, this method improves the data collection performance as well as the network lifetime extension of sensor networks. The network lifetime is increased to 20% when sensor nodes are divided into from 4 clusters to 15 clusters.

A Platform for Supporting Automatic Data Storing and Visualization of Public and Private Big Data

With the arrival of the Fourth Industrial Revolution, large amounts of data are being produced every day in various fields such as industry, culture, politics, education, medicine, and meteorology. In recent years, as the importance of data has increased, data that have been provided free of charge, such as existing social networks, have become available, and the supply period is being limited. Commercialization of data is a continuous process in all over the world; therefore, the data industry, which collects, processes, analyzes, predicts, and provides data is expected to be more active. In this research, using the universal resource locator (URL)and application programming interface (API) of the dataset provided by various public and personal data providers, we propose an automatic database building and visualization platform that can automatically convert the collected data into database (DB), regardless of the data format. Particularly, it is possible to restructure the dataset on the proposed platform, which allows the user to select only the necessary items in the collected dataset and visualize the data set by fusing it.