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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Internet Computing and Services
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Korean Society for Internet Information
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Volume & Issues
Volume 14, Issue 6 - Dec 2013
Volume 14, Issue 5 - Oct 2013
Volume 14, Issue 4 - Aug 2013
Volume 14, Issue 3 - Jun 2013
Volume 14, Issue 2 - Apr 2013
Volume 14, Issue 1 - Feb 2013
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Performance Analysis of Frequent Pattern Mining with Multiple Minimum Supports
Ryang, Heungmo ; Yun, Unil ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 1~8
DOI : 10.7472/jksii.2013.14.6.01
Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.
Design and Implementation of RSSI-based Intelligent Location Estimation System
Lim, Chang Gyoon ; Kang, O Seong Andrew ; Lee, Chang Young ; Kim, Kang Chul ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 9~18
DOI : 10.7472/jksii.2013.14.6.09
In this paper, we design and implement an intelligent system for finding objects with RFID(Radio Frequency IDentification) tag in which an mobile robot can do. The system we developed is a learning system of artificial neural network that uses RSSI(Received Signal Strength Indicator) value as input and absolute coordination value as target. Although a passive RFID is used for location estimation, we consider an active RFID for expansion of recognition distance. We design the proposed system and construct the environment for indoor location estimation. The designed system is implemented with software and the result related learning is shown at test bed. We show various experiment results with similar environment of real one from earning data generation to real time location estimation. The accuracy of location estimation is verified by simulating the proposed method with allowable error. We prepare local test bed for indoor experiments and build a mobile robot that can find the objects user want.
The Implementation of Remote Machine Health Monitoring System using Internet
Kim, Woong-Sik ; Kim, Jong-Ki ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 19~23
DOI : 10.7472/jksii.2013.14.6.19
This research is about the Implementation of Remote Machine Health Monitoring System using Internet. This research will help users in the un-installed office to save a lot of cost and time from checking and managing machines` condition installed in the factory. We have made an experiment and developed the application program and the monitoring terminal which can monitor the machine`s condition. This research will contribute to the development of internet and remote instrumentation engineering in the future. Finally the performance of the proposed system was evaluated through experiments, it showed the good performance and the possibility of commercialization.
A fuzzy ART Approach for IS Personnel Selection and Evaluation
Uprety, Sudan Prasad ; Jeong, Seung Ryul ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 25~32
DOI : 10.7472/jksii.2013.14.6.25
Due to increasing competition of globalization and fast technological improvements the appropriate method for evaluating and selecting IS-personnel is one of the key factors for an organization`s success. Personnel selection is a multi-criteria decision-making (MCDM) problem which consists of both qualitative and quantitative metrics. Although many articles have discussed various knowledge and skills IS personnel should possess, no specific model for IS personnel selection and evaluation, to our knowledge, has been published up to now. After reviewing the IS personnel`s important characteristics, we propose an approach for categorizing the IS personnel based on their skills, ability, and knowledge during evaluation and selection process. Our proposed approach is derived from a model of neural network algorithm. We have adapted and implemented the fuzzy ART algorithm with Jaccard choice function. The result of an illustrative numerical example is proposed to demonstrate the easiness and effectiveness of our approach.
Joint Angles Analysis of Intelligent upper limb and lower extremities Wheelchair Robot System
Song, Byoung-Ho ; Kim, Kwang Jin ; Lee, Chang Sun ; Lim, Chang Gyoon ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 33~39
DOI : 10.7472/jksii.2013.14.6.33
When the eldery with limited mobility and disabled use a wheelchairs to move, it can cause decreased exercise ability like decline muscular strength in upper limb and lower extremities. The disabled people suffers with spinal cord injuries or post stroke hemiplegia are easily exposed to secondary problems due to limited mobility. In this paper, We designed intelligent wheelchair robot system for upper limb and lower extremities exercise/rehabilitation considering the characteristics of these severely disabled person. The system consists of an electric wheelchair, biometrics module for Identification characteristics of users, upper limb and lower extremities rehabilitation. In this paper, describes the design and configurations and of developed robot. Also, In order to verify the system function, conduct performance evaluation targeting non-disabled about risk context analysis with biomedical signal change and upper limb and lower extremities rehabilitation over wheelchair robot move. Consequently, it indicate sufficient tracking performance for rehabilitation as at about 86.7% average accuracy for risk context analysis and upper limb angle of 2.5 and lower extremities angle of 2.3 degrees maximum error range of joint angle.
Implementation of parallel blocked LU decomposition program for utilizing cache memory on GP-GPUs
Kim, Youngtae ; Kim, Doo-Han ; Yu, Myoung-Han ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 41~47
DOI : 10.7472/jksii.2013.14.6.41
GP-GPUs are general purposed GPUs for numerical computation based on multiple threads which are originally for graphic processing. GP-GPUs provide cache memory in a form of shared memory which user programs can access directly, unlikely typical cache memory. In this research, we implemented the parallel block LU decomposition program to utilize cache memory in GP-GPUs. The parallel blocked LU decomposition program designed with Nvidia CUDA C run 7~8 times faster than nun-blocked LU decomposition program in the same GP-GPU computation environment.
A Design of Analysis System on TV Advertising Effect of Social Networking Using Hadoop
Hur, Seoyeon ; Kim, Yoonhee ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 49~57
DOI : 10.7472/jksii.2013.14.6.49
As `Big data` has been one of challenging issues, development of new services using Social Network Service (SNS) which is its typical example became active. SNS has developed as a media where everyone communicates at real time and the number of SNS opinion analyzing services is increasing. Meanwhile, new approach to acquire and analyze twitter data becomes necessary in TV advertisement system. This paper proposes LiveAD system, which store and analyze big data such as twitter data as well as analyze TV advertising effect based on twitter data. As a proof of concept, the proposed system has been implemented collecting and analyzing twitter data using Hadoop. The result of collected information over the system increases the chance of analyzing TV advertising effect on twitter in real-time.
A Study on Development of Smart Literacy Standards of Teachers and Students in Smart Learning Environments
Jun, Woochun ; Hong, Suk-Ki ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 59~70
DOI : 10.7472/jksii.2013.14.6.59
With advances in information and communication technologies, many innovative technologies have been developed. Those technologies are changing every aspect of our daily life. Especially smart technologies are changing our life dramatically. Smart devices such as tablet PCs and smart phones are used in education so that new concept called "smart learning" is created and used. Currently smart learning becomes popular in accordance with wide distribution of smart devices and smart contents in schools. In order to compare and check the current status and progress of individuals in smart environment, we need smart literacy standards. However, there has been only few works for smart literacy standards for teachers and students. Also, those standards need to be improved. The purpose of this paper is to develop smart literacy standards for teachers and students in smart learning environment. The proposed literacy standards are developed based on the existing ICT literacy standards. In this work, smart literacy standards consist of four main areas, smart education, smart knowledge, smart application, and smart ethics, respectively. For development of smart literacy, wide experts from teachers, professors, and researchers are selected and surveyed. Their responses are analyzed using through statistical analysis so that final smart literacy standards are obtained.
Design and Implementation of MongoDB-based Unstructured Log Processing System over Cloud Computing Environment
Kim, Myoungjin ; Han, Seungho ; Cui, Yun ; Lee, Hanku ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 71~84
DOI : 10.7472/jksii.2013.14.6.71
Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client`s business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client`s business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure`s analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user`s various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system`s superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.
User Oriented clustering of news articles using Tweets Heterogeneous Information Network
Shoaib, Muhammad ; Song, Wang-Cheol ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 85~94
DOI : 10.7472/jksii.2013.14.6.85
With the emergence of world wide web, in particular web 2.0 the rapidly growing amount of news articles has created a problem for users in selection of news articles according to their requirements. To overcome this problem different clustering mechanism has been proposed to broadly categorize news articles. However these techniques are totally machine oriented techniques and lack users` participation in the process of decision making for membership of clustering. In order to overcome the issue of zero-participation in the process of clustering news articles in this paper we have proposed a framework for clustering news articles by combining users` judgments that they post on twitter with the news articles to cluster the objects. We have employed twitter hash-tags for this purpose. Furthermore we have computed the credibility of users` based on frequency of retweets for their tweets in order to enhance the accuracy of the clustering membership function. In order to test performance of proposed methodology, we performed experiments on tweets messages tweeted during general election 2013 in Pakistan. Our results proved over claim that using users` output better outcome can be achieved then ordinary clustering algorithms.
Performance of Collaboration Activities upon SME`s Idiosyncrasy
Lee, Hye Sun ; Oh, Junseok ; Lee, Jaeki ; Lee, Bong Gyou ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 95~105
DOI : 10.7472/jksii.2013.14.6.95
Recently, SME`s Collaboration activities have become one of a vital factor for sustaining competitive edge. This is because of the rapidly changing and competitive market environment, and also to leverage performance by overcoming obstacles of having limited internal resources. Discussing about the effects and relationships of the firm`s collaboration activities and its outputs are not new. However, as ICT and various technologies have been diffused into the traditional industries, boundaries and practice capabilities within the industries are becoming ambiguous. Thus contents of the products/services and their development methods are also go and come over the industries. Although many researchers suggested the relations of SME`s collaboration activities and innovation performances, most of the previous literatures are focusing on broad perspectives of firm`s environmental factors rather than considering various SME`s idiosyncrasy factors such as their major product and customer types at once. Therefore, the purpose of this paper is to analyze how SME(Small Medium Enterprise)`s external collaboration activities by their idiosyncrasy act as an input to types of innovation performance. In order to analyze collaboration effects in detail, we defined factors that can represent the SME`s business environment - Perceived importance of using external resources, Perceived importance of external partnership, Collaboration and Collaboration levels of Major Product types, Customer types and lastly the Firm Sizes. We have also specifically divided the performance of innovation types as product innovation and process innovation based on existing research. In this study, the empirical analysis is based on Probit Regression Model to observe the correlations with the impact of each SME`s business environment and their activities. For the empirical data, 497 samples were collected which, this sample data was extracted from the `Korean Open Innovation Survey` performed by ETRI(Korean Electronics Telecommunications Research Institute) in 2010. As a result, empirical test results indicated that the impact of collaboration varies depend on the innovation types (Product and Process Innovation). The Impact of the collaboration level for the product innovation tend to be more effective when SMEs are developing for a final product, targeting on for individual customers (B2C). But on the other hand, the analysis result of the Process innovation tend to be higher than the product innovation, when SMEs are developing raw materials for their partners or to other firms targeting on for manufacturing industries(B2B). Also perceived importance of using external resources has effected to both product and process innovation performance. But Perceived importance of external partnership was statistically insignificant. Interesting finding was that the service product has negative effects on for the process innovation performance. And Relationship between size of the firms and their external collaboration activities with their performance of the innovations indicated that the bigger firms(over 100 of employees) tend to have better for both product and process innovations. Finally, implications of the results can be suggested as performance of innovation can be varied depends on firm`s unique business idiosyncrasy as well as levels of external collaboration activities. The Implication of this research can be considered for firms in selecting an appropriate strategy as well as for policy makers.
Analysis of Assortativity in the Keyword-based Patent Network Evolution
Choi, Jinho ; Kim, Junguk ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 107~115
DOI : 10.7472/jksii.2013.14.6.107
Various networks can be observed in the world. Knowledge networks which are closely related with technology and research are especially important because these networks help us understand how knowledge is produced. Therefore, many studies regarding knowledge networks have been conducted. The assortativity coefficient represents the tendency of connections between nodes having a similar property as figures. The relevant characteristics of the assortativity coefficient help us understand how corresponding technologies have evolved in the keyword-based patent network which is considered to be a knowledge network. The relationships of keywords in a knowledge network where a node is depicted as a keyword show the structure of the technology development process. In this paper, we suggest two hypotheses basedon the previous research indicating that there exist core nodes in the keyword network and we conduct assortativity analysis to verify the hypotheses. First, the patents network based on the keyword represents disassortativity over time. Through our assortativity analysis, it is confirmed that the knowledge network shows disassortativity as the network evolves. Second, as the keyword-based patents network becomes disassortavie, clustering coefficients become lower. As the result of this hypothesis, weconfirm the clustering coefficient also becomes lower as the assortative coefficient of the network gets lower. Another interesting result concerning the second hypothesis is that, when the knowledge network is disassorativie, the tendency of decreasing of the clustering coefficient is much higher than when the network is assortative.
A Hybrid Approach of Efficient Facial Feature Detection and Tracking for Real-time Face Direction Estimation
Kim, Woonggi ; Chun, Junchul ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 117~124
DOI : 10.7472/jksii.2013.14.6.117
In this paper, we present a new method which efficiently estimates a face direction from a sequences of input video images in real time fashion. For this work, the proposed method performs detecting the facial region and major facial features such as both eyes, nose and mouth by using the Haar-like feature, which is relatively not sensitive against light variation, from the detected facial area. Then, it becomes able to track the feature points from every frame using optical flow in real time fashion, and determine the direction of the face based on the feature points tracked. Further, in order to prevent the erroneously recognizing the false positions of the facial features when if the coordinates of the features are lost during the tracking by using optical flow, the proposed method determines the validity of locations of the facial features using the template matching of detected facial features in real time. Depending on the correlation rate of re-considering the detection of the features by the template matching, the face direction estimation process is divided into detecting the facial features again or tracking features while determining the direction of the face. The template matching initially saves the location information of 4 facial features such as the left and right eye, the end of nose and mouse in facial feature detection phase and reevaluated these information when the similarity measure between the stored information and the traced facial information by optical flow is exceed a certain level of threshold by detecting the new facial features from the input image. The proposed approach automatically combines the phase of detecting facial features and the phase of tracking features reciprocally and enables to estimate face pose stably in a real-time fashion. From the experiment, we can prove that the proposed method efficiently estimates face direction.
Normal and Malicious Application Pattern Analysis using System Call Event on Android Mobile Devices for Similarity Extraction
Ham, You Joung ; Lee, Hyung-Woo ;
Journal of Internet Computing and Services, volume 14, issue 6, 2013, Pages 125~139
DOI : 10.7472/jksii.2013.14.6.125
Distribution of malicious applications developed by attackers is increasing along with general normal applications due to the openness of the Android-based open market. Mechanism that allows more accurate ways to distinguish normal apps and malicious apps for common mobile devices should be developed in order to reduce the damage caused by the rampant malicious applications. This paper analysed the normal event pattern from the most highly used game apps in the Android open market to analyse the event pattern from normal apps and malicious apps of mobile devices that are based on the Android platform, and analysed the malicious event pattern from the malicious apps and the disguising malicious apps in the form of a game app among 1260 malware samples distributed by Android MalGenome Project. As described, experiment that extracts normal app and malicious app events was performed using Strace, the Linux-based system call extraction tool, targeting normal apps and malicious apps on Android-based mobile devices. Relevance analysis for each event set was performed on collected events that occurred when normal apps and malicious apps were running. This paper successfully extracted event similarity through this process of analyzing the event occurrence characteristics, pattern and distribution on each set of normal apps and malicious apps, and lastly suggested a mechanism that determines whether any given app is malicious.