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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Intelligence and Information Systems
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Volume 17, Issue 4 - Dec 2011
Volume 17, Issue 3 - Sep 2011
Volume 17, Issue 2 - Jun 2011
Volume 17, Issue 1 - Mar 2011
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Product Family Design based on Analytic Network Process
Kim, Tai-Oun ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 1~17
In order to maintain customer satisfaction and to remain productive and efficient in today's global competition, mass customization is adopted in many leading companies. Mass customization through product family and product platform enables companies to develop new products with flexibility, efficiency and quick responsiveness. Thus, product family strategy based on product platform is well suited to realize the mass customization. Product family is defined as a group of related products that share common features, components, and subsystems; and satisfy a variety of market niches. The objective is to propose a product family design strategy that provides priority weights among product components by satisfying customer requirements. The decision making process for a new product development requires a multiple criteria decision making technique with feedback. An analytical network process is adopted for the decision making modeling and procedure. For the implementation, a netbook product known as a small PC which is appropriate for the product family model is adopted. According to the proposed architecture, the priority weight of each component for each product family is derived. The relationship between the customer requirement and product component is analyzed and evaluated using QFD model.
Semantic Search : A Survey
Park, Jin-Soo ; Kim, Nam-Won ; Choi, Min-Jung ; Jin, Zhe ; Choi, Young-Seok ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 19~36
Since the ambitious declaration of the vision of the Semantic Web, a growing number of studies on semantic search have recently been made. However, we recognize that our community has not so much accomplished despite those efforts. We analyze two underlying problems : a lack of a shared notion of semantic search that guides current research, and a lack of a comprehensive view that envisions future work. Based on this diagnosis, we start by defining semantic search as the process of retrieving desired information in response to user's input using semantic technologies such as ontologies. Then, we propose a classification framework in order for the community to obtain the better understanding of semantic search. The proposed classification framework consists of input processing, target source, search methodology, results ranking, and output data type. Last, we apply our proposed framework to prior studies and suggest future research directions.
Automatic Generation of DB Images for Testing Enterprise Systems
Kwon, Oh-Seung ; Hong, Sa-Neung ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 37~58
In general, testing DB applications is much more difficult than testing other types of software. The fact that the DB states as much as the input data influence and determine the procedures and results of program testing is one of the decisive reasons for the difficulties. In order to create and maintain proper DB states for testing, it not only takes a lot of time and efforts, but also requires extensive IT expertise and business knowledge. Despite the difficulties, there are not enough research and tools for the needed help. This article reports the result of research on automatic creation and maintenance of DB states for testing DB applications. As its core, this investigation develops an automation tool which collects relevant information from a variety of sources such as log, schema, tables and messages, combines collected information intelligently, and creates pre- and post-Images of database tables proper for application tests. The proposed procedures and tool are expected to be greatly helpful for overcoming inefficiencies and difficulties in not just unit and integration tests but including regression tests. Practically, the tool and procedures proposed in this research allows developers to improve their productivity by reducing time and effort required for creating and maintaining appropriate DB sates, and enhances the quality of DB applications since they are conducive to a wider variety of test cases and support regression tests. Academically, this research deepens our understanding and introduces new approach to testing enterprise systems by analyzing patterns of SQL usages and defining a grammar to express and process the patterns.
CTKOS : Categorized Tag-based Knowledge Organization System
Yoo, Dong-Hee ; Kim, Gun-Woo ; Choi, Keun-Ho ; Suh, Yong-Moo ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 59~74
As more users are willingly participating in the creation of web contents, flat folksonomy using simple tags has emerged as a powerful instrument to classify and share a huge amount of knowledge on the web. However, flat folksonomy has semantic problems, such as ambiguity and misunderstanding of tags. To alleviate such problems, many studies have built structured folksonomy with a hierarchical structure or relationships among tags. However, structured folksonomy also has some fundamental problems, such as limited tagging to pre-defined vocabulary for new tags and the timeconsuming manual effort required for selecting tags. To resolve these problems, we suggested a new method of attaching a categorized tag (CT), followed by its category, to web content. CTs are automatically integrated into collaboratively-built structured folksonomy (CSF) in real time, reflecting the tag-and-category relationships by majority users. Then, we developed a CT-based knowledge organization system (CTKOS), which builds the CSF to classify organizational knowledge and allows us to locate the appropriate knowledge.
The Impact of Message Characteristics on Online Viral Diffusion in Online Social Media Services : The Case of Twitter
Nam, Young-Woo ; Son, In-Soo ; Lee, Dong-Won ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 75~94
In this paper, we explore the information diffusion mechanism under social network environments by investigating the effect of message characteristics on the volume and speed of retweeting in Twitter, a popular online social media service. To this end, we select eight main keywords (i.e., '무상급식', '반값등록금', '나가수', '평창', '김연아', '박태환', '아이폰', '갤럭시') that have been popular on online social media in recent days. Each keyword represents various social aspects of Korea that recently grab people's attention such as political issues, entertainment, sports celebrities, and the latest digital products, and eventually holds distinctive message characteristics. Analyzing the frequency and velocity of retweeting for each keyword, we find that more than half of the sample messages posted on Twitter contain personal opinions for the certain keyword, but we also find that the tweets which include objective messages with hyperlink are the fastest ones when being retweeted by other followers. In overall, when being retweeted, the group of messages related to the certain keyword present distinctive diffusion patterns and speed according to message characteristics. From academic perspective, the findings in the study broaden our theoretical knowledge of information diffusion mechanism over online social media. For practitioners, the results also provide managerial implications regarding how to strategically utilize online social media for marketing communications with customers.
The Adaptive Personalization Method According to Users Purchasing Index : Application to Beverage Purchasing Predictions
Park, Yoon-Joo ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 95~108
TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.
A Real-Time Stock Market Prediction Using Knowledge Accumulation
Kim, Jin-Hwa ; Hong, Kwang-Hun ; Min, Jin-Young ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 109~130
One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.
A Comparative Study of Information Delivery Method in Networks According to Off-line Communication
Park, Won-Kuk ; Choi, Chan ; Moon, Hyun-Sil ; Choi, Il-Young ; Kim, Jae-Kyeong ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 131~142
In recent years, Social Network Service, which is defined as a web-based service that allows an individual to construct a public or a semi-public profile within a bounded system, articulates a list of other users with whom they share connections, and traverses their list of connections. For example, Facebook and Twitter are the representative sites of Social Network Service, and these sites are the big issue in the world. A lot of people use Social Network Services to connect and maintain social relationship. Recently the users of Social Network Services have increased dramatically. Accordingly, many organizations become interested in Social Network Services as means of marketing, media, communication with their customers, and so on, because social network services can offer a variety of benefits to organizations such as companies and associations. In other words, organizations can use Social Network Services to respond rapidly to various user's behaviors because Social Network Services can make it possible to communicate between the users more easily and faster. And marketing cost of the Social Network Service is lower than that of existing tools such as broadcasts, news papers, and direct mails. In addition, Social network Services are growing in market place. So, the organizations such as companies and associations can acquire potential customers for the future. However, organizations uniformly communicate with users through Social Network Service without consideration of the characteristics of the networks although networks have different effects on information deliveries. For example, members' cohesion in an offline communication is higher than that in an online communication because the members of the offline communication are very close. that is, the network of the offline communication has a strong tie. Accordingly, information delivery is fast in the network of the offline communication. In this study, we compose two networks which have different characteristic of communication in Twitter. First network is constructed with data based on an offline communication such as friend, family, senior and junior in school. Second network is constructed with randomly selected data from users who want to associate with friends in online. Each network size is 250 people who divide with three groups. The first group is an ego which means a person in the center of the network. The second group is the ego's followers. The last group is composed of the ego's follower's followers. We compare the networks through social network analysis and follower's reaction analysis. We investigate density and centrality to analyze the characteristic of each network. And we analyze the follower's reactions such as replies and retweets to find differences of information delivery in each network. Our experiment results indicate that density and centrality of the offline communicationbased network are higher than those of the online-based network. Also the number of replies are larger than that of retweets in the offline communication-based network. On the other hand, the number of retweets are larger than that of replies in the online based network. We identified that the effect of information delivery in the offline communication-based network was different from those in the online communication-based network through experiments. So, you configure the appropriate network types considering the characteristics of the network if you want to use social network as an effective marketing tool.
Electronic Roll Book using Electronic Bracelet.Child Safe-Guarding Device System
Moon, Seung-Jin ; Kim, Tae-Nam ; Kim, Pan-Su ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 143~155
Lately electronic tagging policy for the sexual offenders was introduced in order to reduce and prevent sexual offences. However, most sexual offences against children happening these days are committed by the tagged offenders whose identities have been released. So, for the crime prevention, we need measures with which we could minimize the suffers more promptly and actively. This paper suggests a new system to relieve the sexual abuse related anxiety of the children and solve the problems that electronic bracelet has. Existing bracelets are only worn by serious criminals, and it's only for risk management and positioning, there is no way to protect the children who are the potential victims of sexual abuse and there actually happened some cases. So we suggest also letting the students(children) wear the LBS(Location Based Service) and USN(Ubiquitous Sensor Network) technology based electronic bracelets to monitor and figure out dangerous situations intelligently, so that we could prevent sexual offences against children beforehand, and while a crime is happening, we could judge the situation of the crime intelligently and take swift action to minimize the suffer. And by checking students' attendance and position, guardians could know where their children are in real time and could protect the children from not only sexual offences but also violent crimes against children like kidnapping. The overall system is like follows : RFID Tag for children monitors the approach of offenders. While an offender's RFID tag is approaching, it will transmit the situation and position as the first warning message to the control center and the guardians. When the offender is going far away, it turns to monitoring mode, and if the tag of the child or the offender is taken off or the child and offender stay at one position for 3~5 minutes or longer, then it will consider this as a dangerous situation, then transmit the emergency situations and position as the second warning message to the control center and the guardians, and ask for the dispatch of police to prevent the crime at the initial stage. The RFID module of criminals' electronic bracelets is RFID TAG, and the RFID module for the children is RFID receiver(reader), so wherever the offenders are, if an offender is at a place within 20m from a child, RFID module for children will transmit the situation every certain periods to the control center by the automatic response of the receiver. As for the positioning module, outdoors GPS or mobile communications module(CELL module)is used and UWB, WI-FI based module is used indoors. The sensor is set under the purpose of making it possible to measure the position coordinates even indoors, so that one could send his real time situation and position to the server of central control center. By using the RFID electronic roll book system of educational institutions and safety system installed at home, children's position and situation can be checked. When the child leaves for school, attendance can be checked through the electronic roll book, and when school is over the information is sent to the guardians. And using RFID access control turnstiles installed at the apartment or entrance of the house, the arrival of the children could be checked and the information is transmitted to the guardians. If the student is absent or didn't arrive at home, the information of the child is sent to the central control center from the electronic roll book or access control turnstiles, and look for the position of the child's electronic bracelet using GPS or mobile communications module, then send the information to the guardians and teacher so that they could report to the police immediately if necessary. Central management and control system is built under the purpose of monitoring dangerous situations and guardians' checking. It saves the warning and pattern data to figure out the areas with dangerous situation, and could help introduce crime prevention systems like CCTV with the highest priority. And by DB establishment personal data could be saved, the frequency of first and second warnings made, the terminal ID of the specific child and offender, warning made position, situation (like approaching, taken off of the electronic bracelet, same position for a certain time) and so on could be recorded, and the data is going to be used for preventing crimes. Even though we've already introduced electronic tagging to prevent recurrence of child sexual offences, but the crimes continuously occur. So I suggest this system to prevent crimes beforehand concerning the children's safety. If we make electronic bracelets easy to use and carry, and set the price reasonably so that many children can use, then lots of criminals could be prevented and we can protect the children easily. By preventing criminals before happening, it is going to be a helpful system for our safe life.
An Intelligent Intrusion Detection Model Based on Support Vector Machines and the Classification Threshold Optimization for Considering the Asymmetric Error Cost
Lee, Hyeon-Uk ; Ahn, Hyun-Chul ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 157~173
As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.
A Study on the Effect of Booth Recommendation System on Exhibition Visitors Unplanned Visit Behavior
Chung, Nam-Ho ; Kim, Jae-Kyung ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 175~191
The Relationships among Perceived Value, Use-Diffusion, Loyalty of Mobile Instant Messaging Service
Jo, Dong-Hyuk ; Park, Jong-Woo ; Chun, Hyun-Jae ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 193~212
Mobile instant messaging service is surfacing to an important keyword in the mobile market together with popularization of Smart phones. Mobile instant messaging service in Korea has become popular to the degree of 87.9% usages from total Smartphone holders, and it is expected that using populations will be more enlarged afterwards if considering a fact that its populations of Smartphone is continuously being increased after exceeding 10 million persons (Trend Monitor, June 2011). In the instant messaging market where competitions have been deepened day by day, raising customer's royalties will be the key for company's business survivals and goals of corporate marketing strategies. It could be said that understanding on which factors affect to customer retentions and royalties is very important. Specially, as changing status is being progressed very quickly in case of innovative mobile services like the instant messaging service, research necessities on how many do consumers use the services after accepting them, how much do consumers use them variously, and whether does it connect to long-term relations have been increased, but studies on such matters are in insufficient situations actually. Therefore, this study examined on which effects were affected to use-diffusion and loyalty factors from perceived customer vales' factors having been occurred after accepting the mobile instant messaging service, namely 'functional value', 'monetary value', 'emotional value', and 'social value'. Also, the study looked into what kind of roles do the service usage and using variety play to service's continued using intents as a loyalty index, recommending intents to others, and brand switching intents. And then the study laid the main purpose in trying to provide implications for enhancing customer securities and royalties on the mobile instant messaging service through research's results. The research hypotheses are as follows; H1: Perceived values will affect influences to royalties. H2: Use-Diffusion will affect influences to loyalty. H3: Perceived value will affect influences to loyalty. H4: The use-diffusion will play intermediating roles between perceived values and loyalty. Total 276 cases among collected 284 ones were used for the statistical analysis by SPSS ver. 15 package. Reliability, Factor analysis, regression were done. As the result of research, 'monetary value' and 'emotional value' affected to 'usage' among perceived value factors, and 'emotional value' was appeared as affecting the largest influence. Besides, the usage affected to constant-using intents and recommending intents to others, and using varieties were displayed as affecting to recommending intents to others. On the other hand, 'Using' and 'Using diversity' were appeared as not affecting to 'brand switching intentions'. Meanwhile, as the result of recognizing about effects of perceived values on the loyalty, it was appeared such like 'continued using intents' affected to'functional value', 'monetary value', and 'social value' first, and also 'monetary value', 'emotional value', and 'social value' affected to 'recommending intents to others'. On the other hand, it was shown such like only 'social value' affected influences to 'brand switching intents', and thus contrary results with the factor 'constant-using intents' were displayed. So, it seems that there are many applications to service provides who are worrying about marketing strategies for making consumer retains (constant-using) and new consumer's inductions (brand-switching intents). Finally, as a result of looking into intermediating roles of the use-diffusion factor in relations between conceived values and royalties at hypothesis 4, 'using' and 'using diversity' were displayed as affecting significant influences all together. Regarding to research result's implications, for expanding and promoting continued uses of the mobile instant messaging service by service providers: First, encouraging recognitions on the perceived value connected to users' service usage are necessary. Second, setting up user's use-diffusion strategies are required so as to enhance the loyalty after understanding a fact that use-diffusion patterns affecting to the service's loyalty are different. Finally, methods of raising customer loyalties and making constant relationships have to be grouped by analyzing on what are the customer value's factors that can satisfy users in competitive alterations.
Development of Agent-based Platform for Coordinated Scheduling in Global Supply Chain
Lee, Jung-Seung ; Choi, Seong-Woo ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 213~226
In global supply chain, the scheduling problems of large products such as ships, airplanes, space shuttles, assembled constructions, and/or automobiles are complicated by nature. New scheduling systems are often developed in order to reduce inherent computational complexity. As a result, a problem can be decomposed into small sub-problems, problems that contain independently small scheduling systems integrating into the initial problem. As one of the authors experienced, DAS (Daewoo Shipbuilding Scheduling System) has adopted a two-layered hierarchical architecture. In the hierarchical architecture, individual scheduling systems composed of a high-level dock scheduler, DAS-ERECT and low-level assembly plant schedulers, DAS-PBS, DAS-3DS, DAS-NPS, and DAS-A7 try to search the best schedules under their own constraints. Moreover, the steep growth of communication technology and logistics enables it to introduce distributed multi-nation production plants by which different parts are produced by designated plants. Therefore vertical and lateral coordination among decomposed scheduling systems is necessary. No standard coordination mechanism of multiple scheduling systems exists, even though there are various scheduling systems existing in the area of scheduling research. Previous research regarding the coordination mechanism has mainly focused on external conversation without capacity model. Prior research has heavily focuses on agent-based coordination in the area of agent research. Yet, no scheduling domain has been developed. Previous research regarding the agent-based scheduling has paid its ample attention to internal coordination of scheduling process, a process that has not been efficient. In this study, we suggest a general framework for agent-based coordination of multiple scheduling systems in global supply chain. The purpose of this study was to design a standard coordination mechanism. To do so, we first define an individual scheduling agent responsible for their own plants and a meta-level coordination agent involved with each individual scheduling agent. We then suggest variables and values describing the individual scheduling agent and meta-level coordination agent. These variables and values are represented by Backus-Naur Form. Second, we suggest scheduling agent communication protocols for each scheduling agent topology classified into the system architectures, existence or nonexistence of coordinator, and directions of coordination. If there was a coordinating agent, an individual scheduling agent could communicate with another individual agent indirectly through the coordinator. On the other hand, if there was not any coordinating agent existing, an individual scheduling agent should communicate with another individual agent directly. To apply agent communication language specifically to the scheduling coordination domain, we had to additionally define an inner language, a language that suitably expresses scheduling coordination. A scheduling agent communication language is devised for the communication among agents independent of domain. We adopt three message layers which are ACL layer, scheduling coordination layer, and industry-specific layer. The ACL layer is a domain independent outer language layer. The scheduling coordination layer has terms necessary for scheduling coordination. The industry-specific layer expresses the industry specification. Third, in order to improve the efficiency of communication among scheduling agents and avoid possible infinite loops, we suggest a look-ahead load balancing model which supports to monitor participating agents and to analyze the status of the agents. To build the look-ahead load balancing model, the status of participating agents should be monitored. Most of all, the amount of sharing information should be considered. If complete information is collected, updating and maintenance cost of sharing information will be increasing although the frequency of communication will be decreasing. Therefore the level of detail and updating period of sharing information should be decided contingently. By means of this standard coordination mechanism, we can easily model coordination processes of multiple scheduling systems into supply chain. Finally, we apply this mechanism to shipbuilding domain and develop a prototype system which consists of a dock-scheduling agent, four assembly- plant-scheduling agents, and a meta-level coordination agent. A series of experiments using the real world data are used to empirically examine this mechanism. The results of this study show that the effect of agent-based platform on coordinated scheduling is evident in terms of the number of tardy jobs, tardiness, and makespan.
Keyword Network Analysis for Technology Forecasting
Choi, Jin-Ho ; Kim, Hee-Su ; Im, Nam-Gyu ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 227~240
New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.
Optimization of Support Vector Machines for Financial Forecasting
Kim, Kyoung-Jae ; Ahn, Hyun-Chul ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 241~254
Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.
Exploratory Case Study for Key Successful Factors of Producy Service System
Park, A-Rum ; Jin, Dong-Su ; Lee, Kyoung-Jun ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 255~277
Product Service System(PSS), which is an integrated combination of product and service, provides new value to customer and makes companies sustainable as well. The objective of this paper draws Critical Successful Factors(CSF) of PSS through multiple case study. First, we review various concepts and types in PSS and Platform business literature currently available on this topic. Second, after investigating various cases with the characteristics of PSS and platform business, we select four cases of 'iPod of Apple', 'Kindle of Amazon', 'Zune of Microsoft', and 'e-book reader of Sony'. Then, the four cases are categorized as successful and failed cases according to criteria of case selection and PSS classification. We consider two methodologies for the case selection, i.e., 'Strategies for the Selection of Samples and Cases' proposed by Bent(2006) and the seven case selection procedures proposed by Jason and John(2008). For case selection, 'Stratified sample and Paradigmatic cases' is adopted as one of several options for sampling. Then, we use the seven case selection procedures such as 'typical', 'diverse', 'extreme', 'deviant', 'influential', 'most-similar', and 'mostdifferent' and among them only three procedures of 'diverse', 'most?similar', and 'most-different' are applied for the case selection. For PSS classification, the eight PSS types, suggested by Tukker(2004), of 'product related', 'advice and consulancy', 'product lease', 'product renting/sharing', 'product pooling', 'activity management', 'pay per service unit', 'functional result' are utilized. We categorize the four selected cases as a product oriented group because the cases not only sell a product, but also offer service needed during the use phase of the product. Then, we analyze the four cases by using cross-case pattern that Eisenhardt(1991) suggested. Eisenhardt(1991) argued that three processes are required for avoiding reaching premature or even false conclusion. The fist step includes selecting categories of dimensions and finding within-group similarities coupled with intergroup difference. In the second process, pairs of cases are selected and listed. The second step forces researchers to find the subtle similarities and differences between cases. The third process is to divide the data by data source. The result of cross-case pattern indicates that the similarities of iPod and Kindle as successful cases are convenient user interface, successful plarform strategy, and rich contents. The differences between the successful cases are that, wheares iPod has been recognized as the culture code, Kindle has implemented a low price as its main strategy. Meanwhile, the similarities of Zune and PRS series as failed cases are lack of sufficient applications and contents. The differences between the failed cases are that, wheares Zune adopted an undifferentiated strategy, PRS series conducted high-price strategy. From the analysis of the cases, we generate three hypotheses. The first hypothesis assumes that a successful PSS system requires convenient user interface. The second hypothesis assumes that a successful PSS system requires a reciprocal(win/win) business model. The third hypothesis assumes that a successful PSS system requires sufficient quantities of applications and contents. To verify the hypotheses, we uses the cross-matching (or pattern matching) methodology. The methodology matches three key words (user interface, reciprocal business model, contents) of the hypotheses to the previous papers related to PSS, digital contents, and Information System (IS). Finally, this paper suggests the three implications from analyzed results. A successful PSS system needs to provide differentiated value for customers such as convenient user interface, e.g., the simple design of iTunes (iPod) and the provision of connection to Kindle Store without any charge. A successful PSS system also requires a mutually benefitable business model as Apple and Amazon implement a policy that provides a reasonable proft sharing for third party. A successful PSS system requires sufficient quantities of applications and contents.
Extracting Beginning Boundaries for Efficient Management of Movie Storytelling Contents
Park, Seung-Bo ; You, Eun-Soon ; Jung, Jason J. ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 279~292
Movie is a representative media that can transmit stories to audiences. Basically, a story is described by characters in the movie. Different from other simple videos, movies deploy narrative structures for explaining various conflicts or collaborations between characters. These narrative structures consist of 3 main acts, which are beginning, middle, and ending. The beginning act includes 1) introduction to main characters and backgrounds, and 2) conflicts implication and clues for incidents. The middle act describes the events developed by both inside and outside factors and the story dramatic tension heighten. Finally, in the end act, the events are developed are resolved, and the topic of story and message of writer are transmitted. When story information is extracted from movie, it is needed to consider that it has different weights by narrative structure. Namely, when some information is extracted, it has a different influence to story deployment depending on where it locates at the beginning, middle and end acts. The beginning act is the part that exposes to audiences for story set-up various information such as setting of characters and depiction of backgrounds. And thus, it is necessary to extract much kind information from the beginning act in order to abstract a movie or retrieve character information. Thereby, this paper proposes a novel method for extracting the beginning boundaries. It is the method that detects a boundary scene between the beginning act and middle using the accumulation graph of characters. The beginning act consists of the scenes that introduce important characters, imply the conflict relationship between them, and suggest clues to resolve troubles. First, a scene that the new important characters don't appear any more should be detected in order to extract a scene completed the introduction of them. The important characters mean the major and minor characters, which can be dealt as important characters since they lead story progression. Extra should be excluded in order to extract a scene completed the introduction of important characters in the accumulation graph of characters. Extra means the characters that appear only several scenes. Second, the inflection point is detected in the accumulation graph of characters. It is the point that the increasing line changes to horizontal line. Namely, when the slope of line keeps zero during long scenes, starting point of this line with zero slope becomes the inflection point. Inflection point will be detected in the accumulation graph of characters without extra. Third, several scenes are considered as additional story progression such as conflicts implication and clues suggestion. Actually, movie story can arrive at a scene located between beginning act and middle when additional several scenes are elapsed after the introduction of important characters. We will decide the ratio of additional scenes for total scenes by experiment in order to detect this scene. The ratio of additional scenes is gained as 7.67% by experiment. It is the story inflection point to change from beginning to middle act when this ratio is added to the inflection point of graph. Our proposed method consists of these three steps. We selected 10 movies for experiment and evaluation. These movies consisted of various genres. By measuring the accuracy of boundary detection experiment, we have shown that the proposed method is more efficient.
Design of Serendipity Service Based on Near Field Communication Technology
Lee, Kyoung-Jun ; Hong, Sung-Woo ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 293~304
The world of ubiquitous computing is one in which we will be surrounded by an ever-richer set of networked devices and services. Especially, mobile phone now becomes one of the key issues in ubiquitous computing environments. Mobile phones have been infecting our normal lives more thoroughly, and are the fastest technology in human history that has been adapted to people. In Korea, the number of mobile phones registered to the telecom company, is more than the population of the country. Last year, the numbers of mobile phone sold are many times more than the number of personal computer sold. The new advanced technology of mobile phone is now becoming the most concern on every field of technologies. The mix of wireless communication technology (wifi) and mobile phone (smart phone) has made a new world of ubiquitous computing and people can always access to the network anywhere, in high speed, and easily. In such a world, people cannot expect to have available to us specific applications that allow them to accomplish every conceivable combination of information that they might wish. They are willing to have information they want at easy way, and fast way, compared to the world we had before, where we had to have a desktop, cable connection, limited application, and limited speed to achieve what they want. Instead, now people can believe that many of their interactions will be through highly generic tools that allow end-user discovery, configuration, interconnection, and control of the devices around them. Serendipity is an application of the architecture that will help people to solve a concern of achieving their information. The word 'serendipity', introduced to scientific fields in eighteenth century, is the meaning of making new discoveries by accidents and sagacity. By combining to the field of ubiquitous computing and smart phone, it will change the way of achieving the information. Serendipity may enable professional practitioners to function more effectively in the unpredictable, dynamic environment that informs the reality of information seeking. This paper designs the Serendipity Service based on NFC (Near Field Communication) technology. When users of NFC smart phone get information and services by touching the NFC tags, serendipity service will be core services which will give an unexpected but valuable finding. This paper proposes the architecture, scenario and the interface of serendipity service using tag touch data, serendipity cases, serendipity rule base and user profile.
Social Network Analysis for the Effective Adoption of Recommender Systems
Park, Jong-Hak ; Cho, Yoon-Ho ;
Journal of Intelligence and Information Systems , volume 17, issue 4, 2011, Pages 305~316
Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.