• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.6
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• pp.1210-1230
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• 1997

In this paper, we suggest an architecture of DS/CDMA tranceiver composed of one pilot channel used as reference and multiple traffic channels. The pilot channel-an unmodulated PN code-is used as the reference signal for synchronization of PN code and data demondulation. The coherent demodulation architecture is also exploited for the reverse link as well as for the forward link. Here are the characteristics of the suggested DS/CDMA system. First, we suggest an interlaced quadrature spreading(IQS) method. In this method, the PN coe for I-phase 1st channel is used for Q-phase 2nd channels and the PN code for Q-phase 1st channel is used for I-phase 2nd channel, and so on-which is quite different from the eisting spreading schemes of DS/CDMA systems, such as IS-95 digital CDMA cellular or W-CDMA for PCS. By doing IQS spreading, we can drastically reduce the zero crossing rate of the RF signals. Second, we introduce an adaptive threshold setting for the synchronization of PN code, an initial acquistion method that uses a single PN code generator and reduces the acquistion time by a half compared the existing ones, and exploit the state machines to reduce the reacquistion time Third, various kinds of functions, such as automatic frequency control(AFC), automatic level control(ALC), bit-error-rate(BER) estimator, and spectral shaping for reducing the adjacent channel interference, are introduced to improve the system performance. Fourth, we designed and implemented the DS/CDMA MODEM to be used for variable transmission rate applications-from 16Kbps to 1.024Mbps. We developed and confirmed the DS/CDMA MODEM architecture through mathematical analysis and various kind of simulations. The ASIC design was done using VHDL coding and synthesis. To cope with several different kinds of applications, we developed transmitter and receiver ASICs separately. While a single transmitter or receiver ASC contains three channels (one for the pilot and the others for the traffic channels), by combining several transmitter ASICs, we can expand the number of channels up to 64. The ASICs are now under use for implementing a line-of-sight (LOS) radio equipment.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.293-299
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• 2007

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a (gas or oil) pipeline and acquire signals (also called sensor data) from their surrounding rings of sensors. By analyzing the signals captured in intelligent PIGs, we can detect pipeline defects, such as holes and curvatures and other potential causes of gas explosions. There are two major data access patterns apparent when an analyzer accesses the pipeline signal data. The first is a sequential pattern where an analyst reads the sensor data one time only in a sequential fashion. The second is the repetitive pattern where an analyzer repeatedly reads the signal data within a fixed range; this is the dominant pattern in analyzing the signal data. The existing PIG software reads signal data directly from the server at every user#s request, requiring network transfer and disk access cost. It works well only for the sequential pattern, but not for the more dominant repetitive pattern. This problem becomes very serious in a client/server environment where several analysts analyze the signal data concurrently. To tackle this problem, we devise a fast in-memory cache manager, called T-Cache, by considering pipeline sensor data as multiple time-series data and by efficiently caching the time-series data at T-Cache. To the best of the authors# knowledge, this is the first research on caching pipeline signals on the client-side. We propose a new concept of the signal cache line as a caching unit, which is a set of time-series signal data for a fixed distance. We also provide the various data structures including smart cursors and algorithms used in T-Cache. Experimental results show that T-Cache performs much better for the repetitive pattern in terms of disk I/Os and the elapsed time. Even with the sequential pattern, T-Cache shows almost the same performance as a system that does not use any caching, indicating the caching overhead in T-Cache is negligible.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.305-322
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• 2005

Watermarking is a widely employed method tn protecting copyright of a digital image, the owner's unique image is embedded into the original image. Strengthened level of watermark insertion would help enhance its resilience in the process of extraction even from various distortions of transformation on the image size or resolution. However, its level, at the same time, should be moderated enough not to reach human visibility. Finding a balance between these two is crucial in watermarking. For the algorithm for watermarking, the predefined strength of a watermark, computed from the physical difference between the original and embedded images, is applied to all images uniformal. The mean brightness or contrast of the surrounding images, other than the absolute brightness of an object, could affect human sensitivity for object detection. In the present study, we examined whether the detectability for watermark noise might be attired by image statistics: mean brightness and contrast of the image. As the first step to examine their effect, we made rune fundamental images with varied brightness and control of the original image. For each fundamental image, detectability for watermark noise was measured. The results showed that the strength ot watermark node for detection increased as tile brightness and contrast of the fundamental image were increased. We have fitted the data to a regression line which can be used to estimate the strength of watermark of a given image with a certain brightness and contrast. Although we need to take other required factors into consideration in directly applying this formula to actual watermarking algorithm, an adaptive watermarking algorithm could be built on this formula with image statistics, such as brightness and contrast.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.3
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• pp.192-207
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• 2006

Today, the SSL protocol has been used as core part in various computing environments or security systems. But, the SSL protocol has several problems, because of the rigidity on operating. First, SSL protocol brings considerable burden to the CPU utilization so that performance of the security service in encryption transaction is lowered because it encrypts all data which is transferred between a server and a client. Second, SSL protocol can be vulnerable for cryptanalysis due to the key in fixed algorithm being used. Third, it is difficult to add and use another new cryptography algorithms. Finally. it is difficult for developers to learn use cryptography API(Application Program Interface) for the SSL protocol. Hence, we need to cover these problems, and, at the same time, we need the secure and comfortable method to operate the SSL protocol and to handle the efficient data. In this paper, we propose the SSL component which is designed and implemented using CBD(Component Based Development) concept to satisfy these requirements. The SSL component provides not only data encryption services like the SSL protocol but also convenient APIs for the developer unfamiliar with security. Further, the SSL component can improve the productivity and give reduce development cost. Because the SSL component can be reused. Also, in case of that new algorithms are added or algorithms are changed, it Is compatible and easy to interlock. SSL Component works the SSL protocol service in application layer. First of all, we take out the requirements, and then, we design and implement the SSL Component, confidentiality and integrity component, which support the SSL component, dependently. These all mentioned components are implemented by EJB, it can provide the efficient data handling when data is encrypted/decrypted by choosing the data. Also, it improves the usability by choosing data and mechanism as user intend. In conclusion, as we test and evaluate these component, SSL component is more usable and efficient than existing SSL protocol, because the increase rate of processing time for SSL component is lower that SSL protocol's.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.43-61
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• 2011

In this research, we propose a dynamic decision making using social context based on ontology. Dynamic adaptation is adopted for the high qualified decision making, which is defined as creation of proper information using contexts depending on decision maker's state of affairs in ubiquitous computing environment. Thereby, the context for the dynamic adaptation is classified as a static, dynamic and social context. Static context contains personal explicit information like demographic data. Dynamic context like weather or traffic information is provided by external information service provider. Finally, social context implies much more implicit knowledge such as social relationship than the other two-type context, but it is not easy to extract any implied tacit knowledge as well as generalized rules from the information. So, it was not easy for the social context to apply into dynamic adaptation. In this light, we tried the social context into the dynamic adaptation to generate context-appropriate personalized information. It is necessary to build modeling methodology to adopt dynamic adaptation using the context. The proposed context modeling used ontology and cases which are best to represent tacit and unstructured knowledge such as social context. Case-based reasoning and constraint satisfaction problem is applied into the dynamic decision making system for the dynamic adaption. Case-based reasoning is used case to represent the context including social, dynamic and static and to extract personalized knowledge from the personalized case-base. Constraint satisfaction problem is used when the selected case through the case-based reasoning needs dynamic adaptation, since it is usual to adapt the selected case because context can be changed timely according to environment status. The case-base reasoning adopts problem context for effective representation of static, dynamic and social context, which use a case structure with index and solution and problem ontology of decision maker. The case is stored in case-base as a repository of a decision maker's personal experience and knowledge. The constraint satisfaction problem use solution ontology which is extracted from collective intelligence which is generalized from solutions of decision makers. The solution ontology is retrieved to find proper solution depending on the decision maker's context when it is necessary. At the same time, dynamic adaptation is applied to adapt the selected case using solution ontology. The decision making process is comprised of following steps. First, whenever the system aware new context, the system converses the context into problem context ontology with case structure. Any context is defined by a case with a formal knowledge representation structure. Thereby, social context as implicit knowledge is also represented a formal form like a case. In addition, for the context modeling, ontology is also adopted. Second, we select a proper case as a decision making solution from decision maker's personal case-base. We convince that the selected case should be the best case depending on context related to decision maker's current status as well as decision maker's requirements. However, it is possible to change the environment and context around the decision maker and it is necessary to adapt the selected case. Third, if the selected case is not available or the decision maker doesn't satisfy according to the newly arrived context, then constraint satisfaction problem and solution ontology is applied to derive new solution for the decision maker. The constraint satisfaction problem uses to the previously selected case to adopt and solution ontology. The verification of the proposed methodology is processed by searching a meeting place according to the decision maker's requirements and context, the extracted solution shows the satisfaction depending on meeting purpose.

• Cartoon and Animation Studies
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• s.50
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• pp.75-99
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• 2018

Makoto Shinkai, an animated film maker in Japan, has been featured as a one-person production system and as a 'writer of light', but his 2016 release of "Your Name" was a departure from the elements that characterize his existing works. At the same time, by the combination of the traditional musubi(むすび) story, ending these, it was a big hit due to its rich narratives and attraction of open interpretation possibility. As it can be guessed from the title of this work, this work shows the encounter between the Japanese ancient language and the modern language in relation to the 'name', and presents the image that the role of the name(language) is repeatedly emphasized with various variations in events for the perfect 'encounter'. In this work, the interpretations of $Signifi\acute{e}$ for characters and objects are extended and reserved as a metaphorical role of the similarity, depending on the meaning of the subject which they touch. The relationship between words and objects analyzed through the structure of Signifiant and $Signifi\acute{e}$ is an epoch-making ideological discovery of modern times revealed through F. Saussure. Focusing on "the difference" between being this and that from the notion of Saussure, Derrida dismissed logocentrism, rationalism that fully obeyed the order of Logos. Likewise, dismissing the center, or dismissing the owner had emerged after the exclusive and closed principle of metaphysics in the west was dismissed. Derrida's definition of 'deconstruction' is a philosophical strategy that starts with the insight on the nature of language. 'Dissemination,' a metaphor that he used as a methodological concept to read texts acts as interpretation and practice (or play), but does not pursue an ultimate interpretation. His 'undecidability' does not start with infinity, but ends with infinity. The researcher testifies himself and identifies that we can't be an interpreter of the world because we, as a human are not the subject of language but a user. Derrida also interpreted the world of things composed of Signifiant and $Signifi\acute{e}$ as open texts. In this respect, this study aimed to read Makoto's works telling about the meeting of a thing and a thing with name as a guide, based on Derrida's frame of 'deconstruction' and 'dissemination.' This study intends to re-consider which relationship the Signifiant and $Signifi\acute{e}$ have with human beings who live in modern times, examine the relationship between words and objects presented in this work through Jacques Derrida's destruction and dissemination concepts, and recognize that we are merely a part of Signifiant and $Signifi\acute{e}$. Just as Taki and Mitsuha confirm the existence by asking each other, we are in the world of things, expecting musubi that a world of names calls me.

• The Journal of Korean Society for Radiation Therapy
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• v.17 no.2
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• pp.105-111
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• 2005

Purpose : Daily Q.A is the important step which must be preceded in a radiation treatment. Specially, radiation output measurement and laser alignment, SSD indicator related to a patient set-up recurrence must be confirmed for a reasonable radiation treatment. Daily Q.A proceeds correctness and a prompt way, and needs an objective measurement basis. Manufacture of the device which can facilitate confirmation of output measurement and appliances check at one time was requested. Materials and Methods : Produced the phantom formal daily check device which can confirm a lot of appliances check (output measurement and laser alignment. field size, SSD indicator) with one time of set up at a time, and measurement observed a linear accelerator (4 machine) for four months and evaluated efficiency. Results : We were able to confirm an laser alignment, field size, SSD indicator check at the same time, and out put measurement was possible with the same set up, so daily Q.A time was reduced, and we were able to confirm an objective basis about each item measurement. As a result of having measured for four months, output measurement within ${\pm}2%$, and measured laser alignment, field size, SSD indicator in range within ${\pm}1mm$. Conclusion : We can enforce output measurement and appliances check conveniently, and time was reduced and was able to raise efficiency of business. We were able to bring a cost reduction by substitution expensive commercialized equipment. Further It is necessary to makes a product as strong and slight materials, and improve convenience of use.