• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.19-29
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• 2011

Due to the development of web technologies and the increasing use of smart devices such as smart phone, in recent various web services are widely used in many application fields. In this environment, the topic of supporting personalized and intelligent web services have been actively researched, and an analysis technique on a web-click stream generated from web usage logs is one of the essential techniques related to the topic. In this paper, for efficient analyzing a web-click stream of sequences, a sequential pattern mining technique is proposed, which satisfies the basic requirements for data stream processing and finds a refined mining result. For this purpose, a concept of interesting sequential patterns with a time-interval constraint is defined, which uses not on1y the order of items in a sequential pattern but also their generation times. In addition, A mining method to find the interesting sequential patterns efficiently over a data stream such as a web-click stream is proposed. The proposed method can be effectively used to various computing application fields such as E-commerce, bio-informatics, and USN environments, which generate data as a form of data streams.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.5
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• pp.137-145
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• 2003

Nowadays, people can communicate with each other on any time at my place by development of wireless communications. But, the openness of mobile communications Poses serious security threats and the security is necessary on mobile communications to support the secure communication channel. The most commonly method is stream cipher for mobile communications. Generally, this stream cipher is implemented by LFSR(Linear Feedback Shift Register). On this paper proposes the modified mechanism of the S box is usually used in block cipher to advance security og the stream cipher and this mechanism is the modified three one in consideration og the randomness. Generally, S box that is function with nonlinear property makes data more strong by attack. The randomness test of the proposed algorithm is used Ent Pseudorandom Number Sequence Test Program and by the test result it proves that it has better randomness and serial correlation value than the based stream cipher on respective test.

• Korean Journal of Ichthyology
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• v.29 no.1
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• pp.1-12
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• 2017

A natural hybrid of interspecific between the Coreoleuciscus splendidus and C. aeruginos (Cypriniformes: Cyprinidae) was captured in the Hwang-ji Stream, a tributary of the Nakdong River basin in Korea. An interspecific hybrid between C. splendidus and C. aeruginos was genetically identified based on morphological characteristics and the sequence analysis of nuclear recombination activating gene 1 (RAG1) gene (1,334 bp) and mitochondrial cytochrome c oxidase subunit 1 (CO1) gene (1,551 bp). As a result of morphological variations, the natural hybrid appeared to have an intermediate character between two parental species (C. splendidus and C. aeruginos) in three variations of black array (s) on dorsal, caudal and anal fin rays. Phylogenetic analysis inferred from RAG1 and CO1 sequence data revealed that Coreoleuciscus populations from Hwang-ji stream consist of two pure Coreoleuciscus species and a hybrid individual group. The individuals were clearly identified the cross and reciprocal hybrid by CO1 gene analysis. In RAG1 gene, 13 nucleotide variation loci were detected and the hybrid individuals displayed the double peaks of sequence chromatograms at the 9 diagnostic positions. In this study, molecular data and morphological variations were clearly demonstrated that hybridization did occur between C. splendidus and C. aeruginos. However, F2 hybrid generation and reproductive capacity of F1 hybrid individuals were not demonstrated.

• The KIPS Transactions:PartD
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• v.18D no.4
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• pp.215-224
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• 2011

U-Health collects patient information with various kinds of sensor. Stream data can be summarized as an interval event which has aninterval between start-time-point and end-time-point. Most of temporal mining techniques consider only the event occurrence-time-point and ignore stream data change of state. In this paper, we propose the temporal mining technique considering the event change of state in U-Health. Our method overcomes the restrictions of the environment by sending a significant event in U-Health from sensors to a server. We define four event states of stream data and perform the temporal data mining considered the event change of state. Finally, we can remove an ambiguity of discovered rules by describing cause-and-effect relations among events in temporal relation sequences.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.271-273
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• 2005

In wireless UDP data transfers, the handoff is one of the most breaking down things. To polycast multimedia data to several clients a sewer may connect clients by UDP networks. Mostly, video data must be compressed based on difference between before and next packets. Thus, the packet losses on the video stream may cause the recomposing of video data to be corrupt. This proposal provides simply retransmitting of lost packets according to a sequence and buffering packets in a given order.

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.283-292
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• 2003

One-dimensional time-series data have been studied in various database applications such as data mining and data warehousing. However, in the current complex business environment, multidimensional data sequences (MDS') become increasingly important in addition to one-dimensional time-series data. For example, a video stream can be modeled as an MDS in the multidimensional space with respect to color and texture attributes. In this paper, we propose the effective similarity measures on which the similar pattern retrieval is based. An MDS is partitioned into segments, each of which is represented by various geometric and semantic features. The similarity measures are defined on the basis of these segments. Using the measures, irrelevant segments are pruned from a database with respect to a given query. Both data sequences and query sequences are partitioned into segments, and the query processing is based upon the comparison of the features between data and query segments, instead of scanning all data elements of entire sequences.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1210-1217
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• 2004

LILI-II stream cipher is an upgraded version of the LILI-128, one of candidates in NESSIE. Since the algorithm is a clock-controlled, the speed of the keystream data is degraded structurally in a clock-synchronized hardware logic design. Accordingly, this paper proposes a 4-bit parallel LFSR, where each register bit includes four variable data routines for feedback or shifting within the LFSR. furthermore, the timing of the proposed design is simulated using a Max+plus II from the ALTERA Co., the logic circuit is implemented for an FPGA device (EPF10K20RC240-3), and apply to the Lucent ASIC device (LV160C, 0.13${\mu}{\textrm}{m}$ CMOS ＆ 1.5v technology), and it could achieve a throughput of about 500 Mbps with a 0.13${\mu}{\textrm}{m}$ semiconductor for the maximum path delay below 1.8㎱. Finally, we propose the m-parallel implementation of LILI-II, throughput with 4, 8 or 16 Gbps (m=8, 16 or 32).

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.11
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• pp.581-588
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• 2007

In Digital Right Management, symmetric cipher is used for content encryption to reduce encryption cost, AES, advanced encryption standard is usually used to multimedia encryption under desktop environment because of its reasonable security level and computation cost. But mobile handheld device often uses slow speed processor and operates under battery-powered environment. Therefore it requires low computation cost and low energy consumption. This paper proposes new stream cipher scheme which combines pseudo random number generator(PRNG) and dynamically generated permutations. Proposed scheme activates PRNG and generates original key streams. Then it generates extended key streams by applying permutation to original sequence. These extended key streams are XORed with plaintext and generate ciphertext. Proposed scheme reduces the usage of PRNG. Therefore this scheme is fast and consumes less energy in comparison with normal stream cipher. Especially, this scheme shows great speed up (almost 2 times) than normal stream cipher scheme in random access.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.563-577
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• 2006

A stream processor uses resource sharing method for efficient of limited resource in multiple continuous queries. The previous methods process aggregate queries to consist the level structure. So insert operation needs to reconstruct cost of the level structure. Also a search operation needs to search cost of aggregation information in each size of sliding windows. Therefore this paper uses linear structure for optimization of sliding window aggregations. The method comprises of making decision, generation and deletion of panes in sequence. The decision phase determines optimum pane size for holding accurate aggregate information. The generation phase stores aggregate information of data per pane from stream buffer. At the deletion phase, panes are deleted that are no longer used. The proposed method uses resources less than the method where level structures were used as data structures as it uses linear data format. The input cost of aggregate information is saved by calculating only pane size of data though numerous stream data is arrived, and the search cost of aggregate information is also saved by linear searching though those sliding window size is different each other. In experiment, the proposed method has low usage of memory and the speed of query processing is increased.