• Journal of KIISE:Databases
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• v.31 no.2
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• pp.122-131
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• 2004

Traditional works on indexing have been suggested for low dimensional data under dynamic environments. But recent database applications require efficient processing of huge sire of high dimensional data under static environments. Thus many indexing strategies suggested especially in partitioning ones do not adapt to these new environments. In our study, we point out these facts and propose a new partitioning strategy, which complies with new applications' requirements and is derived from analysis. As a preliminary step to propose our method, we apply a packing technique on the one hand and exploit observations on the Minkowski-sum cost model on the other, under uniform data distribution. Observations predict that unbalanced partitioning strategy may be more query-efficient than balanced partitioning strategy for high dimensional data. Thus we propose our method, called CSP (Cyclic Spliced Partitioning method). Analysis on this method explicitly suggests metrics on how to partition high dimensional data. By the cost model, simulations, and experiments, we show excellent performance of our method over balanced strategy. By experimental studies on other indices and packing methods, we also show the superiority of our method.

• Journal of the Korean Society of Safety
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• v.39 no.1
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• pp.62-69
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• 2024

Ensuring swift on-site access to fire engines is crucial in preserving the golden time and minimizing damage. However, various mobility constraints in alleyways hinder the timely entry of fire engines to the fire scene, significantly impairing their initial response capabilities. Therefore, this study analyzed the significant mobility constraints of fire engines, focusing on Seoul, which has many old town areas. By leveraging survey responses from firefighting experts and actual observations, this study quantitatively assessed the frequency and severity of mobility constraint factors affecting the disaster responses of fire engines. Survey results revealed a consistent set of top five factors regarding the frequency and disturbance level, including illegally parked cars, narrow paths, motorcycles, poles, and awnings/banners. A comparison with actual road-view images showed notable consistency between the survey and observational results regarding the appearance frequency of mobility constraint factors in vulnerable areas in Seoul. Furthermore, the study emphasized the importance of tailored management strategies for each mobility constraint factor, considering its characteristics, such as dynamic or static. The findings of this study can serve as foundational data for creating more detailed fire safety maps and advancing technologies that monitor the mobility of fire engines through efficient vision-based inference using CCTVs in the future.

• Journal of information and communication convergence engineering
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• v.8 no.4
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• pp.427-432
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• 2010

The fast-emerging of cloud computing technology today has sufficiently benefited its wide range of users from individuals to large organizations. It carries an attractive characteristic by renting myriad virtual storages, computing resources and platform for users to manipulate their data or utilize the processing resources conveniently over Internet without the need to know the exact underlying infrastructure which is resided remotely at cloud servers. However due to the loss of direct control over the systems/applications, users are concerned about the risks of cloud services if it is truly secured. In the literature, there are cases where attackers masquerade as cloud users, illegally access to their accounts, by stealing the static login password or breaking the poor authentication gate. In this paper, we propose a two-factor authentication framework to enforce cloud services' authentication process, which are Public Key Infrastructure (PKI) authentication and mobile out-of-band (OOB) authentication. We discuss the framework's security analysis in later session and conclude that it is robust to phishing and replay attacks, prohibiting fraud users from accessing to the cloud services.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.177-185
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• 2010

Ray-object intersection is an important element in ray tracing that takes up a substantial amount of computing time. In general, such spatial data structure as kd-tree has been frequently used for static scenes to accelerate the intersection computation. Recently, a few variants of kd-tree traversal have been proposed suitable for the GPU that has a relatively restricted computing architecture compared to the CPU. In this article, we propose yet another two implementation techniques that can improve those previous ones. First, we present a cached stack method that is aimed to reduce the costly global memory access time needed when the stack is allocated to global memory. Secondly, we present a rope-with-short-stack method that eases the substantial memory requirement, often necessary for the previous rope method. In order to show the effectiveness of our techniques, we compare their performances with those of the previous GPU traversal methods. The experimental results will provide prospective GPU ray tracer developers with valuable information, helping them choose a proper kd-tree traversal method.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).