• Proceedings of the KOR-KST Conference
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• 2000.02a
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• pp.25-47
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• 2000

In route guidance systems fastest-path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this study is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e. Pareto optimal) paths for real-time vehicle routing which reflect the drivers' preferences and route choice behaviors. We propose two pruning algorithms that reduce the search area based on a context-dependent linear utility function and thus reduce the computation time. The basic notion of the proposed approach is that ⅰ) enumerating all nondominated paths is computationally too expensive, ⅱ) obtaining a stable mathematical representation of the drivers' utility function is theoretically difficult and impractical, and ⅲ) obtaining optimal path given a nonlinear utility function is a NP-hard problem. Consequently, a heuristic two-stage strategy which identifies multiple routes and then select the near-optimal path may be effective and practical. As the first stage, we utilize the relaxation based pruning technique based on an entropy model to recognize and discard most of the nondominated paths that do not reflect the drivers' preference and/or the context-dependency of the preference. In addition, to make sure that paths identified are dissimilar in terms of links used, the number of shared links between routes is limited. We test the proposed algorithms in a large real-life traffic network and show that the algorithms reduce CPU time significantly compared with conventional multi-criteria shortest path algorithms while the attributes of the routes identified reflect drivers' preferences and generic route choice behaviors well.

• KIISE Transactions on Computing Practices
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• v.23 no.8
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• pp.498-503
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• 2017

SIMD (Single Instruction Multiple Data) is a representative parallelization architecture that processes multiple data loaded in a SIMD register with a single instruction. Quicksort is a sorting algorithm that picks an element as a pivot from the array and reorders the array such that all elements having the values less than the pivot value are located in the left side on the pivot as well as all elements having the value greater than the pivot value are located in the right side on the pivot and then the algorithm performs the same task on both sublist recursively. In this paper, we propose an efficient Quicksort algorithm applying the SIMD instructions which minimally invokes conditional branches to avoid the performance degradation incurred by branch misprediction in a pipeline architecture. In addition, we improve the performance of the Quicksort algorithm by fetching data into a SIMD register as a byte unit to apply VBP (Vertical Bit Parallel) and the early pruning technique.

• Journal of the Korean Geographical Society
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• v.47 no.6
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• pp.825-835
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• 2012

The aim of this research is to develope technique and mapping for detecting distribution of vegetation dieback areas in the subalpine zone of Mt. Baekdu. A detection technique developed the rule-based model using MODIS images. Dieback areas could be classified as 4 categories of initial dieback, middle dieback, and end dieback by pruning stages of leaves. Dieback area was $28km^2$ from year 2001 to year 2006, intial dieback was $16km^2$, middle dieback was $10km^2$, and end dieback was $2km^2$ by the each stage. Dieback area was $35km^2$ from year 2006 to year 2011. Total area was $35km^2$ from year 2001 to year 2011, areas of middle dieback and end dieback were increased. The research method for this study may help to support in application with preliminary detection of dieback areas in the mountains by the global warming.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.121-133
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• 1998

This study suggests an efficient algorithm for updating discovered association rules in large database, because a database may allow frequent or occasional updates, and such updates may not only invalidate some existing strong association rules, but also turn some weak rules into strong ones. FUP and DMI update efficiently strong association rules in the whole updated database reusing the information of the old large item-sets. Moreover, these algorithms use a pruning technique for reducing the database size in the update process. This study updates strong association rules efficiently in the whole updated database reusing the information of the old large item-sets. An updating algorithm that is suggested in this study generates the whole candidate item-sets at once in an incremental database in view of the fact that it is difficult to find the new set of large item-sets in the whole updated database after an incremental database is added to the original database. This method of generating candidate item-sets is different from that of FUP and DMI. After generating the whole candidate item-sets, if each item-set in the whole candidate item-sets is large at an incremental database, the original database is scanned and the support of each item-set in the whole candidate item-sets is updated. So, the whole large item-sets in the whole updated database is found out. An updating algorithm that is suggested in this study does not use a pruning technique for reducing the database size in the update process. As a result, an updating algoritm that is suggested updates fast and efficiently discovered large item-sets.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.304-314
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• 2007

Content Based Image Retrieval (CBIR) is to store and retrieve images using the feature description of image contents. In order to support more accurate image retrieval, it has become necessary to develop features that can effectively describe image contents. The commonly used low-level features, such as color, texture, and shape features may not be directly mapped to human visual perception. In addition, such features cannot effectively describe a single image that contains multiple objects of interest. As a result, the research on feature descriptions has shifted to focus on higher-level features, which support representations more similar to human visual perception like spatial relationships between objects. Nevertheless, the prior works on the representation of spatial relations still have shortcomings, particularly with respect to supporting rotational invariance, Rotational invariance is a key requirement for a feature description to provide robust and accurate retrieval of images. This paper proposes a high-level feature named 8AB (8 Angular Bin) that effectively describes the spatial relations of objects in an image while providing rotational invariance. With this representation, a similarity calculation and a retrieval technique are also proposed. In addition, this paper proposes a search-space pruning technique, which supports efficient image retrieval using the 8AB feature. The 8AB feature is incorporated into a CBIR system, and the experiments over both real and synthetic image sets show the effectiveness of 8AB as a high-level feature and the efficiency of the pruning technique.

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.434-445
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• 2009

Content-based image retrieval(CBIR) is the retrieval technique which uses the contents of images. However, in contrast to text data, multimedia data are ambiguous and there is a big difference between system's low-level representation and human's high-level concept. So it doesn't always mean that near points in the vector space are similar to user. We call this the semantic-gap problem. Due to this problem, performance of image retrieval is not good. To solve this problem, the relevance feedback(RF) which uses user's feedback information is used. But existing RF doesn't consider user's region-of-interest(ROI), and therefore, irrelevant regions are used in computing new query points. Because the system doesn't know user's ROI, RF is proceeded in the image-level. We propose a new ROI RF method which guides a user to select ROI from relevant images for the retrieval of complex satellite image, and this improves the accuracy of the image retrieval by computing more accurate query points in this paper. Also we propose a pruning technique which improves the accuracy of the image retrieval by using images not selected by the user in this paper. Experiments show the efficiency of the proposed ROI RF and the pruning technique.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.59-70
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• 2003

Spatial data base system provides many query types and most of them are required frequent disk I/O and much CPU time. k-NN search is to find k-th closest object from the query point and up to now, several k-NN search methods have been proposed. Among these, MINMAX distance method has an aim not to access unnecessary node by adapting pruning technique. But this method accesses more disks than necessary while pruning unnecessary nodes. In this paper, we propose new k-NN search algorithm based on density of object. With this method, we predict the radius to be expected to contain k-NN objects using density of data set and search those objects within this radius and then adjust radius if failed. Experimental results show that this method outperforms the previous MINMAX distance method. This algorithm visit less disks than MINMAX method by the factor of maximum 22% and average 7%.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.335-344
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• 2005

Recently open data mining methods focusing on a data stream that is a massive unbounded sequence of data elements continuously generated at a rapid rate are proposed actively. Knowledge embedded in a data stream is likely to be changed over time. Therefore, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. This paper proposes a sliding window technique for finding recently frequent itemsets, which is applied efficiently in open data mining. In the proposed technique, its memory usage is kept in a small space by delayed-insertion and pruning operations, and its mining result can be found in a short time since the data elements within its target range are not traversed repeatedly. Moreover, the proposed technique focused in the recent data elements, so that it can catch out the recent change of the data stream.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.263-270
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• 2010

Data mining is the process of sorting through large amounts of data and picking out useful information. An important goal of data mining is to discover, define and determine the relationship between several variables. Association rule mining is an important research topic in data mining. An association rule technique finds the relation among each items in massive volume database. Association rule technique consists of two steps: finding frequent itemsets and then extracting interesting rules from the frequent itemsets. Some interestingness measures have been developed in association rule mining. Interestingness measures are useful in that it shows the causes for pruning uninteresting rules statistically or logically. This paper explores some problems for two interestingness measures, confidence and net confidence, and then propose a decision process for right association rule generation using these interestingness measures.