• Journal of KIISE:Databases
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• v.30 no.5
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• pp.438-450
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• 2003

Spatial joins find pairs of objects that overlap with each other. In spatial joins using indexes, original-space indexes such as the R-tree are widely used. An original-space index is the one that indexes objects as represented in the original space. Since original-space indexes deal with sizes of objects, it is difficult to develop a formal algorithm without relying on heuristics. On the other hand, transform-space indexes, which transform objects in the original space into points in the transform space and index them, deal only with points but no sites. Thus, spatial join algorithms using these indexes are relatively simple and can be formally developed. However, the disadvantage of transform-space join algorithms is that they cannot be applied to original-space indexes such as the R-tree containing original-space objects. In this paper, we present a novel mechanism for achieving the best of these two types of algorithms. Specifically, we propose a new notion of the transform-space view and present the transform-space view join algorithm(TSVJ). A transform-space view is a virtual transform-space index based on an original-space index. It allows us to interpret on-the-fly a pre-built original-space index as a transform-space index without incurring any overhead and without actually modifying the structure of the original-space index or changing object representation. The experimental result shows that, compared to existing spatial join algorithms that use R-trees in the original space, the TSVJ improves the number of disk accesses by up to 43.1% The most important contribution of this paper is to show that we can use original-space indexes, such as the R-tree, in the transform space by interpreting them through the notion of the transform-space view. We believe that this new notion provides a framework for developing various new spatial query processing algorithms in the transform space.

• Journal of KIISE:Databases
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• v.31 no.4
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• pp.395-408
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• 2004

A conjunctive Boolean text query refers to a query that searches for tort documents containing all of the specified keywords, and is the most frequently used query form in information retrieval systems. Typically, the query specifies a long list of keywords for better precision, and in this case, the order of keyword processing has a significant impact on the query speed. Currently known approaches to this ordering are based on heuristics and, therefore, cannot guarantee an optimal ordering. We can use a systematic approach by leveraging a database query processing algorithm like the dynamic programming, but it is not suitable for a text query with a typically long list of keywords because of the algorithm's exponential run-time (Ο(n2$^{n-1}$)) for n keywords. Considering these problems, we propose a new approach based on a property called the join sequence separability. This property states that the optimal join sequence is separable into two subsequences of different join methods under a certain condition on the joined relations, and this property enables us to find a globally optimal join sequence in Ο(n2$^{n-1}$). In this paper we describe the property formally, present an optimization algorithm based on the property, prove that the algorithm finds an optimal join sequence, and validate our approach through simulation using an analytic cost model. Comparison with the heuristic text query optimization approaches shows a maximum of 100 times faster query processing, and comparison with the dynamic programming approach shows exponentially faster query optimization (e.g., 600 times for a 10-keyword query).

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.325-338
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• 2004

A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored In the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks of designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views so that the total query response time over all queries can be minimized while a limited amount of time for maintaining the views is given(maintenance-cost view selection problem). In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in terms of time complexity over existing search-based approaches that use heuristics. Our analysis shows that the algorithm consistently yields a solution that only has an additional 10% of query cost of over the optimal query cost while at the same time exhibits an impressive performance of only a linear increase in execution time. We have implemented a prototype version of our algorithm that is used to evaluate our approach.

• Journal of the Korean School Mathematics Society
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• v.24 no.2
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• pp.173-190
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• 2021

This study analyzed the proof of the inverse square law, which is said to be the core of Newton's , in relation to the geometric limit. Newton, conscious of the debate over infinitely small, solved the dynamics problem with the traditional Euclid geometry. Newton reduced mechanics to a problem of geometry by expressing force, time, and the degree of inertia orbital deviation as a geometric line segment. Newton was able to take Euclid's geometry to a new level encompassing dynamics, especially by introducing geometric limits such as parabolic approximation, polygon approximation, and the limit of the ratio of the line segments. Based on this analysis, we proposed to use Newton's geometric limit as a tool to show the usefulness of mathematics, and to use it as a means to break the conventional notion that the area of the curve can only be obtained using the definite integral. In addition, to help the desirable use of geometric limits in school mathematics, we suggested the following efforts are required. It is necessary to emphasize the expansion of equivalence in the micro-world, use some questions that lead to use as heuristics, and help to recognize that the approach of ratio is useful for grasping the equivalence of line segments in the micro-world.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.159-185
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• 2016

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users' preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

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.

• Asia Marketing Journal
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• v.13 no.3
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• pp.55-77
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• 2011

This study investigate the interaction effects of bundle price discount framing and message framing on consumer's attitude of bundle component. Although each effect of bundle price discount framing and message framing has been explored individually, few attempts have been made to invest them jointly. This study tests the interaction effects of bundle price discount framing and message framing on consumer's evaluation of bundle component. Moreover, this research focuses on consumer's evaluation of individual bundle component while the existing research on bundling primarily focused on consumer's evaluation of the bundle. Prior research suggests that consumers are sensitive to the framing of prices and discounts in the presentation of the bundle offer. For example, there is considerable evidence that partitioning or consolidating the prices of a bundle can influence the attractiveness of the bundle offer. Similarly, there is evidence that an equivalent price reduction to the overall bundle, one of the individual products in the bundle, or distributed among the individual products in the bundle can alter the perceived attractiveness of the offer (e.g. Chakravarti, Krish, Paul, and Srivastava 2002; Hamilton and Srivastava 2008; Janiszewski and Cunha 2004; Johnson, Herrmann and Bauer 1999; ; Morwitz, Greenleaf, and Johnson 1998; Yadav 1994; 1995). In line with these earlier research, this research suggests that the bundle type can influence the consumer's evaluation of bundle component. There are two types of bundle - mixed-leader bundle and mixed-joint bundle. In mixed-leader bundling, the price of one of the two products is discounted when the other product is purchased at the regular price. In mixed-joint bundling, a single price is set when the two product are purchased jointly. This study supposes that the teeth whitening product is the leader product in a mixed-leader bundle. So bundle price discount framing is manipulated such as "Buy the teeth whitening product (regular price \80,000) and get 50% discount on the functional toothpaste(regular price \40,000), special set price \100,000" or "Buy the functional toothpaste and the teeth whitening product as a set and get discount for the set, special set price \60,000". Message framing is manipulated through the product claims described in an advertising bill. The positive framing presents that "Over 95% of users achieved the expected 2-3 shades of improvement in two weeks" where as the negative framing presents "less than 5% of users did not achieve the expected 2-3 shades of improvement in two weeks". This study uses hypothetical brand name of the teeth whitening product and the functional toothpaste This study is based on a 2x2 factorial design with bundle discount framing (mixed-leader bundle vs. mixed-joint bundle) and massage framing (positive vs. negative). The dependant variables are consumer's perceived quality and attitude of the teeth whitening product The data reveals that two dependant variables are correlated, so the data is analyzed with two-way MANOVA. This research explores the significant interaction effect of bundle discount framing and message framing on consumer's perceived quality and attitude of the teeth whitening product. When the message framing is positive, consumer's perceived quality and attitude of the teeth whitening product is higher in mixed-leader bundle than mixed-joint bundle condition. However, when the message framing is negative, consumer's evaluation is higher in mixed-joint bundle than mixed-leader bundle. The author explains this result by stating that consumers are less likely to use heuristics such as price-quality association and value discounting hypothesis(Raghubir 2004) in the negative message framing condition. Additionally, consumer's perceived risk of the teeth whitening product in the negative message framing condition can be more reduced by the bundle partner(e.g. the toothpaste) in mixed-joint bundle than mixed-leader bundle. Based on the results, marketing managers are advised to use different bundle type based on message framing of their product.