• Journal of Digital Contents Society
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• v.14 no.4
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• pp.463-469
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• 2013

The set-based POI search algorithm showed better performance than the existing hard matching search when inaccurate queries are entered. In the set-based POI search algorithm, however, there is a problem that can't classify duplicate characters within a record. This is due to it's 'set-based' search property. To solve this problem, we improve the existing set-based POI search algorithm. In this paper, we propose and implement an improved set-based POI search algorithm that is able to deal duplicate characters properly. From the experimental results, we can find that our technique for duplicate characters improves the performance of the existing set based POI search algorithm.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.23-29
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• 2009

Recent growth of the geospatial information on the Web has made it possible to easily access a wide variety of geospatial information. An integration of different geospatial objects consists of the following three steps; extracting geospatial objects from the maps, converting the coordinate system and discovering pairs of objects that represent the same real-world entity in the two maps. This paper deals mainly with the third step to correspond conjugate objects and four matching types and criteria is presented. The techniques designed and developed can be utilized to efficiently integrate distributed heterogeneous spatial databases such as the digital maps and POIs from other data sources. To achieve the goal, we presented four types and criteria for the matching schema. The main contributions of this paper are as follows. A complete process of integrating data from maps on the Web is presented. Then, we show how attributes of the objects can be used in the integration process.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.176-184
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• 2023

Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.711-716
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• 2014

With the gradual increase of interest in plagiarism and intelligent file content search, the demand for similarity measuring between two sentences is increasing. There is a lot of researches for sentence similarity measurement methods in various directions such as n-gram, edit-distance and LSA. However, these methods have their own advantages and disadvantages. In this paper, we propose a new sentence similarity measurement method approaching from another direction. The proposed method uses the set-based POI data search that improves search performance compared to the existing hard matching method when data includes the inverse, omission, insertion and revision of characters. Using this method, we are able to measure the similarity between two sentences more accurately and more quickly. We modified the data loading and text search algorithm of the set-based POI data search. We also added a word operation algorithm and a similarity measure between two sentences expressed as a percentage. From the experimental results, we observe that our sentence similarity measurement method shows better performance than n-gram and the set-based POI data search.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1170-1178
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• 2022

Indoor POI data, an essential component of indoor spatial data, has attribute information of a specific place in the room and is the most critical information necessary for the user. Currently, indoor POI data is manually updated by direct investigation, which is expensive and time-consuming. Recently, research on updating POI using the attribute information of indoor photographs has been advanced to overcome these problems. However, the range of use, such as using only photographs with text information, is limited. Therefore, in this study, and to improvement this, I proposed a new method to update indoor POI data using a smartphone camera. In the proposed method, the POI name is obtained by classifying the indoor scene's photograph into artificial intelligence technology CNN and matching the location criteria to indoor spatial data through AR technology. As a result of creating and experimenting with a prototype application to evaluate the proposed method, it was possible to update POI data that reflects the real world with high accuracy. Therefore, the results of this study can be used as a complement or substitute for the existing methodologies that have been advanced mainly by direct research.

• International Journal of Contents
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• v.12 no.2
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• pp.37-41
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• 2016

In this paper, we propose a friend recommendation scheme that takes into consideration the attribute information of a POI and a user's movement patterns. The proposed scheme broadly consists of a part that filters out other users who have different preferences by calculating preferences using the attribute information of users and a part that finds a moving trajectory close to that of a user with a pattern-matching scheme. To verify the superiority of the proposed scheme, we compare it with existing schemes through various performance evaluations.