• Journal of KIISE:Databases
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• v.36 no.4
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• pp.314-320
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• 2009

Skyline queries have recently received considerable attention in the searching services. The skyline contains interesting objects that are not dominated by any other objects on all dimensions. Many related works have processed a skyline on static data or on moving objects in Euclidean space. However, this paper assumes that the point of a skyline query continuously moves in road networks. We propose a new method that efficiently processes continuous skyline queries in road networks through pre-computed shortest range data of objects. Our experiments show that the proposed method is about 100 times faster than previous methods in terms of query processing time.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.141-152
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• 2004

In this paper, we propose the index structure supporting the future position retrievals with efficiently updating continuous positions of moving objects in location based services. For reducing update costs of moving objects, our index structure directly accesses to the leaf node with moving objects using secondary index structure and performs bottom up update when node information is changed. Positions of moving objects are stored in primary index structure. In primary index structure, the split information similar to kd-tree is stored to internal node for increasing node's fanout. And the proposed index structure supports the future position retrievals using velocity of moving objects in the child node.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1337-1342
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• 2014

Increasingly important user based service on the smart media era, and increasing awareness about the user experience. In this study, by considering these realities, what impact location based constructs on smartphone environment, continuous intention to use you want to identification. Thus, this study conducted of preference the influencing factors for location based constructs. First steps, based constructs known empirical studies were categorized information, entertainment, safe&emergency, navigation&tracking and advertising& commerce. Second Steps, the categorized factors were analyzed preference relationship between constructs using AHP(analytic hierarchy process) technique. Questionnaire survey was conducted to those who employees S Telecom in Busan city and Gyeongnam province during 2000. 4. 15 and 2014. 4. 30. The result of the analysis might be summarized that the navigation(0.133) has the highest preference ran in the constructs. Based on these findings, several theoretical and practical implications were suggested and discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1152-1168
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• 2010

Recently, due to the prevalence of various mobile devices and wireless broadband networks, there has been a significant increase in interest and demand for multimedia streaming services such as the mobile IPTV. In such a wireless broadband network, transmitting a continuous stream of multimedia data is difficult to achieve due to mobile stations (MSs) movement. Providing Quality of Service (QoS) for multimedia video streaming applications requires the server and/or client to be network-aware and adaptive. Therefore, in order to deploy a mobile IPTV service in wireless broadband networks, offering users efficient wireless resource utilization and seamlessly offering user perceived QoS are important issues. In this paper, we propose a new adaptive streaming scheme, called MARC (Mobile-aware Adaptive Rate Control), which adjusts the quality of bit-stream and transmission rate of video streaming based on the wireless channel status and network status. The proposed scheme can control the rate of multimedia streaming to be suitable for the wireless channel status by using awareness information of the wireless channel quality and the mobile station location. The proposed scheme can provide a seamless multimedia playback service in wireless broadband networks in addition to improving the QoS of multimedia streaming services. The proposed MARC scheme alleviates the discontinuity of multimedia playback and allocates a suitable client buffer to the wireless broadband network. The simulation results demonstrate the effectiveness of our proposed scheme.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.697-698
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• 2009

모바일 기기 및 무선 통신 기술의 발달로 인하여 위치 기반 서비스의 이용이 확산되었다. 그러나 이와 같이 사용자의 정확한 위치정보를 가지고 LBS 서버에 서비스를 요청하는 것은 심각한 개인 정보 누출의 위협이 될 수 있다. 따라서 안전하고 편리한 위치기반 서비스 사용을 위한 개인 정보 보호 방법이 요구된다. 이를 위해 본 논문에서는 연속적인 위치기반 서비스를 지원하는 분산 그리드 기반 Cloaking 영역 설정 기법을 설계한다. 설계하는 기법은 분산 환경에서 연속적인 서비스를 지원하기 위해 Cloaking 영역 설정 시 필요한 정보를 분산 유지하고, 이동 확률 매트릭스 생성 및 확률 계산을 분산적으로 수행한다. 마지막으로는 모바일 사용자 사이에 발생하는 통신비용을 감소시키기 위해, 대표 노드는 해당 클러스터에서 떠난 사용자에 대한 정보를 유지하고 클러스터 내 부분 확률값의 합산시 병합노드를 사용한다.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.70-75
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• 2015

In this paper, we explore the efficient processing of continuous range queries over a huge number of moving objects, each of which retrieves the moving objects that are currently located within a geographic query region of interest. The moving objects should continually communicate with the server to report their current locations, so as to keep the results of the continuous range queries up-to-date. However, this increases the server workload and involves a enormous amount of communication as the number of continuous range queries and the moving objects becomes enormous. In this paper, we adopt an approach where we leverage available memory and computational resources of the moving objects in order to resolve these problems. To this end, we propose a query indexing structure, referred to as the Space Partitioning Query Index(SPQI), which enables the server to efficiently cooperate with the moving objects for processing continuous range queries. SPQI improves system performance in terms of server workload and communication cost. Through simulations, we show the superiority of SPQI.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.471-482
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• 2007

In this paper, we propose an efficient continuous range query processing scheme using a modified grid based query indexing to reduce storage spaces and to accelerate processing time. The proposed method has two major features. First, each query has a bit identifier and each cell in a grid has a bit pattern that consists of the bit identifiers of the queries. The bit patterns present the relationship between cells and queries. Using the bit patterns, we can compute quickly what queries overlap a cell in a grid and reduce the number of unnecessary operations by comparing the bit patterns without comparing the query identifiers when we compute the relation between cells and queries. Second, the management of cells in the grid by groups prevents from wasting the storage space through the increase of the length of the bit pattern and increasing the comparison costs of bit patterns. We show through the performance evaluation that the proposed method outperforms the existing methods.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.226-228
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• 2004

최근에 이동객체의 위치정보를 활용한 위치기반서비스(L8S, Location Based Services)에 대한 관심이 증가하고 있다. 전통적으로 정적인 위치정보를 갖는 공간 객체는 GIS(Geographic Information System) 서버에 저장, 관리되었다. 이동객체는 시간에 따라 위치의 변화가 매우 빈번하여 위치 정보가 계속 갱신되기 때문에, 전통적인 GIS 서버로는 관리가 어렵다. 본 논문에서는 기존의 연속적인 최근접 이웃탐색 기법에서 데이터의 처리 순서에 따라 탐색공간과 계산비용이 증가하는 문제점을 슬랩을 사용하여 해결한다. 최근접 이웃의 수직연장선 사이의 공간인 슬랩 내부영역에 대해서만 탐색하도록 하여 탐색영역을 줄이고, 그 내부에 있는 점들에 대해서만 처리하여 계산비용을 줄인다.

• 한국공간정보시스템학회:학술대회논문집
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• 2003.11a
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• pp.169-174
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• 2003

이동 객체에 대한 기존 최근접(nearest neighbor, NN) 질의 처리 기법들은 질의 궤적에 대해 연속적으로 정확하게, 질의와 가장 가까운 위치를 유지하면서 움직이는 최근접 객체를 선택할 수 있는 충분한 기준을 가지고 있지 못하다. 이 논문은 질의 객체와 데이터 객체가 모두 이동 객체인 경우에 가장 적합하게 사용되는 객체 궤적에 대한 연속적인 질의 처리를 통해 정확한 결과를 얻을 수 있는 새로운 최근접 질의 처리 기법, 연속 궤적 최근접 질의(CTNN, continuous trajectory nearest neighbor query)를 제안한다. 우리는 두 가지 Approximate, Exact CTNN 기법을 제안하며 이들은 모두 항해 시스템, 교통 통제 시스템, 물류정보 시스템 등 각종 위치 기반 서비스(L8S: location based services) 상에서 다양하게 사용될 수 있다. 이들은 이동 객체 궤적이 미리 알려져 있는 경우 그리고 질의와 데이터 객체가 모두 이동 객체인 경우에 가장 적합하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.