• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.317-320
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• 2005

The LBS (Location-Based Services) are valuable information services combined the location of moving object with various contents such as map, POI (point of Interest), route and so on. The must general service of LBS is route determination service and its applicable parts are FMS (Fleet Management System), travel advisory system and mobile navigation system. The core function of route determination service is determination of optimal route from source to destination in various environments. The MODB (Moving Object Database) system, core part of LBS composition systems, is able to manage current or past location information of moving object and massive trajectory information stored in MODB is value-added data in CRM, ERP and data mining part. Also this past trajectory information can be helpful to determine optimal route. In this paper, we suggest methods to determine optimal route by querying past trajectory information in MODB system and verify the effectiveness of suggested method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3197-3218
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• 2012

Compared with traditional itinerary planning, intention-oriented itinerary recommendations can provide more flexible activity planning without requiring the user's predetermined destinations and is especially helpful for those in unfamiliar environments. The rank and classification of points of interest (POI) from location-based social networks (LBSN) are used to indicate different user intentions. The mining of vehicles' physical trajectories can provide exact civil traffic information for path planning. This paper proposes a POI category-based itinerary recommendation framework combining physical trajectories with LBSN. Specifically, a Voronoi graph-based GPS trajectory analysis method is utilized to build traffic information networks, and an ant colony algorithm for multi-object optimization is implemented to locate the most appropriate itineraries. We conduct experiments on datasets from the Foursquare and GeoLife projects. A test of users' satisfaction with the recommended items is also performed. Our results show that the satisfaction level reaches an average of 80%.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.207-212
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• 2021

Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.103-112
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• 2022

The goal of pattern mining is to identify novel patterns in a database. High utility itemset mining (HUIM) is a research direction for pattern mining. This is different from frequent itemset mining (FIM), which additionally considers the quantity and profit of the commodity. Several algorithms have been used to mine high utility itemsets (HUIs). The original BPSO algorithm lacks local search capabilities in the subsequent stage, resulting in insufficient HUIs to be mined. Compared to the transfer function used in the original PSO algorithm, the V-shaped transfer function more sufficiently reflects the probability between the velocity and position change of the particles. Considering the influence of the acceleration factor on the particle motion mode and trajectory, a nonlinear acceleration strategy was used to enhance the search ability of the particles. Experiments show that the number of mined HUIs is 73% higher than that of the original BPSO algorithm, which indicates better performance of the proposed algorithm.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.59-77
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• 2007

Advances in mobile techknowledges and supporting techniques require an effective representation and analysis of moving objects. Similarity search of moving object trajectories is an active research area in data mining. In this paper, we propose a trajectory search algorithm for spatio-temporal similarity of moving objects on road network. For this, we define spatio-temporal distance between two trajectories of moving objects on road networks, and propose a new method to measure spatio-temporal similarity based on the real road network distance. In addition, we propose a similar trajectory search algorithm that retrieves spatio-temporal similar trajectories in the road network. The algorithm uses a signature file in order to retrieve candidate trajectories efficiently. Finally, we provide performance analysis to show the efficiency of the proposed algorithm.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.15-37
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• 2004

With the growth in the size of datasets, data mining has recently become an important research topic. Especially, interests about spatio-temporal data mining has been increased which is a method for analyzing massive spatio-temporal data collected from a wide variety of applications like GPS data, trajectory data of surveillance system and earth geographic data. In the former approaches, conventional clustering algorithms are applied as spatio-temporal data mining techniques without any modification. In this paper, we focused to SOM that is the most common clustering algorithm applied to clustering analysis in data mining wet and develop the spatio-temporal data mining module based on it. In addition, we analyzed the clustering results of developed SOM module and compare them with those of K-means and Agglomerative Hierarchical algorithm in the aspects of homogeneity, separation, separation, silhouette width and accuracy. We also developed specialized visualization module fur more accurate interpretation of mining result.

• Asian Journal of Atmospheric Environment
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• v.5 no.1
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• pp.47-55
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• 2011

Variations in ambient atmospheric carbon monoxide(CO) observed at an inland mining site in the Indo-Gangetic plains, Jaduguda ($22^{\circ}38'N$, $86^{\circ}21'E$, 122m MSL, ~75 km away from the coast of the Bay of Bengal) during the Tsunami of 26 December 2004 were monitored. CO mixing ratio over this site was measured using a non-dispersive infrared analyzer (Monitor Europe Model 9830 B). Back trajectory analysis data obtained using NOAA Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) Model was also used for this study. Variations in CO mixing ratio at a coastal site, Thiruvananthapuram ($8^{\circ}29'N$, $76^{\circ}57'E$, located ~2 km from the Arabian Sea coast) have also been investigated using CO data retrieved from the Measurement Of Pollution In The Troposphere (MOPITT) instrument. Ground-based measurements indicated abnormal variations in CO mixing ratio at Jaduguda from 25 December 2004 evening (previous day of the Tsunami). MOPITT CO data showed an enhancement in CO mixing ratio over Thiruvananthapuram on the Tsunami day. Back trajectory analyses over Thiruvananthapuram and Jaduguda for a period of 10 days from $21^{st}$ to $30^{th}$ December 2004 depicted that there were unusual vertical movements of air from high altitudes from 25 December 2004 evening. CO as well as the back trajectory analyses data showed that the variations in the wind regimes and consequently wind driven transport are the most probable reasons for the enhancement in CO observed at Jaduguda and Thiruvananthapuram during the Tsunami.

• Geophysics and Geophysical Exploration
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• v.1 no.2
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• pp.92-100
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• 1998

Array CSAMT surveys were conducted in two areas where it was not easy to identify the presence of faults only with geological survey because of thick overburden. The purpose of these surveys were to locate the faults and to delineate the deep resistivity structures around the faults. The steep dip lineaments having high contrast in resistivity laterally and the low resistive zones having some width in the resistivity sections were interpreted as faults and fracture zones associated with faults, respectively, The good applicability of array CSAMT to the investigation of fault was recognized owing to the agreement between the interpretation results of array CSAMT and the conclusive evidences collected by the following geological survey. The evidences includes the recent exposure of fault and the trajectory of fault evidences of the survey line. A comparison of the applicabilities of array CSAMT method and the resistivity method using dipole-dipole array was presented with the results of both methods along a same traverse line.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.109-141
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• 1998

Many processes in the industrial realm exhibit sstochastic and nonlinear behavior. Consequently, an intelligent system must be able to nonlinear production processes as well as probabilistic phenomena. In order for a knowledge based system to control a manufacturing processes as well as probabilistic phenomena. In order for a knowledge based system to control manufacturing process, an important capability is that of prediction : forecasting the future trajectory of a process as well as the consequences of the control action. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes, includinb chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a chaotic process in the presence of various patterns of noise.

• Advances in Energy Research
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• v.3 no.1
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• pp.1-10
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• 2015

In the past sidetracking was the means to bypass a damaged zone or to correct the direction of a wellbore. Nowadays, this method is very common and useful in relocating the bottom of a wellbore in a more productive zone and consequently enhancing the production of a reservoir by saving a significant amount of time and money. In this paper, the stability of the bend area is assessed considering varied conditions of stress regime and sidetrack orientation. In general, the stress regime and the orientation of the principal stresses have negligible effect on the stability of the sidetrack compared to sidetrack inclination. On the other hand, the sidetrack deviation angle from the vertical main well plays the major role in the stability of the bend area.