• Journal of Information Processing Systems
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• v.6 no.4
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• pp.521-536
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• 2010

Spatio-temporal patterns extracted from historical trajectories of moving objects reveal important knowledge about movement behavior for high quality LBS services. Existing approaches transform trajectories into sequences of location symbols and derive frequent subsequences by applying conventional sequential pattern mining algorithms. However, spatio-temporal correlations may be lost due to the inappropriate approximations of spatial and temporal properties. In this paper, we address the problem of mining spatio-temporal patterns from trajectory data. The inefficient description of temporal information decreases the mining efficiency and the interpretability of the patterns. We provide a formal statement of efficient representation of spatio-temporal movements and propose a new approach to discover spatio-temporal patterns in trajectory data. The proposed method first finds meaningful spatio-temporal regions and extracts frequent spatio-temporal patterns based on a prefix-projection approach from the sequences of these regions. We experimentally analyze that the proposed method improves mining performance and derives more intuitive patterns.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.237-248
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• 2009

Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.49-63
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• 2009

The performance of spatio-temporal moving pattern mining depends on how to analyze and process the huge set of spatio-temporal data due to the nature of it. The several method was presented in order to solve the problems in which existing spatio-temporal moving pattern mining methods[1-10] have, such as increasing execution time and required memory size during the pattern mining, but they did not solve properly yet. Thus, we proposed the STMP/MST method[11] as a preceding research in order to extract effectively sequential and/or periodical frequent occurrence moving patterns from the huge set of spatio-temporal moving data. The proposed method reduces patterns mining execution time, using the moving sequence tree based on hash tree. And also, to minimize the required memory space, it generalizes detailed historical data including spatio-temporal attributes into the real world scopes of space and time by using spatio-temporal concept hierarchy. In this paper, in order to verify the effectiveness of the STMP/MST method, we compared and analyzed performance with existing spatio-temporal moving pattern mining methods based on the quantity of mining data and minimum support factor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.79-93
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• 2009

For developing the location based service which is individualized and specialized according to the characteristic of the users, the spatio-temporal pattern mining for extracting the meaningful and useful patterns among the various patterns of the mobile object on the spatio-temporal area is needed. Thus, in this paper, as the practical application toward the development of the location based service in which it is able to apply to the real life through the pattern mining from the huge historical data of mobile object, we are proposed STOMP(using Frequency of sequence and Weight) that is the new mining method for extracting the patterns with spatial and temporal constraint based on the problems of mining the optimal moving pattern which are defined in STOMP(F)[25]. Proposed method is the pattern mining method compositively using weighted value(weights) (a distance, the time, a cost, and etc) for our previous research(STOMP(F)[25]) that it uses only the pattern frequent occurrence. As to, it is the method determining the moving pattern in which the pattern frequent occurrence is above special threshold and the weight is most a little bit required among moving patterns of the object as the optimal path. And also, it can search the optimal path more accurate and faster than existing methods($A^*$, Dijkstra algorithm) or with only using pattern frequent occurrence due to less accesses to nodes by using the heuristic moving history.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.231-242
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• 2007

The existing pattern mining methods[1,2,3,4,5,6,11,12,13] do not use location generalization method on the set of location history data of moving object, but even so they simply do extract only frequent patterns which have no spatio-temporal constraint in moving patterns on specific space. Therefore, it is difficult for those methods to apply to frequent pattern mining which has spatio-temporal constraint such as optimal moving or scheduling paths among the specific points. And also, those methods are required more large memory space due to using pattern tree on memory for reducing repeated scan database. Therefore, more effective pattern mining technique is required for solving these problems. In this paper, in order to develop more effective pattern mining technique, we propose new location generalization method that converts data of detailed level into meaningful spatial information for reducing the processing time for pattern mining of a massive history data set of moving object and space saving. The proposed method can lead the efficient spatial moving pattern mining of moving object using by creating moving sequences through generalizing the location attributes of moving object into 2D spatial area based on $R^*$-Tree and Area Grid Hash Table(AGHT) in preprocessing stage of pattern mining.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.150-162
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• 2011

For the period between 1991 and 2009, the annual average of 448 forest fires occurred in Korea. Above all, approximately 94% of the total fires frequently occurred during the spring and fall seasons. Therefore, we need to minimize the damage of forest fire and manage them systematically. In this study, we analyzed the spatio-temporal distribution patterns for the frequency of forest fire occurrences by each city and gun during dry season between 1990s and 2000s using GIS. Then we compared to analyze the frequency of forest fire occurrence by ten-day intervals in 2000s with that in 1990s. As a result of analysis, early April showed the highest frequency of forest fire occurrence in both 1990s and 2000s. Compared to the 1990s and 2000s, the regional change of forest fire showed the most frequent fire events around Chungcheong province. Especially extra 27 fires increased in Daejeon city, and the second most frequent fire had more than 10 fires in Jeolla province and Incheon. However, the number of fire frequency decreased by 12 fires at the end of April in Hongcheon-gun(the province of Gangwon). This is the largest drop over the study period. We consider that this paper will utilize usefully to establish regional counterplan for forest fire prevention by understanding regional forest fire patterns from seasonal change.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.287-296
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• 2004

Recently, various location-based services are becoming very popular in mobile environments. In this paper, we propose a new concept of a frequent item set, called “associative class set”, for supporting the location-based service which uses a large quantity of a spatial database in mobile computing environments, and then present a new method for efficiently generating the associative class set. The associative class set is generated with considering the temporal relation of queries, the spatial distance of required objects, and access patterns of users. The result of our research can play a fundamental role in efficiently supporting location-based services and in overcoming the limitation of mobile environments. The associative class set can be applied by a recommendation system of a geographic information system in mobile computing environments, mobile advertisement, city development planning, and client cache police of mobile users.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5015-5022
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• 2010

Most of the existing pattern mining techniques are capable of searching patterns according to the continuous change of the spatial information of an object but there is no constraint on the spatial information that must be included in the extracted pattern. Thus, the existing techniques are not applicable to the optimal path search between specific nodes or path prediction considering the nodes that a moving object is required to round during a unit time. In this paper, the precision of the path search according to the spatial hierarchy is analyzed using the Spatial-Temporal Optimal Moving Pattern(with Frequency & Weight) (STOPM(FW)) algorithm which searches for the optimal moving path by considering the most frequent pattern and other weighted factors such as time and cost. The result of analysis shows that the database retrieval time is minimized through the reduction of retrieval range applying with the spatial constraints. Also, the optimal moving pattern is efficiently obtained by considering whether the moving pattern is included in each hierarchical spatial scope of the spatial hierarchy or not.

• Journal of the Korean association of regional geographers
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• v.19 no.3
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• pp.384-400
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• 2013

In this study, spatio-temporal patterns of extreme precipitation events caused by typhoons are examined based on observational daily precipitation data at approximately 340 weather stations of Korea Meterological Administration's ASOS (Automated Synoptic Observation System) and AWS (Automatic Weather System) networks for the recent 10 year period (2002~2011). Generally, extreme precipitation events by typhoons exceeding 80mm of daily precipitation commonly appear in Jeju Island, Gyeongsangnam-do, and the eastern coastal regions of the Korean Peninsula. However, the frequency, intensity and spatial extent of typhoon-driven extreme precipitation events can be modified depending on the topography of major mountain ridges as well as the pathway of and proximity to typhoons accompanying the anti-clockwise circulation of low-level moisture with hundreds of kilometers of radius. Yellow Sea-passing type of typhoons in July cause more frequent extreme precipitation events in the northern region of Gyeonggi-do, while East Sea-passing type or southern-region-landfall type of typhoons in August-early September do in the interior regions of Gyeongsangnam-do. These results suggest that when local governments develop optimal mitigation strategies against potential damages by typhoons, the pathway of and proximity to typhoons are key factors.