Jung-Im Won

Trajectory clustering in road network environment

This paper proposes a new trajectory clustering scheme for objects moving on road networks. A trajectory on road networks can be defined as a sequence of road segments a moving object has passed by. We first propose a similarity measurement scheme that judges the degree of similarity by considering the total length of matched road segments. Then, we propose a new clustering algorithm based on such similarity measurement criteria by modifying and adjusting the FastMap and hierarchical clustering schemes. To evaluate the performance of the proposed clustering scheme, we also develop a trajectory generator considering the fact that most objects tend to move from the starting point to the destination point along their shortest path. The performance result shows that our scheme has the accuracy of over 95%.

Path prediction of moving objects on road networks through analyzing past trajectories

This paper addresses a series of techniques for predicting a future path of an object moving on a road network. Most prior methods for future prediction mainly focus on the objects moving over Euclidean space. A variety of applications such as telematics, however, require us to handle the objects that move over road networks. In this paper, we propose a novel method for predicting a future path of an object in an efficient way by analyzing past trajectories whose changing pattern is similar to that of a current trajectory of a query object. For this purpose, we devise a new function for measuring a similarity between trajectories by considering the characteristics of road networks. By using this function, we search for candidate trajectories whose subtrajectories are similar to a given query trajectory by accessing past trajectories stored in moving object databases. Then, we predict a future path of a query object by analyzing the moving paths along with a current position to a destination of candidate trajectories. Also, we suggest a method that improves the accuracy of path prediction by grouping those moving paths whose differences are not significant.

A study on thermal behaviour of curved steel box girder bridges considering solar radiation

Solar radiation induces non-uniform temperature distribution in the bridge structure depending on the shape of the structure and shadows cast on it. Especially in the case of curved steel box girder bridges, non-uniform temperature distribution caused by solar radiation may lead to unusual load effects enough to damage the support or even topple the whole curved bridge structure if not designed properly. At present, it is very difficult to design bridges in relation to solar radiation because it is not known exactly how varying temperature distribution affects bridges; at least not specific enough for adoption in design. Standard regulations related to this matter are likewise not complete. In this study, the thermal behavior of curved steel box girder bridges is analyzed while taking the solar radiation effect into consideration. For the analysis, a method of predicting the 3-dimensional temperature distribution of curved bridges is used. It uses a theoretical solar radiation energy equation together with a commercial FEM program. The behavior of the curved steel box girder bridges is examined using the developed method, while taking into consideration the diverse range of bridge azimuth angles and radii. This study also provides reference data for the thermal design of curved steel box girder bridges under solar radiation, which can be used to develop design guidelines.

Similarity search of time-warped subsequences via a suffix tree

This paper proposes an indexing technique for fast retrieval of similar subsequences using the time-warping distance. The time-warping distance is a more suitable similarity measure than the Euclidean distance in many applications where sequences may be of different lengths and/or different sampling rates. The proposed indexing technique employs a disk-based suffix tree as an index structure and uses lower-bound distance functions to filter out dissimilar subsequences without false dismissals. To make the index structure compact and hence accelerate the query processing, it converts sequences in the continuous domain into sequences in the discrete domain and stores only a subset of the suffixes whose first values are different from those of the immediately preceding suffixes. Extensive experiments with real and synthetic data sequences revealed that the proposed approach significantly outperforms the sequential scan and LB scan approaches and scales well in a large volume of sequence databases.

Trajectory Clustering in Road Network Environment

Recently, there have been many research efforts proposed on trajectory information. Most of them mainly focus their attention on those objects moving in Euclidean space. Many real-world applications such as telematics, however, deal with objects that move only over road networks, which are highly restricted for movement. Thus, the existing methods targeting Euclidean space cannot be directly applied to the road network space. This paper proposes a new clustering scheme for a large volume of trajectory information of objects moving over road networks. To the end, we first define a trajectory on a road network as a sequence of road segments a moving object has passed by. Next, we propose a similarity measurement scheme that judges the degree of similarity by considering the total length of matched road segments. Based on such similarity measurement, we propose a new clustering algorithm for trajectories by modifying and adjusting the FastMap and hierarchical clustering schemes. To evaluate the performance of the proposed clustering scheme, we also develop a trajectory generator considering the observation that most objects tend to move from the starting point to the destination point along their shortest path, and perform a variety of experiments using the trajectories thus generated. The performance result shows that our scheme has the accuracy of over 95% in comparison with that judged by human beings.

An efficient DNA sequence searching method using position specific weighting scheme

Exact match queries, wildcard match queries, and k mismatch queries are widely used in various molecular biology applications including the searching of ESTs (Expressed Sequence Tags) and DNA transcription factors. In this paper, we suggest an efficient indexing and processing mechanism for such queries. Our indexing method places a sliding window at every possible location of a DNA sequence and extracts its signature by considering the occurrence frequency of each nucleotide. It then stores a set of signatures using a multi-dimensional index such as the R*-tree. Also, by assigning a weight to each position of a window, it prevents signatures from being concentrated around a few spots in indexing space. Our query processing method converts a query sequence into a multi-dimensional rectangle and searches the index for the signatures overlapping with the rectangle. Experiments with real biological data sets have revealed that the proposed approach is at least 4.4 times, 2.1 times, and several orders of magnitude faster than the previous one in performing exact match, wildcard match, and k-mismatch queries, respectively.

Pleomorphic fibroma on the scalp

We present the eleventh case of benign pleomorphic fibroma arising on the scalp in a young Korean woman. Histopathologically these lesions were characterized by a polypoid or dome-shaped cutaneous fibrous mass with sparse cellularity but striking nuclear atypia and rare mitotic figures. They all showed benign clinical behavior, despite these histopathological findings.

A multi-dimensional indexing approach for timestamped event sequence matching

Abstract This paper addresses the problem of timestamped event sequence matching, a new type of similar sequence matching that retrieves the occurrences of interesting patterns from timestamped sequence databases. The sequential-scan-based method, the trie-based method, and the method based on the iso-depth index are well-known approaches to this problem. In this paper, we point out their shortcomings, and propose a new method that effectively overcomes these shortcomings. The proposed method employs an R ∗ -tree, a widely accepted multi-dimensional index structure that efficiently supports timestamped event sequence matching. To build the R ∗ -tree, this method extracts time windows from every item in a timestamped event sequence and represents them as rectangles in n -dimensional space by considering the first and last occurring times of each event type. Here, n is the total number of disparate event types that may occur in a target application. To resolve the dimensionality curse in the case when n is large, we suggest an algorithm for reducing the dimensionality by grouping the event types. Our sequence matching method based on the R ∗ -tree performs with two steps. First, it efficiently identifies a small number of candidates by searching the R ∗ -tree. Second, it picks out true answers from the set of candidates. We prove its robustness formally, and also show its effectiveness via extensive experiments.

A stock recommendation system exploiting rule discovery in stock databases

This paper addresses an approach that recommends investment types to stock investors by discovering useful rules from past changing patterns of stock prices in databases. First, we define a new rule model for recommending stock investment types. For a frequent pattern of stock prices, if its subsequent stock prices are matched to a condition of an investor, the model recommends a corresponding investment type for this stock. The frequent pattern is regarded as a rule head, and the subsequent part a rule body. We observed that the conditions on rule bodies are quite different depending on dispositions of investors while rule heads are independent of characteristics of investors in most cases. With this observation, we propose a new method that discovers and stores only the rule heads rather than the whole rules in a rule discovery process. This allows investors to impose various conditions on rule bodies flexibly, and also improves the performance of a rule discovery process by reducing the number of rules to be discovered. For efficient discovery and matching of rules, we propose methods for discovering frequent patterns, constructing a frequent pattern base, and its indexing. We also suggest a method that finds the rules matched to a query from a frequent pattern base, and a method that recommends an investment type by using the rules. Finally, we verify the effectiveness and the efficiency of our approach through extensive experiments with real-life stock data.

An efficient approach for sequence matching in large DNA databases

In molecular biology, DNA sequence matching is one of the most crucial operations. Since DNA databases contain a huge volume of sequences, fast indexes are essential for efficient processing of DNA sequence matching. In this paper, we first point out the problems of the suffix tree, an index structure widely-used for DNA sequence matching, in respect of storage overhead, search performance, and difficulty in seamless integration with DBMS. Then, we propose a new index structure that resolves such problems. The proposed index structure consists of two parts: the primary part realizes the trie as binary bit-string representation without any pointers, and the secondary part helps fast access to the tries leaf nodes that need to be accessed for post-processing. We also suggest efficient algorithms based on that index for DNA sequence matching. To verify the superiority of the proposed approach, we conduct performance evaluation via a series of experiments. The results reveal that the proposed approach, which requires smaller storage space, can be a few orders of magnitude faster than the suffix tree.