Byung-Yeon Hwang

A Study of the Correlation between the Spatial Attributes on Twitter

In recent years, much attention has been given to the topics on social network analysis. Previous research has shown that each Twitter user can work as a sensor to detect the target events such as the earthquakes. They used the spatial attributes on Twitter to estimate the location of the target event. Although the precision of the location information may affect the performance of the estimation, they did not consider its reliability. In this paper, we investigate the correlation between the profile locations on Twitter and the GPS coordinates in tweets. A text-based grouping method is applied to the spatial attributes to cluster them with considering of the administrative districts. The analysis result shows that nearly 50% of users post the most of their tweets in the profile locations while 30% of users, who may have high mobility in a wide range, do not have any tweets in their locations.

Path bitmap indexing for retrieval of XML documents

The path-based indexing methods such as the three-dimensional bitmap indexing have been used for collecting and retrieving the similar XML documents. To do this, the paths become the fundamental unit for constructing index. In case the document structure changes, the path extracted before the change and the one after the change are regarded as totally different ones regardless of the degree of the change. Due to this, the performance of the path-based indexing methods is usually bad in retrieving and clustering the documents which are similar. A novel method which can detect the similar paths is needed for the effective collecting and retrieval of XML documents. In this paper, a new path construction similarity which calculates the similarity between the paths is defined and a path bitmap indexing method is proposed to effectively load and extract the similar paths. The proposed method extracts the representative path from the paths which are similar. The paths are clustered using this, and the XML documents are also clustered using the clustered paths. This solves the problem of existing three-dimensional bitmap indexing. Through the performance evaluation, the proposed method showed better clustering accuracy over existing methods.

X-Square: A Hybrid Three-Dimensional Bitmap Indexing for XML Document Retrieval

XML is studied and used as a key technology in many research and applications today. Accordingly there is an increasing need for the efficient XML document retrieval. Bitmap indexing is an efficient technique for determining true and false fast and it has been used mainly for reducing search extent rather than retrieving data. BitCube, an existing three-dimensional bitmap indexing for XML document retrieval, constructs bitmap from the entire index. In case high volumes of documents are loaded in a single cluster, however, this causes significant performance degradation in memory usage and operation speed. xPlaneb which is another bitmap indexing technique solves this problem by reconstructing three-dimensional bitmap index of BitCube using linked list. xPlaneb, however, has a high memory usage problem compared with BitCube when low volumes of documents are loaded because BitCube index consists of small field of one bits. This paper proposes X-Square which is a hybrid of BitCube and xPlaneb. X-Square takes both advantages of BitCube and xPlaneb. Experimental results show that X-Square has better performance in memory usage than xPlaneb and BitCube although the operation speed is a bit worse than xPlaneb.

TRED : Twitter based Realtime Event-location Detector

SNS is a web-based online platform service supporting the formation of relations between users. SNS users have usually used a desktop or laptop for this purpose so far. However, the number of SNS users is greatly increasing and their access to the web is improving with the spread of smart phones. They share their daily lives with other users through SNSs. We can detect events if we analyze the contents that are left by SNS users, where the individual acts as a sensor. Such analyses have already been attempted by many researchers. In particular, Twitter is used in related spheres in various ways, because it has structural characteristics suitable for detecting events. However, there is a limitation concerning the detection of events and their locations. Thus, we developed a system that can detect the location immediately based on the district mentioned in Twitter. We tested whether the system can function in real time and evaluated its ability to detect events that occurred in reality. We also tried to improve its detection efficiency by removing noise.

Two-Phase path retrieval method for similar XML document retrieval

The existing three-dimensional bitmap indexing techniques have a performance problem in clustering the similar documents because they cannot detect the similar paths. The existing path clustering method which is based on the path construction similarity is one approach to solve the problem above. This method, however, consumes too much time in measuring the similarities between the similar paths for clustering. This paper defines the expected path construction similarity and proposes two-phase path retrieval method which effectively clustering the paths using it. The proposed method solved the performance degrade problem in path clustering by filtering the paths to be measured using the expected path construction similarity.

Keyword Filtering about Disaster and the Method of Detecting Area in Detecting Real-Time Event Using Twitter

This research suggests the keyword filtering about disaster and the method of detecting area in real-time event detecting system by analyzing contents of twitter. The diffusion of smart-mobile has lead to a fast spread of SNS and nowadays, various researches based on studying SNS are being processed. Among SNS, the twitter has a characteristic of fast diffusion since it is written in 140 words of short paragraph. Therefore, the tweets that are written by twitter users are able to perform a role of sensor. By using these features the research has been constructed which detects the events that have been occurred. However, people became reluctant to open their information of location because it is reported that private information leakage are increasing. Also, problems associated with accuracy are occurred in process of analyzing the tweet contents that do not follow the spelling rule. Therefore, additional designing keyword filtering and the method of area detection on detecting real-time event process were required in order to develop the accuracy. This research suggests the method of keyword filtering about disaster and two methods of detecting area. One is the method of removing area noise which removes the noise that occurred in the local name words. And the other one is the method of determinating the area which confirms local name words by using landmarks. By applying the method of keyword filtering about disaster and two methods of detecting area, the accuracy has improved. It has improved 49% to 78% by using the method of removing area noise and the other accuracy has improved 49% to 89% by using the method of determinating the area.

The Path Inverted Index Technique for XML Document Retrieval

Recently, many XML document management systems using the advantage of RDBMS have been actively developed for the storage, processing and retrieval of XML documents. However, fractional pattern-matching query such as the LIKE operations cannot take the advantage of the index of RDBMS because these operations have deteriorated retrieval performance through its inefficient comparison processing. The hierarchical XML storage technique which stores XML documents in RDBMS efficiently, and the path inverted index technique are proposed in this paper. It regards the element of an XML document as a keyword, and focuses on organizing a posting file with path identifiers and sequences to reduce the retrieval time of path based query. Through simulations, our methods have shown about 60% better performance than the conventional method using RDBMS in searching.

k-Bitmap Clustering Method for XML Data based on Relational DBMS

Use of XML data has been increased with growth of Web 2.0 environment. XML is recognized its advantages by using based technology of RSS or ATOM for transferring information from blogs and news feed. Bitmap clustering is a method to keep index in main memory based on Relational DBMS, and which performed better than the other XML indexing methods during the evaluation. Existing method generates too many clusters, and it causes deterioration of result of searching quality. This paper proposes k-Bitmap clustering method that can generate user defined k clusters to solve above-mentioned problem. The proposed method also keeps additional inverted index for searching excluded terms from representative bits of k-Bitmap. We performed evaluation and the result shows that the users can control the number of clusters. Also our method has high recall value in single term search, and it guarantees the searching result includes all related documents for its query with keeping two indices.

An Indexing System for Retrieving Similar Paths in XML Documents

Since the XML standard was introduced by the W3C in 1998, documents that have been written in XML have been gradually increasing. Accordingly, several systems have been developed in order to efficiently manage and retrieve massive XML documents. BitCube-a bitmap indexing system-is a representative system for this field of research. Based on the bitmap indexing technique, the path bitmap indexing system(LH06), which performs the clustering of similar paths, improved the problem that the existing BitCube system could not solve, namely, determining similar paths. The path bitmap indexing system has the advantage of a higher retrieval speed in not only exactly matched path searching but also similar path searching. However, the similarity calculation algorithm of this system has a few particular problems. Consequently, it sometimes cannot calculate the similarity even though some of two paths have extremely similar relationships; further, it results in an increment in the number of meaningless clusters. In this paper, we have proposed a novel method that clustering, the similarity between the paths in order to solve these problems. The proposed system yields a stable result for clustering, and it obtains a high score in clustering precision during a performance evaluation against LH06.

X-Binder: Path Combining System of XML Documents Based on RDBMS

With the increasing use of XML, considerable research is being conducted on the XML document management systems for more efficient storage and searching of XML documents. Depending on the base systems, these researches can be classified into object-oriented DBMS (OODBMS) and relational DBMS (RDBMS). OODBMS-based systems are better suited to reflect the structure of XML-documents than RDBMS-based ones. However, using an XML parser to map the contents of documents to relational tables is a better way to construct a stable and effective XML document management system. The proposed X-Binder system uses an RDBMS-based inverted index; this guarantees high searching speed but wastes considerable storage space. To avoid this, the proposed system incorporates a path combining module agent that combines paths with sibling relations, and stores them in a single row. Performance evaluation revealed that the proposed system reduces storage wastage and search time.