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This paper proposes a system recommending spatial information what user wants with collecting and analyzing tweets around the user’s location by using the GPS information acquired in mobile. This system has built an emotion dictionary and then derive the recommendation score of morphological analyzed tweets to provide not just simple information but recommendation through the emotion analysis information. The system also calculates distance between the recommended tweets and user’s latitude-longitude coordinates and the results showed the close order. This paper evaluates the result of the emotion analysis in a total of 10 areas with two keyword ‘Restaurants’ and ‘Performance.’ In the result, the number of tweets containing the words positive or negative are 122 of the total 210. In addition, 65 tweets classified as positive or negative by analyzing emotions after a morphological analysis and only 46 tweets contained the meaning of the positive or negative actually. This result shows the system detected tweets containing the emotional element with recall of 38% and performed emotion analysis with precision of 71%.

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.Keywords:SNS, Twitter, Realtime Event Detect, Geographical Name Denoising, Machine Learning

Twitter has the fast propagation and diffusion of information compare to other SNS. Therefore, many researches about detecting real-time event using twitter are progressing. Twitter real-time event detecting system assumes every twitter user as a sensor and analyzes their written tweet in order to detect the event. Researches that are related to this twitter have already obtained good results but confronted the limits because of some problems. Especially, many existing researches are using the method that can trace an event location by using GPS coordinate. However, it can be suggested a definite limitation through the present user's skeptical responses about making personal location information public. Therefore, this paper suggests the method that traces the location information in tweet contents text without using the provided location information from twitter. Associated words were grouped by using the keyword that extracted in tweet contents text. The place that the events have occurred and whether the events have surely occurred are detected by this experiment using this algorithm. Furthermore, this experiment demonstrated the necessity of the suggested methods by showing faster detection compare to the other existing media.

While SNS services such as Twitter or Facebook are rapidly growing, research for the SNS analysis has been concerned. Especially, twitter reacts to social issues in real-time so that it is used to get useful experimental data for researchers of social science or information retrieval. However, it is still lack of research on the methodology to collect data. Therefore, this paper suggests the tweet acquisition system by considering tendency of twitter user oriented location-based event and political·social event. First the system acquires tweets including information of location and keyword about event and secure IDs for acquisition of political·social event. Then we plan ID-analyzer to classify the tendency of users. In addition for measuring reliability of ID-analyzer, it acquires and analyzes the tweet by using high-ranked ID. In analyses result, top-ranked ID shows 88.8% reliability, 2nd-ranked ID shows 76.05% and ID-analyzer shows 77.5%, it shortens collection time by using minority ID.Keywords:Social Network Analysis, Tendency Analysis, Data Acquisition, Information Retrieval

With the huge success of Social Network Services, studies on social network analysis to extract the current issues or to track the symptoms of epidemic disease are being carried out actively. On Twitter, tweets reflect people`s reaction to an event and users` individual status well, so it is possible to detect an event regarding a tweet as a sensory value. Recently, social signals are used to detect the spread of illness like the flu as well as the occurrence of disaster event like an earthquake in early stages. In this paper, we set up a cold as a target event and regarded tweets as Cold Signals. To evaluate the reliability of Cold Signals, we analyzed correlations between weather factors and the cold index provided by Korea Meteorological Administration.

Since XML allows users to define any tags, XML documents with various structures have been created. Accordingly, many studies on clustering and searching the XML documents based on the similarity of paths have been done in order to manage the documents efficiently. To retrieve XML documents having similar structures, the three-dimensional bitmap indexing technique uses a path as a unit when it creates an index. If a path structure is changed, the technique recognizes it as a new path. Thus, another technique to measure the similarity of paths was proposed. To compute the similarity between two paths, the technique compares every node of the paths. It causes unnecessary comparison of the nodes, which do not exist in common between the two paths. In this paper, we propose a new technique that minimizes the comparison using signatures and show the performance evaluation results of the technique. The comparison speed of proposed technique was 20 percent faster than the existing technique.

We have observed huge success in social network services like Facebook and Twitter, and many researchers have done their analysis on these services. As massive data observed by users is produced on Twitter, many researchers have been conducting research to detect an event on Twitter. Some of them developed a system to detect the earthquakes or to find the local festivals. However, they did not consider the credibility of location information on Twitter although their systems were using the location information. In this paper, we analyze the credibility of the profile location and the correlation between the spatial attributes on Twitter as the preliminary research of the event detection system on Twitter. We analyzed 0.5 million Twitter users in Korea and 2.8 million users around the world. 49.73% of the users in Korea and 90.64% of the users in the world posted tweets in their profile locations. This paper will be helpful to understand the credibility of the spatial attributes on Twitter when the researchers develop an application using them.

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.

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.

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.

Many data are stored in database. For getting any information from many data, we use the query sentences. These information is basic and simple. Data mining method is various. In this paper, we manage clustering and association rules. We present a method for finding the better association rules, and we solve a problem of the existing association rules. We propose and apply a new clustering method to fit for association rules. It is not clustering of the existing distance basis or category basis. If we find association rules of each clusters, we can get not only existing rules found in all transaction but also rules that will be characteristics of clusters. Through this study, we can expect that we will reduce the number of many transaction access in large databases and find association of small group.

The cache used by mobile host is an important device that recovers the weak points of limited power and bandwidth, in mobile computing environments. However, it has to stand and maintain the consistency with the server data. In this paper, we propose a `Selective Cache Consistency Scheme`. The server allows an effective broadcasting by selecting data of high usability using `Cache State Table` and `Data Access Table`. Moreover, this scheme prevents the loss of data that nay occur by a long period of disconnection, by asynchronous broadcasting and transmitting those broadcast data preserved in the server. This also allows user to possess the latest data. Through experiments, we have found that the enlargement of autonomy is possible by reducing the dependence of server.

The automatic text categorization (ATC) is an emerging field that is technically based on the machine learning and information retrieval. Traditional machine learning usually builds a model that captures the behavior of the ‘concept’ from the training instances. Following this context, the ATC learns the concept from text data such as digital text documents, web pages, or e-mail texts. In this paper, we categorize the concepts that have been dealt by ATC into two groups: broad concepts and narrow concepts. Broad concepts are difficult to learn than narrow concepts. We propose a multi model approach where multiple local models are constructed to learn the broad concepts. Some experimental results showed that the multi model approach turned out to be effective in learning the confidentiality of company.

Recently, many solutions and sites related to the intelligent agent are created in order to provide good services for customers. Moreover, some new proposals including the collaborative filtering are put forward in the field of electronic commerce (EC) solutions. However, these proposals are lack of the add-on characteristics. In fact, it seems that only a few intelligent systems could provide the recommendations to the customers for the items that they really want to purchase, by means of the collaborative filtering algorithm based on their previous evaluation data. In this paper, we propose the CLASG (Clustering And Similarity Grouplens) collaborative filtering agent algorithm. The CLASG algorithm is the one that uses both the GroupLens algorithm and the clustering method. We have evaluated its performance with enough experiments, and the results show that the proposed method provides more stable recommendations than GroupLens does. We developed the MindReader, which makes it possible to have the correct predictions and recommendations with less response time than ever, as an automated recommendation system that includes both of CLASG algorithm and WhoLiked agent. It can be readily integrated into the existing EC solutions since it has an add-on characteristic, which is lacked in the past solutions.