Kil Hong Joo

An integrated proteome database for two-dimensional electrophoresis data analysis and laboratory information management system

We describe an integrated proteome database, termed Yonsei Proteome Research Center Proteome Database (YPRC‐PDB) which can store, retrieve and analyze various information including two‐dimensional electrophoresis (2‐DE) images and associated spot information that were obtained during studies of hepatocellular carcinoma (HCC). YPRC‐PDB is also designed to perform as a laboratory information management system that manages sample information, clinical background, conditions of both sample preparation and 2‐DE, and entire sets of experimental results. It also features query system and data‐mining applications, which are amenable to automatically analyze expression level changes of a specific protein and directly link to clinical information. The user interface is web‐based, so that the results from other laboratories can be shared effectively. In particular, the master gel image query is equipped with a graphic tool that can easily identify the relationship between the specific pathological stage of HCC and expression levels of a potential marker protein on the master gel image. Thus, YPRC‐PDB is a versatile integrated database suitable for subsequent analyses. The information in YPRC‐PDB is updated easily and it is available to authorized users on the World Wide Web (http://yprcpdb.proteomix.org/∼damduck/).

An Adaptive Teaching and Learning System for Efficient Ubiquitous Learning

In this paper we present our pedagogical and technological approach for supporting the design of novel situated teaching and learning activities that can be conducted both, outside the school and in the classroom. Education has undergone major changes in recent years, with the development of digital information transfer, storage and communication methods having a significant effect. This development has allowed for access to global communications and the number of resources available to today’s students at all levels of schooling. Therefore, ubiquitous learning is a new educational paradigm made possible in part by the affordances of digital information. Ubiquitous learning is characterized by providing intuitive ways for identifying right learning collaborators, right learning contents and right learning services in the right place at the right time. This paper first creates ubiquitous environment, providing function enabling learning to take place anytime and anywhere with any available learning device, for ubiquitous learning according to various properties. Also, in order to improve of proposed ubiquitous system, this paper proposes the scaffolding and mentoring system. If the scaffolding and the mentoring in the ubiquitous learning are provided, studying efficiency would be maximized. Furthermore, the adaptive teaching and learning system with ubiquitous computing may offer great innovation in the delivery of education, allowing for personalization and customization to student needs. The experiments in studying achievements and attitudes of students are performed and show the application possibility of the ubiquitous teaching and learning model.

An incremental document clustering algorithm based on a hierarchical agglomerative approach

Document clustering is classifying a data set of documents into groups of closely related documents, so that its resulting clusters can be used in browsing and searching the documents of a specific topic. In most cases of such as application, a set of new documents are incrementally added to the data set and there can be a large variation in the number of words in each document. This paper proposes an incremental document clustering method for an incrementally increasing data set of documents. The normalized inverse document frequency of a word in the data set is introduced to cope with the variation of the number of words in each document. Furthermore, an average link method for document clustering instead of using one similarity measure used in two similarity measures: a cluster cohesion rate and a cluster participation rate. Furthermore, a category tree for a set of identified clusters is introduced to assist the incremental document clustering of newly added documents. In this paper, the performance of the proposed method is analyzed by a series of experiments to identify their various characteristics.

An Adaptive Materialization Method for Globally Optimized Query Evaluation in a Hierarchical Mediator System

Semantically related mediators form a hierarchical structure based on the definition of views in each mediator, since a view in a mediator is defined by views in other mediators or base tables in several local information servers. A global query is transformed into a set of its sub-queries; and, each sub-query is the unit of evaluation in an information server or a child mediator. Therefore, it is possible to speed up the execution of a global query if the previous results of frequently requested sub-queries are materialized in a mediator. However, the integrating schema in a mediator can be incrementally modified; and, the evaluation frequency of a global query can also be continuously varied. In order to select the optimized set of materialized sub-queries, with respect to their current evaluation frequencies, the proposed method applies a decay factor for modeling the recent access behavior of each sub-query. As a result, it is possible to adjust the optimized set of materialized sub-queries adaptively according to the recent changes in the evaluation frequencies of sub-queries. Consequently, this paper proposes an algorithm for selecting the optimized set of sub-queries to be materialized in each mediator of a hierarchical mediator system adaptively such that the overall query evaluation cost of the hierarchical mediator system can be minimized at all times.

An efficient query evaluation in a mediator based on implementation plan

Recent research which purports to integrate distributed information has been concentrated on developing efficient mediator systems that not only provide a high degree of autonomy for local users, but also support the flexible integration of required functions for global users. However, there has been little attention paid to the implementation of the global views defined in a mediator. It is possible to speed up the execution of a global query if the previous result of a view is materialized in a mediator. Since the integration schema of a mediator can be incrementally modified and its usage can also be continuously varied, view usage should be carefully monitored to determine the optimized set of materialized views. Furthermore, as the number of views increases, the optimization process itself may take too long, so that the optimized set identified by a long optimization process becomes obsolete due to the recent change of view usage. This paper proposes the adaptive selection of a practical implementation method for each global view, such that available storage in a mediator can be highly utilized at any time. In order to differentiate the recent usage of a view from the past, the accumulated usage frequency of a view decays as time goes by. As a result, it is possible to change the implementation of each view adaptively with respect to the recent variation of view usage.

An incremental document clustering for the large document database

With the development of the internet and computer, the amount of information through the internet is increasing rapidly and it is managed in document form. For this reason, the research into the method to manage for a large amount of document in an effective way is necessary. The document clustering is integrated documents to subject by classifying a set of documents through their similarity among them. Accordingly, the document clustering can be used in exploring and searching a document and it can increase accuracy of search. This paper proposes an efficient incremental clustering algorithm for a set of documents increase gradually. The incremental document clustering algorithm assigns a set of new documents to the legacy clusters which have been identified in advance. In addition, to improve the correctness of the clustering, removing the stop words can be proposed and the weight of the word can be calculated by the proposed TF × NIDF function. In this paper, the performance of the proposed method is analyzed by a series of experiments to identify their various characteristics.

Efficient detection and resolution of OR deadlocks in distributed systems

This paper proposes a distributed algorithm for resolving deadlocks under the OR request model. The algorithm builds a distributed spanning tree by propagating probes. An encoding scheme is devised to deduce the ancestor-descendant relationship between tree nodes, so that the initiator of the algorithm collects only non-tree edge information to detect deadlock, whereas the current algorithms require all the edge information for deadlock detection. The proposed algorithm resolves all deadlocks reachable from the initiator. Its performance in terms of number of messages and execution time is better than or comparable to that of the existing algorithms. We further showed through analytic evaluation that the suggested algorithm substantially shortens deadlock detection time.

Adaptive selection of materialized queries in a mediator for the integration of distributed information resources

A global query in a mediator system to integrate distributed information is transformed into a set of its sub-queries and each sub-query is the unit of evaluation in a remote server. Therefore, it is possible to speed up the execution of a global query if the previous results of frequently evaluated sub-queries are materialized in a mediator. Since the integration schema of a mediator can be incrementally modified and the evaluation frequency of a global query can also be continuously varied, query usage should be carefully monitored to determine the optimized set of materialized sub-queries. Furthermore, as the number of sub-queries increases, the optimization process itself may take too long, so that the optimized set identified by a long optimization process may become obsolete due to the recent change of query usage. This paper proposes the adaptive selection of materialized sub-queries such that available storage in a mediator can be highly utilized at any time. In order to differentiate the recent usage of a query from the past, the accumulated usage frequency of a query decays as time goes by. As a result, it is possible to change the set of materialized sub-queries adaptively according to the recent variation of query usage.

Adaptive materialization in a hierarchical mediator system

Recent research studies which purport to integrate distributed information have been concentrated on developing efficient mediators that provide a high degree of autonomy for a local user. Since a view in a mediator is defined by views in other mediators or base tables in several information resources, semantically related mediators form a hierarchical structure based on the definition of views in each mediator. It has been shown that the query evaluation cost of a mediator can be greatly reduced if the results of frequently requested queries are materialized in the mediator. Unlike a database system, the schema of a mediator can be evolved. In addition, the evaluation frequency of each query as well as the update frequency of each related information resource can be easily varied as time goes by. Consequently, they should be adaptively monitored. The effect of such materialization is greatly increased in a set of mediators in a hierarchical mediator system. This paper, proposes an algorithm for selecting the optimized set of materialized queries in each mediator such that the overall query evaluation cost of a hierarchical mediator system can be minimized at any time. In addition, the performance of the proposed algorithm is analyzed by a series of experiments to identify their various characteristics.

An Adaptive Query Optimization in a Hierarchical Mediator System

To hide the heterogeneous environment from a global user as much as possible while preserving the autonomy of each individual system to the highest degree, the study of a mediator has been actively carried out as an integration method for distributed heterogeneous information sources. To evaluate a global query on a view that integrates several distributed information sources, the query can be rewritten into a set of transformed queries based on the definition of the view. Each transformed query is targeted to one of the information sources. To speed up the execution of a global query, the previous results of frequently requested sub-queries are materialized in a mediator. This paper proposes a method of choosing the optimized set of materialized queries in each mediator such that available storage in each mediator can be highly utilized at any time. The integrating schema in a mediator can be incrementally modified and the evaluation frequency of a global query can also be continuously varied. In order to select the optimized set of materialized sub-queries with respect to their current evaluation frequencies, the proposed method for modeling the recent access behavior of each sub-query. As a result, it is possible to adjust the optimized set of materialized sub-queries adaptively according to the recent changes in the evaluation frequencies of sub-queries.