• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.789-800
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• 2002

XML is a standard format of web data and is currently used as a prevailing language for exchanging data. Most of the commercial data are stored in a relational database. It is quite important to convert these conventionally stored data into those for exchange and use them in data exchange, or to get the query results effectively by utilizing XQL on XML data which are store in a relational database. Thus, it is absolutely required to have a proper query processing mechanism for XML data and to maintain many XML data properly. Up to now, many cases of researches on the storage and retrieval of XML data have been carried out and under study. But, effective retrieval and storage system for path queries like XQL has yet to be contrived. Thus, in this paper, a schema to store XML data is designed, in which DFS-Numbegering method is used to store data effectively. And an effective path query processing method is also designed and implemented, in which a traditional relational database engine is used. That is, XQL is converted into SQL with a XQL processor if a user makes query XQL in a system. A database system executes SQL, and a XML generator uses a generated record and makes a XML document.

• Spatial Information Research
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• v.20 no.3
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• pp.95-106
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• 2012

Recently, with increase and development of the wireless access network area, u-GIS Service is supported in various fields. Especially, spatio-temporal data is used in the mobile environment for the u-GIS service. However, there is no standard for the spatio-temporal data used in different spaces, spatio-temporal data processing technology is necessary to makes interoperability among mobile u-GIS services. Furthermore, it is also necessary to develop the system of gathering, storing, and managing the spatio-temporal data in consideration of small capacity and low performance of mobile devices. Therefore, in this paper, we designed and implemented a spatio-temporal query processing system based on GML to manage spatio-temporal data efficiently in the mobile environment. The spatio-temporal query processing system based on GML can offer a structured storage method which maps a GML schema to a storage table and a binary XML storage method which uses the Fast Infoset technique, so as to support interoperability that is an important feature of GML and increase storage efficiency. we can also provide spatio-temporal operators for rapid query processing of spatio-temporal data of GML documents. In addition, we proved that this system can be utilized for the u-GIS service to implement a virtual scenario.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• KIISE Transactions on Computing Practices
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• v.24 no.1
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• pp.24-32
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• 2018

This paper proposes a Big Data system for energy Big Data which is aggregated in real-time from industrial and public sources. The constructed Big Data system is based on Hadoop and the Spark framework is simultaneously applied on Big Data processing, which supports in-memory distributed computing. In the paper, we focus on Big Data, in the form of heat energy for district heating, and deal with methodologies for storing, managing, processing and analyzing aggregated Big Data in real-time while considering properties of energy input and output. At present, the Big Data influx is stored and managed in accordance with the designed relational database schema inside the system and the stored Big Data is processed and analyzed as to set objectives. The paper exemplifies a number of heat demand plants, concerned with district heating, as industrial sources of heat energy Big Data gathered in real-time as well as the proposed system.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.769-778
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• 2002

The LEX system is a XML-based framework for medical information consolidation. The Lex makes it possible for heterogeneous HISs(Hospital Information Systems) exchange and share HL7 messages by storing the messages into a single Central Clinical Database. In this paper, we propose a HL7 message server independently interoperable from existing HIS to generate HL7 messages, and design an XML database schema suitable for storing and manipulating such data. We also propose a new DTD for efficient transformation of HL7 messages to XML documents for storage saving as well as supporting patient-oriented information retrieval.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2696-2707
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• 2009

Although, a number of DBMS vendors are scrambling to extend their products to handle XML, there is a need for a lightweight, DBMS and platform-independent XML repository as well. In this paper, we describe such an XML integrated repository system, that solves the following functions : generating relational schema from XML DTDs for storage of XML documents, importing data from XML documents into relational tables, creating XML documents according to a XMLQL(XML Query Language) from data extracted from a database and synchronizing the replicated XML documents. In the XML repository systems, the efficient change detection techniques for XML documents is required to maintain the consistency of replicated XML data because the same data in the repository can be replicated between so many different XML documents. In this paper, we propose a message digest based change detection technique to maintain the consistency of replicated data between client XML documents and a XML data in XML repository systems.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.84-88
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• 2007

In a general tendency that DBMS is used as a tool for storing large size of triple knowledge, it still remains in issue that which DBMS schema should be designed for storing, managing, inferring, and querying the triple knowledge efficiently. In this paper, we present, in the view point of efficient query process, a method that processes a query using Expanded Class-Property Views (ECPV) and, as a result, improvement in response time. The response time of DBMS-based inference systems is proportioned to table size and the number of table join operations. The more query is complex, the more join operations it requires, and the longer response time it requires. ECPV is a table obtained by processing possible join operations before queries. To use ECPV in the query process, SPARQL queries should be converted into corresponding ECPV-based SQL queries. This paper describes the conversion process and shows the improvement in response time by experiments.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.155-168
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• 2008

This paper proposes a relational security model-based RDF Web ontology access control model. The Semantic Web is recognized as a next generation Web and RDF is a Web ontology description language to realize the Semantic Web. Much effort has been on the RDF and most research has been focused on the editor, storage, and inference engine. However, little attention has been given to the security issue, which is one of the most important requirements for information systems. Even though several researches on the RDF ontology security have been proposed, they have overhead to load all relevant data to memory and neglect the situation that most ontology storages are being developed based on relational database. This paper proposes a novel RDF Web ontology security model based on relational database to resolve the issues. The proposed security model provides high practicality and usability, and also we can easily make it stable owing to the stability of the relational database security model.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.1-12
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• 2004

As network systems are developed rapidly and network architectures are more complex than before, it needs to use PBNM(Policy-Based Network Management) in network system. Generally, architecture of the PBNM consists of two hierarchical layers: management layer and enforcement layer. A security policy server in the management layer should be able to generate new policy, delete, update the existing policy and decide the policy when security policy is requested. And the security policy server should be able to analyze and manage the alert messages received from Policy enforcement system in the enforcement layer for the available information. In this paper, we propose an alert analyzer using data mining. First, in the framework of the policy-based network security management, we design and implement an alert analyzes that analyzes alert data stored in DBMS. The alert analyzer is a helpful system to manage the fault users or hosts. Second, we implement a data mining system for analyzing alert data. The implemented mining system can support alert analyzer and the high level analyzer efficiently for the security policy management. Finally, the proposed system is evaluated with performance parameter, and is able to find out new alert sequences and similar alert patterns.

• Journal of KIISE:Databases
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• v.29 no.2
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• pp.89-98
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• 2002

As XML has become an emerging standard for information exchange on the World Wide Web it has gained attention in database communities to extract information from XML seen as a database model. Recently, many researchers have addressed the problem of storing XML data and processing XML queries using traditional database engines. Here, most of them have used relational database systems. In this paper, we show that OODBSs can be another solution. Our technique generates an OODB schema from DTDs and processes XML queries, Especially, we show that the semi-structural part of XML data can be represented by the 'inheritance' and that this can be used to improve query processing.