• Journal of Information Processing Systems
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• v.10 no.3
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• pp.355-364
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• 2014

Internet accessibility has been growing due to the diffusion of smartphones in today's society. Therefore, people can generate data anywhere and are confronted with the challenge that they should process a large amount of data. Since the appearance of relational database management system (RDBMS), most of the recent information systems are built by utilizing it. RDBMS uses foreign-keys to avoid data duplication. The transactions in the database use attributes, such as atomicity, consistency, isolation, durability (ACID), which ensures that data integrity and processing results are stably managed. The characteristic of RDBMS is that there is high data reliability. However, this results in performance degradation. Meanwhile, from among these information systems, some systems only require high-performance rather than high reliability. In this case, if we only consider performance, the use of NoSQL provides many advantages. It is possible to reduce the maintenance cost of the information system that continues to increase in the use of open source software based NoSQL. And has a huge advantage that is easy to use NoSQL. Therefore, in this study, we prove that the leverage of NoSQL will ensure high performance than RDBMS by applying NoSQL to database systems that implement RDBMS.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.88.3-89
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• 2017

We present our experiences with open-source NoSQL database systems in analyzing spatial and temporal astronomical data. We conduct experiments of using Redis in-memory NoSQL database system by modifying and exploiting its support of geohash for astronmical spatial data. Our experiment focuses on performance, cost, difficulty, and scalability of the database system. We also test OpenTSDB as a possible NoSQL database system to process astronomical time-series data. Our experiments include ingesting, indexing, and querying millions or billions of astronomical time-series measurements. We choose our KMTNet data and the public VVV (VISTA Variables in the Via Lactea) catalogs as test data. We discuss issues in using these NoSQL database systems in astronomy.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.2
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• pp.95-104
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• 2017

As the technology in smart device is growing and Social Network Services(SNS) are becoming more common, the data which is difficult to be processed by existing RDBMS are increasing. As a result of this, NoSQL databases are getting popular as an alternative for processing massive and unstructured data generated in real time. The demand for the technique of digital investigation of NoSQL databases is increasing as the businesses introducing NoSQL database in their system are increasing, although the technique of digital investigation of databases has been researched centered on RDMBS. New techniques of digital forensic investigation are needed as NoSQL Database has no schema to normalize and the storage method differs depending on the type of database and operation environment. Research on document-based database of NoSQL has been done but it is not applicable as itself to other types of NoSQL Database. Therefore, the way of operation and data model, grasp of operation environment, collection and analysis of artifacts and recovery technique of deleted data in HBase which is a NoSQL column-based database are presented in this paper. Also the proposed technique of digital forensic investigation to HBase is verified by an experimental scenario.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.631-634
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• 2014

When to build a Web and Cloud Computing environment, it is essential to used a database system. Database systems includes commercial programs, such as Oracle and MS-SQL, but also similar to the performance of commercial applications, there are many free programs. In particular, PostgreSQL, MySQL, MariaDB are no costs, but the source is open to the public can be applied to a variety of environments. This paper presents an open source relational database management system, the trends are examined.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.469-476
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• 2016

Relational Database Management Systems have become de facto database model among most developers and users since the inception of Data Science. From IoT devices, sensors, social media and other sources, data is generated in structured, semi-structured and unstructured formats, in huge volumes, thereby the difficulty of data management greatly increases. Organizations that collect large amounts of data are increasingly turning to non relational databases - NoSQL databases. In this paper, through experiments with real field data, we demonstrate that MongoDB, a document-based NoSQL database, is a better alternative for building a Telco Subscriber Data Management System which hitherto is mainly built with Relational Database Management Systems. We compare the existing system in various phases of data flow with our proposed system powered by MongoDB. We show how various workloads at some phases of the existing system were either completely removed or significantly simplified on the new system. Based on experiment results, using MongoDB for managing telco subscriber data turned out to offer performance better than the existing system built with MSSQL Server.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.123-134
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• 2014

As the data gets bigger recently, the demand for relational database management system (RDBMS) and NoSQL DBMS to process big data has been increased consistently. The digital forensic investigation method for RDBMS has been studied actively, but that for NoSQL DBMS, which is popularly used nowadays, has almost no research. This paper proposes the digital forensic investigation process and method for MongoDB, the most popularly used among NoSQL DBMS.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.9
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• pp.425-432
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• 2016

As the importance of information audit is getting bigger, the public corporations invest many expenses at information system audit to build a high quality system. For this purpose, there are much research to proceed an audit effectively. In database audit works, it could audit utilizing a variety of monitoring tools. However, when auditing SQLs which might be affected to database performance, there are several limits related to SQL audit functionality. For this reason, most existing monitoring tools process based on meta information, it is difficult to proceed SQL audit works if there is no meta data or inaccuracy. Also, it can't detect problems by analysis of SQL's syntax structure. In this paper, we design and implement the SQL Inspector using ANTLR which is applied by syntax analysis technique. The overall conclusion is that the implemented SQL Inspector can work effectively much more than eye-checked way. Finally, The SQL inspector which we proposed can apply much more audit rules by compared with other monitoring tools. We expect the higher stability of information system to apply SQL Inspector from development phase to the operation phase.

• Journal of Information Processing Systems
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• v.12 no.2
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• pp.263-274
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• 2016

The exceptional development of electronic device technology, the miniaturization of mobile devices, and the development of telecommunication technology has made it possible to monitor human biometric data anywhere and anytime by using different types of wearable or embedded sensors. In daily life, mobile devices can collect wireless body area network (WBAN) data, and the co-collected location data is also important for disease analysis. In order to efficiently analyze WBAN data, including location information and support medical analysis services, we propose a geohash-based spatial index method for a location-aware WBAN data monitoring system on the NoSQL database system, which uses an R-tree-based global tree to organize the real-time location data of a patient and a B-tree-based local tree to manage historical data. This type of spatial index method is a support cloud-based location-aware WBAN data monitoring system. In order to evaluate the proposed method, we built a system that can support a JavaScript Object Notation (JSON) and Binary JSON (BSON) document data on mobile gateway devices. The proposed spatial index method can efficiently process location-based queries for medical signal monitoring. In order to evaluate our index method, we simulated a small system on MongoDB with our proposed index method, which is a document-based NoSQL database system, and evaluated its performance.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.35-41
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• 2024

As online activities have rapidly increased due to recent digital changes and the impact of COVID-19, the importance of large-scale data processing and maintenance is increasing. This study compares the performance of the two main types of databases widely used for data storage and management: Relational Database Management Systems (RDBMS) and Non-Relational Databases (NoSQL). Specifically, we measured and evaluated the execution time of data insertion, query, and deletion functions using MySQL, a representative example of RDBMS, and Redis, a representative example of NoSQL. The experimental results showed that Redis showed performance about 5.84 times faster in data insertion, 6.61 times faster in query, and 12.33 times faster in deletion than MySQL. These results demonstrate that Redis provides superior performance, especially in environments requiring large-scale data processing and maintenance. Therefore, companies and online service providers can choose NoSQL databases such as Redis to ensure more efficient data management solutions. We hope this study will be an essential reference when selecting a database based on data processing performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2067-2075
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• 2017

Lately big data processing is considered as an emerging issue. As a huge amount of data is generated, data processing capability is getting important. In processing big data, both Hadoop distributed file system and unstructured date processing-based NoSQL data store are getting a lot of attention. However, there still exists problems and inconvenience to use NoSQL. In case of low volume data, MapReduce of NoSQL normally consumes unnecessary processing time and requires relatively much more data retrieval time than RDBMS. In order to address the NoSQL problem, in this paper, an interworking scheme between NoSQL and the conventional RDBMS is proposed. The developed auto-mapping scheme enables to choose an appropriate database (NoSQL or RDBMS) depending on the amount of data, which results in fast search time. The experimental results for a specific data set shows that the database interworking scheme reduces data searching time by 35% at the maximum.