Earticle

2002년에 제정된 전자정부법에 따라 행정업무 환경이 전자문서 중심으로 전환되면서 전자문서 관리를 위한 1만 6,228개 행정정보시스템과 1만9,533개 데이터베이스가 설치되었다. 2020년에는 공공기록물 관리에 관한 시행령 개정 및 행정정보 데이터세트 기록관리 기준에 관한 공공표준을 제정하여 행정정보 데이터세트에 대한 기록관리를 본격적으로 추진하였다. 이로써 전자문서로 한정되어 있던 기록관리 범위를 데이터세트까지 포괄하게 되었다. 전자 문서는 파일에 보관되므로 파일 단위의 기록관리가 이루어졌지만 데이터세트는 실행 중인 DBMS(관계형, NoSQL 등)에서 관리되므로 파일 단위로 무결성을 검증하는 기존 방식이 적용되기 어렵다. 그래서 본 연구에서는 블록체인 을 기반으로 관계형 그리고 NoSQL 데이터베이스에서 관리되고 있는 데이터세트에 대해서 무결성을 검증할 수 있 는 방안을 제시한다. 실제 프로토타입 구현을 통해 제안한 방안이 DBMS에 관리되는 데이터세트에 대해서도 효과 적으로 무결성을 검증할 수 있음을 증명하였다. 본 논문의 연구결과는 향후 대용량의 전자기록이 생산되는 현재의 기록관리 분야에서 무결성을 검증할 수 있는 기술 기반을 제공할 수 있을 것으로 기대된다.

According to the E-Government Act enacted in 2002, as the government work environment shifted to electronic documents, 16,228 administrative information systems and 19,533 databases were installed to manage electronic documents. In 2020, public records management for administrative information datasets was promoted in earnest by amending the enforcement ordinance on public records management and enacting public standards for administrative information dataset records management standards. The records management system, which was limited to electronic documents, has now been encompassed to the dataset. Since each electronic document is stored in a file, datasets are managed in the currently running DBMS(RDB, NoSQL, etc.), unlike records management in units of files, it is difficult to introduce the existing method of verifying the integrity of each file to datasets. Therefore, in this study, we propose a method to verify the integrity of datasets managed in relational and NoSQL databases based on blockchain. By implementing a prototype, we proved that the proposed method can effectively verify the integrity of datasets in the DBMS. The research results of this paper are expected to provide a technological base to verify the integrity of the current records management field where large-capacity electronic records are produced in the future.

대한민국의 국민 스포츠는 야구다. 2012년 700만 관중을 돌파했고, 2013년에는 600만 관중을 돌파했다. 2014년에도 2013년보다 빠른 속도로 600만을 돌파하며 관중몰이를 하고 있다. 우리나라 프로스포츠의 특성상 야구단은 기업이 하는데, 투자를 줄이거나 해체도 고려중인 팀이 다른 스포츠종목들과는 달리 오히려 두 개의 구단이 생겨났다. 철 저히 이윤을 추구하는 기업들에게도 야구는 매력적인 스포츠인 것이다. 따라서 기업들은 다양한 야구 마케팅을 하 고 있다. 기업들의 다양한 야구 마케팅을 효과적으로 진행하기 위해서는 관중에 대한 심도 있는 분석이 필요하다. 본 논문에 서는 야구의 수많은 관중 데이터를 NoSQL의 MongoDB 기반으로 저장 및 처리를 하고, 맵리듀스 기법을 이용하여 관중에 대해 분석을 하고자 한다. 그리고 분석결과를 이용한다면 다음 경기의 관중들에 대한 예측이 가능할 것이다 . 최종적으로 이러한 결과물을 통하여 효율적인 마케팅 방법을 찾는 방식을 제안하고자 한다. 다양한 특성을 가진 많은 수의 관중의 데이터와 각 종 외부요소들을 NoSQL로 데이터화 하여 정리하고, 이를 맵리듀스 기법을 이용한 데이터 분석을 통하여 체계적인 관중 정보를 얻고자 한다. 또한 이 기법을 응용한다면 다른 불특정 다수가 오가는 문화시설이나 관광지 등에서도 사용이 가능할 것이다.

The vision of the future Internet of Things is posing new challenges and opportunities for data management and analysis technology. Gigabytes of data are generated everyday by millions of sensors, actuators, RFID tags, and other devices. As the volume of data is growing dramatically, so is the demand for performance enhancement. When it comes to this Big Data problem, much attention has been paid to cloud computing and virtualization for their unlimited resource capacity, exible resource allocation and management, and distributed processing ability that promise high scalability and availability. On the other hand, the types and nature of data are getting more and more various. Data can come in any format, structured or unstructured, ranging from text and number to audio, picture, or even video. Data are generated, stored, and transferred across multiple nodes. Data can be updated and queried in real time or on demand. Hence, the traditional and dominant relational database systems have been questioned whether they can still be the best choice for current systems with all the new requirements. It has been realized that the emphasis on data consistency and the constraint of using relational data model cannot well with the variety of modern data and their distributed trend. This led to the emergence of NoSQL databases with their support for a schema-less data model and horizontal scaling on clusters of nodes. NoSQL databases have gained much attention from the community and are increasingly considered as a viable alternative to traditional databases.

본 논문에서는 서로 다른 항목의 정보를 담고 있는 다종다형자료의 메타 정보를 하나의 시스템에서 통합하여 검색 및 관리할 수 있으며, 관리되고 있는 메타 데이터에서 사용자가 필요한 검색 항목을 자유롭게 정의할 수 있는 사용자 인터페이스를 제공할 수 있는 시스템에 대해 연구하였다. 본 시스템의 특징은 다종다형 메타정보 관리가 가능하며 다양한 시스템 간 손쉬운 통합을 지원할 수 있는 데이터베이스 통합 버스, 다종다형 자료들의 사용자별 맞춤 서비스가 가능한 사용자별 검색 항목 지정 설정이 있다. 본 논문에서 연구한 시스템은 현재 ICT 분야에서 각광받고 있는 빅데이터 관리가 가능할 것으로 판단되며 향후 다양한 분야의 다종 다형자료들을 효과적으로 관리하고 다양한 환경을 가지고 있는 시스템 간 통합을 손쉽게 수행할 수 있을 것으로 판단된다.

In this paper, we present the system that user can search and manage data using united interface and user can define search field dynamically. The feature of this system is that it is possible to manage multiple polymorphic meta information first. Second, there is a database integration bus that can support easy integration between the various systems. Third, it is possible to set the search item for each user which can customize polymorphism data for each user. The system studied in this paper is expected to be capable of managing big data, which is currently well received in the field of ICT. In addition, it will be possible to effectively manage multi-species polymorphic data in various fields in the future and to easily integrate between systems having various environments.

최근 미세먼지로 인한 문제가 대두되고 있다. 마스크 착용 및 기상 경보가 발령하게 되면서 많은 관심을 받고 있으며 미세먼지의 원인과 절감 방안 등에 대한 연구와 정책 등이 활발하게 벌어지고 있으나 연구결과는 다양하게 나타나고 있다. 기상학적으로 미세먼지로 인한 피해가 가장 큰 경우는 기온역전이 함께 발생하는 경우이다. 본 연구에서는 기온역전과 풍향 등을 함께 분석하여 미세먼지를 사전에 경고할 수 있는 시스템을 구상하였다. 이러한 기상정보 시스템은 다양한 센서 정보를 센서 제어 및 데이터 교환 등을 위해 OGC Sensor Web Enablement(SWE)를 따르는 시스템과 NoSQL 스토리지를 사용하여 확장성과 병렬 처리성을 높이는 시스템을 제안한다.

Recently, it has become a social problem due to fine particles. There are more people wearing masks, weather alerts and disaster notices. Research and policy are actively underway. Meteorologically, the biggest damage caused by fine particles is the inversion layer phenomenon. In this study, we designed a system to warn fine Particles by analyzing inversion layer and wind direction. This weather information system proposes a system that can efficiently perform scalability and parallel processing by using OGC sensor web enablement system and NoSQL storage for sensor control and data exchange.

Web sites can be dynamic and static sometimes a combination of both. To assure security all the websites need protections to their database. This paper proposes a static analysis of various technique of detecting recently discovered web-based vulnerabilities such as cross-site scripting and HTTP splitting attacks with NoSQL injection. Today, the situation is¬ better, and traditional databases have introduced built-in protection mechanisms. But does this mean that NoSQL systems are immune to injections? Our study shows that although the security of the query language has largely improved; there are still techniques for injecting malicious queries. Some works already provide reports of NoSQL injection techniques. We also show the methods of accessing the dark web. We use TOR browser for the explanation of accessing dark web. The user entering into the website with different format rather than the original will be noticed, and they are not allowed to access our website. Instead of passing SQL queries into the input fields the user can also directly pass the objects inside it. Once the user enters in, with the actual format and attempts to upload .exe file the website tends to block that user's account.

We present the No-SQL based Web service system for sharing of emotion information regarding the individual emotional states and location information of users using by a MongoDB in cloud computing environments. Pre-processed emotion information represents the individual emotional states categorized as a nine domain based on physiological signals recognition within the database system. However, in order to present individual emotional states regarding to pre-processed user’s emotional states and locations on a Web-based map, the computational overload with large amount of data for a location-query process is to be solved for support sharing Web services. Meanwhile, previous RDBMS-based systems are inadequate due to insufficient scalability and processing time of data processing nodes. Therefore, in this paper, we have designed and implemented an emotion sharing web service by reducing the load on the system using NoSQL database. In addition, it was confirmed that it is possible to effectively utilize resources and visualized by using Google’s FusionTable and it is possible to share on a Web map.

In order to utilize various data provided by Korea public open data portal, data should be systematically managed using a database. Since the range of open data is enormous, and the amount of data continues to increase, it is preferable to use a database capable of processing big data in order to analyze and utilize the data. This paper proposes data modeling and implementation method suitable for public data. The target data is subway related data provided by the public open data portal. Schema of the public data related to Seoul metro stations are analyzed and problems of the schema are presented. To solve these problems, this paper proposes a method to normalize and structure the subway data and model it in NoSQL database. In addition, the implementation result is shown by using MongDB which is a document-based database capable of processing big data.

오늘날 우리는 전쟁과 기후 위기 등에 의해 에너지 위기 요소를 안고 있게 되었다. 이러한 에너지 위기를 대비 하기 위해 많은 연구자가 에너지 관리 시스템이라는 에너지 절감 및 관리와 같은 에너지 모니터링 및 에너지 절감에 대한 시스템에 대한 연구를 지속하고, 이에 발맞춰 국가에서도 에너지 다소비 사업장에서 이를 의무화하고 있다. 이러한 공장은 공간과 에너지적 한계가 존재하여 이를 개선 하고자 낮은 성능의 임베디드 디바이스로 데이터 수집 시스템을 구동 하는 방안에 대해 연구를 진행한다. 이때 임베디드 디바이스에서 기존의 데이터베이스가 아닌 Key-value store인 RocksDB의 최적화 버전이 시계열 데이터에 우수한 성능을 보임을 평가를 통해 보인다. 또한 이를 평가하기 위한 범용 데이터베이스 평가 도구를 통해 이종 데이터베이스와 평가를 진행한다. 그 결과 낮은 성능의 디바이스에서 타 데이터베이스 대비 11배 짧은 소요 시간을 기록하는 것을 볼 수 있었다.

In today's world, we find ourselves facing energy crises due to factors such as war and climate crises. To prepare for these energy crises, many researchers continue to study systems related to energy monitoring and conservation, such as energy management systems, energy monitoring, and energy conservation. In line with these efforts, nations are making it mandatory for energy-consuming facilities to implement these systems. However, these facilities, limited by space and energy constraints, are exploring ways to improve. This research explores the operation of a data collection system using low-performance embedded devices. In this context, it proves that an optimized version of RocksDB, a Key-Value store, outperforms traditional databases when it comes to time-series data. Furthermore, a comprehensive database evaluation tool was employed to assess various databases, including optimized RocksDB and regular RocksDB. In addition, heterogeneous databases and evaluations are conducted using a UD Benchmark tool to evaluate them. As a result, we were able to see that on devices with low performance, the time required was up to 11 times shorter than that of other databases.

최근 인터넷과 SNS의 발전으로 미디어 데이터가 늘어나고 있으며. 사진이나 동영상은 공간 태그를 가지고 있는 경우가 많으므로 이를 분석하기 위한 많은 기술이 발전하고 있다. SNS와 같은 자유도가 높은 데이터를 처리하기 위해서 NoSQL이 각곽을 받고 있으나 대부분의 NoSQL은 공간 데이터에 대한 연산 및 질의가 미비하다. 따라서 본 논문에서는 대표 적인 NoSQL 중 MongoDB를 이용하여 공간 연산자를 추가하기 위한 시스템을 설계 및 구현하였다. 본 연구를 통해 다양한 연산자를 사용할 수 있음을 확인하였으며 연산자를 이용하여 다양한 서비스를 할 수 있을 것으로 기대된다.

Recently, media data is increasing due to the development of Internet and SNS. Since photographs and videos often have geo-tags, many techniques have been developed to analyze them. In order to process various kind of such as SNS, NoSQL has been covered. However, most NoSQL does not have enough computation and query about spatial data. Therefore, in this paper, we designed and implemented a system for adding spatial operators using MongoDB among the representative NoSQL. Through this study, it is confirmed that various operators can be used and it is expected that various services can be performed using operators.

Web Map Tile Services (WMTS) have been widely used for quick and convenient sharing of geospatial information. In practice, when streaming requests to servers increase in scale, unacceptable response times and service unavailability might result. To address this scalability problem, we implemented a scalable WMTS framework on a high performance cluster (HPC), enabling the realization of elastic deployment as the client users grow in number. This scalable and high-concurrency WMTS is built totally with open-source software, including Nginx, GeoWebCache, and MongoDB. In this architecture, Nginx acts as a powerful load balancer for routing client requests; GeoWebCache is customized to publish the required WMTS and process client requests; while MongoDB is used to store the large volume of tile images in the HPC. Evaluation experiments were carried out to assess the efficiency and scalability of our WMTS system, using one synthetic workload. Experimental results illustrate that this distributed WMTS framework can achieve about 15% performance improvement when the service nodes are increased with a 0.5~2s reduction in the load time and a 5~10MB increase in network throughput.

One of the key advances in resolving the “big-data” problem has been the emergence of an alternative database technology. Today, classic RDBMS are complemented by a rich set of alternative Data Management Systems (DMS) specially designed to handle the volume, variety, velocity and variability of Big Data collections; these DMS include NoSQL, NewSQL and Search-based systems. NewSQL is a class of modern relational database management systems (RDBMS) that provide the same scalable performance of NoSQL systems for online transaction processing (OLTP) read-write workloads while still maintaining the ACID guarantees of a traditional database system. This paper discusses about NewSQL data management system; and compares with NoSQL and with traditional database system. This paper covers architecture, characteristics, classification of NewSQL databases for online transaction processing (OLTP) for Big data management. It also provides the list of popular NoSQL as well as NewSQL databases in separate categorized tables. This paper compares SQL based RDBMS, NoSQL and NewSQL databases with set of metrics; as well as, addressed some research issues of NoSQL and NewSQL.

Content syndication is the process of pushing the information out into third-party information providers. The idea is to drive more engagement with your content by wiring it into related digital contexts. However, there are some shortages of current related products, such as search challenges on massive feeds, synchronization performance, and user experience. To address these limitations, we aim to propose an improved architecture of content syndication and recommendation. First, we design a source listener to extract feed changes from different RSS sources, and propagate the incremental changes to target schema-free document stores to improve the search performance. Second, the proposed recommendation algorithm is to tidy, filter, and sort all the feeds before pushing them to the users automatically. Third, we provide some OAuth2-authorization RESTful feed sharing APIs for the integration with the third-party systems. The experimental result shows that this architecture speeds up the search and synchronization process, and provides friendlier user experience.

For internet servers, how to handle colossal data volumes and maintain data consistency is a major challenge. To ensure each database maintains the latest information and messages do not conflict with one another, we have a new type of database NoSQL (Not Only SQL). A number of atomic broadcast protocols, including libPaxos, Mencius and RingPaxos, have been established to help maintain transmission consistency for cloud computing services. This paper introduces a new atomic broadcast protocol to improve the performance of existing protocols. Our new protocol divides the network topology into various areas to process messages in a distributed way and uses a rotating mechanism to effectively balance the server loads. Experimental evaluation has been conducted to compare the performance of different protocols. The results show that our new protocol yields higher throughput and lower latency under different client loads in contrast to other protocols.

Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as ‘Big Data’ that is a global phenomenon. This is typically considered to be a data collection that has grown so large it can’t be effectively managed or exploited using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines. To handle this problem, traditional RDBMS are complemented by specifically designed a rich set of alternative DBMS; such as - NoSQL, NewSQL and Search-based systems. This paper motivation is to provide - classification, characteristics and evaluation of NoSQL databases in Big Data Analytics. This report is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data.

In cloud environment, data intensive applications required to interact with various kinds of databases ranged from SQL (Structured Query Language) to NoSQL. NoSQL databases have wide-ranging physical level data models like key-value, document, column oriented, graph etc. Designing such databases are complex due to absence of well-accepted common meta-modeling concepts for varied physical level data model. Thus, research challenges are exist towards achieving common interpretation of data and their semantics at conceptual level over disparate databases. In this paper, an ontology driven meta-model, called Ontology Driven NoSQL Data Model (ODNSDM), is proposed to conceptualize data representation facets over heterogeneous kinds of databases. The novelty of the proposed approach is to provide common conceptual level abstraction based on semantically enriched formal vocabularies for both NoSQL and SQL databases. The formal vocabularies are further implemented using an ontology editorial tool Protégé based on OWL (Web Ontology Language). Several crucial properties of the proposed model are also prescribed in this paper. In addition, the proposed framework is illustrated using case studies in order to show its practical exhibition. Moreover, step wise algorithms are to map the proposed conceptualization towards SQL and NoSQL based databases.

음원 서비스의 주요 기능 중 하나인 내용 기반 검색을 위해 음원의 지문을 채취하여 데이타베이스에 저장하고 색인하여 검색에 활용하는 기법이 널리 사용되고 있다. 그런데 지속적으로 추가되는 신규 음원의 지문이 기존의 데이타베이스에 계속 삽입되면 공간 효율 및 음원 검색 성능의 저하가 점차 초래되는 문제점이 있다. 따라서 시스템 운용 비용의 증가를 가져오는 주기적인 데이터 베이스 재구성 없이 효율적인 음원 데이타베이스의 확장을 지원하는 기법이 요구된다. 본 논문에서는 샤잠의 지문 채취 알고리즘을 기반으로 클러스터 컴퓨팅 환경에서 맵리듀스 및 NoSQL 데이타베이스를 사용하여 이러한 문제를 해결하는 내용 기반 음원 검색 시스템의 설계를 제시하고 실제 음원 데이터를 이용한 다양한 실험을 통해 그 성능을 평가한다.

For content-based audio retrieval which is one of main functions in audio service, the techniques for extracting fingerprints from the audio source, storing and indexing them in a database are widely used. However, if the fingerprints of new audio sources are continually inserted into the database, there is a problem that space efficiency as well as audio retrieval performance are gradually deteriorated. Therefore, there is a need for techniques to support efficient expansion of audio database without periodic reorganization of the database that would increase the system operation cost. In this paper, we design a content-based audio retrieval system that solves this problem by using MapReduce and NoSQL database in a cluster computing environment based on the Shazam's fingerprinting algorithm, and evaluate its performance through a detailed set of experiments using real world audio data.

인터넷과 클라우드 서비스 사용이 증가하면서 패킷의 양과 사이버 위협이 증가하였다. 본 논문에서는 빅데이터를 처리하기 위해 사용되는 NoSQL을 보안이벤트의 신속한 처리를 위한 침입탐지시스템에 적용하였다. 다양한 데이터 모델 유형의 NoSQL 데이터베이스 중에서 빅데이터 보안이벤트를 처리하는데 가장 적합한 시스템을 찾기 위해 세 가지 유형의 Snort 룰 기반 보안이벤트 분산 처리 프로토타입 시스템들을 구축하였고 각 시스템의 성능을 평가하였다. 그 결과로 MongoDB 기반의 보안이벤트 분산 처리 시스템이 가장 속도가 빠른 것을 확인하였다.

인터넷과 클라우드 서비스 사용이 증가하면서 패킷의 양과 사이버 위협이 증가하였다. 본 논문에서는 빅데이터를 처리하기 위해 사용되는 NoSQL을 보안이벤트의 신속한 처리를 위한 침입탐지시스템에 적용하였다. 다양한 데이터 모델 유형의 NoSQL 데이터베이스 중에서 빅데이터 보안이벤트를 처리하는데 가장 적합한 시스템을 찾기 위해 세 가지 유형의 Snort 룰 기반 보안이벤트 분산 처리 프로토타입 시스템들을 구축하였고 각 시스템의 성능을 평가하였다. 그 결과로 MongoDB 기반의 보안이벤트 분산 처리 시스템이 가장 속도가 빠른 것을 확인하였다.

The increasing number of multimedia content offered to the user demands content recommendation. In this paper, we propose a system for recommending content related to the content that user is watching. In the proposed system, relationship information between content is generated using relationship information between representative keywords of content. Relationship information between keywords is generated by analyzing keyword collocation frequencies in Internet news corpus. In order to handle big corpus data, we design an architecture that consists of a distributed search engine and a distributed data processing engine. Furthermore, we store relationship information between keywords and relationship information between keywords and content in NoSQL to handle big relationship data. Because the query optimizer of NoSQL is not as well developed as RDBMS, we propose query optimization techniques to efficiently process complex queries for recommendation. Experimental results show that the performance is improved by up to 69 times by using the proposed techniques, especially when the number of requested related keywords is small.