• Title, Summary, Keyword: MongoDB

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Performance Comparisons on MongoDB with B-Tree Indexes and Fractal Tree Indexes (MongoDB에서 B-트리 인덱스와 Fractal 트리 인덱스를 이용한 성능 비교)

  • Jang, Seongho;Kim, Suhee
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.622-625
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    • 2014
  • As Big data began to produce a variety of values, a database that allows for huge amount of data with varieties became to be needed. Therefore, for the purpose of overcoming the limitations of the complexity and capacity of the existing RDBMS, NoSQL databases were introduced. Among the different types of NoSQL databases, MongoDB is most commonly used and is offered as open sources. The B-Tree index, used in MongoDB, experiences a significant decrease in performance as the amount of data increases. The fractal tree index enables to enhance the performance of B-Tree substantially by improving B-Tree's insertion algorithm. In this paper, the performances of MongoDB when using B-Tree Index and when using Fractal Tree Index are compared.

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Evaluation of Distributed Intrusion Detection System Based on MongoDB (MongoDB 기반의 분산 침입탐지시스템 성능 평가)

  • Han, HyoJoon;Kim, HyukHo;Kim, Yangwoo
    • KIPS Transactions on Computer and Communication Systems
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    • v.8 no.12
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    • pp.287-296
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    • 2019
  • Due to the development and increased usage of Internet services such as IoT and cloud computing, a large number of packets are being generated on the Internet. In order to create a safe Internet environment, malicious data that may exist among these packets must be processed and detected quickly. In this paper, we apply MongoDB, which is specialized for unstructured data analysis and big data processing, to intrusion detection system for rapid processing of big data security events. In addition, building the intrusion detection system(IDS) using some of the private cloud resources which is the target of protection, elastic and dynamic reconfiguration of the IDS is made possible as the number of security events increase or decrease. In order to evaluate the performance of MongoDB - based IDS proposed in this paper, we constructed prototype systems of IDS based on MongoDB as well as existing relational database, and compared their performance. Moreover, the number of virtual machine has been increased to find out the performance change as the IDS is distributed. As a result, it is shown that the performance is improved as the number of virtual machine is increased to make IDS distributed in MongoDB environment but keeping the overall system performance unchanged. The security event input rate based on distributed MongoDB was faster as much as 60%, and distributed MongoDB-based intrusion detection rate was faster up to 100% comparing to the IDS based on relational database.

Implementation of motor control system using NodeJS and MongoDB (NodeJS와 MongoDB를 활용한 모터 동작 제어시스템 구현)

  • Kang, Jin Young;Lee, Young-dong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.748-750
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    • 2017
  • With the development of intelligent technologies, the Internet of Things(IoT) has been applied to various applications. A platform technology including a sensor-server-DB for easily managing data at a remote site is required. In this paper, we implemented a servo motor control system that moves by the smart phone tilt value using NodeJS and MongoDB. The system consists of Rasberry Pi, servo motor and smart phone and the servo motor sensor data is transmitted to NodeJS so that data can be stored in database.

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An implementation of MongoDB based Distributed Triple Store on Jena Framework (MongoDB를 활용한 Jena 프레임워크 기반의 분산 트리플 저장소 구현)

  • Ahn, Jinhyun;Yang, Sungkwon;Lee, Munhwan;Jung, Jinuk;Kim, Eung-Hee;Im, Dong-Hyuk;Kim, Hong-Gee
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.1615-1617
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    • 2015
  • 웹을 통한 데이터 공유에 대한 관심의 증가로 RDF 트리플 형태의 데이터가 폭발적으로 증가하고 있다. 대용량 RDF 데이터를 저장하고 빠른 SPARQL 질의 처리를 지원하는 트리플 저장소의 개발이 중요하다. 아파치 프로젝트 중 하나인 Jena-TDB는 가장 잘 알려진 오픈소스 트리플 저장소 중 하나로서 Jena 프레임워크 기반으로 구현됐다. 하지만 Jena-TDB 의 경우 단일 컴퓨터에서 작동하기 때문에 대용량 RDF 데이터를 다룰 수 없다는 문제점이 있다. 본 논문에서는 MongoDB를 활용한 Jena 프레임워크 기반의 트리플 저장소인 Jena-MongoDB를 제안한다. Jena 프레임워크를 사용했기 때문에 기존 Jena-TDB와 동일한 인터페이스로 사용할 수 있고 최신 표준 SPARQL 문법도 지원한다. 또한 MongoDB를 사용했기 때문에 분산환경에서도 작동할 수 있다. 대용량 LUBM 데이터셋에 대한 SPARQL 질의 처리 실험결과 Jena-MongoDB가 Jena-TDB 보다 빠른 질의 응답 속도를 보여줬다.

Full Stack Platform Design with MongoDB (MongoDB를 활용한 풀 스택 플랫폼 설계)

  • Hong, Sun Hag;Cho, Kyung Soon
    • Journal of the Institute of Electronics and Information Engineers
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    • v.53 no.12
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    • pp.152-158
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    • 2016
  • In this paper, we implemented the full stack platform design with MongoDB database of open source platform Raspberry PI 3 model. We experimented the triggering of event driven with acceleration sensor data logging with wireless communication. we captured the image of USB Camera(MS LifeCam cinema) with 28 frames per second under the Linux version of Raspbian Jessie and extended the functionality of wireless communication function with Bluetooth technology for the purpose of making Android Mobile devices interface. And therefore we implemented the functions of the full stack platform for recognizing the event triggering characteristics of detecting the acceleration sensor action and gathering the temperature and humidity sensor data under IoT environment. Especially we used MEAN Stack for developing the performance of full stack platform because the MEAN Stack is more akin to working with MongoDB than what we know of as a database. Afterwards, we would enhance the performance of full stack platform for IoT clouding functionalities and more feasible web design with MongoDB.

Analysis of Encryption Algorithm Performance by Workload in BigData Platform (빅데이터 플랫폼 환경에서의 워크로드별 암호화 알고리즘 성능 분석)

  • Lee, Sunju;Hur, Junbeom
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.29 no.6
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    • pp.1305-1317
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    • 2019
  • Although encryption for data protection is essential in the big data platform environment of public institutions and corporations, much performance verification studies on encryption algorithms considering actual big data workloads have not been conducted. In this paper, we analyzed the performance change of AES, ARIA, and 3DES for each of six workloads of big data by adding data and nodes in MongoDB environment. This enables us to identify the optimal block-based cryptographic algorithm for each workload in the big data platform environment, and test the performance of MongoDB by testing various workloads in data and node configurations using the NoSQL Database Benchmark (YCSB). We propose an optimized architecture that takes into account.

Design and Implementation of MongoDB-based Unstructured Log Processing System over Cloud Computing Environment (클라우드 환경에서 MongoDB 기반의 비정형 로그 처리 시스템 설계 및 구현)

  • Kim, Myoungjin;Han, Seungho;Cui, Yun;Lee, Hanku
    • 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.

Spatial Operator for Spatial MongoDB (Spatial MongoDB를 위한 공간 연산자)

  • Kwak, Kwang-Jin;Yoon, Ha-Young;Shin, Dong-Yoon;Shin, Dong-Jin;Park, Jeong-Min;Kim, Jeong-Joon
    • The Journal of The Institute of Internet, Broadcasting and Communication
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    • v.18 no.6
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    • pp.237-242
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    • 2018
  • 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.

Digital Forensic Investigation of MongoDB (MongoDB에 대한 디지털 포렌식 조사 기법 연구)

  • Yoon, Jong-Seong;Jung, Doo-Won;Kang, Chul-Hoon;Lee, Sang-Jin
    • 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.

A Comparison of Performance Between MSSQL Server and MongoDB for Telco Subscriber Data Management (통신 가입자 데이터 관리를 위한 MSSQL Server와 NoSQL MongoDB의 성능 비교)

  • Nichie, Aaron;Koo, Heung-Seo
    • 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.