• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.455-464
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• 2020

With a variety of data types and high utilization of data, non-relational databases are a popular data storage because it supports better availability and scalability. The increasing use of this technology also brings the risk of NoSQL injection attacks. Existing works mostly discuss the rule-based detection of NoSQL injection attacks that it is hard to deal with NoSQL queries beyond the coverage of the rules. In this paper, we propose a model for detecting NoSQL injection attacks. Our model is based on deep learning algorithms that select features from NoSQL queries using CNN, and classify NoSQL queries using RNN. Also, we experiment the proposed model to compare with existing models, and find that our model outperforms traditional models in terms of detection rate.

• Journal of Biosystems Engineering
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• v.28 no.1
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• pp.59-64
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• 2003

This study was carried out to develop database system using internet fur greenhouse. The database system consisted of group monitor, client monitor. server monitor and DBMS. The results of the study are summarized as follows. 1. The monitoring module, as data collection system for database. could monitor the state of the control device and the greenhouse environment. 2 The group monitor was connected to the house monitors by RS-485 communication method. Data received by the group monitor were sent to the server monitor and then stored in database server by TCP/IP and MIDAS. 3. The data of database consisted of on growing environment. control devices, operation and cultivation data in greenhouse. It was developed using MS-SQL server. 4. Remote monitoring of greenhouse was realized in a client/server environment. The client module. which was named as the client monitor. made requests to access the measurement data of greenhouse through the remote data module of the server monitor in internet environment.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.127-133
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• 2019

SQL is the most common language in data processing. Therefore, most of the colleges offer SQL in their curriculum. In this research, an auto scoring SQL test is proposed for the efficient results of SQL education. The system was treated with algorithms instead of using expensive DBMS(Data Base Management System) for automatic scoring, and satisfactory results were produced. For this system, the test question bank was established out of 'personnel management' and 'academic management'. It provides users with different sets of test each time. Scoring was done by dividing tables into two sections. The one that does not change the table(select) and the other that actually changes the table(update, insert, delete). In the case of a search, the answer and response were executed at first and then the results were compared and processed, the user's answers are evaluated by comparing the table with the correct answer. Modification, insertion, and deletion of table actually changes the data table, so data was restored by using ROLLBACK command. This system was implemented and tested 772 times on the 88 students in Computer Information Division of our college. The results of the implementation show that the average scoring time for a test consisting of 10 questions is 0.052 seconds, and the performance of this system is distinguished considering that multiple responses cannot be processed at the same time by a human grader, we want to develop a problem system that takes into account the difficulty of the problem into account near future.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.347-356
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• 2015

Due to the rapid development of IoT(Internet of Things) technology, traditional taxis are connected through dispatchers and location systems. Typically, modern taxis have embedded with GPS(Global Positioning System), which aims for obtaining the route information. By analyzing the frequency of taxi trip events, we can find the frequent route for a given query time. However, a scalability problem would occur when we convert the raw location data of taxi trip events into the analyzed frequency information due to the volume of location data. For this problem, we propose a NoSQL based top-K query system for taxi trip events. First, we analyze raw taxi trip events and extract frequencies of all routes. Then, we store the frequency information into hash-based index structure of MongoDB which is a document-oriented NoSQL database. Efficient top-K query processing for frequent route is done with the top of the MongoDB. We validate the efficiency of our algorithms by using real taxi trip events of New York City.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.2 s.6
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• pp.29-42
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• 1995

Route design in topographical plane map brings many problems in efficiency and requires much time and labor by hand Recently, the active studies of efficient route design method using 3-D terrain information are being developed according to increasing concentration on GSIS. In order to analyze terrain information for route design efficiently, this study presents objective and overall datum by applying SQL in construction and analysis of database and the possibility of three-dimensional terrain information analysis, This study generates 3-D base map on topographical map of scale 1:5,000 and acquires terrain information that have various thematic map data; contour, land use, roadway, and drange. This is a study on the application of SQL in route design and construction of the terrain information that linked by graphic datum of completed topographical map and attributed datum of database. As the result of this study, we can produce promptly and efficiently design datum of profile annotation, cross section, and volume computations to the preliminary route for route design and apply this efficient method to route design by understanding visual DTM which is composed of the roadway and the natural scene after design.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1706-1712
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• 2022

Recently, the safety of chemical substances has gained attention due to incidents occurring in petrochemical industrial complexes, such as gas leaks and fires. In particular, industrial complexes in Ulsan and Yeosu (South Korea) are valuable as they significantly contribute to the petrochemical industry, but accidents may occur due to chemical leakage. Therefore, in this study, sensor nodes are configured at an interval of 20 [m] based on outdoor facilities standards to respond to chemical accidents, and exposure consideration of 8 h (TWA) and 15 min (STEL) are proposed in TLVs. The proposed system pre-processes data collected in multi-hop communication at a cycle of 0.6-0.75 [s] using Python and stores it in the MySQL database through SQL and a real-time remote monitoring system that updates the stored data once every 5 s is implemented by linking MySQL and Grafana.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.163-180
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• 2022

Real estate data can be big data. Because the amount of real estate data is growing rapidly and real estate data interacts with various fields such as the economy, law, and crowd psychology, yet is structured with complex data layers. The existing Relational Database tends to show difficulty in handling various relationships for managing real estate big data, because it has a fixed schema and is only vertically extendable. In order to improve such limitations, this study constructs the real estate data in a Graph Database and verifies its usefulness. For the research method, we modeled various real estate data on MySQL, one of the most widely used Relational Databases, and Neo4j, one of the most widely used Graph Databases. Then, we collected real estate questions used in real life and selected 9 different questions to compare the query times on each Database. As a result, Neo4j showed constant performance even in queries with multiple JOIN statements with inferences to various relationships, whereas MySQL showed a rapid increase in its performance. According to this result, we have found out that a Graph Database such as Neo4j is more efficient for real estate big data with various relationships. We expect to use the real estate Graph Database in predicting real estate price factors and inquiring AI speakers for real estate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.223-225
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• 2013

For a long time, Relational Databases have been widely used in many enterprises. Even though the Relational Databases have merits such as excellent power of data preservation, concurrency controlling as a standard model in databases, there are a demerit in the inconsistency of objects and relations. Against this backdrop, NoSQL is being magnified as a new solution for Big Data since it can be operated without any schema. In this paper, we research on not only merits and demerits of Relational Databases but also the comparison between application databases and integrated databases. And then, we define NoSQL for Big Data and check its characteristics.

• Spatial Information Research
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• v.19 no.2
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• pp.57-64
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• 2011

This paper describes how to storing and querying trajectory information on PostgreSQL/PostGIS. Recently as technology of mobile devices is advancing, many researches for location-based services and moving object's trajectory have been studied. Trajectory is the set of information of the location by the time, and is one of the most im portant information for location-based services. Traditional spatial database systems do not support trajectory data types and functions. In this paper, we propose a trajectory data type and query functions for moving objects on PostgreSQL/PostGIS.

• 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.