MongoDB and NoSQL Databases

Jain, Vidushi; Upadhyay, Aviral

doi:10.5120/ijca2017914385

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…NoSQL databases, which can meet the requirement of high concurrent access, overcome the shortcomings of SQL databases to some extent. Owing to their excellent features, NoSQL databases have been used in many successful Internet applications [14,31]. NoSQL databases have many data storage models, and the most commonly used categories of data models are key-value store, wide-column store, document store and graph store [32].…”

Section: Sooc Hybrid Sql/nosql Servicementioning

confidence: 99%

“…However, unstructured SOOC data, such as videos, images, and text, are usually serialized and stored in the local file system [13]. Although the storage scheme based on SQL databases can effectively reduce data redundancy and efficiently process complex data queries for structured data [14], it is not only inconvenient for the management and retrieval of unstructured SOOC data but also faces difficulties in maintaining the association between the structured data in the SQL database and the unstructured data. As a solution to this problem, the SQL database was used to store structured data and the metadata of unstructured data while the Hadoop Distributed File System (HDFS) and Apache HBase were used to store unstructured SOOC files of different sizes [15].…”

Section: Introductionmentioning

confidence: 99%

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SOOCP: A Platform for Data and Analysis of Space Object Optical Characteristic

Lan

2019

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With the advancement of various technologies, the research and application of space object optical characteristic (SOOC), one of the main characteristics of space objects, are faced with new challenges. Current diverse structures of massive SOOC data cannot be stored and retrieved effectively. Moreover, SOOC processing and application platforms are inconvenient to build and deploy, while researchers’ innovative algorithms cannot be applied effectively, thereby limiting the promotion of the research achievements. To provide a scaffolding platform for users with different needs, this paper proposes SOOCP, a SOOC data and analysis service platform based on microservice architecture. Using the hybrid Structured Query Language (SQL)/NoSQL service, the platform provides efficient data storage and retrieval services for users at different levels. For promoting research achievements and reusing existing online services, the proposed heterogeneous function integration service assists researchers and developers in independently integrating algorithmic modules, functional modules, and existing online services to meet high concurrency requests with a unified interface. To evaluate the platform, three research cases with different requirement levels were considered. The results showed that SOOCP performs well by providing various data and function integration services for different levels of demand.

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Section: Sooc Hybrid Sql/nosql Servicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SOOCP: A Platform for Data and Analysis of Space Object Optical Characteristic

Lan

2019

Information

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“…redis), etc. [53]. In terms of unstructured data, NoSQL databases have a huge advantage over RDBMS [51,54].…”

Section: Sql and Nosqlmentioning

confidence: 99%

Microservice-Based Platform for Space Situational Awareness Data Analytics

Lan

et al. 2020

International Journal of Aerospace Engineering

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The development, deployment, and maintenance of the current space situational awareness (SSA) information system have become increasingly complex. However, researchers cannot flexibly and conveniently apply the research results to practical applications due to the lack of basic research platforms for SSA. Inspired by X as a Service (XaaS), we propose the microservice-based platform for SSA data analytics to provide a scaffold-like platform for researchers. Based on microservice, the architecture for this platform is proposed to meet the requirements of flexible development and loosely coupled deployment. To facilitate the use of the platform, the hybrid data service layer is established to provide basic data for research and the functional service layer is designed to provide services for clients and applications. Due to the massive data processing requirements, the data analysis architecture and processing model, which can easily integrate various user-defined algorithms and significantly improve the computational efficiency, are proposed based on the Lambda architecture. To verify the platform's effectiveness, two cases are established and implemented. The results show that this platform can provide a convenient, flexible, and efficient platform for the requirements of algorithm integration, experiment, and data display from users and researchers.

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“…• Application latency: writing data into NoSQL database is more efficient than that in rational database [31]. Given a specific query order, NoSQL may not provide better performance than rational database.…”

Section: Related Workmentioning

confidence: 99%

An Implementation of Scalable High Throughput Data Platform for Logging Semiconductor Testing Results

2019

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Unlike graded data of common semiconductor test results storing in relational databases, log data in the standard test data format (STDF) contain millions of test data entries. In a semiconductor packaging and testing factory, a semiconductor wafer or integrated circuit tests generate thousands of STDF files each day; therefore, how to store these massive databases is a crucial topic. Different products correspond to different test items and STDF content; if a relational database is used to store all forms of data, the practical operation becomes challenging. This paper used a NoSQL document-oriented database collocated with a Docker container to build a system, named the scalable STDF data (SSD) framework, for storing semiconductor test data. According to semiconductor test operations, the SSD framework first converts STDF files into an open standard format for data transmission and subsequently transfers them to the database. The use of NoSQL databases allows for flexibility of specifications of STDF content, and a Docker container exhibits features such as rapid deployment and high scalability. The SSD framework meets the requirements of semiconductor testing for throughput, latency, and parallel experimental projects; possesses excellent execution efficiency; and provides flexible data storage services in a semiconductor testing environment where processing a large quantity of data is required. From our simulation results, the major performance of the proposed system depends on the hardware properties. The higher hardware distribution degree provides better performance. Docker container provides more connections and the scalability of storage, but higher software distribution contributes limited performance enhancement. INDEX TERMS Flexible data storage, scalable STDF data, semiconductor testing, standard test data format.

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MongoDB and NoSQL Databases

Cited by 9 publications

References 3 publications

SOOCP: A Platform for Data and Analysis of Space Object Optical Characteristic

SOOCP: A Platform for Data and Analysis of Space Object Optical Characteristic

Microservice-Based Platform for Space Situational Awareness Data Analytics

An Implementation of Scalable High Throughput Data Platform for Logging Semiconductor Testing Results

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