Big data in memory: Benchimarking in memory database using the distributed key-value store for machine to machine communication

Iwazume, Michiaki; Takahiro, Iwase; Tanaka, Kouji; Fujii, Hodaka; Hijiya, Makoto; Haraguchi, Hiroshi

doi:10.1109/snpd.2014.6888748

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…These databases provide a platform for distributed storage services [25]. KVS databases enhanced the universal communication technology due to their ability to handle the large amount of unstructured data quite efficiently [26]. KVS has no constraints and no foreign key.…”

Section: Key-value Storementioning

confidence: 99%

Challenges in NoSQL-Based Distributed Data Storage: A Systematic Literature Review

et al. 2019

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Key-Value stores (KVSs) are the most flexible and simplest model of NoSQL databases, which have become highly popular over the last few years due to their salient features such as availability, portability, reliability, and low operational cost. From the perspective of software engineering, the chief obstacle for KVSs is to achieve software quality attributes (consistency, throughput, latency, security, performance, load balancing, and query processing) to ensure quality. The presented research is a Systematic Literature Review (SLR) to find the state-of-the-art research in the KVS domain, and through doing so determine the major challenges and solutions. This work reviews the 45 papers between 2010–2018 that were found to be closely relevant to our study area. The results show that performance is addressed in 31% of the studies, consistency is addressed in 20% of the studies, latency and throughput are addressed in 16% of the studies, query processing is addressed in 13% of studies, security is addressed in 11% of the studies, and load balancing is addressed in 9% of the studies. Different models are used for execution. The indexing technique was used in 20% of the studies, the hashing technique was used in 13% of the studies, the caching and security techniques were used together in 9% of the studies, the batching technique was used in 5% of the studies, the encoding techniques and Paxos technique were used together in 4% of the studies, and 36% of the studies used other techniques. This systematic review will enable researchers to design key-value stores as efficient storage. Regarding future collaborations, trust and privacy are the quality attributes that can be addressed; KVS is an emerging facet due to its widespread popularity, opening the way to deploy it with proper protection.

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Section: Key-value Storementioning

confidence: 99%

Challenges in NoSQL-Based Distributed Data Storage: A Systematic Literature Review

et al. 2019

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“…In order to improve the processing performance of application system of Transportation IoT further, the distributed data processing technology is able to execute data computing tasks concurrently by mean of scattering the massive data on the multiple server nodes of cloud platform. Based on this, the distributed memory database is very suitable for real-time processing of massive traffic data [24], that is expected to solve the bottleneck problem of Transportation IoT in calculating massive information data [25].…”

Section: Cloud Computing and Big Data Processing Technologymentioning

confidence: 99%

System Architecture and Core Technologies for Transportation Internet of Things

Zhu

Zou

Deng

2015

Advances in Intelligent Systems and Computing

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Considering the recent application and research status for Internet of things (IoT) in transportation industry, this paper proposed the concept, system architecture and technical systems of the Transportation IoT. In this paper, several key technical problems in the development of current transportation IoT were discussed, including sensing and recognition technology, network and communications technology, cloud computing and big data processing technology and so on, which aims at exploring the future development and application of the transportation IoT further.

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“…These databases are becoming popular because they are providing a high level of scalability. Additionally, they are very efficient in handling the unstructured data to facilitate universal data communication [7] in modern information systems. Relational databases follow the ACID (Atomicity, Consistency, Isolation, Durability) and BASE (Basically available, Soft-state, Eventual-consistency) properties.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Approach for Handling Complexity by Migrating from Conventional Databases to Big Data

2018

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Handling complexity in the data of information systems has emerged into a serious challenge in recent times. The typical relational databases have limited ability to manage the discrete and heterogenous nature of modern data. Additionally, the complexity of data in relational databases is so high that the efficient retrieval of information has become a bottleneck in traditional information systems. On the side, Big Data has emerged into a decent solution for heterogenous and complex data (structured, semi-structured and unstructured data) by providing architectural support to handle complex data and by providing a tool-kit for efficient analysis of complex data. For the organizations that are sticking to relational databases and are facing the challenge of handling complex data, they need to migrate their data to a Big Data solution to get benefits such as horizontal scalability, real-time interaction, handling high volume data, etc. However, such migration from relational databases to Big Data is in itself a challenge due to the complexity of data. In this paper, we introduce a novel approach that handles complexity of automatic transformation of existing relational database (MySQL) into a Big data solution (Oracle NoSQL). The used approach supports a bi-fold transformation (schema-to-schema and data-to-data) to minimize the complexity of data and to allow improved analysis of data. A software prototype for this transformation is also developed as a proof of concept. The results of the experiments show the correctness of our transformations that outperform the other similar approaches.

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Big data in memory: Benchimarking in memory database using the distributed key-value store for machine to machine communication

Cited by 5 publications

References 2 publications

Challenges in NoSQL-Based Distributed Data Storage: A Systematic Literature Review

Challenges in NoSQL-Based Distributed Data Storage: A Systematic Literature Review

System Architecture and Core Technologies for Transportation Internet of Things

An Intelligent Approach for Handling Complexity by Migrating from Conventional Databases to Big Data

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