Architecture Knowledge for Evaluating Scalable Databases

Gorton, Ian; Klein, John R.; Nurgaliev, Albert

doi:10.1109/wicsa.2015.26

Cited by 20 publications

(8 citation statements)

References 17 publications

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“…Furthermore, Riak has also been analysed in different research studies [25][26][27], by taking into account various factors such as the trade-off between CAP properties (Consistency, Availability, Partition tolerance) and performance. For instance, the work in [25] compares Cassandra, MongoDB and Riak.…”

Section: Related Workmentioning

confidence: 99%

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Transaction processing in consistency-aware user’s applications deployed on NoSQL databases

González-Aparicio

Ogunyadeka

Younas

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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NoSQL databases are capable of storing and processing big data which is characterized by various properties such as volume, variety and velocity. Such databases are used in a variety of user applications that need large volume of data which is highly available and efficiently accessible. But they do not enforce or require strong data consistency nor do they support transactions. This paper investigates into the transaction processing in consistency-aware applications hosted on MongoDB and Riak which are two representatives of Document and Key-Value NoSQL databases, respectively. It develops new transaction schemes in order to provide NoSQL databases with transactional facilities as well as to analyze the effects of transactions on data consistency and efficiency in user’s applications. The proposed schemes are evaluated using the YCSB + T benchmark which is based on Yahoo! Cloud Services Benchmark (YCSB). Experimental results show that using the proposed schemes, strong consistency can be achieved in MongoDB and Riak without severely affecting their efficiency. We also conduct experiments in order to analyse the level of consistencyof MongoDB and Riak transactional systems.

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Section: Related Workmentioning

confidence: 99%

“…Therefore, it has a negative impact on the execution latency of database operations. Though, studies like [25] and [26] provide useful insights into the comparison of NoSQL databases various other factors, such as database architectures, data models and query capabilities, could have been taken into account in the evaluation process [27].…”

Section: Related Workmentioning

confidence: 99%

Transaction processing in consistency-aware user’s applications deployed on NoSQL databases

González-Aparicio

Ogunyadeka

Younas

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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“…The first level contained Feature Categories, which were identified as dimensions where the COTS product features had significant impact on architecture of the software using the product. The complete NoSQL feature model is described in [11].…”

Section: Nosql Feature Modelmentioning

confidence: 99%

Design Assistant for NoSQL Technology Selection

Klein¹,

Gorton²

2015

Proceedings of the 1st International Workshop on Future of Software Architecture Design Assistants

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NoSQL databases create tight coupling between data model, deployment topology, and application architecture, and so this technology selection must be one of the earliest architecture decisions. The NoSQL technology landscape is large and evolving rapidly, so architects need efficient and trusted design assistance to explore the solution space. Our solution was to create a queryable knowledge base, populated with curated information, which is rendered dynamically as content grows and changes. We built the knowledge base using Semantic MediaWiki, implementing a novel knowledge model that enables reasoning from quality attributes to architecture patterns and tactics to features implemented in specific NoSQL products. We also provide tabular and graphical visualizations to support both systematic and ad hoc exploration. Our contributions to the field of architecture design assistants are the knowledge model, its implementation in a semantic platform, and the resulting populated knowledge base for big data system architects.

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“…search for AK, and codify it) from multiple sources, and subsequently structure and store it in a repository (e.g. [9]). This supports software engineers to directly find relevant AK concepts, without navigating through many sources of AK depending on the concept.…”

Section: Introductionmentioning

confidence: 99%

Exploring Web Search Engines to Find Architectural Knowledge

Soliman

Wiese

et al. 2021

2021 IEEE 18th International Conference on Software Architecture (ICSA)

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Software engineers need relevant and up-to-date architectural knowledge (AK), in order to make well-founded design decisions. However, finding such AK is quite challenging. One pragmatic approach is to search for AK on the web using traditional search engines (e.g. Google); this is common practice among software engineers. Still, we know very little about what AK is retrieved, from where, and how useful it is. In this paper, we conduct an empirical study with 53 software engineers, who used Google to make design decisions using the Attribute-Driven-Design method. Based on how the subjects assessed the nature and relevance of the retrieved results, we determined how effective web search engines are to find relevant architectural information. Moreover, we identified the different sources of AK on the web and their associated AK concepts.

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Architecture Knowledge for Evaluating Scalable Databases

Cited by 20 publications

References 17 publications

Transaction processing in consistency-aware user’s applications deployed on NoSQL databases

Transaction processing in consistency-aware user’s applications deployed on NoSQL databases

Design Assistant for NoSQL Technology Selection

Exploring Web Search Engines to Find Architectural Knowledge

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