Quantitative Analysis of Scalable NoSQL Databases

Swaminathan, Surya Narayanan; Elmasri, Ramez

doi:10.1109/bigdatacongress.2016.49

Cited by 19 publications

(14 citation statements)

References 15 publications

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“…2 [35]. The YCSB core software package is designed to evaluate different aspects of system performance, including a series of workloads, to assess the suitability of the system for different workload characteristics at different points in the performance space [33,34].…”

Section: Yahoo! Cloud Serving Benchmark (Ycsb)mentioning

confidence: 99%

Evaluating Riak Key Value Cluster for Big Data

Rmis

Topcu

2020

Teh. vjesn.

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NoSQL database has become an important alternative to traditional relational databases. Those databases are prepared by the management of large, continuously and variably changing data sets. They are widely used in cloud databases and distributed systems. With NoSQL databases, static schemes and many other restrictions are avoided. In the era of big data, such databases provide scalable high availability solutions. Their key-value feature allows fast retrieval of data and the ability to store a lot of it. There are many kinds of NoSQL databases with various performances. Therefore, comparing those different types of databases in terms of performance and verifying the relationship between performance and database type has become very important. In this paper, we test and evaluate the Riak key-value database for big data clusters using benchmark tools, where huge amounts of data are stored and retrieved in different sizes in a distributed database environment. Execution times of the NoSQL database over different types of workloads and different sizes of data are compared. The results show that the Riak key-value is stable in execution time for both small and large amounts of data, and the throughput performance increases as the number of threads increases.

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Section: Yahoo! Cloud Serving Benchmark (Ycsb)mentioning

confidence: 99%

Evaluating Riak Key Value Cluster for Big Data

Rmis

Topcu

2020

Teh. vjesn.

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“…From the collected of 18 articles, we found 8 articles [8][9][10][11][12][13][14][15] that were related to general overview of NoSQL databases and Big Data, 5 articles [16][17][18][19][20] that discussed performance comparison and evaluation between different NoSQL databases, 2 articles [21][22] that were associated to NoSQL data model and classifying NoSQL databases based on the Consistency, Availability, tolerance of network Partition of database which is known as CAP theorem, [23] this research has concentrated on the storage aspect of cloud computing systems using NoSQL. Other two [24][25] study reported the SQL query condition transformation to any NoSQL based database using Espresso Heuristic algorithm and analyzed the effects of transaction on data consistency & efficiency.…”

Section: Descriptive Analysismentioning

confidence: 99%

“…In this present study, we provide evaluation of four categories NoSQL databases including document based (MongoDB, Couch-Base), column based (Cassandra, HBase), key value base (Redis) and graph based (Neoj4). Moreover, MongoDB, CouchBase, Cassandra, HBase, Redis are the most prominent NoSQL databases that are evaluated using YCSB [16,18]. Surya et al [16] showed their experiment for MongoDB, CouchBase, Cassandra, HBase using YCSB, where Cassandra had a better performance than others while the execution of workload is 50% read and 50% write.…”

Section: Comparison Of Nosql Databasesmentioning

confidence: 99%

“…Moreover, MongoDB, CouchBase, Cassandra, HBase, Redis are the most prominent NoSQL databases that are evaluated using YCSB [16,18]. Surya et al [16] showed their experiment for MongoDB, CouchBase, Cassandra, HBase using YCSB, where Cassandra had a better performance than others while the execution of workload is 50% read and 50% write. They also found that HBase had a better performance with a small datasets of database, whereas MongoDB had the best performance while working with 100% read and 100% blind write [16].…”

Section: Comparison Of Nosql Databasesmentioning

confidence: 99%

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A literature review on NoSQL database for big data processing

Ahmed

Khatun²,

Ali³

et al. 2018

IJET

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Objective: Aim of the present study was to literature review on the NoSQL Database for Big Data processing including the structural issues and the real-time data mining techniques to extract the estimated valuable information.Methods: We searched the Springer Link and IEEE Xplore online databases for articles published in English language during the last seven years (between January 2011 and December 2017). We specifically searched for two keywords (“NoSQL” and “Big Data”) to find the articles. The inclusion criteria were articles on the use of performance comparison on valuable information processing in the field of Big Data through NoSQL databases.Results: In the 18 selected articles, this review identified 8 articles which provided various suitable recommendations on NoSQL databases for specific area focus on the value chain of Big Data, 5 articles described the performance comparison of different NoSQL databases, 2 articles presented the background of basics characteristics data model for NoSQL, 1 article denoted the storage in respect of cloud computing and 2 articles focused the transactions of NoSQL.Conclusion: In this literature, we presented the NoSQL databases for Big Data processing including its transactional and structural issues. Additionally, we highlight research directions and challenges in relation to Big Data processing. Therefore, we believe that the information contained in this review will incredible support and guide the progress of the Big Data processing.

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“…Web applications are being exponentially populated and as a result information is stored in huge databases that utilize various technologies for that purpose such as Datacubes (Edoh-Alove, 2016), (Höffner, 2016) or noSQL databases (Swaminathan, 2016), (Gantz, 2016). But the information stored in these databases are either semi-relational, un-relational (in case of noSQL) or hidden from user activities and existing search engines.…”

Section: Related Workmentioning

confidence: 99%

Modeling a context-aware friend-of-a-friend(foaf) applications for mobile platforms

Raufi¹,

Ismaili²,

Ajdari³

et al. 2017

Pressacademia

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We live in an era where the evolution of social networks and developments in the field of information technology witnesses and exponential growth. On a daily basis, we have an ever growing, an ever expanding social network users and as a result we affirm an increase of data that is distributed across different platforms. In this paper, we aim to treat the semantic web from the prism of creating a suitable infrastructure for data integration on the web. One of the most promising applications of the semantic web is the presentation of profiles using an RDF (Resource Description Framework) schema called Friend-of-Friend (FOAF) which represents a machine-processable ontology for describing persons, their activities and relations to other people and objects. In order to demonstrate the concept on how to integrate, relate and share information using FOAF, we model and develop a mobile application called "Find professional" which consists in creating, finding and interacting in a context aware fashion with other users via FOAF. The "Find Professional" mobile app can create a FOAF profile either by asking user's direct input or it can utilize other social network profiles such as Facebook for importing user public profiles into FOAF. User profiles represent RDF files that can be read and queried by semantic query languages like SPARQL, which returns data to their user profiles as well as finds professionals in the FOAF app based on user's geographic location. The application is also evaluated with a field usability testing performed against ten randomly picked user from various profiles and backgrounds. Usability testing process did not yield severe problems during application evaluation and comments are more related to user interface rather than difficulty in user task completion.

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Quantitative Analysis of Scalable NoSQL Databases

Cited by 19 publications

References 15 publications

Evaluating Riak Key Value Cluster for Big Data

Evaluating Riak Key Value Cluster for Big Data

A literature review on NoSQL database for big data processing

Modeling a context-aware friend-of-a-friend(foaf) applications for mobile platforms

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