NoSQL databases for big data

Oussous, Ahmed; Benjelloun, Fatima Zahra; Lahcen, Ayoub Ait; Belfkih, Samir

doi:10.1504/ijbdi.2017.085537

Cited by 12 publications

(9 citation statements)

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“…NoSQL data storage systems have been developed based on BASE (Basically Available, Soft State, and Eventual Consistency) features to relax the restriction of ACID properties. NoSQL data storage systems are presented under the following categories that are key-value, document, wide-column, and graph [73][74][75] [76]. Popular key-value stores are presented in Section V/C/1.…”

Section: ) Permenant Storage Technologiesmentioning

confidence: 99%

“…Comparing these storage technologies are presented in Table VIII using distinctive features. The shared features for these graph stores are fault tolerance, concurrency, performance, durability, not supporting Map Reduce, graph data model support, and supporting ACID [73]. Another essential data store category is NewSQL [70].…”

Section: ) Permenant Storage Technologiesmentioning

confidence: 99%

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A Comparative Perspective on Technologies of Big Data Value Chain

Aydin

2023

IEEE Access

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Data is one of the most valuable assets in the digital era because it may conceal hidden valuable insights. Diverse organizations in diverse domains overcome the challenges of the big data value chain by employing a wide range of technologies to meet their needs and achieve a variety of goals to support their decision-making. Due to the significance of data-oriented technologies, this paper presents a model of the big data value chain based on technologies used in the acquisition, storage, and analysis of data. The following are the paper's contributions: First, a model of the big data value chain is developed to illustrate a comprehensive representation of the big data value chain that depicts the relationships between the characteristics of big data and the technologies associated with each category. Second, in contrast to previous research, this paper presents an overview of technologies for each category of the big data value chain. The third contribution of this paper is to assist researchers and developers of data-intensive systems in selecting the appropriate technology for their specific application development use cases by providing examples of applications and use cases from prominent papers in a variety of fields and by describing the capabilities and stages of the technologies being presented so that the right technology is used at the right time in the big data collection, processing, storage, and analytics tasks.

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Section: ) Permenant Storage Technologiesmentioning

confidence: 99%

Section: ) Permenant Storage Technologiesmentioning

confidence: 99%

A Comparative Perspective on Technologies of Big Data Value Chain

Aydin

2023

IEEE Access

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“…Many experiments have been conducted to compare characteristics of non-relational and relational databases including their scalability, performance, flexibility, power of querying, and security [3,17,58,68,69,70,86]. Experiments conducted a decade ago proved quite inconclusive as performance varied significantly according to the type of operation performed and the type of data used [58,86].…”

Section: Experiment-based Comparisonsmentioning

confidence: 99%

“…A more recent study analysing performance of non-relational databases for spatial and aggregate functions suggests that the performance of MongoDB has since improved [3]. Focusing on applications handling large volumes of data (i.e., terabytes), it was concluded that non-relational databases were preferable because they offer flexible architectures which can accommodate a large variety of data storage needs [68,70]. Similar results were obtained in a performance comparison of various types of non-relational databases against MySQL [35].…”

Section: Experiment-based Comparisonsmentioning

confidence: 99%

Cloud based evaluation of databases for stock market data

Singh

Martyr

Medland³

et al. 2022

J Cloud Comp

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About fifty years ago, the world’s first fully automated system for trading securities was introduced by Instinet in the US. Since then the world of trading has been revolutionised by the introduction of electronic markets and automatic order execution. Nowadays, financial institutions exploit the associated flow of daily data using more and more advanced analytics to gain valuable insight on the markets and inform their investment decisions. In particular, time series of Open High Low Close prices and Volume data are of special interest as they allow identifying trading patterns useful for forecasting both stock prices and volumes. Traditionally, relational databases have been used to store this data; however, the ever-growing volume of this data, the adoption of the hybrid cloud model, and the availability of novel non-relational databases which claim to be more scalable and fault tolerant raise the question whether relational databases are still the most appropriate. In this study, we define a set of criteria to evaluate performance of a variety of databases on a hybrid cloud environment. There, we conduct experiments using standard and custom workloads. Results show that migration to a MongoDB database would be most beneficial in terms of cost, storage space, and throughput. In addition, organisations wishing to take advantage of autoscaling and the maintenance power of the cloud should opt for a cloud native solution.

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“…The data management phase encompasses tasks such as data acquisition & recording; extraction, cleaning & annotation; integration, aggregation & representation. Ensuring that the data collected is reliable, accessible, manageable, properly stored and secured is crucial (Oussous et al, 2017). The data analysis phase is about modelling and interpretation.…”

Section: Big Data In Innovationmentioning

confidence: 99%

The role of design thinking in Big Data innovations

2021

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Digital technologies are disrupting the way companies manage business. In today's society, where a plethora of different digital solutions are booming and the attention of both practitioners and scholars is growing, little is known about how to make the most of such technologies. Given the complexity of the phenomenon, this paper refers to a single digital technology, Big Data. It is commonly recognised that Big Data can represent a competitive advantage source for companies. However, there is still a lack of literature on how to exploit the opportunities provided by this technology, while the amount of data is so vast that their interpretation is complex. The focus on human centricity, iteration, and prototyping of Design Thinking brought the academic world towards the recognition that this approach could be valuable in steering and managing this technology. Thus, the paper investigates how Design Thinking can foster innovation based on Big Data technologies. It leverages three explicative case studies to shed light on a new set of hybrid practices. By comparing and contrasting cases, the four practices of (i) Cropping Big Data Landscapes, (ii) Reframing Big Data applications, (iii) Unveiling Big Data Opportunities, and (iv) Proving Big Data Releases are proposed.These hybrid sets of practices support both practitioners and academics in opening up the black box to interpret and value Big Data.

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NoSQL databases for big data

Cited by 12 publications

References 0 publications

A Comparative Perspective on Technologies of Big Data Value Chain

A Comparative Perspective on Technologies of Big Data Value Chain

Cloud based evaluation of databases for stock market data

The role of design thinking in Big Data innovations

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