A view of programming scalable data analysis: from clouds to exascale

Talia, Domenico

doi:10.1186/s13677-019-0127-x

Cited by 18 publications

(8 citation statements)

References 27 publications

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“…We however recorded no study focused on assessing the efficiency and scalability of their tools; studying the efficiency informs how much time the tools take to produce their outcomes; whereas scalability informs how the time changes when the input of the tools increase. Efficiency and scalability are fundamental qualities of analytics tools (Talia 2019); app review mining tools are no exception. The number of reviews that an app receives can vary from a few to more than thousands.…”

Section: Pay Attention To Efficiency and Scalability Of Mining Toolsmentioning

confidence: 99%

Analysing app reviews for software engineering: a systematic literature review

et al. 2022

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App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.

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Section: Pay Attention To Efficiency and Scalability Of Mining Toolsmentioning

confidence: 99%

Analysing app reviews for software engineering: a systematic literature review

et al. 2022

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“…Furthermore, the novel Exascale systems pose new requirements for addressing architectures composed of a very large number of cores. In particular, in the near future, existing frameworks will have to address a wide range of issues related to energy consumption, scheduling, data distribution and access, communication and synchronization, in order to enable the scalable implementation of real Big Data analysis applications [71].…”

Section: Final Remarksmentioning

confidence: 99%

Programming big data analysis: principles and solutions

et al. 2022

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In the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. This data, commonly referred to as Big Data, is challenging current storage, processing, and analysis capabilities. New models, languages, systems and algorithms continue to be developed to effectively collect, store, analyze and learn from Big Data. Most of the recent surveys provide a global analysis of the tools that are used in the main phases of Big Data management (generation, acquisition, storage, querying and visualization of data). Differently, this work analyzes and reviews parallel and distributed paradigms, languages and systems used today to analyze and learn from Big Data on scalable computers. In particular, we provide an in-depth analysis of the properties of the main parallel programming paradigms (MapReduce, workflow, BSP, message passing, and SQL-like) and, through programming examples, we describe the most used systems for Big Data analysis (e.g., Hadoop, Spark, and Storm). Furthermore, we discuss and compare the different systems by highlighting the main features of each of them, their diffusion (community of developers and users) and the main advantages and disadvantages of using them to implement Big Data analysis applications. The final goal of this work is to help designers and developers in identifying and selecting the best/appropriate programming solution based on their skills, hardware availability, application domains and purposes, and also considering the support provided by the developer community.

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“…The design and implementation of Big Data management and analysis solutions has received many benefits and improvements via the utilization of high-performance computing (HPC) systems. Today, complex processing and analysis of real-world massive data sources in AI, machine learning, and large simulations require using HPC infrastructures such as highly parallel clusters, supercomputers, and clouds (Talia, 2019). However, as parallel research and technologies advance, in the next few years, exascale computing systems will be used for implementing scalable Big Data analysis solutions in science and business (Reed and Dongarra, 2015).…”

Section: Editorial On the Research Topic Towards Exascale Solutions F...mentioning

confidence: 99%