Programming models and systems for Big Data analysis

Belcastro, Loris; Marozzo, Fabrizio; Talia, Domenico

doi:10.1080/17445760.2017.1422501

Cited by 42 publications

(29 citation statements)

References 27 publications

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“…Every day millions of people use social media and produce huge amount of digital data that can be effectively exploited to extract valuable information concerning human dynamics and behaviors. Such data, commonly referred as Big Data, contains valuable information about user activities, interests, and behaviors, which makes it intrinsically suited to a very large set of applications [1]. Big Social Data analysis is a subfield of Big Data analysis aimed at studying the interactions of users on social media for extracting useful information, such as moods or opinions on topics or events of interest [2].…”

Section: Introductionmentioning

confidence: 99%

Learning Political Polarization on Social Media Using Neural Networks

et al. 2020

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Social media analysis is a fast growing research area aimed at extracting useful information from social media platforms. This paper presents a methodology, called IOM-NN (Iterative Opinion Mining using Neural Networks), for discovering the polarization of social media users during election campaigns characterized by the competition of political factions. The methodology uses an automatic incremental procedure based on feed-forward neural networks for analyzing the posts published by social media users. Starting from a limited set of classification rules, created from a small subset of hashtags that are notoriously in favor of specific factions, the methodology iteratively generates new classification rules. Such rules are then used to determine the polarization of people towards a faction. The methodology has been assessed on two case studies that analyze the polarization of a large number of Twitter users during the 2018 Italian general election and 2016 US presidential election. The achieved results are very close to the real ones and more accurate than the average of the opinion polls, revealing the high accuracy and effectiveness of the proposed approach. Moreover, our approach has been compared to the most relevant techniques used in the literature (sentiment analysis with NLP, adaptive sentiment analysis, emoji-and hashtag-based polarization) by achieving the best accuracy in estimating the polarization of social media users. INDEX TERMS Social media analysis, opinion mining, user polarization, neural networks, sentiment analysis, political events.

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Section: Introductionmentioning

confidence: 99%

Learning Political Polarization on Social Media Using Neural Networks

et al. 2020

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“…While [9] cites a time of about 0.125s for generating a schedule for an instance with P = N = 512 and unspecified arrival times on a Xeon E5-2680@2.7 GHz CPU. 1 With our implementation we were able to reach schedule generation time of about 10s for the same sized instances with randomly generated arrival times. Our tests were run on a different CPU, a Xeon E5-2670 v3 @2.3 GHz, but such a large run time difference cannot be explained by neither hardware nor compiler or implementation differences alone.…”

Section: Improvements To the Algorithmmentioning

confidence: 97%

“…The addition of the conditional instruction moved the original lines 35-41 to lines 39-45. 1: Let M be a state matrix of size P • N . Set M( * , * ) = A, i.e.. mark all segments as available.…”

Section: Skipping Idle Roundsmentioning

confidence: 99%

“…Thus, we can observe a rapid progress in parallelization of currently existing compute methods and applications, especially ones requiring the large compute resources. We perceive that the improvements of HPC communication algorithms and protocols, including our works, will enable faster optimization of the above crucial areas (IoT, AI, Big Data), especially in such topics like parallelization of hybrid parallel FTDT methods [22], intelligent home systems supported by neural networks [26], Big Data related programming models and systems [1], and voice evaluation mechanisms involving such complex mechanisms like bio-inspired algorithms or spiking neural network [14].…”

Section: Introductionmentioning

confidence: 99%

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Improving Clairvoyant: reduction algorithm resilient to imbalanced process arrival patterns

Proficz

Ocetkiewicz

2020

J Supercomput

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The Clairvoyant algorithm proposed in “A novel MPI reduction algorithm resilient to imbalances in process arrival times” was analyzed, commented and improved. The comments concern handling certain edge cases in the original pseudocode and description, i.e., adding another state of a process, improved cache friendliness more precise complexity estimations and some other issues improving the robustness of the algorithm implementation. The proposed improvements include skipping of idle loop rounds, simplifying generation of the ready set and management of the state array and an about 90-fold reduction in memory usage. Finally an extension enabling process arrival times (PATs) prediction was added: an additional background thread used to exchange the data with the PAT estimations. The performed tests, with a dedicated mini-benchmark executed in an HPC environment, showed correctness and improved performance of the solution, with comparison to the original or other state-of-the-art algorithms.

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“…Development of analytical platforms due to advances in computational capacity and computer science can accommodate, link, and analyze large, diverse datasets. One such example is Apache Hadoop (Belcastro et al 2018 ). Big data imply the use of data science methods, such as data mining or machine learning.…”

Section: Cardiac Big Data Repositories Iot and Diagnosticsmentioning

confidence: 99%

Futuristic biosensors for cardiac health care: an artificial intelligence approach

et al. 2018

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Biosensor-based devices are pioneering in the modern biomedical applications and will be the future of cardiac health care. The coupling of artificial intelligence (AI) for cardiac monitoring-based biosensors for the point of care (POC) diagnostics is prominently reviewed here. This review deciphers the most significant machine-learning algorithms for the futuristic biosensors along with the internet of things, computational techniques and microchip-based essential cardiac biomarkers for real-time health monitoring and improving patient compliance. The present review also discusses the recently developed cardiac biosensors along with technical strategies involved in their mechanism of working and their applications in healthcare. Additionally, it provides a key for the ontogeny of an effective and supportive hierarchical protocol for clinical decision-making about personalized medicine through combinatory information analysis, and integrated multidisciplinary AI approaches.

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Programming models and systems for Big Data analysis

Cited by 42 publications

References 27 publications

Learning Political Polarization on Social Media Using Neural Networks

Learning Political Polarization on Social Media Using Neural Networks

Improving Clairvoyant: reduction algorithm resilient to imbalanced process arrival patterns

Futuristic biosensors for cardiac health care: an artificial intelligence approach

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