The Method of Analysis of Data from Social Networks Using Rapidminer

Boranbayev, Askar; Shuitenov, Gabit; Boranbayev, Seilkhan

doi:10.1007/978-3-030-52246-9_49

Cited by 16 publications

(6 citation statements)

References 14 publications

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“…We used the Rapid Miner auto-model (Hofmann and Klinkenberg, 2016;Boranbayev et al, 2020). The auto model divides the original dataset into a 60:40 split (Train: Test).…”

Section: An Inductive Data Mining and Predictive Modelling Approachmentioning

confidence: 99%

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

Hocherman

Mizrachi

Ben‐Gal

2022

Preprint

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Digital Phenotyping (DP) entails exploring digital expressions of human personality using behavioral cues drawn from smartphones’ digital footprints. Most personality-oriented DP studies focus narrowly on the Big5 model. This research aims to broaden this approach, using fifty-four personality constructs rooted in fifteen leading personality theories beyond the Big5. Our sample consisted of 104 respondents from whom smartphone data was collected over 7–10 days. We implemented both deductive (hypothesis-testing) and inductive (machine learning) modelling methods. Results show that fifteen of the sixteen broad personality constructs were successfully predicted from smartphone data (forty-eight sub-personality items of the fifty-nine types and personality traits). The best overall predictive model was Gradient Boosted Trees with communication-related features having the highest predictive weight. DP has the potential to transform the field of personality research and may be applied in areas such as HR analytics, personality-based targeted marketing, individualized homeland security, financial risk assessments, personalized medicine, and more.

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“…We used the Rapid Miner auto-model (Hofmann and Klinkenberg, 2016;Boranbayev et al, 2020). The auto model divides the original dataset into a 60:40 split (Train: Test).…”

Section: An Inductive Data Mining and Predictive Modelling Approachmentioning

confidence: 99%

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

Hocherman

Mizrachi

Ben‐Gal

2022

Preprint

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“…В данной статье мы предлагаем сравнительный анализ Node.js и Scala, рассматривая их преимущества и недостатки, области применения и примеры успешных масштабируемых проектов. Мы проведем обзор соответствующих сообществ разработчиков, экосистем инструментов и ресурсов для обучения, которые помогут вам выбрать наиболее подходящую технологию для вашего проекта [1]. Scala (от "scalable language" -масштабируемый язык) -это мультипарадигменный язык программирования, разработанный для общего назначения.…”

Section: Introductionunclassified

Node. Js И Scala: Интеграция Для Создания Распределенных Масштабируемых Приложений Для Анализа Научных Текстов

Тургинбаева,

Серік,

Турусбекова

et al. 2024

kazutb

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Node.js и Scala представляют собой две популярные технологии разработки, которые обладают уникальными особенностями и применяются в различных сферах. Node.js – серверный JavaScript, который стал основой для множества веб-приложений и сервисов. Scala, с другой стороны, является мощным языком программирования, который комбинирует преимущества функционального и объектно-ориентированного программирования. В этой статье мы сравниваем эти две технологии, рассматриваем их особенности, преимущества и недостатки, основные характеристики каждой технологии, сферы их применения. Node.js или Scala, оба эти инструмента предоставляют широкие возможности для создания мощных, масштабируемых и эффективных приложений и в статье рассматриваются возможности их интеграции, с целью создания масштабируемого приложения.

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“…Apache Hadoop [14] [15] [16], RapidMiner [17] [18] [19], and Microsoft Azure [20] [21] [22] [23]. Aside from data utilization, data privacy [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] must also be considered when datasets are allowedly utilized in big data analytics.…”

Section: Introductionmentioning

confidence: 99%

Aggregate Query Frameworks for Preserving Privacy Data in Big Data Analytics

Riyana

Sasujit

Homdoung

2023

Preprint

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Data utility and data privacy are both serious issues that must be considered when datasets are released to use in big data analytics because they are traded-off issues. That is, high-utility datasets are generally high risks in terms of privacy violation issues. Likewise, datasets are formed to be high security in terms of privacy preservation, they often lead to data utility issues. To address these issues in datasets, several privacy preservation models have been proposed such as k-Anonymity, l-Diversity, t-Closeness, Anatomy, k-Likeness, and (lp1, . . . , lpn)-Privacy, i.e., all users’ explicit identifier values and all unique quasi-identifier values in datasets are removed and distorted respectively. Unfortunately, these privacy preservation models are static data models and still have data utility issues that must be addressed. Thus, they could be insufficient to address privacy violation issues in big data analytics. For this reason, a new efficient and effective privacy preservation model is proposed in this work such that it is based on aggregate query answers that can guarantee the confidence of the range and the minimized number of values that can be re-identified. The aims of the proposed privacy preservation model are that aside from privacy preservation constraints, the complexity and data utility are also maintained as much as possible. Furthermore, we show that the proposed model is a privacy preservation model that is efficient and effective by using extensive experiments.

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The Method of Analysis of Data from Social Networks Using Rapidminer

Cited by 16 publications

References 14 publications

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

RETRACTED: Digital Phenotyping of Smartphone Data Successfully Predicts a Broad Range of Personality Constructs

Node. Js И Scala: Интеграция Для Создания Распределенных Масштабируемых Приложений Для Анализа Научных Текстов

Aggregate Query Frameworks for Preserving Privacy Data in Big Data Analytics

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