A Study on Privacy Preserving Big Data Mining: Techniques and Challenges

Dahiya, Anuradha

doi:10.26483/ijarcs.v13i5.6906

Cited by 2 publications

(2 citation statements)

References 16 publications

(24 reference statements)

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“…It is critical to increase the number of comments on social media platforms. Future research should focus on evaluating data on a million-opinion mining scale accurately portraying the volume, velocity, and variety of big data (Sowmya et al, 2015), which this study did not accomplish.…”

Section: Discussionmentioning

confidence: 89%

Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry

Siahaan¹,

Prasetio²

2022

ajtm

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Abstract. Insights gained from social media platforms are pivotal for businesses to understand their products’ present position. While it is possible to use consulting services focusing on surveys about a product or brand, such methods may yield limited insights. By contrast, on social media, people frequently express their individual and unique feelings about products openly and informally. With this in mind, we aim to provide rigorous methodologies to enable businesses to gain significant insights on their brands and products in terms of representations on social media. This study employs conjoint analysis to lay the analytical groundwork for developing positive and negative sentiment frameworks to evaluate the brands of three prominent emerging automotive companies in Indonesia, anonymized as “HMI,” “YMI,” and “SMI.” We conducted a survey with a sample size of n=67 to analyze the phrasings of importance for our wording dictionary construction. A series of data processing operations were carried out, including the collection, capture, formatting, cleansing, and transformation of data. Our study’s findings indicate a distinct ranking of the most positively and negatively perceived companies among social media users. As a direct management-related implication, our proposed data analysis methods could assist the industry in applying the same rigor to evaluating companies’ products and brands directly from social media users’ perspective. Keywords: Brand image, social media, data analytics, sentiment analysis, conjoint analysis

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Section: Discussionmentioning

confidence: 89%

Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry

Siahaan¹,

Prasetio²

2022

ajtm

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“…Big data (BD) is a consequence of data explosion, experienced by all industries. [35] describe big data as a data mining evolution that became big data as accumulated data grew exponentially. Because of the large amount of data accumulated daily in industry, the traditional DM process had to adopt the growing data explosion.…”

Section: Big Data (Bd)mentioning

confidence: 99%

A framework for a structured problem-solving approach with experimental design as the focus for industrial processing environment

van Blerk,

van der Vyver

2023

ORION

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This article focuses on developing a framework for industrial environments to provide a structured problem-solving approach based on experimentation as a basis to assist analysts and management for strategic decision making for process improvement. The process for developing this generic framework was through an analytical process improvement process case study for a company in an industrial environment. The research methodology includes an interpretivist approach followed by a positivist approach and ends with a constructivism worldview due to the Experimental design approach design depicted by this framework. The summarized goals were, expanding Design of Experiments (DOE) as a statistical approach to complement existing methods and methodologies used for Data Mining, validate the integrity of data through a refining process and applying DOE in combination with traditional Data Mining techniques. The importance for developing this framework was to experiment with historical data, based on real process data then to predict future process behaviour, using full and fractional DOE design scenarios which allows the analyst not to have a one-dimensional analytical approach but to evaluate which design fits the data best. No risk of costly process failures due to experimenting into the unknown by utilizing historic data by applying experimentation when evaluating different processing scenarios for possible product improvement and to provide an alternative statistical approach for Data Mining in an industrial environment for screening independent and dependent variables for a DOE model.

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A Study on Privacy Preserving Big Data Mining: Techniques and Challenges

Cited by 2 publications

References 16 publications

Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry

Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry

A framework for a structured problem-solving approach with experimental design as the focus for industrial processing environment

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