data visualization – Buonaiuto@Work

Unveiling the intricacies of Hashtag Sense Clustering Based on Temporal Similarity for a marketing campaign of a big coffee Company

Massimo2023-07-170305 views

In the ever-evolving world of social media, hashtags have become a cornerstone in shaping digital conversations. They are not just mere labels but are pivotal in categorizing and identifying the pulse of social narratives. However, with this utility comes a challenge: the dynamic and polysemous nature of hashtags. This complexity is where the innovative approach of “Hashtag Sense Clustering Based on Temporal Similarity” comes into play. The challenges of hashtags in Twitter (X) Traditionally, hashtags have been used as simple markers to categorize posts or as symbols of community affiliation. But their usage varies greatly, often leading to ambiguity. The same hashtag can represent different topics at different times, and conversely, various hashtags can denote the same subject. This polymorphic nature, coupled…

Data management in practice

Massimo2023-03-140429 views

Addressing challenges in data management involves designing robust strategies, implementing quality frameworks, and ensuring compliance with regulations. From data migration to security protocols, each step plays a crucial role in maintaining accurate, accessible, and secure data for organizational success.

Unlocking the Power of Data Standardization: A Guide to CDISC and FAIRplus Cookbook for Streamlined Research. Transforming data management to foster adoption of FAIR principles

Massimo2022-01-010425 views

In the realm of life sciences research, the FAIRplus Cookbook emerges as a pivotal guide for integrating the FAIR principles—Findability, Accessibility, Interoperability, and Reusability—into data management practices. This invaluable resource offers a series of practical recipes for the FAIRification of data, catering to a diverse audience, including researchers and data stewards. It underscores the significance of data standardization, as demonstrated through the application of CDISC-SDTM models for clinical trial data, to enhance the quality and utility of scientific data. By leveraging the FAIRplus Cookbook, the scientific community can navigate the complexities of data management, ensuring that research data are not only standardized but also aligned with ethical considerations and regulatory requirements. This guide thus stands as a testament to the collaborative effort to foster a culture of open, standardized, and ethically responsible data sharing in life sciences.

How many fields in data science?

Massimo2021-06-270399 views

Data Science is a research activity… mostly Data-driven scientific discovery is regarded as the fourth science paradigm The twenty-first century has ushered in a new age that is coined as data science and big data analytics. Data-driven scientific discovery is regarded as the fourth science paradigm. Data science has been a core driver of the new-generation science, technologies and economy, and is driving new researches, innovation, profession, applications and education across both disciplines and business domains. There are many scientific and technical challenges associated with big data, ranging from data capture, creation, storage, search, sharing, modeling, representation, analysis, learning, visualization, explanation, and decision making. Among the many data characteristics and complexities to be addressed, I mention the hybridization of heterogeneous, multisource, hierarchical, interactive, dynamic,…

Curvilinear Component Analysis

Massimo2015-01-040403 views

Curvilinear Component Analysis (CCA) is a powerful method for dimensionality reduction, preserving local data structure. Unlike PCA, it handles nonlinear relationships, vital for tasks like image compression, speech recognition, and data visualization. In business, CCA aids customer segmentation, fraud detection, and marketing analytics, enhancing decision-making and cost efficiency.