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Published February 8, 2024

Data & Metadata

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Edited By:

Editorial Salud, Ciencia y Tecnología

Title:

Data and Metadata

ISSN-L: 2953-4917

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2024-03-30 Original
Social Capital in Small Industrial Firms and Its Link with Innovation

By Edith Georgina Surdez Pérez, María del Carmen Sandoval Caraveo, Maribel Flores Galicia

Introduction: Social Capital in organizations is an intangible asset that represents the favourable relationships that exist between work teams, within an organization and externally, to different interest groups.
Objective: This study examined the link between internal relational social capital (RSC) and external RSC with innovation in small industrial firms in Tabasco, Mexico. There was also an inquiry into how much internal RSC and external RSC explain innovation.
Methods: The design was nonexperimental, cross-sectional, descriptive, correlational, and explanatory. Linear regression analysis was used.
Results: Significant positive relationships was identified between internal RSC and external RSC and innovation. The internal RSC and external RSC contributed significantly to the explaining of innovation. Areas of opportunity were identified for these firms in process design and formal research activities for new raw materials, production procedures and patent generation. Conclusion: To promote innovation, managers of small industrial companies must continue to establish strategies and practices to strengthen RSC.

2024-02-08 Reviews
Overview on Data Ingestion and Schema Matching

By Oumaima El Haddadi, Max Chevalier, Bernard Dousset, Ahmad El Allaoui, Anass El Haddadi, Olivier Teste

This overview traced the evolution of data management, transitioning from traditional ETL processes to addressing contemporary challenges in Big Data, with a particular emphasis on data ingestion and schema matching. It explored the classification of data ingestion into batch, real-time, and hybrid processing, underscoring the challenges associated with data quality and heterogeneity. Central to the discussion was the role of schema mapping in data alignment, proving indispensable for linking diverse data sources. Recent advancements, notably the adoption of machine learning techniques, were significantly reshaping the landscape. The paper also addressed current challenges, including the integration of new technologies and the necessity for effective schema matching solutions, highlighting the continuously evolving nature of schema matching in the context of Big Data.

2024-06-24 Systematic reviews or meta-analyses
Artificial intelligence in potential customer segmentation: machine learning approach

By Eduardo Rafael Jauregui Romero, Javier Alca Gomez, Manuel Eduardo Vilca Tantapoma, Orlando Tito Llanos Gonzales

Integrating artificial intelligence (AI) into sales processes at a business level, specifically, in the segmentation of potential customers, is currently a very important issue for the promotion of your products and services. The present study focused on the analysis of the effectiveness of the machine learning approach used in mass consumption companies for the segmentation of potential customers. To achieve this objective, a systematic review of the literature will be carried out with a qualitative approach and supported by the PRISMA methodology. The results achieved in the review carried out showed that machine learning algorithms present better results compared to other approaches; Furthermore, regarding customer segmentation, this can be done through grouping, which is one of the most recognized machine learning techniques. It is concluded that it is necessary to expand the methods provided by this approach, using them to extract knowledge from unstructured, monitoring, and network data to achieve descriptive, causal, and prescriptive analyses; In addition, to outline the journey that customers take when purchasing and deploy decision support capabilities. All these benefits, at a business level, are provided by machine learning, reason enough for the proposed marketing strategies to be based on the information it offers.

2024-05-28 Short communications
Blockchain Technology in Digital Identity Management and Verification

By Edith Mariela Quispe Sanabria, Julio Cesar Pizarro Avellaneda, Edward Eddie Bustinza Zuasnabar, Ana Mónica Huaraca García, Lizet Doriela Mantari Mincam, Hilario Romero Giron, Yesser Soriano Quispe

This study analyzes the potential of Blockchain technology to improve security and privacy in the management and verification of digital identities, aspects that currently face challenges. Through a literature review, it was found that Blockchain offers a decentralized approach that provides greater control to users over their data through cryptographic mechanisms. The cases examined demonstrate benefits such as efficiency and automation in identity processes. However, further research is required to address pending challenges and achieve widespread application considering the particularities of each context. The objective is to analyze how this technology can positively transform the way digital identity is managed in an inclusive and privacy-respecting manner.

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