scholarly journals Ética y transparencia para la detección de sesgos algorítmicos de género

2019 ◽  
Vol 25 (3) ◽  
Author(s):  
Lucía Benítez Eyzaguirre
Keyword(s):  
Big Data ◽  
Search Engine ◽  
Toma De Decisiones

La creciente importancia de los algoritmos pone en evidencia la discriminación que se registra, especialmente en género y minorías, así como la necesidad de transparencia en la aplicación de estas fórmulas frente a la opacidad de las corporaciones. A pesar de estos sesgos, en los algoritmos se apoya la toma de decisiones de casi todos los campos del conocimiento y de las actividades sociales, políticas y económicas a causa de una confianza casi ciega en los procesamientos informáticos, de un imaginario tecnológico sobre su capacidad para eliminar el error y el sesgo. El Efecto Manipulador del Motor de Búsqueda o Search Engine Manipulation Effect (SEME) (Epstein y Robertson, 2015) muestra efectos muy claros sobre el comportamiento del voto. También Caliskan y Bryson (2017) han detectado la reproducción de sesgos de género y étnicos, a partir de datos ya sesgados que conducen a desviaciones estadísticas muy importantes en el Big Data.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

scholarly journals ¿Y de repente big data? No, nuevas competencias, evolución de ''viejas'' prácticas

COMeIN ◽  
2016 ◽  
Author(s):  
Montserrat Garcia Alsina
Keyword(s):  
Big Data ◽  
Toma De Decisiones

Datos para construir información de la que extraer conocimiento es una cadena de valor que ha existido siempre. Subyace a los avances de la humanidad. Conlleva unos procesos y un tratamiento para que los datos sean fiables, actuales y pertinentes. La gestión de estos datos fundamenta la toma de decisiones. A más volúmenes de datos más precisión en la información extraída. De ahí la relevancia de las tecnologías que gestionan los datos: ERP, CRM… y big data. ¿Qué competencias y perfiles profesionales hay detrás?

scholarly journals Microbiome Search Engine 2: a Platform for Taxonomic and Functional Search of Global Microbiomes on the Whole-Microbiome Level

mSystems ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Gongchao Jing ◽  
Lu Liu ◽  
Zengbin Wang ◽  
Yufeng Zhang ◽  
Li Qian ◽  
...  
Keyword(s):  
Big Data ◽  
User Interface ◽  
Search Engine ◽  
Functional Similarity ◽  
Metagenomic Data ◽  
Data Sets ◽  
Data Space ◽  
Link Type ◽  
Database Platform ◽  
Microbiome Data

ABSTRACT Metagenomic data sets from diverse environments have been growing rapidly. To ensure accessibility and reusability, tools that quickly and informatively correlate new microbiomes with existing ones are in demand. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes in the global metagenome data space based on the taxonomic or functional similarity of a whole microbiome to those in the database. MSE 2 consists of (i) a well-organized and regularly updated microbiome database that currently contains over 250,000 metagenomic shotgun and 16S rRNA gene amplicon samples associated with unified metadata collected from 798 studies, (ii) an enhanced search engine that enables real-time and fast (<0.5 s per query) searches against the entire database for best-matched microbiomes using overall taxonomic or functional profiles, and (iii) a Web-based graphical user interface for user-friendly searching, data browsing, and tutoring. MSE 2 is freely accessible via http://mse.ac.cn. For standalone searches of customized microbiome databases, the kernel of the MSE 2 search engine is provided at GitHub (https://github.com/qibebt-bioinfo/meta-storms). IMPORTANCE A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird’s-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile. Key improvements include database extension, data compatibility, a search engine kernel, and a user interface. The new ability to search the microbiome space via functional similarity greatly expands the scope of search-based mining of the microbiome big data.

scholarly journals ‘Issues’ y ‘big data’ en la gestión de relaciones públicas. El caso de la implementación del nuevo sistema de tratamiento de residuos “Recuperando Valor” en Córdoba, Argentina

Tripodos ◽  
2021 ◽  
pp. 73-87
Author(s):  
Antonio Castillo-Esparcia ◽  
Alejandro Álvarez-Nobell ◽  
María Belén Barroso
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Latin America ◽  
Public Relations ◽  
Analytical Models ◽  
Issues Management ◽  
Redes Sociales ◽  
Political Function ◽  
Toma De Decisiones ◽  
The Impact

El LCM 2016-2017 (Moreno et al., 2017) mostró el déficit en Latinoaméri­ca en el uso de big data para la toma de decisiones basada en issues; una de las grandes transformaciones actuales en relaciones públicas. El objetivo de esta investigación fue analizar el impacto de la implementación de estrategias de is­sues management y big data para el nuevo sistema de residuos de Córdoba (Argentina) —“Recuperando Valor”— durante diciembre 2018. Se analizaron más de 10.000 publicaciones en redes sociales mediante un sistema de aler­tas programadas (QSocial) por temas, actores, impactos y frecuencia a través de distintos modelos analíticos: Imagen de Gestión; Sentimientos; Preocupacio­nes Ciudadanas, Género, Humor Social y Valoraciones. Las organizaciones no solo comunican estratégicamente: son comunicación estratégica (Grandien y Johansson, 2016). Ello implica una función de dirección y asesoramiento (Zerfass y Franke, 2013) —o función política (Simões, 2001 inspirado en Matrat, 1971)—, atendiendo la opi­nión pública mediante la gestión de issues (Nothhaft, 2010). En la prácti­ca implica construir, administrar y mo­nitorear en tiempo real el desarrollo e impacto de un conjunto de temas que cobran relevancia en las distintas agen­das y por consecuencia en la producción de contenidos y la gestión de relaciones con los distintos públicos en función de sus intereses. Issues and Big Data in Public Relations Management. The Case of the Implementation of the New Garbage System Called “Recuperando Valor” in Córdoba, ArgentinaThe LCM 2016-2017 (Moreno et al., 2017) showed the deficit in the use of big data for making decisions based on issues in Latin America; this is one of the great transformations that we currently envision in public relations. The objec­tive of this research was to analyze the impact of the implementation of Issues Management and big data strategies for the new garbage system in Córdoba (Ar­gentina) —“Recuperando Valor”— du­ring December 2018. More than 10,000 publications on social networks were analyzed through a system of program­med alerts (QSocial) taking into accou­nt topics, actors, impact and frequency through different analytical models: measurement of Management Ima­ge; Feelings; Citizen Concerns, Gender, Social Humor and Evaluations. Orga­nizations not only communicate strate­gically: they are indeed strategic com­munication (Grandien and Johansson, 2016). This requires a management and advisory function (Zerfass and Franke, 2013) —or political function (Simões, 2001 as inspired by Matrat, 1971)—, considering public opinion through is­sues management (Nothhaft, 2010). In practice it involves building, managing and monitoring in real time the develo­pment and impact of a set of issues that become relevant in the different agendas and, consequently, in the production of contents and the management of rela­tions with the different stakeholders ba­sed on their interests.Palabras clave: issues, big data, rela­ciones públicas, ambiente, residuos en Argentina.Key words: issues; big data, public rela­tions, environment, garbage in Argen­tina.

Forecasting daily attraction demand using big data from search engines and social media

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fengjun Tian ◽  
Yang Yang ◽  
Zhenxing Mao ◽  
Wenyue Tang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Search Engine ◽  
Elastic Net ◽  
Percentage Error ◽  
Mean Square ◽  
Social Media Data ◽  
Content Type ◽  
Forecasting Error ◽  
Media Data

Purpose This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media. Design/methodology/approach Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy. Findings Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error. Practical implications Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions. Originality/value This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

Putting Students “In Whitman’s Hand”

2018 ◽  
pp. 153-166
Author(s):  
Catherine Waitinas
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
General Education ◽  
Search Engine ◽  
New Technologies ◽  
Walt Whitman ◽  
Big Data Analysis ◽  
Manuscript Project ◽  
Graduate Seminars

Catherine Waitinas leads readers step-by-step through a digital manuscript project on Walt Whitman’s poetry that she created for a variety of courses from general education to graduate seminars. Using handwritten manuscripts digitized in the Walt Whitman Archive, Waitinas’s students meld old and new technologies, placing penmanship in conversation with big data analysis and The Walt Whitman’s Archive’s tools like the archive’s search engine. Waitinas describes how archival assignments like these are infinitely scalable; they can be used in relation to many other archives, and Waitinas gives suggestions for one-day to full-unit versions of the assignment.

scholarly journals Similarity Measurement Technique for Measuring the Performance of Page Rank Algorithm Based on Hadoop

2020 ◽  
Vol 8 (5) ◽  
pp. 4712-4717
Keyword(s):  
Big Data ◽  
Search Engine ◽  
Measurement Technique ◽  
Similarity Measurement ◽  
Web Pages ◽  
Ranking Algorithm ◽  
Web Data ◽  
Mapreduce Framework ◽  
Page Rank ◽  
Hadoop Mapreduce

In this century big data manipulation is a challenging task in the field of web mining because content of web data is massively increasing day by day. Using search engine retrieving efficient, relevant and meaningful information from massive amount of Web Data is quite impossible. Different search engine uses different ranking algorithm to retrieve relevant information easily. A new page ranking algorithm is presented based on synonymous word count using Hadoop MapReduce framework named as Similarity Measurement Technique (SMT). Hadoop MapReduce framework is used to partition Big Data and provides a scalable, economical and easier way to process these data. It stores intermediate result for running iterative jobs in the local disk. In this algorithm, SMT takes a query from user and parse it using Hadoop and calculate rank of web pages. For experimental purpose wiki data file have been used and applied page rank algorithm (PR), improvised page rank algorithm (IPR) and proposed SMT method to calculate page rank of all web pages and compare among these methods. Proposed method provides better scoring accuracy than other approaches and reduces theme drift problem.

scholarly journals La interactividad y la conectividad al servicio de la industria de la confección

Lámpsakos ◽  
2019 ◽  
pp. 106-122
Author(s):  
Leidy Yaneth Vega Rodríguez ◽  
Fabio Andrés Gaviria ◽  
Luz Eugenia Botero
Keyword(s):  
Big Data ◽  
Nuevas Tecnologías ◽  
Toma De Decisiones

Los rápidos avances en la industrialización e informatización han estimulado el desarrollo de procesos automáticos, precisos y sostenibles. La Industria 4.0 representa la evolución tecnológica integrada a los sistemas ciberfísicos, que combina sensores inteligentes, inteligencia artificial y análisis de datos para optimizar la fabricación en tiempo real. En este documento se explora el panorama de las nuevas tecnologías en el ámbito de la Industria 4.0, con la intención de brindar una perspectiva diferente que permita mejorar las técnicas tradicionales de desarrollo y fabricación de prendas de vestir y que facilite la respuesta permanente y rápida a los retos que se presentan en el mundo empresarial mediante la toma de decisiones inteligentes y responsables. El uso de tecnologías como Big Data o Cloud brinda la oportunidad de optimizar las operaciones y proporcionar valor agregado al integrar productos y servicios si se considera que se tiene una cadena de valor más compleja, canales digitales cada vez más importantes y un cliente más exigente. Muchas empresas del sector de la confección han adoptado estas tecnologías disruptivas, comprobando que tienen un profundo impacto en términos de productividad, calidad y servicio. Sin embargo, la falta de herramientas poderosas representa un obstáculo importante para explotar todo su potencial.

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