Autor a quien debe ser dirigida la correspondencia. ResumenEste artículo presenta un proceso de vigilancia tecnológica que considera las actividades asociadas al proceso de realizar revisiones sistemáticas de literatura. La vigilancia tecnológica forma parte del sistema de gestión de innovación e investigación en contextos industriales. La propuesta tiene como objetivo el brindar acceso, gestión y contextualización de conocimientos científicos y técnicos que están a la vanguardia en tecnologías de soporte a la estrategia del negocio. Las revisiones sistemáticas de la literatura consideran un proceso detallado para estudiar los avances en un campo particular del conocimiento. Este tipo de revisiones constituye un facilitador de procesos efectivos de vigilancia tecnológica. El carácter innovador del proceso que se presenta es bidimensional. Por una parte se integran buenas prácticas para realizar vigilancia tecnológica y revisiones sistemáticas, y por otra se propone una herramienta de soporte (LOXO) para la ejecución de procesos de vigilancia tecnológica. Palabras clave: vigilancia tecnológica; revisiones sistemáticas de literatura; buenas prácticas; herramienta de soporte; contextos industrialesAbstract This paper presents a technology surveillance process that takes into account activities associated with the process of conducting systematic literature reviews. Technology surveillance is a part of the research and innovation management system in industrial contexts. The aim of the proposal is to provide access, management and contextualization of scientific and technical knowledge to be at the forefront of technologies for supporting business strategy. Systematic literature reviews consider a detailed process to study the state of the art in a particular field of knowledge. This type of reviews constitutes a facilitator of effective technology surveillance processes. The innovative nature of the process is two-fold. On the one hand, integrates best practices to carry out technology forecasting and systematic literature reviews and on the other, a support tool (LOXO) is proposed for the execution of technology surveillance processes.

Self-adaptive enterprise applications have the ability to continuously reconfigure themselves according to changes in their execution contexts or user requirements. The infrastructure managing such systems is based on IBM's MAPE-K reference model: a Monitor and an Analyzer to sense and interpret context data, a Planner and an Executor to create and apply structural adaptation plans, and a Knowledge manager to share relevant information. In this paper we present a formal model, built on the principles of constraint satisfaction, to address dynamic adaptation planning for self-adaptive enterprise applications. We formalize, modify and extend the approach presented in [H. Arboleda, J. F. Díaz, V. Vargas, and J.-C. Royer, "Automated reasoning for derivation of modeldriven spls," in SPLC'10 MAPLE'10, 2010, pp. 181-188] for working with self-adaptation infrastructures in order to provide automated reasoning on the dynamic creation of structural adaptation plans. We use a running example to demonstrate the applicability of such model, even in situations where complex interactions arise between context elements and the target self-adaptive enterprise application. © 2016 The Authors

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For more than a decade now, with the emergence of Internet 2.0, users have been able to generate their own content and share it publicly with relative ease. This boom has witnessed a huge surge in the popularity of Online Social Networks (OSN), in particular the Twitter microblogging platform which allows its users to share text messages of up to 140 characters with their family, friends and followers [12]. More than 500 million messages, commonly known as tweets, are posted every day [20, 34]. Given that the main reason behind these publications is to express the opinion of the users, they have proven to be great interest to be analysed [14, 38, 48]. This huge amount of content circulating in OSN has attracted the attention of marketing agencies that seek capitalize on the behaviour of clients (current or future) to adjust their online campaigns and even use the social content (number of likes, retweets, etc.) to predict consumer behaviour in the real world [45, 53]. Recently this type of analysis

Abstract The avalanche of personal and social data circulating in Online Social Networks over the past 10 years has attracted a great deal of interest from Scholars and Practitioners who seek to analyse not only their value, but also their limits. Predicting election results using Twitter data is an example of how data can directly influence the politic domain and it also serves an appealing research topic. This article aims to predict the results of the 2019 Spanish Presidential election and the voting share of each candidate, using Tweeter. The method combines sentiment analysis and volume information and compares the performance of five Machine Learning algorithms. Several data scrutiny uncertainties arose that hindered the prediction of the outcome. Consequently, the method develops a political lexicon-based framework to measure the sentiments of online users. Indeed, an accurate understanding of the contextual content of the tweets posted was vital in this work. Our results correctly ranked the candidates and determined the winner by means of a better prediction of votes than official research institutes.

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Colombia over the last decade has experienced a historic economic boom and Information Technology (IT) has been emerging as a tool to enable the competitiveness of companies. The last government (2014)(2015)(2016)(2017)(2018) took different actions to explain how the use of data science and open data improve the business activity. The question to identify if there is a relationship between IT capacities, the organizational structure and the performance of the companies remains unresolved and is certainly an urgent issue for new government of Iv an Duque. Purpose: Our study analyses the relationship between data structure and business performance measured through the efficiency of customer experience and provider operations processes. Methodology: our methodology is novel compared to previous researches which develop linear regression. It is based on the use of a fuzzy-set qualitative comparative analysis (fsQCA). Originality/value: our method allows to reveal multiple and complementary paths to achieve possible correlations between data and business performance. Findings: Our results show that data consistency, data usage and data protection are the three more frequent conditions to a better customer experience and provider operations efficiency. Surprisingly, data-driven profile is a necessary but not sufficient condition. Practical implications: our conclusions allow practitioners to uncover the strength of the data to orientate their digital strategy. Our recommendations could be used for the new governmental program of digital revival for Small and Medium Enterprises.