Big Data sources and methods for social and economic analyses

[EN] The Data Big Bang that the development of the ICTs has raised is providing us with a stream of fresh and digitized data related to how people, companies and other organizations interact. To turn these data into knowledge about the underlying behavior of the social and economic agents, organizations and researchers must deal with such amount of unstructured and heterogeneous data. Succeeding in this task requires to carefully plan and organize the whole process of data analysis taking into account the particularities of the social and economic analyses, which include the wide variety of heterogeneous sources of information and a strict governance policy. Grounded on the data lifecycle approach, this paper develops a Big Data architecture that properly integrates most of the non-traditional information sources and data analysis methods in order to provide a specifically designed system for forecasting social and economic behaviors, trends and changes.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2013-43913-R; and by the Spanish Ministry of Education under Grant FPU14/02386.Blazquez, D.; Domenech, J. (2018). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change. 130:99-113. https://doi.org/10.1016/j.techfore.2017.07.027S9911313

Predicting SME's default: Are their websites informative?

[EN] We propose the use of online indicators, scraped from the firms¿ websites, to predict default risk for a sample of Spanish firms via nonlinear discriminant analysis and the logistic regression model.This work was partially supported by the Ca' Foscari University of Venice, Italy and by Agencia Estatal de Investigacion, Spain under grant PID2019107765RBI00. We also acknowledge helpful comments by an anonymous referee.Crosato, L.; Domenech, J.; Liberati, C. (2021). Predicting SME's default: Are their websites informative?. Economics Letters. 204:1-3. https://doi.org/10.1016/j.econlet.2021.109888S1320

Predicting SME's default: some old facts and a new idea

[EN] The Small Business Act of the European Commission in 2008 acknowledge s the key role of Small and Medium Enterprises (SMEs) in the EU economy. Th is is particularly relevant for Italy, which has the largest share of SMEs in Europe, as well as for other countries such as Portugal, Spain and Greece. On the other hand, SMEs experience more difficulties in their early stages mainly due to high market competition and credit constraints, as highlighted by Fritsch and Weyh (2006). For these reasons, the study of SMEs default risk is always relevant. There are several papers studying firm default factors in a single country (see Ciampi, 2015, Fantazzini and Figini, 2009, Flix and dos Santos, 2018). The literature concentrates mainly on financial indicators built on businesses’ balance sheets, which are available about two years late wi th respect to their reference period. This diminishes the significance of the results, both for credit risk and policy aims, and particularly in a forecasting perspective. The purpose of this paper is to provide a preliminary study on a sample of spanish firms selected from the SABI, Sistema de Análisis de Balances Ibéricos, which is listed among Bureau van Dijk databases. The analysis will be carried out according to both parametric and non-parametric discrimination techniques, with the standard construction of a training set on which to build a model and a validation set to test the validity and robustness of the results, and, in the end, the reliability of the model in predicting default. Finally we present a new proposal: a scheme to understand to what ext ent firms’ default can be predicted by substituting the traditional data sour ces (offline information) with data collected from their corporate websites (onli ne information) in order to exploit more up-to-date information.Crosato, L.; Domenech, J.; Liberati, C. (2020). Predicting SME's default: some old facts and a new idea. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/149606OC

Non-conventional data and default prediction: the challenge of companies’ websites

Small and Medium Enterprises (SMEs) contribution to the European Union economy has always been relevant, for both value added and the creation of jobs. That is why the prediction of their survival is considered one of the economic pillars UE keeps under observation. Default prediction models, accounting for SMEs idiosyncratic traits, are based on several types of data, mainly accounting indicators. Balance sheet data, indeed, are considered the standard predictors for classification models in this field, although they do not allow to completely overcome the information opacity that is one of the main barriers preventing these firms from accessing credit. In our work, we explore the possibility of complementing accounting information with data scraped from the firms’ websites. We modeled the data using a nonlinear discriminant analysis and we benchmarked the results with the Logistic Regression. The evidence of our study is promising although the combination of online and offline data shows better results in case of survival firms than for defaulted companies

The impact of companies’ websites on competitiveness and productivity performance

Resumen de la ponencia[EN] We investigate the role of the Internet in inducing market competition and firm productivity performance using a sample of UK and Spanish firms over the 1995-2010 period. For each firm we collect unique information on its online status (website) and the number of years of Internet activity. Our results show that the Internet is associated with reduced market concentration in both countries. Using a semiparametric estimation algorithm we find that firms’ Internet presence is positively associated with the level of TFP but not with its rate of growth. This suggests that selection is likely to drive website adoption and that the Internet by itself cannot replace traditional sources of competitive and productivity advantage.Domenech, J.; Rizov, M.; Vecchi, M. (2016). The impact of companies’ websites on competitiveness and productivity performance. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 126-126. https://doi.org/10.4995/CARMA2016.2016.3120OCS12612

Divulgación de las ciencias: Richard Feynman, Stephen Hawking y Jorge Wagensberg

[ES] Este año 2018 se celebra el centenario del nacimiento de Richard Feynman y, por desgracia, la pérdida de dos científicos y divulgadores excepcionales, Stephen Hawking y Jorge Wagensberg. La divulgación de la ciencia es hacer accesible a toda la sociedad los descubrimientos científicos, anteriores y actuales. Por ello, se han propuesto una serie de trabajos y charlas, sobre estos tres científicos, al alumnado de cièncias de diversos centros de Enseñanza Secundaria de Barcelona y, también, a nuestro alumnado universitario. El resultado, muy positivo, muestra que el alumnado de los distintos centros educativos, ha obtenido una visión más real de dichos científicos y de la difusión de la ciencia. Esta innovación educativa es adaptable a todos los niveles desde primaria hasta el alumnado universitario de primer año.Fernández Novell, J.; Zaragoza Domenech, C. (2019). Divulgación de las ciencias: Richard Feynman, Stephen Hawking y Jorge Wagensberg. En INNODOCT/18. International Conference on Innovation, Documentation and Education. Editorial Universitat Politècnica de València. 861-871. https://doi.org/10.4995/INN2018.2018.8891OCS86187

Enhancing the Programmability of Cloud Object Storage

En un món que depèn cada vegada més de la tecnologia, les dades digitals es generen a una escala sense precedents. Això fa que empreses que requereixen d'un gran espai d'emmagatzematge, com Netflix o Dropbox, utilitzin solucions d'emmagatzematge al núvol. Mes concretament, l'emmagatzematge d'objectes, donada la seva simplicitat, escalabilitat i alta disponibilitat. No obstant això, aquests magatzems s'enfronten a tres desafiaments principals: 1) Gestió flexible de càrregues de treball de múltiples usuaris. Normalment, els magatzems d'objectes són sistemes multi-usuari, la qual cosa significa que tots ells comparteixen els mateixos recursos, el que podria ocasionar problemes d'interferència. A més, és complex administrar polítiques d'emmagatzematge heterogènies a gran escala en ells. 2) Autogestió de dades. Els magatzems d'objectes no ofereixen molta flexibilitat pel que fa a l'autogestió de dades per part dels usuaris. Típicament, són sistemes rígids, la qual cosa impedeix gestionar els requisits específics dels objectes. 3) Còmput elàstic prop de les dades. Situar els càlculs prop de les dades pot ser útil per reduir la transferència de dades. Però, el desafiament aquí és com aconseguir la seva elasticitat sense provocar contenció de recursos i interferències en la capa d'emmagatzematge. En aquesta tesi presentem tres contribucions innovadores que resolen aquests desafiaments. En primer lloc, presentem la primera arquitectura d'emmagatzematge definida per programari (SDS) per a magatzems d'objectes que separa les capes de control i de dades. Això permet gestionar les càrregues de treball de múltiples usuaris d'una manera flexible i dinàmica. En segon lloc, hem dissenyat una nova abstracció de polítiques anomenada "microcontrolador" que transforma els objectes comuns en objectes intel·ligents, permetent als usuaris programar el seu comportament. Finalment, presentem la primera plataforma informàtica "serverless" guiada per dades i elàstica, que mitiga els problemes de col·locar el càlcul prop de les dades.En un mundo que depende cada vez más de la tecnología, los datos digitales se generan a una escala sin precedentes. Esto hace que empresas que requieren de un gran espacio de almacenamiento, como Netflix o Dropbox, usen soluciones de almacenamiento en la nube. Mas concretamente, el almacenamiento de objectos, dada su escalabilidad y alta disponibilidad. Sin embargo, estos almacenes se enfrentan a tres desafíos principales: 1) Gestión flexible de cargas de trabajo de múltiples usuarios. Normalmente, los almacenes de objetos son sistemas multi-usuario, lo que significa que todos ellos comparten los mismos recursos, lo que podría ocasionar problemas de interferencia. Además, es complejo administrar políticas de almacenamiento heterogéneas a gran escala en ellos. 2) Autogestión de datos. Los almacenes de objetos no ofrecen mucha flexibilidad con respecto a la autogestión de datos por parte de los usuarios. Típicamente, son sistemas rígidos, lo que impide gestionar los requisitos específicos de los objetos. 3) Cómputo elástico cerca de los datos. Situar los cálculos cerca de los datos puede ser útil para reducir la transferencia de datos. Pero, el desafío aquí es cómo lograr su elasticidad sin provocar contención de recursos e interferencias en la capa de almacenamiento. En esta tesis presentamos tres contribuciones que resuelven estos desafíos. En primer lugar, presentamos la primera arquitectura de almacenamiento definida por software (SDS) para almacenes de objetos que separa las capas de control y de datos. Esto permite gestionar las cargas de trabajo de múltiples usuarios de una manera flexible y dinámica. En segundo lugar, hemos diseñado una nueva abstracción de políticas llamada "microcontrolador" que transforma los objetos comunes en objetos inteligentes, permitiendo a los usuarios programar su comportamiento. Finalmente, presentamos la primera plataforma informática "serverless" guiada por datos y elástica, que mitiga los problemas de colocar el cálculo cerca de los datos.In a world that is increasingly dependent on technology, digital data is generated in an unprecedented way. This makes companies that require large storage space, such as Netflix or Dropbox, use cloud object storage solutions. This is mainly thanks to their built-in characteristics, such as simplicity, scalability and high-availability. However, cloud object stores face three main challenges: 1) Flexible management of multi-tenant workloads. Commonly, cloud object stores are multi-tenant systems, meaning that all tenants share the same system resources, which could lead to interference problems. Furthermore, it is now complex to manage heterogeneous storage policies in a massive scale. 2) Data self-management. Cloud object stores themselves do not offer much flexibility regarding data self-management by tenants. Typically, they are rigid, which prevent tenants to handle the specific requirements of their objects. 3) Elastic computation close to the data. Placing computations close to the data can be useful to reduce data transfers. But, the challenge here is how to achieve elasticity in those computations without provoking resource contention and interferences in the storage layer. In this thesis, we present three novel research contributions that solve the aforementioned challenges. Firstly, we introduce the first Software-defined Storage (SDS) architecture for cloud object stores that separates the control plane from the data plane, allowing to manage multi-tenant workloads in a flexible and dynamic way. For example, by applying different service levels of bandwidth to different tenants. Secondly, we designed a novel policy abstraction called microcontroller that transforms common objects into smart objects, enabling tenants to programmatically manage their behavior. For example, a content-level access control microcontroller attached to an specific object to filter its content depending on who is accessing it. Finally, we present the first elastic data-driven serverless computing platform that mitigates the resource contention problem of placing computation close to the data

Productivity, Digital Footprint and Sustainability in the Textile and Clothing Industry

[EN] In recent years, there has been a shift from the linear economic model on which the textile and clothing industry is based to a more sustainable model. However, to date, limited research on the relationship between sustainability commitment and firm productivity has focused on the textile and clothing industry. This study addresses this gap and aims to explore whether the digital footprint of small and medium-sized textile companies in terms of their sustainable performance is related to their productivity. To this end, the paper proposes an innovative model to monitor the companies’ commitment to sustainable issues by analyzing online data retrieved from their corporate websites. This information is merged with balance sheet data to examine the impact of sustainability practices, capital and human capital on productivity. The estimated firm’s total factor productivity is explained as a function of the sustainability digital footprint measures and additional control variables for a sample of 315 textile firms located in the region of Comunidad Valenciana, Spain.This work was partially funded by MCIN/AEI/10.13039/501100011033 under grant PID2019-107765RB-I00.Domenech, J.; Garcia-Bernabeu, A.; Diaz-Garcia, P. (2023). Productivity, Digital Footprint and Sustainability in the Textile and Clothing Industry. Editorial Universitat Politècnica de València. 319-326. https://doi.org/10.4995/CARMA2023.2023.1644631932

Changes in corporate websites and business activity: automatic classification of corporate webpages

[EN] Every time a firm or institution performs an activity on the Web, this is registered, leaving a "digital footprint”. Part this digital footprint is reflected on their websites as these officially represent them on the Web. We plan to automatically monitor the changes that periodically occur in a website to relate them with the business activity. The aim of this paper is to propose a theoretical classification of corporate webpages to associate changes that occur on them with the regular activity of the firms, and to evaluate the possibility of an automatic categorization using classification models. To generate the classification of corporate webpages, a significant number of today corporate webpages were analyzed and observed, distinguishing four theoretical types of corporate webpages. To evaluate the automatic categorization of corporate webpages, a dataset of 1005 today corporate pages was generated by manually labeling them and evaluating their automatic categorization using classification models.This work was partially supported by grants PID2019-107765RB-I00 and funded by MCIN/AEI/10.13039/501100011033.Valenzuela Rubilar, JM.; Domenech, J.; Pont, A. (2022). Changes in corporate websites and business activity: automatic classification of corporate webpages. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 213-220. https://doi.org/10.4995/CARMA2022.2022.1509021322

Do corporate websites' changes reflect firms' survival?

This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here http://doi.org/10.1108/OIR-11-2016-0321. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited[EN] Purpose The purpose of this paper is to analyze to what extent changes in corporate websites reflect firms' survival. Since keeping a website online involves some costs, it is likely that firms would invest resources on it only when they are active and healthy. Therefore, when a firm dies, this event is likely to be manifested on its website as lacking updates or being down. Design/methodology/approach Changes in the corporate websites of a panel of Spanish firms were tracked between 2008 and 2014 in order to evaluate the approach. The status of websites, classified according to the type of change undergone, was used to infer firms' activity status (active or inactive). Multi-period logistic regressions and a duration model were applied to study the relationship among the website status and the firm's status. Findings Results showed that changes in website contents clearly reflect the firm's status. Active firms were mainly associated with updated corporate websites, while inactive firms were more associated with down websites. In fact, results confirmed that the firms' death hazard increases when the website activity lowers. Originality/value Although online information is increasingly being used to monitor the economy, this is the first study to connect online data to firms' survival. The results revealed a new source of information about business demography and evidenced corporate websites as a fresh source of high granularity business data.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness with Grants TIN2013-43913-R and MTM2013-45381-P-AR, and by the Spanish Ministry of Education with Grant FPU14/02386. The authors thank the participants of the "1st International Conference on Advanced Research Methods and Analytics (CARMA2016)" for their invaluable comments.Blazquez, D.; Domenech, J.; Debón Aucejo, AM. (2018). Do corporate websites' changes reflect firms' survival?. Online Information Review. 42(6):956-970. https://doi.org/10.1108/OIR-11-2016-0321S95697042