18,642 research outputs found

    Social media analytics: a survey of techniques, tools and platforms

    Get PDF
    This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

    Design and Evaluation of Web-Based Economic Indicators: A Big Data Analysis Approach

    Full text link
    Tesis por compendio[ES] En la Era Digital, el creciente uso de Internet y de dispositivos digitales está transformando completamente la forma de interactuar en el contexto económico y social. Miles de personas, empresas y organismos públicos utilizan Internet en sus actividades diarias, generando de este modo una enorme cantidad de datos actualizados ("Big Data") accesibles principalmente a través de la World Wide Web (WWW), que se ha convertido en el mayor repositorio de información del mundo. Estas huellas digitales se pueden rastrear y, si se procesan y analizan de manera apropiada, podrían ayudar a monitorizar en tiempo real una infinidad de variables económicas. En este contexto, el objetivo principal de esta tesis doctoral es generar indicadores económicos, basados en datos web, que sean capaces de proveer regularmente de predicciones a corto plazo ("nowcasting") sobre varias actividades empresariales que son fundamentales para el crecimiento y desarrollo de las economías. Concretamente, tres indicadores económicos basados en la web han sido diseñados y evaluados: en primer lugar, un indicador de orientación exportadora, basado en un modelo que predice si una empresa es exportadora; en segundo lugar, un indicador de adopción de comercio electrónico, basado en un modelo que predice si una empresa ofrece la posibilidad de venta online; y en tercer lugar, un indicador de supervivencia empresarial, basado en dos modelos que indican la probabilidad de supervivencia de una empresa y su tasa de riesgo. Para crear estos indicadores, se han descargado una diversidad de datos de sitios web corporativos de forma manual y automática, que posteriormente se han procesado y analizado con técnicas de análisis Big Data. Los resultados muestran que los datos web seleccionados están altamente relacionados con las variables económicas objeto de estudio, y que los indicadores basados en la web que se han diseñado en esta tesis capturan en un alto grado los valores reales de dichas variables económicas, siendo por tanto válidos para su uso por parte del mundo académico, de las empresas y de los decisores políticos. Además, la naturaleza online y digital de los indicadores basados en la web hace posible proveer regularmente y de forma barata de predicciones a corto plazo. Así, estos indicadores son ventajosos con respecto a los indicadores tradicionales. Esta tesis doctoral ha contribuido a generar conocimiento sobre la viabilidad de producir indicadores económicos con datos online procedentes de sitios web corporativos. Los indicadores que se han diseñado pretenden contribuir a la modernización en la producción de estadísticas oficiales, así como ayudar a los decisores políticos y los gerentes de empresas a tomar decisiones informadas más rápidamente.[CA] A l'Era Digital, el creixent ús d'Internet i dels dispositius digitals està transformant completament la forma d'interactuar al context econòmic i social. Milers de persones, empreses i organismes públics utilitzen Internet a les seues activitats diàries, generant d'aquesta forma una enorme quantitat de dades actualitzades ("Big Data") accessibles principalment mitjançant la World Wide Web (WWW), que s'ha convertit en el major repositori d'informació del món. Aquestes empremtes digitals poden rastrejar-se i, si se processen i analitzen de forma apropiada, podrien ajudar a monitoritzar en temps real una infinitat de variables econòmiques. En aquest context, l'objectiu principal d'aquesta tesi doctoral és generar indicadors econòmics, basats en dades web, que siguen capaços de proveïr regularment de prediccions a curt termini ("nowcasting") sobre diverses activitats empresarials que són fonamentals per al creixement i desenvolupament de les economies. Concretament, tres indicadors econòmics basats en la web han sigut dissenyats i avaluats: en primer lloc, un indicador d'orientació exportadora, basat en un model que prediu si una empresa és exportadora; en segon lloc, un indicador d'adopció de comerç electrònic, basat en un model que prediu si una empresa ofereix la possibilitat de venda online; i en tercer lloc, un indicador de supervivència empresarial, basat en dos models que indiquen la probabilitat de supervivència d'una empresa i la seua tasa de risc. Per a crear aquestos indicadors, s'han descarregat una diversitat de dades de llocs web corporatius de forma manual i automàtica, que posteriorment s'han analitzat i processat amb tècniques d'anàlisi Big Data. Els resultats mostren que les dades web seleccionades estan altament relacionades amb les variables econòmiques objecte d'estudi, i que els indicadors basats en la web que s'han dissenyat en aquesta tesi capturen en un alt grau els valors reals d'aquestes variables econòmiques, sent per tant vàlids per al seu ús per part del món acadèmic, de les empreses i dels decisors polítics. A més, la naturalesa online i digital dels indicadors basats en la web fa possible proveïr regularment i de forma barata de prediccions a curt termini. D'aquesta forma, són avantatjosos en comparació als indicadors tradicionals. Aquesta tesi doctoral ha contribuït a generar coneixement sobre la viabilitat de produïr indicadors econòmics amb dades online procedents de llocs web corporatius. Els indicadors que s'han dissenyat pretenen contribuïr a la modernització en la producció d'estadístiques oficials, així com ajudar als decisors polítics i als gerents d'empreses a prendre decisions informades més ràpidament.[EN] In the Digital Era, the increasing use of the Internet and digital devices is completely transforming the way of interacting in the economic and social framework. Myriad individuals, companies and public organizations use the Internet for their daily activities, generating a stream of fresh data ("Big Data") principally accessible through the World Wide Web (WWW), which has become the largest repository of information in the world. These digital footprints can be tracked and, if properly processed and analyzed, could help to monitor in real time a wide range of economic variables. In this context, the main goal of this PhD thesis is to generate economic indicators, based on web data, which are able to provide regular, short-term predictions ("nowcasting") about some business activities that are basic for the growth and development of an economy. Concretely, three web-based economic indicators have been designed and evaluated: first, an indicator of firms' export orientation, which is based on a model that predicts if a firm is an exporter; second, an indicator of firms' engagement in e-commerce, which is based on a model that predicts if a firm offers e-commerce facilities in its website; and third, an indicator of firms' survival, which is based on two models that indicate the probability of survival of a firm and its hazard rate. To build these indicators, a variety of data from corporate websites have been retrieved manually and automatically, and subsequently have been processed and analyzed with Big Data analysis techniques. Results show that the selected web data are highly related to the economic variables under study, and the web-based indicators designed in this thesis are capturing to a great extent their real values, thus being valid for their use by the academia, firms and policy-makers. Additionally, the digital and online nature of web-based indicators makes it possible to provide timely, inexpensive predictions about the economy. This way, they are advantageous with respect to traditional indicators. This PhD thesis has contributed to generating knowledge about the viability of producing economic indicators with data coming from corporate websites. The indicators that have been designed are expected to contribute to the modernization of official statistics and to help in making earlier, more informed decisions to policy-makers and business managers.Blázquez Soriano, MD. (2019). Design and Evaluation of Web-Based Economic Indicators: A Big Data Analysis Approach [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/116836TESISCompendi

    Simple identification tools in FishBase

    Get PDF
    Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

    Research and Development Workstation Environment: the new class of Current Research Information Systems

    Get PDF
    Against the backdrop of the development of modern technologies in the field of scientific research the new class of Current Research Information Systems (CRIS) and related intelligent information technologies has arisen. It was called - Research and Development Workstation Environment (RDWE) - the comprehensive problem-oriented information systems for scientific research and development lifecycle support. The given paper describes design and development fundamentals of the RDWE class systems. The RDWE class system's generalized information model is represented in the article as a three-tuple composite web service that include: a set of atomic web services, each of them can be designed and developed as a microservice or a desktop application, that allows them to be used as an independent software separately; a set of functions, the functional filling-up of the Research and Development Workstation Environment; a subset of atomic web services that are required to implement function of composite web service. In accordance with the fundamental information model of the RDWE class the system for supporting research in the field of ontology engineering - the automated building of applied ontology in an arbitrary domain area, scientific and technical creativity - the automated preparation of application documents for patenting inventions in Ukraine was developed. It was called - Personal Research Information System. A distinctive feature of such systems is the possibility of their problematic orientation to various types of scientific activities by combining on a variety of functional services and adding new ones within the cloud integrated environment. The main results of our work are focused on enhancing the effectiveness of the scientist's research and development lifecycle in the arbitrary domain area.Comment: In English, 13 pages, 1 figure, 1 table, added references in Russian. Published. Prepared for special issue (UkrPROG 2018 conference) of the scientific journal "Problems of programming" (Founder: National Academy of Sciences of Ukraine, Institute of Software Systems of NAS Ukraine

    Revista Economica

    Get PDF

    A study of BIM collaboration requirements and available features in existing model collaboration systems

    Get PDF
    Established collaboration practices in the construction industry are document centric and are challenged by the introduction of Building Information Modelling (BIM). Document management collaboration systems (e.g. Extranets) have significantly improved the document collaboration in recent years; however their capabilities for model collaboration are limited and do not support the complex requirements of BIM collaboration. The construction industry is responding to this situation by adopting emerging model collaboration systems (MCS), such as model servers, with the ability to exploit and reuse information directly from the models to extend the current intra-disciplinary collaboration towards integrated multi-disciplinary collaboration on models. The functions of existing MCSs have evolved from the manufacturing industry and there is no concrete study on how these functions correspond to the requirements of the construction industry, especially with BIM requirements. This research has conducted focus group sessions with major industry disciplines to explore the user requirements for BIM collaboration. The research results have been used to categorise and express the features of existing MCS which are then analysed in selected MCS from a user’s perspective. The potential of MCS and the match or gap in user requirements and available model collaboration features is discussed. This study concludes that model collaborative solutions for construction industry users are available in different capacities; however a comprehensive custom built solution is yet to be realized. The research results are useful for construction industry professionals, software developers and researchers involved in exploring collaborative solutions for the construction industry

    Design Considerations for Multi-Stakeholder Display Analytics

    Get PDF
    Measuring viewer interactions through detailed analytics will be crucial to improving the overall performance of future open display networks. However, in contrast to traditional sign and web analytics systems, such display networks are likely to feature multiple stakeholders each with the ability to collect a subset of the required analytics information. Combining analytics data from multiple stakeholders could lead to new insights, but stakeholders may have limited willingness to share information due to privacy concerns or commercial sensitivities. In this paper, we provide a comprehensive overview of analytics data that might be captured by different stakeholders in a display network, make the case for the synthesis of analytics data in such display networks, present design considerations for future architectures designed to enable the sharing of display analytics information, and offer an example of how such systems might be implemented

    Monitoring E-commerce Adoption from Online Data

    Full text link
    [EN] The purpose of this paper is to propose an intelligent system to automatically monitor the firms¿ engagement in e-commerce by analyzing online data retrieved from their corporate websites. The design of the proposed system combines web content mining and scraping techniques with learning methods for Big Data. Corporate websites are scraped to extract more than 150 features related to the e-commerce adoption, such as the presence of some keywords or a private area. Then, these features are taken as input by a classification model that includes dimensionality reduction techniques. The system is evaluated with a data set consisting of 426 corporate websites of firms based in France and Spain. The system successfully classified most of the firms into those that adopted e-commerce and those that did not, reaching a classification accuracy of 90.6%. This demonstrates the feasibility of monitoring e-commerce adoption from online data. Moreover, the proposed system represents a cost-effective alternative to surveys as method for collecting e-commerce information from companies, and is capable of providing more frequent information than surveys and avoids the non-response errors. This is the first research work to design and evaluate an intelligent system to automatically detect e-commerce engagement from online data. This proposal opens up the opportunity to monitor e-commerce adoption at a large scale, with highly granular information that otherwise would require every firm to complete a survey. In addition, it makes it possible to track the evolution of this activity in real time, so that governments and institutions could make informed decisions earlier.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness with Grant TIN2013-43913-R, and by the Spanish Ministry of Education with Grant FPU14/02386.Blazquez, D.; Domenech, J.; Gil, JA.; Pont Sanjuan, A. (2018). Monitoring E-commerce Adoption from Online Data. Knowledge and Information Systems. 1-19. https://doi.org/10.1007/s10115-018-1233-7S119Arias M, Arratia A, Xuriguera R (2013) Forecasting with Twitter data. ACM Trans Intell Syst Technol 5:1–24. https://doi.org/10.1145/2542182.2542190Arora SK, Youtie J, Shapira P, Gao L, Ma T (2013) Entry strategies in an emerging technology: a pilot web-based study of graphene firms. Scientometrics 95:1189–1207. https://doi.org/10.1007/s11192-013-0950-7Barcaroli G, Nurra A, Scarnò M, Summa D (2014) Use of web scraping and text mining techniques in the istat survey on information and communication technology in enterprises. In: Proceedings of quality conference, pp 33–38Barcaroli G, Nurra A, Salamone S, Scannapieco M, Scarnò M, Summa D (2015) Internet as data source in the istat survey on ict in enterprises. Austrian J Stat 44:31. https://doi.org/10.17713/ajs.v44i2.53Blazquez D, Domenech J (2014) Inferring export orientation from corporate websites. Appl Econ Lett 21:509–512. https://doi.org/10.1080/13504851.2013.872752Blazquez D, Domenech J (2017) Big data sources and methods for social and economic analyses. Technol Forecast Soc Change. https://doi.org/10.1016/j.techfore.2017.07.027Blazquez D, Domenech J (2017) Web data mining for monitoring business export orientation. Technol Econ Dev Econ. https://doi.org/10.3846/20294913.2016.1213193Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2:1–8. https://doi.org/10.1016/j.jocs.2010.12.007Bughin J (2015) Google searches and twitter mood: nowcasting telecom sales performance. NETNOMICS: Econ Res Electron Netw 16:87–105. https://doi.org/10.1007/s11066-015-9096-5Bulligan G, Marcellino M, Venditti F (2015) Forecasting economic activity with targeted predictors. Int J Forecast 31:188–206. https://doi.org/10.1016/j.ijforecast.2014.03.004Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357Choi H, Varian H (2009) Predicting the present with Google Trends. http://static.googleusercontent.com/external_content/untrusted_dlcp/www.google.com/en//googleblogs/pdfs/google_predicting_the_present.pdf . Accessed 9 Dec 2016Choi H, Varian H (2012) Predicting the present with Google Trends. Econ Record 88:2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.xCooley R, Mobasher B, Srivastava J (1997) Web mining: information and pattern discovery on the world wide web. In: Proceedings of the ninth ieee international conference on tools with artificial intelligence. IEEE Computer Society, Newport Beach, CA, USA, pp 558–567. https://doi.org/10.1109/TAI.1997.632303Domenech J, de la Ossa B, Pont A, Gil JA, Martinez M, Rubio A (2012) An intelligent system for retrieving economic information from corporate websites. In: IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT), Macau, China, pp 573–578. https://doi.org/10.1109/WI-IAT.2012.92Ecommerce Foundation (2016) Global B2C E-commerce Report 2016Edelman B (2012) Using internet data for economic research. J Econ Perspect 26:189–206. https://doi.org/10.1257/jep.26.2.189Einav L, Levin J (2014) The data revolution and economic analysis. Innov Policy Econ 14:1–24. https://doi.org/10.1086/674019Eurostat (2008) NACE Rev. 2 Statistical classification of economic activities in the European Communities. EUROSTAT Methodologies and Working papers, Office for Official Publications of the European Communities, LuxembourgEurostat (2016) ICT usage and e-commerce in enterprises. http://ec.europa.eu/eurostat/statistics-explained/index.php/E-commerce_statistics . Accessed 12 Dec 2016Fan J, Han F, Liu H (2014) Challenges of Big Data analysis. Natl Sci Rev 1:293–314. https://doi.org/10.1093/nsr/nwt032Fondeur Y, Karamé F (2013) Can Google data help predict French youth unemployment? Econ Model 30:117–125. https://doi.org/10.1016/j.econmod.2012.07.017Griffis SE, Goldsby TJ, Cooper M (2003) Web-based and mail surveys: A comparison of response, data, and cost. J Bus Logist 24:237–258. https://doi.org/10.1002/j.2158-1592.2003.tb00053.xHand C, Judge G (2012) Searching for the picture: forecasting UK cinema admissions using google trends data. Appl Econ Lett 19:1051–1055. https://doi.org/10.1080/13504851.2011.613744Hao W, Walden J, Trenkamp C (2013) Accelerating e-commerce sites in the cloud. 10th Anual Consumer Communications and Networking Conference (CCNC). IEEE, IEEE, pp 605–608Hasan B (2016) Perceived irritation in online shopping: the impact of website design characteristics. Comput Hum Behav 54:224–230. https://doi.org/10.1016/j.chb.2015.07.056Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and prediction, 2nd edn. Springer, BerlinHastie T, Tibshirani R, Friedman J (2013) The elements of statistical learning: data mining, inference and prediction, 3rd edn. Springer, BerlinHe LJ (2012) The application of web mining ontology system in e-commerce based on FCA, vol 149. Springer, Berlin, pp 429–432. https://doi.org/10.1007/978-3-642-28658-2_65Hernández B, Jiménez J, Martín MJ (2009) Key website factors in e-business strategy. Int J Inf Manag 29:362–371. https://doi.org/10.1016/j.ijinfomgt.2008.12.006INE (2016) Encuesta de uso de TIC y Comercio Electrónico en las empresas 2015-2016. http://ine.es/dynt3/inebase/?path=/t09/e02/a2015-2016 , http://ine.es/dynt3/inebase/?path=/t09/e02/a2015-2016 . Accessed 9 Oct 2016James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning, vol 112. Springer Texts in Statistics. Springer, New YorkJungherr A, Jürgens P (2013) Forecasting the pulse. Internet Res 23:589–607. https://doi.org/10.1108/IntR-06-2012-0115Kim T, Hong J, Kang P (2015) Box office forecasting using machine learning algorithms based on SNS data. Int J Forecast 31:364–390. https://doi.org/10.1016/j.ijforecast.2014.05.006Kosala R, Blockeel H (2000) Web mining research. ACM SIGKDD Explor Newsl 2:1–15. https://doi.org/10.1145/360402.360406Kuhn M, Johnson K (2013) Applied predictive modeling, vol 810. Springer, BerlinKulkarni G, Kannan P, Moe W (2012) Using online search data to forecast new product sales. Decision Support Syst 52:604–611. https://doi.org/10.1016/j.dss.2011.10.017Lee Y, Kozar KA (2006) Investigating the effect of website quality on e-business success: an analytic hierarchy process (ahp) approach. Decision Support Syst 42:1383–1401. https://doi.org/10.1016/j.dss.2005.11.005Li Y, Arora S, Youtie J, Shapira P (2016) Using web mining to explore Triple Helix influences on growth in small and mid-size firms. Technovation. https://doi.org/10.1016/j.technovation.2016.01.002Menardi G, Torelli N (2014) Training and assessing classification rules with imbalanced data. Data Min Knowl Discov 28:92–122. https://doi.org/10.1007/s10618-012-0295-5Munzert S, Rubba C, Meißner P, Nyhuis D (2015) Automated data collection with R: a practical guide to web scraping and text mining. Wiley, ChichesterOliveira T, Martins MF (2010) Understanding e-business adoption across industries in European countries. Ind Manag Data Syst 110:1337–1354. https://doi.org/10.1108/02635571011087428ONS (2016) E-commerce and ICT Activity: 2015. https://www.ons.gov.uk/businessindustryandtrade/itandinternetindustry/bulletins/ecommerceandictactivity/2015 . Accessed 5 Dec 2016Ordanini A, Rubera G (2010) How does the application of an it service innovation affect firm performance? A theoretical framework and empirical analysis on e-commerce. Inf Manag 47:60–67. https://doi.org/10.1016/j.im.2009.10.003Peytchev A (2013) Consequences of survey nonresponse. Ann Am Acad Political Soc Sci 645:88–111. https://doi.org/10.1177/0002716212461748Poggi N, Carrera D, Gavaldà R, Ayguadé E, Torres J (2014) A methodology for the evaluation of high response time on e-commerce users and sales. Inf Syst Front 16:867–885. https://doi.org/10.1007/s10796-012-9387-4Pokorný J, Škoda P, Zelinka I, Bednárek D, Zavoral F, Kruliš M, Šaloun P (2015) Big Data movement: a challenge in data processing, Studies in Big Data, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-11056-1_2R Core Team (2015) R: a language and environment for statistical computing, Vienna, Austria. https://www.R-project.org/ . Accessed 25 Mar 2015Roche X (2014) HTTrack. http://www.httrack.com . Accessed 10 Nov 2014Rodríguez-Ardura I, Meseguer-Artola A (2010) Toward a longitudinal model of e-commerce: environmental, technological, and organizational drivers of B2C adoption. Inf Soc 26:209–227. https://doi.org/10.1080/01972241003712264Rosaci D, Sarnè G (2014) Multi-agent technology and ontologies to support personalization in B2C e-commerce. Electron Commer Res Appl 13:13–23. https://doi.org/10.1016/j.elerap.2013.07.003Shih HY (2012) The dynamics of local and interactive effects on innovation adoption: the case of electronic commerce. J Eng Technol Manag 29:434–452. https://doi.org/10.1016/j.jengtecman.2012.06.001Sohrabi B, Mahmoudian P, Raeesi I (2012) A framework for improving e-commerce websites usability using a hybrid genetic algorithm and neural network system. Neural Comput Appl 21:1017–1029. https://doi.org/10.1007/s00521-011-0674-7Stoll KU, Hepp M (2013) Detection of e-commerce systems with sparse features and supervised classification. In: 10th international conference on e-business engineering (ICEBE), IEEE, Coventry, United Kingdom, pp 199–206. https://doi.org/10.1109/ICEBE.2013.30Suchacka G, Borzemski L (2013) Simulation-based performance study of e-commerce Web server system-results for FIFO scheduling. Springer, Berlin, pp 249–259Swets J (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293. https://doi.org/10.1126/science.3287615Thorleuchter D, Van den Poel D (2012) Predicting e-commerce company success by mining the text of its publicly-accessible website. Expert Syst Appl 39:13,026–13,034. https://doi.org/10.1016/j.eswa.2012.05.096Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B (Methodol) 58:267–288Varian HR (2014) Big Data: new tricks for econometrics. J Econ Perspect 28:3–28. https://doi.org/10.1257/jep.28.2.3Vicente MR, López-Menéndez AJ, Pérez R (2015) Forecasting unemployment with internet search data: does it help to improve predictions when job destruction is skyrocketing? Technol Forecast Soc Change 92:132–139. https://doi.org/10.1016/j.techfore.2014.12.005Youtie J, Hicks D, Shapira P, Horsley T (2012) Pathways from discovery to commercialisation: using web sources to track small and medium-sized enterprise strategies in emerging nanotechnologies. Technol Anal Strateg Manag 24:981–995. https://doi.org/10.1080/09537325.2012.724163Zhang Y, Fang Y, Wei KK, Ramsey E, McCole P, Chen H (2011) Repurchase intention in B2C e-commerce—a relationship quality perspective. Inf Manag 48:192–200. https://doi.org/10.1016/j.im.2011.05.003Zhao WX, Li S, He Y, Wang L, Wen JR, Li X (2016) Exploring demographic information in social media for product recommendation. Knowl Inf Syst 49:61–8

    Model-driven design, simulation and implementation of service compositions in COSMO

    Get PDF
    The success of software development projects to a large extent depends on the quality of the models that are produced in the development process, which in turn depends on the conceptual and practical support that is available for modelling, design and analysis. This paper focuses on model-driven support for service-oriented software development. In particular, it addresses how services and compositions of services can be designed, simulated and implemented. The support presented is part of a larger framework, called COSMO (COnceptual Service MOdelling). Whereas in previous work we reported on the conceptual support provided by COSMO, in this paper we proceed with a discussion of the practical support that has been developed. We show how reference models (model types) and guidelines (design steps) can be iteratively applied to design service compositions at a platform independent level and discuss what tool support is available for the design and analysis during this phase. Next, we present some techniques to transform a platform independent service composition model to an implementation in terms of BPEL and WSDL. We use the mediation scenario of the SWS challenge (concerning the establishment of a purchase order between two companies) to illustrate our application of the COSMO framework

    Adaptation of domestic state governance to international governance models

    Get PDF
    The purpose of the article is to provide the evolving international trends of modern management models and authorial vision of model of state governance system in Ukraine, its subsystems, in particular, the system of provision of administrative services that is appropriate for the contemporary times. Methodology. On the basis of scientific and theoretical approaches to the definitions of terms “state governance” and “public governance”, there was an explanation of considerable difference between them and, taking into consideration, the mentality of Ukrainian society and peculiar weak side in self-organization, the authors offered to form authorial model of governance on the basis of historically traditional for Ukraine model of state governance and to add some elements of management concepts that proved their significance, efficiency and priority in practice. Results. The authors emphasized the following two prevailing modern management models in the international practice: “new state management” and “good governance”. The first concept offered for consideration served as a basis for the semantic content of state activity that reflects more the state of administrative reformation. Practical meaning. A practical introduction of management to the domestic model of governance creates the range of contradictions that do not allow implementing herein concept. Pursuant to authors, the second one allows in considerable measure to reform state governance, considering historically developed peculiarities of this model. Moreover, the involvement of concept herein into introduction of informational and communicational technologies in the process of governance eliminates the necessity of power decentralization, it allows to form real net structure and, at the same, to keep vertical power structure, to involve citizens for formation and taking of management decisions, to form electronic communicational channel of feedback, to provide citizens with electronic administrative services. All indicated advantages of the concept certify about the necessity to reform state governance exactly in this field. Meaning/ Distinction. This article raises a question about the significance of formation and sequence of state policy in Ukraine aimed at creating an information-oriented society, space, as well as informational and technological infrastructure
    corecore