Literatura académica sobre el tema "Data-rich environments"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Data-rich environments".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Data-rich environments"
Bharadwaj, Neeraj y Charles H. Noble. "Innovation in Data-Rich Environments". Journal of Product Innovation Management 32, n.º 3 (3 de marzo de 2015): 476–78. http://dx.doi.org/10.1111/jpim.12266.
Texto completoWedel, Michel y P. K. Kannan. "Marketing Analytics for Data-Rich Environments". Journal of Marketing 80, n.º 6 (noviembre de 2016): 97–121. http://dx.doi.org/10.1509/jm.15.0413.
Texto completoBharadwaj, Neeraj y Charles Noble. "Finding Innovation in Data Rich Environments". Journal of Product Innovation Management 34, n.º 5 (2 de agosto de 2017): 560–64. http://dx.doi.org/10.1111/jpim.12407.
Texto completoBell, Kathleen P. y Timothy J. Dalton. "Spatial Economic Analysis in Data-Rich Environments". Journal of Agricultural Economics 58, n.º 3 (septiembre de 2007): 487–501. http://dx.doi.org/10.1111/j.1477-9552.2007.00123.x.
Texto completoMiller, Harvey J. y Jiawei Han. "Discovering geographic knowledge in data rich environments". ACM SIGKDD Explorations Newsletter 1, n.º 2 (enero de 2000): 105–7. http://dx.doi.org/10.1145/846183.846208.
Texto completoMedeiros, Marcelo C. y Gabriel F. R. Vasconcelos. "Forecasting macroeconomic variables in data-rich environments". Economics Letters 138 (enero de 2016): 50–52. http://dx.doi.org/10.1016/j.econlet.2015.11.017.
Texto completoCubadda, Gianluca y Alain Hecq. "Testing for common autocorrelation in data-rich environments". Journal of Forecasting 30, n.º 3 (9 de junio de 2010): 325–35. http://dx.doi.org/10.1002/for.1186.
Texto completoDong, John Qi. "Online Information Practices for User Innovation in Data-Rich Environments". Academy of Management Proceedings 2016, n.º 1 (enero de 2016): 11729. http://dx.doi.org/10.5465/ambpp.2016.11729abstract.
Texto completoBERTOLI, GIUSEPPE, SANDRO CASTALDO, PAOLA CILLO, GABRIELE TROILO y GIANMARIO VERONA. "Guest Editorial: Knowledge and trust in data-rich business environments". Sinergie Italian Journal of Management 40, n.º 1 (30 de abril de 2022): 11–14. http://dx.doi.org/10.7433/s117.2022.01.
Texto completoLee, Ickjai y Vladimir Estivill-Castro. "Fast Cluster Polygonization and its Applications in Data-Rich Environments". GeoInformatica 10, n.º 4 (diciembre de 2006): 399–422. http://dx.doi.org/10.1007/s10707-006-0340-x.
Texto completoTesis sobre el tema "Data-rich environments"
Drobek, Marc. "Data-driven system dynamics modelling : model formulation and KPI prediction in data-rich environments". Thesis, Queen's University Belfast, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725834.
Texto completoNilekar, Shirish K. "A system-oriented analysis of team decision making in data rich environments". Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/90698.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 78-80).
The information processing view of organizations [1] and subsequent works highlight the primary role of information processing in the effective functioning of markets and organizations. With the current wave of "big data" and related technologies, data-oriented decision making is being widely discussed [2] as a means of using this vast amount of available data for better decisions which can lead to improved business results. The focus of many of these studies is at the organization level. However, decisions are made by teams of individuals and this is a complex socio-technical process. The quality of a decision depends on many factors including technical capabilities for data analysis and human factors like team dynamics, cognitive capabilities of the individuals and the team. In this thesis, we developed a systems theory based framework for decision making and identified four socio technical factors viz., data analytics, data sensing, power distribution, and conflict level which affect the quality of decisions made by teams. We then conducted "thought experiments" to investigate the relative contribution of each of these factors to the quality of decisions. Our experiments and subsequent analyses show that while improved data analytics does result in better decisions, human factors have an out-sized contribution to the quality of decisions, even in data rich environments. Moreover, when the human factors in a team improve, the predictability of the positive impacts due to improvements in technical capabilities of the team also increases.
by Shirish K. Nilekar.
S.M. in Engineering and Management
Lasky, Alan. "Slipstream, a data rich production environment". Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/68242.
Texto completoBarsoum, Fady [Verfasser]. "Econometric Modelling in a Mixed-Frequency and Data-Rich Environment / Fady Barsoum". Konstanz : Bibliothek der Universität Konstanz, 2016. http://d-nb.info/1112944699/34.
Texto completoAhmadi, Pooyan Amir. "Essays in empirical macroeconomics with application to monetary policy in a data-rich environment". Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://dx.doi.org/10.18452/16153.
Texto completoThis thesis consists of four self-contained chapters. The first chapter provides an introduction with a literature overview. In Chapter 2 we estimate the effects of monetary policy shocks in a Bayesian Factor- Augmented vector autoregression (BFAVAR). We propose to employ as an identification strategy sign restrictions on the impulse response function of pertinent variables according to conventional wisdom. The key strength of our factor based approach is that sign restrictions can be imposed on many variables in order to pin down the impact of monetary policy shocks. Thus an exact identification of shocks can be approximated and monitored. In chapter 3 the role of monetary policy during the interwar Great Depression is analyzed. The prominent role of monetary policy in the U.S. interwar depression has been conventional wisdom since Friedman and Schwartz [1963]. This paper attempts to capture the pertinent dynamics through a BFAVAR methodology of the previous chapter. We find the effects of monetary policy shocks and the systematic component to have been moderate. Our results caution against a predominantly monetary interpretation of the Great Depression. This final chapter 4 analyzes macroeconomic dynamics within the Euro area. To tackle the questions at hand I propose a novel approach to jointly estimate a factor-based DSGE model and a structural dynamic factor model that simultaneously captures the rich interrelations in a parsimonious way and explicitly involves economic theory in the estimation procedure. To identify shocks I employ both sign restrictions derived from the estimated DSGE model and the implied restrictions from the DSGE model rotation. I find a high degree of comovement across the member countries, homogeneity in the monetary transmission mechanism and heterogeneity in transmission of technology shocks. The suggested approach results in a factor generalization of the DSGE-VAR methodology of Del Negro and Schorfheide [2004].
Neumann, Bradley C. "Is All Open Space Created Equal? A Hedonic Application within a Data-Rich GIS Environment". Fogler Library, University of Maine, 2005. http://www.library.umaine.edu/theses/pdf/NeumannBC2005.pdf.
Texto completovon, Wenckstern Michael. "Web applications using the Google Web Toolkit". Master's thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2013. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-115009.
Texto completoDiese Diplomarbeit beschreibt die Erzeugung desktopähnlicher Anwendungen mit dem Google Web Toolkit und die Umwandlung klassischer Java-Programme in diese. Das Google Web Toolkit ist eine Open-Source-Entwicklungsumgebung, die Java-Code in browserunabhängiges als auch in geräteübergreifendes HTML und JavaScript übersetzt. Vorgestellt wird der Großteil des GWT Frameworks inklusive des Java zu JavaScript-Compilers sowie wichtige Sicherheitsaspekte von Internetseiten. Um zu zeigen, dass auch komplizierte graphische Oberflächen mit dem Google Web Toolkit erzeugt werden können, wird das bekannte Brettspiel Agricola mittels Model-View-Presenter Designmuster implementiert. Zur Ermittlung der richtigen Technologie für das nächste Webprojekt findet ein Vergleich zwischen dem Google Web Toolkit und JavaServer Faces statt
Avasarala, Viswanath. "Multi-agent systems for data-rich, information-poor environments". 2006. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-1506/index.html.
Texto completoAmankwah-Amoah, J. y Samuel Adomako. "Big Data Analytics and Business Failures in Data-Rich Environments: An Organizing Framework". 2018. http://hdl.handle.net/10454/16746.
Texto completoIn view of the burgeoning scholarly works on big data and big data analytical capabilities, there remains limited research on how different access to big data and different big data analytic capabilities possessed by firms can generate diverse conditions leading to business failure. To fill this gap in the existing literature, an integrated framework was developed that entailed two approaches to big data as an asset (i.e. threshold resource and distinctive resource) and two types of competences in big data analytics (i.e. threshold competence and distinctive/core competence). The analysis provides insights into how ordinary big data analytic capability and mere possession of big data are more likely to create conditions for business failure. The study extends the existing streams of research by shedding light on decisions and processes in facilitating or hampering firms’ ability to harness big data to mitigate the cause of business failures. The analysis led to the categorization of a number of fruitful avenues for research on data-driven approaches to business failure.
Amir, Ahmadi Pooyan [Verfasser]. "Essays in empirical macroeconomics with application to monetary policy in a data-rich environment / von Pooyan Amir Ahmadi". 2009. http://d-nb.info/1008017671/34.
Texto completoLibros sobre el tema "Data-rich environments"
Bernanke, Ben. Monetary policy in a data-rich environment. Cambridge, MA: National Bureau of Economic Research, 2001.
Buscar texto completoJean, Boivin. DSGE models in a data-rich environment. Cambridge, Mass: National Bureau of Economic Research, 2006.
Buscar texto completoMazzoni, Stefania y Franca Pecchioli, eds. The Uşaklı Höyük Survey Project (2008-2012). Florence: Firenze University Press, 2016. http://dx.doi.org/10.36253/978-88-6655-902-3.
Texto completoVerloo, Nanke y Luca Bertolini, eds. Seeing the City. NL Amsterdam: Amsterdam University Press, 2020. http://dx.doi.org/10.5117/9789463728942.
Texto completoGupta, Manisha, Deergha Sharma y Himani Gupta. Revolutionizing Business Practices Through Artificial Intelligence and Data-Rich Environments. IGI Global, 2022.
Buscar texto completoGupta, Manisha, Deergha Sharma y Himani Gupta, eds. Revolutionizing Business Practices Through Artificial Intelligence and Data-Rich Environments. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4950-9.
Texto completoGupta, Manisha, Deergha Sharma y Himani Gupta. Revolutionizing Business Practices Through Artificial Intelligence and Data-Rich Environments. IGI Global, 2022.
Buscar texto completoGupta, Manisha, Deergha Sharma y Himani Gupta. Revolutionizing Business Practices Through Artificial Intelligence and Data-Rich Environments. IGI Global, 2022.
Buscar texto completoGupta, Manisha, Deergha Sharma y Himani Gupta. Revolutionizing Business Practices Through Artificial Intelligence and Data-Rich Environments. IGI Global, 2022.
Buscar texto completoGupta, Manisha, Deergha Sharma y Himani Gupta. Revolutionizing Business Practices Through Artificial Intelligence and Data-Rich Environments. IGI Global, 2022.
Buscar texto completoCapítulos de libros sobre el tema "Data-rich environments"
Romero, David, Ovidiu Noran y Peter Bernus. "Green Virtual Enterprise Breeding Environments Enabling the RESOLVE Framework". En Collaboration in a Data-Rich World, 603–13. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65151-4_53.
Texto completoLee, Ickjai. "Geospatial Clustering in Data-Rich Environments: Features and Issues". En Lecture Notes in Computer Science, 336–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11554028_47.
Texto completoLee, Ickjai. "Data Mining Coupled Conceptual Spaces for Intelligent Agents in Data-Rich Environments". En Lecture Notes in Computer Science, 42–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11554028_7.
Texto completoGao, Song, Yu Liu, Yuhao Kang y Fan Zhang. "User-Generated Content: A Promising Data Source for Urban Informatics". En Urban Informatics, 503–22. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_28.
Texto completoBernardi, Mauro, Giovanni Bonaccolto, Massimiliano Caporin y Michele Costola. "Volatility Forecasting in a Data Rich Environment". En Macroeconomic Forecasting in the Era of Big Data, 127–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31150-6_5.
Texto completoArosio, Laura. "What People Leave Behind Online: Digital Traces and Web-Mediated Documents for Social Research". En Frontiers in Sociology and Social Research, 311–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11756-5_20.
Texto completoUtz, Wilfrid y Robert Woitsch. "A Model-Based Environment for Data Services: Energy-Aware Behavioral Triggering Using ADOxx". En Collaboration in a Data-Rich World, 265–75. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65151-4_25.
Texto completoBallarino, Andrea, Carlo Brondi, Alessandro Brusaferri y Guido Chizzoli. "The CPS and LCA Modelling: An Integrated Approach in the Environmental Sustainability Perspective". En Collaboration in a Data-Rich World, 543–52. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65151-4_48.
Texto completoGilworth, Bob. "Starting Points and Journeys: Careers and Employability in a Data-Rich Environment". En The SAGE Handbook of Graduate Employability, 452–74. 1 Oliver's Yard, 55 City Road London EC1Y 1SP: SAGE Publications Ltd, 2023. http://dx.doi.org/10.4135/9781529791082.n27.
Texto completoWipf, Heinz. "Safety Versus Security in Aviation". En The Coupling of Safety and Security, 29–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47229-0_4.
Texto completoActas de conferencias sobre el tema "Data-rich environments"
Karafili, Erisa, Emil C. Lupu, Alan Cullen, Bill Williams, Saritha Arunkumar y Seraphin Calo. "Improving data sharing in data rich environments". En 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258270.
Texto completoBreitkreutz, David y Ickjai Lee. "Voronoi representation for areal data processing in data-rich environments". En 2009 IEEE International Conference on Intelligence and Security Informatics. IEEE, 2009. http://dx.doi.org/10.1109/isi.2009.5137291.
Texto completoHong, Shenda, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li y Jimeng Sun. "RDPD: Rich Data Helps Poor Data via Imitation". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/817.
Texto completoDeLuca, V. William, Aaron Clark, Jeremy Ernst y Nasim Lari. "Work in progress: Data-rich learning environments for engineering education". En 2011 Frontiers in Education Conference (FIE). IEEE, 2011. http://dx.doi.org/10.1109/fie.2011.6142698.
Texto completode Sousa, Bruno y Dulce Gomes. "Facing the challenges from different realities: e-learning approaches for Africa and Europe". En Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17603.
Texto completoDe Giusti, Giovanna. "Using digital tools to engage Kenyan development students with data". En Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17602.
Texto completoLee, Wei Ching. "Understanding Adult Learning Participation and Problem Solving in Technology-Rich Environments Through International Survey Data". En 2020 AERA Annual Meeting. Washington DC: AERA, 2020. http://dx.doi.org/10.3102/1576975.
Texto completoBruce, Mary. "The Use of Random Data in Online Discussion Boards to Promote Student Understanding of Sampling Distributions". En IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.hirmq.
Texto completoShort, Adam R., Zachary Mimlitz y Douglas L. Van Bossuyt. "Autonomous System Design and Controls Design for Operations in High Risk Environments". En ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60144.
Texto completoPocquette, Nicholas, Hwasung Yeom, Hemant Agiwal, Frank Pfefferkorn, Kumar Sridharan, Kenneth Ross, John Kessler y Gary Cannel. "Cold Spray Process to Mitigate Potential Stress-Corrosion Cracking in Used Nuclear Fuel Storage Canisters". En ITSC2021, editado por F. Azarmi, X. Chen, J. Cizek, C. Cojocaru, B. Jodoin, H. Koivuluoto, Y. C. Lau et al. ASM International, 2021. http://dx.doi.org/10.31399/asm.cp.itsc2021p0623.
Texto completoInformes sobre el tema "Data-rich environments"
Boivin, Jean y Marc Giannoni. DSGE Models in a Data-Rich Environment. Cambridge, MA: National Bureau of Economic Research, diciembre de 2006. http://dx.doi.org/10.3386/t0332.
Texto completoBernanke, Ben y Jean Boivin. Monetary Policy in a Data-Rich Environment. Cambridge, MA: National Bureau of Economic Research, julio de 2001. http://dx.doi.org/10.3386/w8379.
Texto completoBoivin, Jean y Marc Giannoni. DSGE Models in a Data-Rich Environment. Cambridge, MA: National Bureau of Economic Research, diciembre de 2006. http://dx.doi.org/10.3386/w12772.
Texto completoNeyedley, K., J. J. Hanley, Z. Zajacz y M. Fayek. Accessory mineral thermobarometry, trace element chemistry, and stable O isotope systematics, Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328986.
Texto completoDownes, Jane, ed. Chalcolithic and Bronze Age Scotland: ScARF Panel Report. Society for Antiquaries of Scotland, septiembre de 2012. http://dx.doi.org/10.9750/scarf.09.2012.184.
Texto completoAndrabi, Tahir, Natalie Bau, Jishnu Das y Asim I. Khwaja. Heterogeneity in School Value-Added and the Private Premium. Research on Improving Systems of Education (RISE), noviembre de 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/116.
Texto completoBonfil, David J., Daniel S. Long y Yafit Cohen. Remote Sensing of Crop Physiological Parameters for Improved Nitrogen Management in Semi-Arid Wheat Production Systems. United States Department of Agriculture, enero de 2008. http://dx.doi.org/10.32747/2008.7696531.bard.
Texto completoBorch, Thomas, Yitzhak Hadar y Tamara Polubesova. Environmental fate of antiepileptic drugs and their metabolites: Biodegradation, complexation, and photodegradation. United States Department of Agriculture, enero de 2012. http://dx.doi.org/10.32747/2012.7597927.bard.
Texto completoKing, E. L., A. Normandeau, T. Carson, P. Fraser, C. Staniforth, A. Limoges, B. MacDonald, F. J. Murrillo-Perez y N. Van Nieuwenhove. Pockmarks, a paleo fluid efflux event, glacial meltwater channels, sponge colonies, and trawling impacts in Emerald Basin, Scotian Shelf: autonomous underwater vehicle surveys, William Kennedy 2022011 cruise report. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331174.
Texto completoChefetz, Benny y Jon Chorover. Sorption and Mobility of Pharmaceutical Compounds in Soils Irrigated with Treated Wastewater. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7592117.bard.
Texto completo