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Relevant bibliographies by topics / Big Data

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Academic literature on the topic 'Big Data'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 April 2025

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Journal articles on the topic "Big Data"

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A, Eicher,. "Big business with Big Data Big Business mit Big Data." GIS Business 12, no. 3 (June 12, 2019): 20–25. http://dx.doi.org/10.26643/gis.v12i3.5173.

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Sizov, Ivan Aleksandrovich. "BIG DATA – BIG DATA IN BUSINESS." Economy. Business. Computer science, no. 3 (January 1, 2016): 8–23. http://dx.doi.org/10.19075/2500-2074-2016-3-8-23.

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Habegger, Benjamin. "Big Data vs. Privacy Big Data." Services Transactions on Big Data 1, no. 1 (January 2014): 25–35. http://dx.doi.org/10.29268/stbd.2014.1.1.3.

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Weber, A. S. "The Big Student Big Data Grab." International Journal of Information and Education Technology 6, no. 1 (2016): 65–70. http://dx.doi.org/10.7763/ijiet.2016.v6.660.

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Mager, Astrid. "The politics of big data. Big data, big brother?" Information, Communication & Society 22, no. 10 (January 22, 2019): 1523–25. http://dx.doi.org/10.1080/1369118x.2019.1567804.

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Viceconti, Marco, Peter Hunter, and Rod Hose. "Big Data, Big Knowledge: Big Data for Personalized Healthcare." IEEE Journal of Biomedical and Health Informatics 19, no. 4 (July 2015): 1209–15. http://dx.doi.org/10.1109/jbhi.2015.2406883.

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Riahi, Youssra, and Sara Riahi. "Big Data and Big Data Analytics: concepts, types and technologies." International Journal of Research and Engineering 5, no. 9 (November 2018): 524–28. http://dx.doi.org/10.21276/ijre.2018.5.9.5.

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Lesk, Michael. "Big Data, Big Brother, Big Money." IEEE Security & Privacy 11, no. 4 (July 2013): 85–89. http://dx.doi.org/10.1109/msp.2013.81.

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West, Stephen W., Jo Clubb, Tracy A. Blake, James Fern, Harry Bowles, and Torstein Dalen-Lorentsen. "Big data. Big potential. Big problems?" BMJ Open Sport & Exercise Medicine 10, no. 2 (April 2024): e001994. http://dx.doi.org/10.1136/bmjsem-2024-001994.

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Martin, Joseph. "Big data, big future." BioTechniques 68, no. 4 (April 2020): 166–68. http://dx.doi.org/10.2144/btn-2020-0027.

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Dissertations / Theses on the topic "Big Data"

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Hansen, Simon, and Erik Markow. "Big Data : Implementation av Big Data i offentlig verksamhet." Thesis, Högskolan i Halmstad, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-38756.

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Lundvall, Helena. "Big data = Big money? : En kvantitativ studie om big data, förtroende och köp online." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-451065.

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Tidigare forskning har entydigt visat på att ett ökat förtroende hos kunder i köpsituationer ökar deras vilja att genomföra köp. Vilka faktorer som påverkar kunders förtroende har även det undersökts flitigt och faktorer som kan kopplas till hantering av kunders data tas allt oftare upp som avgörande. Dock behandlas dessa faktorer många gånger på ett övergripande plan och studier som djupdyker i vilka underliggande faktorer kopplat till datahantering som påverkar kunders förtroende saknas. Genom att samla in kvantitativ data om hur kunder förhåller sig till företags insamling och användande av big data, deras förtroende för e-handelsföretag, samt deras vilja att genomföra köp online ämnar denna studie till att besvara syftet att undersöka effekten av företags insamling och användande av big data på kunders förtroende för företag inom e-handel, samt att undersöka effekten av kunders förtroende på deras vilja att genomföra köp. Studiens resultat visar att företags insamling av big data har en signifikant negativ effekt på kundernas förtroende, samt att kunders förtroende har ett signifikant positivt samband med kunders köpintention. Gällande företags användande av big data kunde däremot inte en signifikant negativ effekt på kundernas förtroende påvisas.

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Rizk, Raya. "Big Data Validation." Thesis, Uppsala universitet, Informationssystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353850.

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With the explosion in usage of big data, stakes are high for companies to develop workflows that translate the data into business value. Those data transformations are continuously updated and refined in order to meet the evolving business needs, and it is imperative to ensure that a new version of a workflow still produces the correct output. This study focuses on the validation of big data in a real-world scenario, and implements a validation tool that compares two databases that hold the results produced by different versions of a workflow in order to detect and prevent potential unwanted alterations, with row-based and column-based statistics being used to validate the two versions. The tool was shown to provide accurate results in test scenarios, providing leverage to companies that need to validate the outputs of the workflows. In addition, by automating this process, the risk of human error is eliminated, and it has the added benefit of improved speed compared to the more labour-intensive manual alternative. All this allows for a more agile way of performing updates on the data transformation workflows by improving on the turnaround time of the validation process.

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Jaber, Carolin. "Big data visualisering." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-79898.

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Visualisering av data i grafiska presentationer är viktigt inom många olika områden för attenklare förstå information och relationer av insamlad data. Mängden data växer snabbt tillstora skalor som är svåra att hantera och bidrar till nya utmaningar vid visualisering av data igrafiska presentationer. System är beroende av data visualisering för att upptäcka defekteroch fel av produktion. Genom att förbättra prestandan av tidsseriedata visualisering ökar detmöjligheten att upptäcka fel och defekter av produktion.Rapporten tar upp metoder för visualisering av tidsseriedata med snabb prestanda ochdiskuterar hur Big data av multivaribler kan visualiseras med PCA.
Presenting data in graphical forms is important in many different industries in order tounderstand information asset from data that is being collected. The amount of data is growingfast and brings new challenges for visualizing the data in graphical representations. Systemsare dependent on data visualization for detecting defects and faults of productions. Byimproved performance of time series data visualization increases the ability of detectingfaults and defects of productions.This report takes up a methods for visualizing time series data with high velocity in toaccount and discusses how big data of multivariable can be visualized with PCA.

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Blahová, Leontýna. "Big Data Governance." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-203994.

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This master thesis is about Big Data Governance and about software, which is used for this purposes. Because Big Data are huge opportunity and also risk, I wanted to map products which can be easily use for Data Quality and Big Data Governance in one platform. This thesis is not only on theoretical knowledge level, but also evaluates five key products (from my point of view). I defined requirements for every kind of domain and then I set up the weights and points. The main objective is to evaluate software capabilities and compere them.

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Casagrande, Federico <1994&gt. "Big Data Valuation." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19687.

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Kämpe, Gabriella. "How Big Data Affects UserExperienceReducing cognitive load in big data applications." Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163995.

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We have entered the age of big data. Massive data sets are common in enterprises, government, and academia. Interpreting such scales of data is still hard for the human mind. This thesis investigates how proper design can decrease the cognitive load in data-heavy applications. It focuses on numeric data describing economic growth in retail organizations. It aims to answer the questions: What is important to keep in mind when designing an interface that holds large amounts of data? and How to decrease the cognitive load in complex user interfaces without reducing functionality?. It aims to answer these questions by comparing two user interfaces in terms of efficiency, structure, ease of use and navigation. Each interface holds the same functionality and amount of data, but one is designed to increase user experience by reducing cognitive load. The design choices in the second application are based on the theory found in the literature study in the thesis.

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Sherikar, Vishnu Vardhan Reddy. "I2MAPREDUCE: DATA MINING FOR BIG DATA." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/437.

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This project is an extension of i2MapReduce: Incremental MapReduce for Mining Evolving Big Data . i2MapReduce is used for incremental big data processing, which uses a fine-grained incremental engine, a general purpose iterative model that includes iteration algorithms such as PageRank, Fuzzy-C-Means(FCM), Generalized Iterated Matrix-Vector Multiplication(GIM-V), Single Source Shortest Path(SSSP). The main purpose of this project is to reduce input/output overhead, to avoid incurring the cost of re-computation and avoid stale data mining results. Finally, the performance of i2MapReduce is analyzed by comparing the resultant graphs.

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Giordano, Manfredi. "Autonomic Big Data Processing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14837/.

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Apache Spark è un framework open source per la computazione distribuita su larga scala, caratterizzato da un engine in-memory che permette prestazioni superiori a soluzioni concorrenti nell’elaborazione di dati a riposo (batch) o in movimento (streaming). In questo lavoro presenteremo alcune tecniche progettate e implementate per migliorare l’elasticità e l’adattabilità del framework rispetto a modifiche dinamiche nell’ambiente di esecuzione o nel workload. Lo scopo primario di tali tecniche è di permettere ad applicazioni concorrenti di condividere le risorse fisiche disponibili nell’infrastruttura cluster sottostante in modo efficiente. Il contesto nel quale le applicazioni distribuite vengono eseguite difficilmente può essere considerato statico: le componenti hardware possono fallire, i processi possono interrompersi, gli utenti possono allocare risorse aggiuntive in modo imprevedibile nel tentativo di accelerare la computazione o di allegerire il carico di lavoro. Infine, non soltanto le risorse fisiche ma anche i dati in input possono variare di dimensione e complessità durante l’esecuzione, così che sia dati sia risorse non possano essere considerati statici. Una configurazione immutabile del cluster non riuscirà a ottenere la migliore efficienza possibile per tutti i differenti carichi di lavoro. Ne consegue che un framework per il calcolo distribuito che sia "consapevole" delle modifiche ambientali e delle modifiche al workload e che sia in grado di adattarsi a esse puo risultare piu performante di un framework che permetta unicamente configurazioni statiche. Gli esperimenti da noi compiuti con applicazioni Big Data altamente parallelizzabili mostrano come il costo della soluzione proposta sia minimo e come la nostra version di Spark più dinamica e adattiva possa portare a benefici in termini di flessibilità, scalabilità ed efficienza.

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Francke, Angela, and Sven Lißner. "Big Data im Radverkehr." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-230730.

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Für einen attraktiven Radverkehr bedarf es einer qualitativ hochwertigen Infrastruktur. Bisher liegen durch den hohen Aufwand von Vor-Ort-Erfassungen nur punktuelle Radverkehrsstärken vor. Die aktuell wohl zuverlässigsten und tauglichsten Werte liefern bisher fest installierte automatische Radverkehrszählstellen, wie sie bereits viele Kommunen installiert haben. Ein Nachteil ist hierbei, dass für eine flächige Abdeckung mit einer besseren Aussagekraft für die gesamte Stadt oder Kommune die Anzahl der Erhebungspunkte meist deutlich zu gering ist. Die Bedeutung des Nebennetzes für den Radverkehr wird somit nur unvollständig erfasst. Für weitere Parameter, wie Wartezeiten, Routenwahl oder Geschwindigkeiten der Radfahrenden, fehlen dagegen meist die Daten. Perspektivisch kann diese Lücke unter anderem durch GPS-Routendaten gefüllt werden, was durch die mittlerweile sehr hohe Verbreitung von Smartphones und den entsprechenden Tracking-Apps ermöglicht wird. Die Ergebnisse des im Leitfaden vorgestellten Projektes sind durch das BMVI im Rahmen des Nationalen Radverkehrsplans 2020 gefördert wurden. Das Forschungsprojekt untersucht dabei die Nutzbarkeit von mit Smartphones generierten Nutzerdaten einer App für die kommunale Radverkehrsplanung. Zusammenfassend lässt sich sagen, dass unter Beachtung der im folgenden Leitfaden beschriebenen Faktoren GPS-Daten, im vorliegenden Fall die der Firma Strava Inc., mit einigen Einschränkungen für die Radverkehrsplanung nutzbar sind. Bereits heute sind damit Auswertungen möglich, die zeigen, wo, wann und wie sich Radfahrende im gesamten Netz bewegen. Die mittels Smartphone-App generierten Daten können sehr sinnvoll als Ergänzung zu bestehenden Dauerzählstellen von Kommunen genutzt werden. Berücksichtigt werden sollten bei der Auswertung und Interpretation der Daten jedoch einige Aspekte, wie der eher sportlich orientierte Kontext der erfassten Routen in den untersuchten Beispielen. Des Weiteren werden aktuell die Daten zum Teil noch als Datenbank- oder GIS-Dateien zur Verfügung gestellt, bzw. befinden sich online Masken zur einfacheren Nutzung im Aufbau oder einem ersten Nutzungsstadium. Die Auswertung und Interpretation erfordert also weiterhin Fachkompetenz und auch personelle Ressourcen. Der Einsatz dieser sinkt jedoch voraussichtlich zukünftig durch die Weiterentwicklung von Web-Oberflächen und unterstützenden Auswertemasken. Hier gilt es zukünftig, in Zusammenarbeit mit den Kommunen, die benötigten Parameter sowie die geeignetsten Aufbereitungsformen zu erarbeiten. Im Forschungsprojekt erfolgte ein Ansatz der Hochrechnung von Radverkehrsstärken aus Stichproben von GPS-Daten auf das gesamte Netz. Dieser konnte auch erfolgreich in einer weiteren Kommune verifiziert werden. Jedoch ist auch hier in Zukunft noch Forschungsbedarf vorhanden bzw. die Anpassung auf lokale Gegebenheiten notwendig. In naher Zukunft ist es notwendig, den Praxisnachweis für die Nutzbarkeit von GPS-Daten zu erbringen. Vorbilder hierfür können die Städte Bremen, Dresden, Leipzig oder Mainz sein, die jeweils bereits erste Schritte zur Nutzung von GPS-Daten in der Radverkehrsplanung und -förderung unternehmen. Diese Schritte sind vor dem Hintergrund der weiteren Digitalisierung von Mobilität und Verkehrsmitteln und dem damit wachsenden Datenangebot – auch trotz der bisherigen Einschränkungen der Daten – sinnvoll, um in den Verwaltungen frühzeitig entsprechende Kompetenzen aufzubauen. Langfristig bietet die Nutzung von GPS-Daten einen Mehrwert für die Radverkehrsplanung. Der aktive Einbezug von Radfahrenden eröffnet zudem neue Möglichkeiten in der Kommunikation und der Bürgerbeteiligung – auch ohne Fachwissen vorauszusetzen. Der vorliegende Leitfaden liefert dafür einen praxisorientierten Einstieg in das Thema und weist umfassend auf Angebote, Hindernisse und Potenziale von GPS-Daten hin.

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Books on the topic "Big Data"

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Mei, Hong, Weiguo Zhang, Wenfei Fan, Zili Zhang, Yihua Huang, Jiajun Bu, Yang Gao, and Li Wang, eds. Big Data. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0705-9.

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Liao, Xiangke, Wei Zhao, Enhong Chen, Nong Xiao, Li Wang, Yang Gao, Yinghuan Shi, Changdong Wang, and Dan Huang, eds. Big Data. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9709-8.

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Xu, Zongben, Xinbo Gao, Qiguang Miao, Yunquan Zhang, and Jiajun Bu, eds. Big Data. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2922-7.

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King, Stefanie. Big Data. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06586-7.

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Fasel, Daniel, and Andreas Meier, eds. Big Data. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-11589-0.

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Mohanty, Hrushikesha, Prachet Bhuyan, and Deepak Chenthati, eds. Big Data. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2494-5.

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Jin, Hai, Xuemin Lin, Xueqi Cheng, Xuanhua Shi, Nong Xiao, and Yihua Huang, eds. Big Data. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1899-7.

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König, Christian, Jette Schröder, and Erich Wiegand, eds. Big Data. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20083-1.

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Gottlob, Georg, Giovanni Grasso, Dan Olteanu, and Christian Schallhart, eds. Big Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39467-6.

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Chen, Min, Shiwen Mao, Yin Zhang, and Victor C. M. Leung. Big Data. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06245-7.

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Book chapters on the topic "Big Data"

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Bautista, Elizabeth, Cary Whitney, and Thomas Davis. "Big Data Behind Big Data." In Conquering Big Data with High Performance Computing, 163–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33742-5_8.

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Estrada, Raul, and Isaac Ruiz. "Big Data, Big Challenges." In Big Data SMACK, 3–7. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2175-4_1.

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Estrada, Raul, and Isaac Ruiz. "Big Data, Big Solutions." In Big Data SMACK, 9–16. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2175-4_2.

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Leclerc, Benoit, and Jesse Cale. "Big data." In Big Data, 117–31. Milton Park, Abingdon, Oxon ; New York, NY : Routledge, 2020. | Series: Criminology at the edge: Routledge, 2020. http://dx.doi.org/10.4324/9781351029704-8.

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Schmitz, Uwe. "Big Data." In Data Science, 3–25. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33403-1_1.

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Suciu, Dan. "Big Data Begets Big Database Theory." In Big Data, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39467-6_1.

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Amirian, Pouria, Francois van Loggerenberg, and Trudie Lang. "Big Data and Big Data Technologies." In Big Data in Healthcare, 39–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62990-2_3.

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Shi, Yong. "Big Data and Big Data Analytics." In Advances in Big Data Analytics, 3–21. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3607-3_1.

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Anderson, Billie. "Big Data." In Intelligent Credit Scoring, 149–72. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119282396.ch9.

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Richter, Philipp. "Big Data." In Handbuch Medien- und Informationsethik, 210–16. Stuttgart: J.B. Metzler, 2016. http://dx.doi.org/10.1007/978-3-476-05394-7_28.

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Conference papers on the topic "Big Data"

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Becker, David, Trish Dunn King, and Bill McMullen. "Big data, big data quality problem." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7364064.

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Makhlouf, Mohamed. "BIG: Big Data Intelligence Governance Framework." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020244.

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Gopalkrishnan, Vivekanand, David Steier, Harvey Lewis, and James Guszcza. "Big data, big business." In the 1st International Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2351316.2351318.

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Gote, Christoph, Pavlin Mavrodiev, Frank Schweitzer, and Ingo Scholtes. "Big data = big insights?" In ICSE '22: 44th International Conference on Software Engineering. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3510003.3510619.

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Wang, Wei. "Big Data, Big Challenges." In 2014 IEEE International Conference on Semantic Computing (ICSC). IEEE, 2014. http://dx.doi.org/10.1109/icsc.2014.65.

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Shamsuddin, Siti Mariyam, and Shafaatunnur Hasan. "Data science vs big data @ UTM big data centre." In 2015 International Conference on Science in Information Technology (ICSITech). IEEE, 2015. http://dx.doi.org/10.1109/icsitech.2015.7407766.

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Mahoney, Bill. "Big Earth — Big data!" In 2012 Conference on Intelligent Data Understanding (CIDU). IEEE, 2012. http://dx.doi.org/10.1109/cidu.2012.6382180.

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Sun, Zhaohao. "Big Data 4.0 = Meta4 (Big Data) = The Era of Big Intelligence." In ICSIM 2024: 2024 the 7th International Conference on Software Engineering and Information Management. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3647722.3647725.

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Benjamins, V. Richard. "Big Data." In the 4th International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2611040.2611042.

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Gross, Thomas. "Big data." In the 6th International Systems and Storage Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2485732.2485757.

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Reports on the topic "Big Data"

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Zwitter, Andrej J., and Amelia Hadfield. Governing Big Data. Librello, January 2014. http://dx.doi.org/10.12924/pag2014.02010001.

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Gawade, Rushikesh. Big data and big dollars are changing cricket. Edited by Chris Bartlett. Monash University, February 2023. http://dx.doi.org/10.54377/783a-d051.

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Gildea, Timothy R. Big Data health Physics. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1603973.

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Goldstein, Itay, Chester Spatt, and Mao Ye. Big Data in Finance. Cambridge, MA: National Bureau of Economic Research, March 2021. http://dx.doi.org/10.3386/w28615.

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Bhattacharya, Tanmoy. Data-aware distributed scientific computing for big-data problems in bio-surveillance. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1092438.

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Big data en salud digital. Chair Alberto Urueña López and José María San Segundo Encinar. ONTSI : Fundación Vodafone España, March 2017. http://dx.doi.org/10.30923/5896-8.

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Alewijn, M. Big data - Banana origin determination. Wageningen: Wageningen Food Safety Research, 2020. http://dx.doi.org/10.18174/516096.

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Doucet, Rachel A., Deyan M. Dontchev, Javon S. Burden, and Thomas L. Skoff. Big Data Analytics Test Bed. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada589903.

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Farboodi, Maryam, Roxana Mihet, Thomas Philippon, and Laura Veldkamp. Big Data and Firm Dynamics. Cambridge, MA: National Bureau of Economic Research, January 2019. http://dx.doi.org/10.3386/w25515.

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Li, Junteng. ADL Data Capturing System: A Big Data Approach. Ames (Iowa): Iowa State University, January 2019. http://dx.doi.org/10.31274/cc-20240624-116.

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