Relevant bibliographies by topics / Data Analytics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Analytics'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Data Analytics"

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Kumar, S. Senthil, and Ms V. Kirthika. "Big Data Analytics Architecture and Challenges, Issues of Big Data Analytics." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (2017): 669–73. http://dx.doi.org/10.31142/ijtsrd4673.

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Gade, Kishore. "Data Analytics: Data Privacy, Data Ethics, Data Monetization." International Journal of Science and Research (IJSR) 9, no. 2 (2020): 1953–59. http://dx.doi.org/10.21275/sr20027110931.

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Sri Vardhan Pentapati, Harsha. "Management Data and Business Analytics." International Journal of Science and Research (IJSR) 10, no. 9 (2021): 1603–7. https://doi.org/10.21275/sr21927211738.

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C V, Pooja Suresh, and Dr S. K. Manju Bargavi. "IoT for Healthcare Data Analytics." International Journal of Research Publication and Reviews 5, no. 5 (2024): 6365–69. http://dx.doi.org/10.55248/gengpi.5.0524.1421.

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Husamaldin, Laden, and Nagham Saeed. "Big Data Analytics Correlation Taxonomy." Information 11, no. 1 (2019): 17. http://dx.doi.org/10.3390/info11010017.

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Big data analytics (BDA) is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. In the literature, researchers have focused on classifying big data according to data type, data security or level of difficulty, and many research papers reveal that there is a lack of information on evidence of a real-world link of big data analytics methods and its associated techniques. Thus, many organisations are still struggling to realise the actual value of big data analytic methods and its associated techniq
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Shastri, Shankarayya, M. Pooja, and R. M. Soumyashree. "Data Analytics for Linked Data in Real Time." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (2016): 32–36. http://dx.doi.org/10.9756/bijsesc.8238.

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Mehdizade, M. "BIG DATA ANALYTICS." Sciences of Europe, no. 142 (June 9, 2024): 77–81. https://doi.org/10.5281/zenodo.11535739.

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In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social
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Oddleifson, Evan. "The Effects of Modern Data Analytics in Electoral Politics." Political Science Undergraduate Review 5, no. 1 (2020): 46–52. http://dx.doi.org/10.29173/psur130.

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New implementations of data analytical processes in democratic politics deeply affect voter-representative relationships and constitute a substantive challenge to voter agency. This paper examines the effects of social media driven data analytics on voter microtargeting and electoral politics using Cambridge Analytica’s (CA) involvement in the 2016 US Presidential election and the 2010 Trinidad and Tobago General election. It finds that data-driven voter targeting strategies developed by Cambridge Analytica from 2014-2015 are substantially more effective than previously employed strategies. Mo
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S., Senthil Kumar, and Ms.V.Kirthika. "Big Data Analytics Architecture and Challenges, Issues of Big Data Analytics." International Journal of Trend in Scientific Research and Development 1, no. 6 (2017): 669–73. https://doi.org/10.31142/ijtsrd4673.

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Big Data technologies uses a new generation of technologies and architectures, designed for organizations can extract value from very large volumes of a wide variety of data by enabling high velocity capture, discovery, and or analysis. Big data is a massive amount of digital data being collected from various sources that are too large. Big data deals with challenges like complexity, security, risks to privacy. Big data is redefining as data management from extraction, transformation, cleaning and reducing. The size and variety of data lead us to think ahead and develop new and faster methods
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Kalvakolanu, Sripathi, and Madhavaiah Chendragiri. "A NEW WAVE OF DIGITALIZING HR: HR ANALYTICS." International Research Journal of Human Resources and Social Sciences 5, no. 4 (2018): 103–12. https://doi.org/10.5281/zenodo.4452026.

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Analytics gained strategic importance with the use of bigdata. Emergence of more sophisticated analytical tools increased the credibility of business analytics. Gradually all organizations are making use of analytics. Organizations are quick enough to catch up the new developments and adopt the technologies that increase business efficiency. The use of analytic capabilities to analyze and develop insights on HR related data is broadly termed as HR Analytics, or People Analytics, or Human Capital Analytics. Many HR Professional organizations have recognized Analytics as an important capability
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Dissertations / Theses on the topic "Data Analytics"

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Tata, Maitreyi. "Data analytics on Yelp data set." Kansas State University, 2017. http://hdl.handle.net/2097/38237.

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Master of Science<br>Department of Computing and Information Sciences<br>William H. Hsu<br>In this report, I describe a query-driven system which helps in deciding which restaurant to invest in or which area is good to open a new restaurant in a specific place. Analysis is performed on already existing businesses in every state. This is based on certain factors such as the average star rating, the total number of reviews associated with a specific restaurant, the price range of the restaurant etc. The results will give an idea of successful restaurants in a city, which helps you decide where
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Le, Quoc Do. "Approximate Data Analytics Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234219.

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Today, most modern online services make use of big data analytics systems to extract useful information from the raw digital data. The data normally arrives as a continuous data stream at a high speed and in huge volumes. The cost of handling this massive data can be significant. Providing interactive latency in processing the data is often impractical due to the fact that the data is growing exponentially and even faster than Moore’s law predictions. To overcome this problem, approximate computing has recently emerged as a promising solution. Approximate computing is based on the observation
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Canon, Moreno Javier Mauricio 1977. "Leading data analytics transformations." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111472.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 77-79).<br>The phenomenal success of big technology companies founded with a strong emphasis on data, has epitomized the rise of the new "digital economy." Large traditional organizations, that were not long ago "on top of the world" are now at a crossroads. Their business models seem threatened by newcomers as they face pressure to "transform" and "modernize." Publicity has reinforced the perception that data can now be
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Ahsan, Ramoza. "Time Series Data Analytics." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/529.

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Given the ubiquity of time series data, and the exponential growth of databases, there has recently been an explosion of interest in time series data mining. Finding similar trends and patterns among time series data is critical for many applications ranging from financial planning, weather forecasting, stock analysis to policy making. With time series being high-dimensional objects, detection of similar trends especially at the granularity of subsequences or among time series of different lengths and temporal misalignments incurs prohibitively high computation costs. Finding trend
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Erlandsson, Niklas. "Game Analytics och Big Data." Thesis, Mittuniversitetet, Avdelningen för arkiv- och datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29185.

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Game Analytics är ett område som vuxit fram under senare år. Spelutvecklare har möjligheten att analysera hur deras kunder använder deras produkter ned till minsta knapptryckning. Detta kan resultera i stora mängder data och utmaning ligger i att lyckas göra något vettigt av sitt data. Utmaningarna med speldata beskrivs ofta med liknande egenskaper som används för att beskriva Big Data: volume, velocity och variability. Detta borde betyda att det finns potential för ett givande samarbete. Studiens syfte är att analysera och utvärdera vilka möjligheter Big Data ger att utveckla området Game Ana
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Doucet, Rachel A., Deyan M. Dontchev, Javon S. Burden, and Thomas L. Skoff. "Big data analytics test bed." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37615.

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Approved for public release; distribution is unlimited<br>The proliferation of big data has significantly expanded the quantity and breadth of information throughout the DoD. The task of processing and analyzing this data has become difficult, if not infeasible, using traditional relational databases. The Navy has a growing priority for information processing, exploitation, and dissemination, which makes use of the vast network of sensors that produce a large amount of big data. This capstone report explores the feasibility of a scalable Tactical Cloud architecture that will harness and utiliz
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Naumanen, Hampus, Torsten Malmgård, and Eystein Waade. "Analytics tool for radar data." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353857.

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Analytics tool for radar data was a project that started when radar specialists at Saab needed to modernize their tools that analyzes binary encoded radar data. Today, the analysis is accomplished using inadequate and ineffective applications not designed for that purpose, and consequently this makes the analysis tedious and more difficult compared to using an appropriate interface. The applications had limitations regarding different radar systems too, which restricted their usage significantly. The solution was to design a new software that imports, translates and visualizes the data indepen
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Komolafe, Tomilayo A. "Data Analytics for Statistical Learning." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/87468.

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The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing oper
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Miloš, Marek. "Nástroje pro Big Data Analytics." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199274.

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The thesis covers the term for specific data analysis called Big Data. The thesis firstly defines the term Big Data and the need for its creation because of the rising need for deeper data processing and analysis tools and methods. The thesis also covers some of the technical aspects of Big Data tools, focusing on Apache Hadoop in detail. The later chapters contain Big Data market analysis and describe the biggest Big Data competitors and tools. The practical part of the thesis presents a way of using Apache Hadoop to perform data analysis with data from Twitter and the results are then visual
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Santhanagopalan, Meena. "Biopsychosocial Data Analytics and Modeling." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/177435.

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Sustained customisation of digital health intervention (DHI) programs, in the context of community health engagement, requires strong integration of multi-sourced interdisciplinary biopsychosocial health data. The biopsychosocial model is built upon the idea that biological, psychological and social processes are integrally and interactively involved in physical health and illness. One of the longstanding challenges of dealing with healthcare data is the wide variety of data generated from different sources and the increasing need to learn actionable insights that drive performance improvement
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Books on the topic "Data Analytics"

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Badiru, Adedeji B. Data Analytics. CRC Press, 2020. http://dx.doi.org/10.1201/9781003083146.

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Huang, Shuai, and Houtao Deng. Data Analytics. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003102656.

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Calì, Andrea, Peter Wood, Nigel Martin, and Alexandra Poulovassilis, eds. Data Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60795-5.

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Runkler, Thomas A. Data Analytics. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-14075-5.

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Runkler, Thomas A. Data Analytics. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29779-4.

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Runkler, Thomas A. Data Analytics. Vieweg+Teubner Verlag, 2012. http://dx.doi.org/10.1007/978-3-8348-2589-6.

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Cuadrado-Gallego, Juan J., and Yuri Demchenko. Data Analytics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-39129-3.

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Runkler, Thomas A. Data Analytics. Springer Fachmedien Wiesbaden, 2025. http://dx.doi.org/10.1007/978-3-658-45951-2.

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Tyagi, Amit Kumar. Data Science and Data Analytics. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003111290.

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Sedkaoui, Soraya. Data Analytics and Big Data. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119528043.

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Book chapters on the topic "Data Analytics"

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Cuadrado-Gallego, Juan J., and Yuri Demchenko. "Data." In Data Analytics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-39129-3_2.

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Hargreaves, Carol. "Healthcare Analytics." In Data Science. CRC Press, 2019. http://dx.doi.org/10.1201/9780429263798-7.

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Pincetl, Stephanie, Hannah Gustafson, Felicia Federico, Eric Daniel Fournier, Robert Cudd, and Erik Porse. "Data Analytics." In Energy Use in Cities. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55601-3_6.

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Balali, Farhad, Jessie Nouri, Adel Nasiri, and Tian Zhao. "Data Analytics." In Data Intensive Industrial Asset Management. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35930-0_7.

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Fleckenstein, Mike, and Lorraine Fellows. "Data Analytics." In Modern Data Strategy. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68993-7_13.

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Akhtar, Saaema. "Data Analytics." In Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things. CRC Press, 2023. http://dx.doi.org/10.1201/9781003383505-5.

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Treder, Martin. "Data Analytics." In Das Management-Handbuch für Chief Data Officer. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9346-1_20.

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Sun, Zhaohao. "Data Analytics." In Data Analytics for Business Intelligence. Chapman and Hall/CRC, 2024. https://doi.org/10.1201/9781003450504-7.

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Karim, Ramin, Diego Galar, and Uday Kumar. "Data Analytics." In AI Factory. CRC Press, 2023. http://dx.doi.org/10.1201/9781003208686-4.

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Martens, Jan. "Data Analytics." In Digitales Produktmanagement. Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-41880-9_19.

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Conference papers on the topic "Data Analytics"

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Hedayati, Helia, and Saeed Samet. "DataInsight: Big Data Analytics Services." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10824998.

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Franceschina, Luciano. "Data Analytics." In CCS'16: 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2016. http://dx.doi.org/10.1145/2996429.2996441.

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Triantafillou, Peter. "Data-Less Big Data Analytics (Towards Intelligent Data Analytics Systems)." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00205.

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Chandler, Tom, Maxime Cordeil, Tobias Czauderna, et al. "Immersive Analytics." In 2015 Big Data Visual Analytics (BDVA). IEEE, 2015. http://dx.doi.org/10.1109/bdva.2015.7314296.

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ElSayed, Neven, Bruce Thomas, Kim Marriott, Julia Piantadosi, and Ross Smith. "Situated Analytics." In 2015 Big Data Visual Analytics (BDVA). IEEE, 2015. http://dx.doi.org/10.1109/bdva.2015.7314302.

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Zhang, Chi, and Hossain Shahriar. "Health Data Analytics." In SIGITE '18: The 19th Annual Conference on Information Technology Education. ACM, 2018. http://dx.doi.org/10.1145/3241815.3241887.

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Madden, Samuel. "Interactive data analytics." In SoCC '15: ACM Symposium on Cloud Computing. ACM, 2015. http://dx.doi.org/10.1145/2806777.2809956.

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Yang, Tianbao, Qihang Lin, and Rong Jin. "Big Data Analytics." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. http://dx.doi.org/10.1145/2783258.2789989.

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Bourassa, Norman, Walker Johnson, Jeff Broughton, et al. "Operational Data Analytics." In ICPP 2019: Workshops. ACM, 2019. http://dx.doi.org/10.1145/3339186.3339210.

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Singh, Sachchidanand, and Nirmala Singh. "Big Data analytics." In 2012 International Conference on Communication, Information & Computing Technology (ICCICT). IEEE, 2012. http://dx.doi.org/10.1109/iccict.2012.6398180.

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Reports on the topic "Data Analytics"

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Murphy, David Patrick, and Matthew Thomas Calef. Data Analytics for SAR. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1396159.

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Zhang, Jovan Yang, Hari Viswanathan, Jeffery Hyman, and Richard Middleton. Data Analytics of Hydraulic Fracturing Data. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1304742.

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Gungor, Osman, Imad Al-Qadi, and Navneet Garg. Pavement Data Analytics for Collected Sensor Data. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-034.

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The Federal Aviation Administration instrumented four concrete slabs of a taxiway at the John F. Kennedy International Airport to collect pavement responses under aircraft and environmental loading. The study started with developing preprocessing scripts to organize, structure, and clean the collected data. As a result of the preprocessing step, the data became easier and more intuitive for pavement engineers and researchers to transform and process. After the data were cleaned and organized, they were used to develop two prediction models. The first prediction model employs a Bayesian calibra
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Hollis, Andrew Nathan. Data Visualization for Threat Analytics. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1212630.

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Krishnamurthy, Narayanan, Siddharth Maddali, Amit Verma, et al. Data Analytics for Alloy Qualification. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1456238.

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Ruch, Marc Lavi. Data Analytics for Nonproliferation Applications. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1529508.

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

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Calderon, Marta, and Luis Jugo. Leveraging Data Analytics Beyond Assurance. Inter-American Development Bank, 2014. http://dx.doi.org/10.18235/0006992.

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This presentation gives an overview of the importance of creating a data analytics strategy in an internal audit department. It describes reasons for having a data analytics strategy, where to start (steps) when formulating a strategy, typical challenges to be addressed, what should be the focus of the strategy, and how to analyze the data analytics capability and usage maturity. Finally, it presents the IDB experience implementing a data analytics strategy for its internal audit activities.
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Phillips, Thurman B., and Raymond J. Lanclos III. Data Analytics in Procurement Fraud Prevention. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada626749.

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Allen, Courtney, Samuel Faluade, Cameron Payseno, Zachary Thomander, Cody Kasten, and Alex Darwin. Data Analytics: Project-level Cost Estimates. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1762650.

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