Data-Intensive Computing

Baker, Nathan A. Barr, Jonathan L. Bonheyo, George T. Joslyn, Cliff A. Krishnaswami, Kannan Oxley, Mark E. Quadrel, Rich Sego, Landon H. Tardiff, Mark F. and Wynne, Adam S. 2013. Research towards a systematic signature discovery process. p. 301.

Sahli, Majed Mansour, Essam and Kalnis, Panos 2015. StarDB. Proceedings of the VLDB Endowment, Vol. 8, Issue. 12, p. 1844.

Feng, Jianyuan Turksoy, Kamuran and Cinar, Ali 2016. Prediction Methods for Blood Glucose Concentration. p. 243.

Semenov, N. A. and Poltavtsev, A. A. 2019. Cloud-Based Data Architecture Security. Automatic Control and Computer Sciences, Vol. 53, Issue. 8, p. 1056.

Poltavtsev, A. A. Khabarov, A. R. and Selyankin, A. O. 2020. Inference Attacks and Information Security in Databases. Automatic Control and Computer Sciences, Vol. 54, Issue. 8, p. 829.

Srinivasan, Vignesh and K, Chandrasekaran 2021. Data Aggregation Of Tweets And Topic Modelling Based On The Twitter Dataset. p. 15.

Bocciarelli, Paolo D’Ambrogio, Andrea Panetti, Tommaso and Giglio, Andrea 2022. E-MDAV: A Framework for Developing Data-Intensive Web Applications. Informatics, Vol. 9, Issue. 1, p. 12.

Anderson, Eric D. Erxleben, Jennifer R. Qi, Sharon L. Monroe, Adrian P. and Dahm, Katharine G. 2023.

Al Jawarneh, Isam Mashhour Foschini, Luca and Bellavista, Paolo 2023. Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees. Future Internet, Vol. 15, Issue. 8, p. 263.

Edited by Ian Gorton , Pacific Northwest National Laboratory, Washington , Deborah K. Gracio , Pacific Northwest National Laboratory, Washington

Publisher: Cambridge University Press Online publication date: December 2012 Print publication year: 2012 Online ISBN: 9780511844409 Digital access for individuals (PDF download and/or read online) Added to cart Digital access for individuals (PDF download and/or read online)

Book description

The world is awash with digital data from social networks, blogs, business, science and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice.

Reviews

"Overall, I recommend this book for researchers and advanced graduate students. The collection presents different essays for a very rich and diversified overview of one of the most recent and fast-paced revolutions in computer science."
Radu State, Computing Reviews