Sublinear Algorithms for Big Data Applications by Dan Wang
The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.-
Concise Computer Mathematics
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The Data Grid
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Resource Management for Satellite-Terrestrial Vehicular Networks
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Computational Intelligence for Remote Sensing Image Change Detection
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Algorithmic Fairness in AI-Mediated Institutional Communication
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AbstractSwarm Multi-Agent Modeling and Simulation
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Federated Learning for Smart Mobility
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Introduction to Ethical Software Development
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Digital Image Forgery Detection
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Blockchain Without Barriers
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Human Reconstruction Using mmWave Technology
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Universal Time-Series Forecasting with Mixture Predictors
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Turbo Message Passing Algorithms for Structured Signal Recovery
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Developing Sustainable and Energy-Efficient Software Systems
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Latent Factor Analysis for High-dimensional and Sparse Matrices
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Cognitive Security
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Dynamic Network Representation Based on Latent Factorization of Tensors
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Security-aware Cooperation in Cognitive Radio Networks
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Robust Latent Feature Learning for Incomplete Big Data
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Seamless and Secure Communications over Heterogeneous Wireless Networks
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The AI Act and The Agile Safety Plan
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Representation in Machine Learning
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Enabling Secure and Privacy Preserving Communications in Smart Grids
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Energy Detection for Spectrum Sensing in Cognitive Radio
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Enabling Content Distribution in Vehicular Ad Hoc Networks
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Machine Learning Empowered Intelligent Data Center Networking
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Energy-Efficient Area Coverage for Intruder Detection in Sensor Networks
“Wang and Han’s book focuses on sublinear algorithms for processing big data… For the researcher, this book also shows that there is room for improvement and new discoveries in this flourishing area. … The book is thus recommended mainly to researchers, but just as a piece of the bigger puzzle of sublinear algorithms for big data processing and applications.” (Corrado Mencar, Computing Reviews, computingreviews.com, August, 2016)
Han, Zhu: - Zhu Han is currently an Assistant Professor in the Electrical and Computer Engineering Department at Boise State University, Idaho. In 2003, he was awarded his Ph.D. in electrical engineering from the University of Maryland, College Park. Zhu Han has also worked for a period in industry, as an R & D Engineer for JDSD. Dr Han is PHY/MAC Symposium vice chair of IEEE Wireless Communications and Networking Conference, 2008.
| SKU | Unavailable |
| ISBN 13 | 9783319204475 |
| ISBN 10 | 3319204475 |
| Title | Sublinear Algorithms for Big Data Applications |
| Author | Dan Wang |
| Series | Springerbriefs In Computer Science |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2015-08-20 |
| Number of pages | 85 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |