Smart data : state-of-the-art perspectives in computing and applications / edited by Kuan-Ching Li, Kuan-Ching Zhang, Laurence T. Yang, Beniamino Di Martino
| Publisher | (Boca Raton, Florida : CRC Press) |
|---|---|
| Year | [2019] |
| Authors | Li, Kuan-Ching editor |
Hide book details.
| Links to the text | Location | Volume | Call No. | Barcode No. | Status | Comments | ISBN | Printed | Restriction | Reserve |
|---|---|---|---|---|---|---|---|---|---|---|
| Links to the text | Library Off-campus access |
|
OB00178706 | Taylor & Francis eBooks (電子ブック) | 9780429018022 |
|
|
Hide details.
| Material Type | E-Book |
|---|---|
| Media type | 機械可読データファイル |
| Size | 1 online resource |
| Notes | Cover; Half Title; Title Page; Copyright Page; Table of Contents; Foreword; Acknowledgments; Editors; List of Contributors; CHAPTER 1 : Extreme Heterogeneity in Deep Learning Architectures; 1 Introduction; 1.1 Deep Learning; 1.2 Deep Learning Operations; 1.3 Deep Learning Network Communications; 1.3.1 Data and Model Parallelism; 2 Hardware Architectures for Deep Learning; 2.1 Microprocessors; 2.2 Digital Signal Processors; 2.3 Graphics Processing Units; 2.4 Coarse Grained Reconfigurable Architectures; 2.5 Tensor Processing Units; 2.6 Mapping Deep Learning to an Architecture 3 FPGAs in Deep Learning3.1 FPGA Optimizations for NN Operations; 3.1.1 Communications Path Optimizations; 3.1.2 Processing Element Optimizations; 3.1.3 NN Model Optimizations; 3.2 FPGA Reconfigurability for NNs; 3.2.1 FPGA Reconfigurability in Inference Processing; 3.2.2 FPGA Reconfigurability in NN Training; 4 Discussion; 4.1 Neural Network Models for Heterogeneous Systems; 5 Conclusion; Further Reading; References; CHAPTER 2 : GPU PaaS Computation Model in Aneka Cloud Computing Environments; 1 Introduction; 2 Background; 2.1 Cloud Computing; 2.2 GPU Computing 2.3 Aneka: Cloud Application Platform3 Motivation; 3.1 Challenges for GPU Programming in Cloud; 4 Related Work; 4.1 GPU Virtualization Through API Remoting; 4.1.1 vCUDA; 4.1.2 gVirtus; 4.1.3 rCUDA; 4.2 Using Resource Management Systems; 4.3 Using Framework Schedulers; 4.4 GPUs in Cloud; 5 Methodology for Aneka GPU Computing; 5.1 Methodology; 5.2 Extended Aneka Architecture; 5.2.1 GPU Resources in IaaS; 5.2.2 Task Programming Model for GPU; 5.2.3 APIs and SDKs for GPGPU on Aneka Cloud; 5.3 GPU-Aware Scheduling of Tasks; 5.4 Template for Aneka GPU Task Programming 6 Image Edge Detection on Aneka Cloud Using GPU: A Case Study6.1 Edge Detection Algorithm; 6.2 Parallel Implementation of Sobel Edge Detection on GPUs; 6.3 Results and Analysis; 7 Future Directions; 8 Summary and Conclusions; References; CHAPTER 3 : Toward Complex Search for Encrypted Mobile Cloud Data via Index Blind Storage; 1 Background; 2 Searchable Symmetric Encryption (SSE) and Searchable Asymmetric Encryption (SAE); 2.1 Searchable Symmetric Encryption; 2.2 Searchable Asymmetric Encryption; 2.3 Trapdoor; 3 Index Blind Storage Scheme Overview; 3.1 System Model; 3.2 Security Guarantees 3.3 Data Structures3.4 Index Blind Storage Operations; 4 The Algorithms Implementations for IBS-SSE; 4.1 The Implementation Flow; 4.2 Access Control Algorithms; 5 Security Analysis; 5.1 Confidentiality of Data and Index; 5.2 Trapdoor Security; 5.3 Complete Concealing of Access Pattern; 5.4 The Probability of Aborting; 6 Performance Evaluation; 6.1 Functionality; 6.2 Computation Costs; 6.3 Communication Cost; 7 Conclusion; CHAPTER 4 : Encrypted Big Data Deduplication in Cloud Storage; 1 Introduction; 2 Related Work Review and Open Research Issues; 2.1 Access Control on Encrypted Data Smart Data: State-of-the-Art Perspectives in Computing and Applications explores smart data computing techniques to provide intelligent decision making and prediction services support for business, science, and engineering. It also examines the latest research trends in fields related to smart data computing and applications, including new computing theories, data mining and machine learning techniques. The book features contributions from leading experts and covers cutting-edge topics such as smart data and cloud computing, AI for networking, smart data deep learning, Big Data capture and representation, AI for Big Data applications, and more. Features Presents state-of-the-art research in big data and smart computing Provides a broad coverage of topics in data science and machine learning Combines computing methods with domain knowledge and a focus on applications in science, engineering, and business Covers data security and privacy, including AI techniques Includes contributions from leading researchers OCLC-licensed vendor bibliographic record HTTP:URL=https://www.taylorfrancis.com/books/9780429507670 Information=Taylor & Francis HTTP:URL=http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf Information=OCLC metadata license agreement |
| Subjects | LCSH:Big data LCSH:Decision making -- Data processing All Subject Search LCSH:Computer network resources FREE:COMPUTERS / Databases / Data Warehousing FREE:BUSINESS & ECONOMICS / Statistics FREE:COMPUTERS / General FREE:COMPUTERS / Database Management / Data Mining |
| Classification | LCC:QA76.9.B45 DC23:005.7 |
| ID | 8000080684 |
| ISBN | 9780429018022 |
Similar Items
Usage statistics of this contents
Number of accesses to this page：7times