Systems Simulation and Modelling for Cloud Computing and Big Data Applications provides readers with the most current approaches to solving problems through the use of models and simulations, presenting SSM based approaches to performance testing and benchmarking that offer significant advantages. For example, multiple big data and cloud application developers and researchers can perform tests in a controllable and repeatable manner. Inspired by the need to analyze the performance of different big data processing and cloud frameworks, researchers have introduced several benchmarks, including BigDataBench, BigBench, HiBench, PigMix, CloudSuite and GridMix, which are all covered in this book.
Despite the substantial progress, the research community still needs a holistic, comprehensive big data SSM to use in almost every scientific and engineering discipline involving multidisciplinary research. SSM develops frameworks that are applicable across disciplines to develop benchmarking tools that are useful in solutions development.
1. Cover image - p. page_i 2. Title page - p. page_i 3. Table of Contents - p. page_i 4. Copyright - p. page_iv 5. Contributors - p. page_xi 6. Preface - p. page_xv 7. Chapter 1: Differential color harmony: A robust approach for extracting harmonic color features and perceiving aesthetics in a large image dataset - p. page_xv 8. Chapter 2: Physiological parameter measurement using wearable sensors and cloud computing - p. page_13 9. Chapter 3: Social media data analytics using feature engineering - p. page_27 10. Chapter 4: A novel framework for quality care in assisting chronically impaired patients with ubiquitous computing and ambient intelligence technologies - p. page_59 11. Chapter 5: Dynamic and static system modeling with simulation of an eco-friendly smart lighting system - p. page_79 12. Chapter 6: Predictive analysis of diabetic women patients using R - p. page_97 13. Chapter 7: IoT-based smart mirror for health monitoring - p. page_113 14. Chapter 8: Discovering human influenza virus using ensemble learning - p. page_121 15. Chapter 9: Mining and monitoring human activity patterns in smart environment-based healthcare systems - p. page_135 16. Chapter 10: Early detection of cognitive impairment of elders using wearable sensors - p. page_145 17. Index - p. page_161
Systems Simulation and Modelling for Cloud Computing and Big Data Applications provides readers with the most current approaches to solving problems through the use of models and simulations, presenting SSM based approaches to performance testing and benchmarking that offer significant advantages. For example, multiple big data and cloud application developers and researchers can perform tests in a controllable and repeatable manner. Inspired by the need to analyze the performance of different big data processing and cloud frameworks, researchers have introduced several benchmarks, including BigDataBench, BigBench, HiBench, PigMix, CloudSuite and GridMix, which are all covered in this book.
Despite the substantial progress, the research community still needs a holistic, comprehensive big data SSM to use in almost every scientific and engineering discipline involving multidisciplinary research. SSM develops frameworks that are applicable across disciplines to develop benchmarking tools that are useful in solutions development.
1. Cover image - p. page_i 2. Title page - p. page_i 3. Table of Contents - p. page_i 4. Copyright - p. page_iv 5. Contributors - p. page_xi 6. Preface - p. page_xv 7. Chapter 1: Differential color harmony: A robust approach for extracting harmonic color features and perceiving aesthetics in a large image dataset - p. page_xv 8. Chapter 2: Physiological parameter measurement using wearable sensors and cloud computing - p. page_13 9. Chapter 3: Social media data analytics using feature engineering - p. page_27 10. Chapter 4: A novel framework for quality care in assisting chronically impaired patients with ubiquitous computing and ambient intelligence technologies - p. page_59 11. Chapter 5: Dynamic and static system modeling with simulation of an eco-friendly smart lighting system - p. page_79 12. Chapter 6: Predictive analysis of diabetic women patients using R - p. page_97 13. Chapter 7: IoT-based smart mirror for health monitoring - p. page_113 14. Chapter 8: Discovering human influenza virus using ensemble learning - p. page_121 15. Chapter 9: Mining and monitoring human activity patterns in smart environment-based healthcare systems - p. page_135 16. Chapter 10: Early detection of cognitive impairment of elders using wearable sensors - p. page_145 17. Index - p. page_161
