Chapter 6 Real-time big data analytics
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Abstract
Real-Time Big Data Analytics (RTBDA) is a tool or a software feature that has the capability of analyzing huge volumes of data gathered and stored by IT enterprises. RTBDA is a combination of real-time analytics and big data. It is a good initiative in today's business world. Big data that comes in from websites, logs, and enterprises can be processed as they arrive, and a decision can be taken in real time, based on the data. Enterprise Security Software and Enterprise Event Management software have huge sets of real-time data to be processed. Enterprises deploy their applications on cloud. Each application creates an event and log records. This real data can be processed efficiently to make better decisions. Examining big data is possible due to enhanced analytical capabilities, efficient access to various data sources, and inexpensive and improved computing power in the form of cloud computing. Mathematical and logical concepts are applied over the huge volume of data to make better real-time decisions. This big data analytics can also be business analytics, app analytics, web data analytics, and so on.
Abstract
Real-Time Big Data Analytics (RTBDA) is a tool or a software feature that has the capability of analyzing huge volumes of data gathered and stored by IT enterprises. RTBDA is a combination of real-time analytics and big data. It is a good initiative in today's business world. Big data that comes in from websites, logs, and enterprises can be processed as they arrive, and a decision can be taken in real time, based on the data. Enterprise Security Software and Enterprise Event Management software have huge sets of real-time data to be processed. Enterprises deploy their applications on cloud. Each application creates an event and log records. This real data can be processed efficiently to make better decisions. Examining big data is possible due to enhanced analytical capabilities, efficient access to various data sources, and inexpensive and improved computing power in the form of cloud computing. Mathematical and logical concepts are applied over the huge volume of data to make better real-time decisions. This big data analytics can also be business analytics, app analytics, web data analytics, and so on.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- List of contributing authors IX
- Chapter 1 Digital transformation technology and tools: shaping the future of primary health care 1
- Chapter 2 Predictive maintenance of industrial machines using data collected through IoT sensors and analyzed by machine learning algorithms 27
- Chapter 3 A deep survey on quantum computing technologies 49
- Chapter 4 Machine learning and deep learning 71
- Chapter 5 From evolution to revolution: the contemporary development of quantum computing 85
- Chapter 6 Real-time big data analytics 107
- Chapter 7 Quantum processors/networks/sensors 129
- Chapter 8 Quantum computing in automata theory 147
- Chapter 9 Quantum computing: future of artificial intelligence and its applications 163
- Chapter 10 A leap among quantum ML and DL models: a review 185
- Chapter 11 A perspective study on quantum machine learning models for the areas of medicine, materials, sensing, and communication 205
- Chapter 12 Quantum computing: application-specific need of the hour 225
- Chapter 13 Industrial Internet of things and Industry 4.0: a learner’s perspectives toward quantum technologies 243
- Chapter 14 Applications of quantum AI for healthcare 271
- Biography 289
- Index 291
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- List of contributing authors IX
- Chapter 1 Digital transformation technology and tools: shaping the future of primary health care 1
- Chapter 2 Predictive maintenance of industrial machines using data collected through IoT sensors and analyzed by machine learning algorithms 27
- Chapter 3 A deep survey on quantum computing technologies 49
- Chapter 4 Machine learning and deep learning 71
- Chapter 5 From evolution to revolution: the contemporary development of quantum computing 85
- Chapter 6 Real-time big data analytics 107
- Chapter 7 Quantum processors/networks/sensors 129
- Chapter 8 Quantum computing in automata theory 147
- Chapter 9 Quantum computing: future of artificial intelligence and its applications 163
- Chapter 10 A leap among quantum ML and DL models: a review 185
- Chapter 11 A perspective study on quantum machine learning models for the areas of medicine, materials, sensing, and communication 205
- Chapter 12 Quantum computing: application-specific need of the hour 225
- Chapter 13 Industrial Internet of things and Industry 4.0: a learner’s perspectives toward quantum technologies 243
- Chapter 14 Applications of quantum AI for healthcare 271
- Biography 289
- Index 291
