Social Sensing
Building Reliable Systems on Unreliable Data
Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Internet
| Author: | Dong Wang, Tarek Abdelzaher, Lance Kaplan | ISBN: | 9780128011317 |
| Publisher: | Elsevier Science | Publication: | April 17, 2015 |
| Imprint: | Morgan Kaufmann | Language: | English |
| Author: | Dong Wang, Tarek Abdelzaher, Lance Kaplan |
| ISBN: | 9780128011317 |
| Publisher: | Elsevier Science |
| Publication: | April 17, 2015 |
| Imprint: | Morgan Kaufmann |
| Language: | English |
Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion.
- Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability
- Presents novel theoretical foundations for assured social sensing and modeling humans as sensors
- Includes case studies and application examples based on real data sets
- Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book
Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion.
- Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability
- Presents novel theoretical foundations for assured social sensing and modeling humans as sensors
- Includes case studies and application examples based on real data sets
- Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book
