Social Sensing Building Reliable Systems on Unreliable Data
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- Taschenbuch
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Form:Einzelkauf Download
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Sprache:Englisch
71,95 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
17.04.2015
Verlag
Elsevier Science & Techn.Seitenzahl
232 (Printausgabe)
Sprache
Englisch
EAN
9780128011317
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
- 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
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