Social Sensing: Building Reliable Systems on Unreliable Data by Dong Wang, Tarek Abdelzaher, Lance Kaplan

By Dong Wang, Tarek Abdelzaher, Lance Kaplan

Increasingly, humans are sensors enticing at once with the cellular net. contributors can now percentage real-time stories at an extraordinary scale. Social Sensing: development trustworthy platforms on Unreliable info looks at fresh advances within the rising box of social sensing, emphasizing the major challenge confronted by way of software designers: tips to extract trustworthy details from facts gathered from mostly unknown and probably unreliable assets. The ebook explains how a myriad of societal purposes may be derived from this huge quantity of knowledge amassed and shared via typical participants. The identify deals theoretical foundations to help rising data-driven cyber-physical purposes and touches on key concerns similar to privateness. The authors current suggestions in response to contemporary examine and novel rules that leverage strategies from cyber-physical platforms, sensor networks, computing device studying, information mining, and knowledge fusion.

  • Offers a distinct interdisciplinary standpoint bridging social networks, mammoth facts, cyber-physical platforms, and reliability
  • Presents novel theoretical foundations for guaranteed social sensing and modeling people as sensors
  • Includes case reviews and alertness examples in keeping with genuine information sets
  • Supplemental fabric comprises pattern datasets and fact-finding software program that implements the most algorithms defined within the book

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Additional resources for Social Sensing: Building Reliable Systems on Unreliable Data

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They built a Latent Truth Model (LTM) based on maximum a posterior (MAP), which in general needs the prior on both source reliability and claim truthfulness. In particular, the LTM explicitly models two aspects of source quality by considering both false positive and false negative errors made by a source. They solved the MAP estimation problem by using the collapsed Gibbs sampling method. An incremental approximation algorithm was also developed to efficiently handle streaming data. There exists some limitations of LTM that originate from several assumptions made by the model.

The prior knowledge p(B) is the marginal probability of a patient to have a cardiac disease, not knowing anything beyond the fact he/she is a 50-year-old. We call this information prior knowledge because it exists before the test. Suppose we know from previous research and statistics that the probability of a 50-year-old to have a cardiac disease is 5% in the population. 111. , the positive test result). The small posterior probability is somewhat counter-intuitive given a test with so-called “95%” accuracy.

Once the answers are given, it is in principle possible to define an error based on a comparison of what the fact-finder believed and what was actually true in the external physical world. The distinction is important because it allows us to formulate fact-finding problems as ones of minimizing the difference between exact and estimated states of systems. In other words, we cast them as sensing problems and are thus able to apply results from traditional estimation theory. 2 Overview of fact-finders in information networks It remains to make two more points clear.

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