The expansion of the World Wide Web to include information that is generated by physical devices with embedded sensing and actuation capabilities entails a surge of high-frequency real-time data that is mostly published without further processing in its raw form. To derive "smart" decisions from this data and thus use it to enable a "smart world" requires the distilling of more abstract, higher-level knowledge from it. We propose the concept of a computational marketplace as a framework to enable the analysis and aggregation of real-time data. Here, multiple tiers of hyperlinked algorithms from different providers interact to refine data within computational graphs, which are linked structures of cascaded processing steps. In the associated paper, S. Mayer and D. Karam, A Computational Space for the Web of Things, we present an analysis of the key constraints on such a framework and provide a corresponding implementation as well as results from evaluations in an experimental use case scenario.

1. A Computational Space for the Web of Things
3rd International Workshop on the Web of Things (WoT 2012), Newcastle, UK
Simon Mayer, Distributed Systems Group, ETH Zurich, simon.mayer@inf.ethz.ch
David S. Karam, Chair of Scientific Computing, TU Munich, karam@in.tum.de

2. Motivation / Background
 Earthquake and Tsunami on March 11, 2011
 Nuclear meltdown in three reactors of the Fukushima NPP
 Releases of radioactive materials
US federal occupational limit: 0.5 μSv/h
www.pref.fukushima.jp
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3. Motivation / Background
Cheap
Geiger
counters
Radiation
data
streams
 “Wind from Fukushima” App
Mashing of radiation and wind data
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4. Motivation / Background
Commodity
Hardware
Information
Markets
 Apps like “Wind from Fukushima” allow to
mashup this data to provide a service
 What happens with the refined data?
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5. Motivation / Background
Crowd-sourced information creation
Commodity
Hardware
Information sharing and aggregation
Information
Markets
Comput
ational
Markets Information processing, refinement, and
analysis
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6. Motivation / Background
Smartphones, Smart Consumer
Crowd-sourced information creation
Products, Electricity Meters, ...
Commodity
Hardware
Cosm, Sen.Se, Twitter,
Information sharing and aggregation
FB Graph API, ...
Information
Markets
Comput
ational
Markets Information processing, refinement, and
analysis
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7. Algorithms should be linked together in an open
and extensible fashion to enable multi-tier
computations in a construct that we call a
computational marketplace

8. Constraints on a Computational Marketplace
 Clients need to be able to find computations on the
marketplace and select appropriate computations to
achieve their goal
 The marketplace should provide meta-information about
algorithms and thus help to optimize traversal
 Mechanisms required for security and billing
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9. Constraints on a Computational Marketplace
 Clients need to be able to find computations on the
marketplace and select appropriate computations to
achieve their goal
 The marketplace should provide meta-information about
algorithms and thus help to optimize traversal
 Mechanisms required for security and billing
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10. Interface Discovery
 Marketplace API exposes paths
GET marketplace/node_arrhythmia
{
“service” : “www.arrhythmia_detection.net”
“forward_paths” : {
“emergency” : “node_dispatch”,
“default” : “node_start_arrhythmia”
}
}
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11. Interface Discovery
GET marketplace/node_arrhythmia
{
 Marketplace API exposes paths “service” :
“www.arrhythmia_detection.net”
“forward_paths” : {
“emergency” : “node_dispatch”,
“default” : “node_start_arrhythmia”
}
}
 “Graph Crawler” maps computational graph
 Basically an ordinary search machine…
Crawler
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12. Constraints on a Computational Marketplace
 Clients need to be able to find computations on the
marketplace and select appropriate computations to
achieve their goal
 The marketplace should provide meta-information about
algorithms and thus help to optimize traversal
 Mechanisms required for security and billing
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13. Path Traversal Guidance
 Humans do it...
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14. Path Traversal Guidance
 Machines can, too!
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15. Path Traversal Guidance using Path Deciders
 Application-specific (here: Arrhythmia Detection)
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16. Path Traversal Guidance will work if...
 Machines can understand and interpret path names
 Long shot, but could work...
 Definitely future work!
 Path descriptions (e.g., “emergency”) are fixed/meaningful
 Easy!
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17. Constraints on a Computational Marketplace
 Clients need to be able to find computations on the
marketplace and select appropriate computations to
achieve their goal
 The marketplace should provide meta-information about
algorithms and thus help to optimize traversal
 Mechanisms required for security and billing
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18. Computational Paths Optimization
 Optimize traversal according to application needs
 Multi-dimensional cost metrics: time, money, quality,...
routes → Generic path deciders
 Marketplace offers this information, but does not decide on
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19. Computational Paths Optimization using Path
Deciders
 Generic (here: Lowest time cost path)
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20. Constraints on a Computational Marketplace
 Clients need to be able to find computations on the
marketplace and select appropriate computations to
achieve their goal
 The marketplace should provide meta-information about
algorithms and thus help to optimize traversal
 Mechanisms required for security and billing
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21. Security and Billing
 Authentication, authorization, and billing schemes to
restrict access to computational resources
 Don’t create a centralized repository for login data: trust
problems, security problems, scaling problems...
 Instead use third-party schemes (OAuth 2.0!) and inline
this functionality as a linked computational node!
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22. Demo 1: Arrhythmia Patient Scenario
 Four separate computational mashups
 Arrhythmia Arrhythmia probability from heartbeat data
 Ambulance Optimal ambulance dispatch
 Traffic Predicts traffic from location of people and cars and
weather data from yahoo/google predict
 Composite mashup: Weather + Traffic + Ambulance +
Arrhythmia
 OAuth-based authentication for prediction API
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23. Demo 2: Marketplace Exchange
 Time cost-based traversal optimization of multiple clients
 Uses generic time-cost-based path decider...
 Three arrhythmia detectors: 𝐶𝐶𝐶𝐶 𝑖 = 𝑑 + 𝑘 ∗ #𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑖
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24. Demo 2: Marketplace Exchange
 Time cost-based traversal optimization of multiple clients
 Uses generic time-cost-based path decider...
 Three arrhythmia detectors: 𝐶𝐶𝐶𝐶 𝑖 = 𝑑 + 𝑘 ∗ #𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑖
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25. Conclusion
 Concept of computational marketplace
 Constraints for scalable, fault-tolerant, and change-tolerant
N-tiered computational model
 Proof of concept marketplace implementation + scenarios
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26. Acknowledgements
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