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Skyhook’s Location Platform:
Precision Positioning at Global Scale
1 (c) Skyhook Wireless, Inc. 2013 - Confidential
1,000,000,000
Sensor Points
13,500,000
Cell Towers
50,000,000+
Devices
200+
Countries
850,000,000
People
Covered
Many Billions
Location
Transactions /
month
Geography and Population Volume
WiFi
Access Points
Fueled by Big Data
Skyhook surveys with proprietary scanning devices to
establish baseline coverage
• High Precision Data
• Low Update Frequency
• Errors Generally Random
Diverse consumer devices return a constant stream of
feedback data
• Extremely Variable Quality
• Very High Update Frequency
• Errors Follow Multiple Distributions
Service providers, institutions, and individual users
submit data about their own networks
• Typically High Quality
• Sporadic Batch Delivery
• Errors Often Systematic or Malicious
Key Insights
Detect Pathological or Malicious Data Sources
• Any data source can develop problems
• Filter out bad actors and model larger-scale
patterns
Characterize Data Quality at Source and Sample
Level
• Use side information to inform data classes
• Use empirical data to create quantitative metrics
Build Algorithms for Variable Quality Data
• Leverage quality metrics in “soft” algorithms
• Plan for and handle outliers
Review, Refresh, and Recalibrate
• Systems will change and drift over time
• Constantly re-evaluate parameters and
assumptions

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Data mashups skyhook

  • 1. Skyhook’s Location Platform: Precision Positioning at Global Scale 1 (c) Skyhook Wireless, Inc. 2013 - Confidential 1,000,000,000 Sensor Points 13,500,000 Cell Towers 50,000,000+ Devices 200+ Countries 850,000,000 People Covered Many Billions Location Transactions / month Geography and Population Volume WiFi Access Points
  • 2. Fueled by Big Data Skyhook surveys with proprietary scanning devices to establish baseline coverage • High Precision Data • Low Update Frequency • Errors Generally Random Diverse consumer devices return a constant stream of feedback data • Extremely Variable Quality • Very High Update Frequency • Errors Follow Multiple Distributions Service providers, institutions, and individual users submit data about their own networks • Typically High Quality • Sporadic Batch Delivery • Errors Often Systematic or Malicious
  • 3. Key Insights Detect Pathological or Malicious Data Sources • Any data source can develop problems • Filter out bad actors and model larger-scale patterns Characterize Data Quality at Source and Sample Level • Use side information to inform data classes • Use empirical data to create quantitative metrics Build Algorithms for Variable Quality Data • Leverage quality metrics in “soft” algorithms • Plan for and handle outliers Review, Refresh, and Recalibrate • Systems will change and drift over time • Constantly re-evaluate parameters and assumptions