geolocation and big data...2018/10/04 · big data conference, moscow 2018 Экономика...
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Geolocation and Big Data
Victor Rudoy, the Head of DA&C RU&CIS, HERE TechnologiesBig Data Conference, Moscow 2018
Экономика спроса
9.5 B ARробототехникаКвантовые
вычисления
Умный город
Искусственный интеллект Устойчивость
Нейронные сетиДата аналитика
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Geolocation services became an essential part of our life
Geolocation for business
Big Data for
Maps
Maps for
Big Data
Maps and
Big Data
Map is a key element
The three Vs of big data
Volume
VarietyVelocity
Volume: the diversity of Map products
Off-Road Outdoor Data Parking Data Pedestrian Content Places Extract Point Addressing Postal Code Boundaries & Points Restaurant Guide Road Roughness Safety Cameras Scenic Routes Signs, Signals and Warnings Speed Limits Supplemental Listings Toll Costs Trucks Venues Voice Phonetic Transcriptions Yellow Pages
2D & 3D Junction Visuals 2D & 3D Landmarks
Basic & Advanced 3D City Models Bicycling Data
Built-Up Area Roads Census Boundaries & IDs
Core POI Distance Markers
Enhanced Curvature Enhanced Geometry
Enhanced Height and Slope Enterprise Admin Boundaries
Environmental Zones Extended Lanes and Lane Markings
Extended Listings EV Charging Stations
Fuel TypesNatural Guidance
collectionvehicles40
0
HERE+
50Local GIS
expert teams
>
5Billionrecords per month by thousands of expert communities and developers
> 400.000Rich sensor data from
vehicles
105B
Basic probe points
/month
sources &suppliers
80k
Volume: Map Leadership requires Big Data Orchestration
Velocity
EV Charging Stations
Fuel PricesParking
Mobility
Safety Electric mobility
AutomotiveTraffic
Hazard Warnings Road Signs
+ 24 min+ 16 min+ 12 min
Departure in
02:00
DepartureAlert
Destination Weather
Automotive Search
Static Data
Map Platform
Crowdsourcing
Imageries
Video collection
Dynamic data
Sensors data
Variety
12
FreshnessRichest contentQuality
Three key challenges for geolocation
Big Data for Maps: Example #1
Example #1: Probe data usage
Community
Probe
Imageries
Processed Community
Processed Probe
Template
Map
Example #1: how the process looks like
Example #1: probe data analysis part
Big Data for Maps: Example #2
Example #2: Places
Example #2: Predictive Model creation
TRAINING DATA PREDICTIVE MODELMachine Learning Algorithms
Example #2: training data example
Rating Location (Y/N)
…. Ever updated?
Likes result/prediction
2.8 Yes … Yes … True
3.1 No … No … False
4 Yes … Yes … True
4.5 No … No … True
4 “” … Yes … False
3.2 Yes … Yes … True
1.7 Yes … No … False
SCORING DATA PREDICTIONSPredictive model
Example #2: the result
Avg. Rating
Likes
Example #2: Training data quality factor
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00%
% o
f Er
rors
in M
odel
Pre
dict
ion
% of Errors in training data
Model Error increase
Example 3
Big Data for maps: summary
- Effective time resource usage
- Financial expenses optimization
- Scale effect
Maps for Big Data
Smart safety
Maps and Big Data
The intelligent car
Assistance LearningAutomation
•
Чем больше данных, тем больше возможностей
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