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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