Chair of Software Engineering for Business Information Systems

Department of Informatics

Technische Universität München

DC

A Pattern Catalog for GDPR Compliant Data Protection

“Data Cooperatives”

„Privacy is the claim of individuals, groups or institutions to

determine for themselves when, how and to what extent information about them is communicated to others“ (Alan Westin)

€

Research goals

Alternative operating models for GDPR compliant data organizations

Data economy Regulation

We have come to expect Personalized services

require extensive personal data

With the use of smartphones, IoT devices and

personalized services, we leave vast amounts of

digital traces in the hands of companies

Regulators have the difficult task of balancing the

protection of individuals with the promotion of

technological & business development

Companies have to efficiently implement privacy

regulation

GDPR key elements

• New territorial scope,

definitions,…

• Extended rights for data

subjects: transparency,

portability, objection, notification

of data breach, rectification,

erasure,…

• Principle of accountability, data

protection by design and default

• Records of processing activities,

data protection impact

assessments

• Designation of Data Protection

Officer, certification

mechanisms

• Fines of up to 4% revenue for

non-compliance

Set of fundamental requirements Set of fundamental solution patterns

e.g. right to data

portability

Conceptual

Strategic

Organizational

Technical

Cultural

e.g.

require

portability

from

processor

e.g.

implement

export

functionality

Which conceptual frameworks can be

instrumented to describe regulatory

requirements and the organization of

possible solutions?

What are the elementary requirements of

the GDPR?

How is GDPR compliance achieved in

practice?

What is the value or effectivity of observed

solutions?

How can solution patterns be assessed

and how are they interrelated with each

other?

2

GDPR compliant storage of full genome, option to identify common

(family) predispositions in order to proactively work against them –

e.g. high blood iron

Collects user data from social media and stores it locally or in the

customer’s cloud storage

Uses anonymized trip data to provide information about traffic

density and estimated travel times between city areas

Provides a safe solution for members of the cooperative to store

their health data and optionally share it with research institutions

• How could an organization support data privacy?

• What would be feasible models? The “Data Cooperative” as an advisor to the

end user (1), as intermediary between services and the user (2), as a provider

of data protection services for companies (3) or as a provider of services to

the end user (4)?

• Who would initiate such an organization?

Dominik Huth

dominik.huth@tum.de

DC

DC

DC

On Google “my activity”, personal data can be viewed and deleted

in accordance with the current EU-US Privacy Shield

(4)(3)(2)(1)

Prof. Dr. Florian Matthes

matthes@tum.de

5

5

4

4

3

1

1

2

Instantiated GDPR Project

e.g. update

privacy policy

3

MEGENO

Chair of Software Engineering for Business Information Systems

Department of Informatics

Technical University of Munich

Motivation

• The way we consume mobility is drastically changing

• Mobility is no longer only provided by traditional public

transport and goods we own, like cars and bicycles, but

also by service providers which offer mobility as a service,

like car sharing companies

• This new mobility ecosystem enables more flexibility, but

also introduces additional complexity, especially for

intermodal travel, i.e. reaching one’s destination by using

multiple means of transportation

• One of the main obstacles towards an integrated solution

of multiple mobility services is the lack of cooperation

between mobility service providers.

NLU Technology

Comparison of:

• Microsoft LUIS

• IBM Watson Conversation

• API.ai

• RASA (Open Source)

https://github.com/sebischair/NLU-Evaluation-Corpora

Approach

Approach

• Combine APIs of different mobility services

• Introduction of an abstraction layer in order to be

independent from service providers

• Creation of a central routing algorithm which enables

intermodal mobility

• Make it accessible through a chat bot to simplify the

planning process for users

Example Chat

This work has been part of the Vertical Social Software Project

and has been funded by Siemens Corporate Technology

Customer-Centered Intermodal Combination of

Mobility Services with Conversational Interfaces

daniel.braun@tum.de, adrian.hernandez@tum.de, manfred.langen@siemens.com, matthes@tum.de

Daniel Braun, Adrian Hernandez Mendez, Manfred Langen, and Florian Matthes

Evaluation results

Context-AwareVerticalSocial

SoftwarePlatform

ConversationalInterface

NLU

Routing

MVG

DB

…

MVGConnector

DBConnector

… Connector

Chat

Chat

BotConnector

User

How can I get from München to Augsburg?

TravelCompanionBot

Take the 🚂 RJ 111 from München Hbf to Paris Est at 06:23. You will arrive at 06:54 at Augsburg Hbf.

User

From Garching Forschungszentrum to Flughafen

TravelCompanionBot

First, take the 🚍 Bus 230 from Garching, Forschungszentrum to Ismaning at 10:11. You will arrive at

10:29. Then, take the 🚆 S-Bahn 8 from Ismaning to Flughafen, Besucherpark at 10:42. You will arrive at

10:53. Your journey will take 🕜 42 minutes.

User

I want to travel from Boltzmannstraße to Neuperlach Süd

TravelCompanionBot

First, 🚶 walk to 🚈 U station Garching-Forschungszentrum. You will arrive at 14:06. Then, take the 🚈 U 6

from Garching-Forschungszentrum to Odeonsplatz at 14:06. You will arrive at 14:30. Then, take the 🚈 U 5

from Odeonsplatz to Neuperlach Süd at 14:37. You will arrive at 14:52. Your journey will take 🕜 50

minutes.

This work is part of the TUM Living Lab Connected Mobility (TUM LLCM) project and has been funded by the Bavarian Ministry of Economic

Affairs and Media, Energy and Technology (StMWi) through the Center Digitisation.Bavaria, an initiative of the Bavarian State Government.

ACKNOWLEDGMENTS

Stream Analytics in IoT Mashup toolsIoannis Varsamidakis, Tanmaya Mahapatra, Ilias Gerostathopoulos, Christian Prehofer

{ioannis.varsamidakis; mahapatr; gerostat; prehofer}@in.tum.de

Software- and Systems Engineering

Approach

Big Picture

Scenario 1: Twitter Sentiment Real – Time Analysis

Objectives

Challenges

Enable stream analytics in an IoT mashup tool

No technical background needed to analyze streaming data

No coding skills needed to analyze streaming data

Support real-time stream processing

Support asynchronous and non-blocking stream processing

Parameterize stream processing properties through a user-

friendly UI

Implement a stream analytics suite & integrate it in a IoT

mashup tool

Support Content & Time-based Window processing

Support various overflow mechanisms (e.g. backpressure)

Simplified visual notations for specifying stream processing

properties

Support design & deployment of streaming processes for Spark

and Flink (Future Work)

Get all tweets for a specific topic (e.g. “Raspberry”) as a stream and calculate the sentiment of each tweet.

Then calculate the moving average of the sentiment of the topic and publish the results on a Raspberry-Pi

Stream analytics jobs are invoked to calculate the moving average for the sentiment of a twitter topic in

real time

Develop a Stream Analytics suite supporting various Stream Analytics functions (e.g. filter, merge) using the Akka Stream library, based on

the Reactive Streams specifications

Integrate the Stream Analytics suite in the aFlux IoT Mashup tool (multi-threaded, based on Akka’s actor system)

Development of a set of visual semantics to facilitate specification of stream analytics jobs within the IoT mashup tool

Development of various overflow strategies to ensure that the receiving side is not forced to buffer arbitrary amounts of data (overflow

strategies are defined by the user, e.g. back-pressure)

IoT Mashup

Tool

Stream Analytics

Real-time insights of data

Scenario 2: Stream Analytics on SUMO Traffic Simulator

Traffic Monitoring System records live traffic data and detects congestion scenarios. On detection of a new

congestion incident, a new lane might open to counter act the congestion

Stream analytics jobs are invoked to calculate congestion rates & trigger appropriate counter measures

Twitter StreamSA Moving

AverageSentiment MQTT Publisher

Kafka ConsumerSA Moving

AverageJSON Parser Kafka Producer

aFlux flow example:

aFlux flow example:

This work is part of the TUM Living Lab Connected Mobility (TUM LLCM) project and has been funded by the Bavarian Ministry of Economic

Affairs and Media, Energy and Technology (StMWi) through the Center Digitisation.Bavaria, an initiative of the Bavarian State Government.

ACKNOWLEDGMENTS

Pricing Models of Shared Autonomous Vehicle SystemsAndreas Hein¹, Julia Veitl², Christopher Kohl¹, Lisa Kissmer², Helmut Krcmar¹

{andreas.hein; christopher.kohl; krcmar}@in.tum.de; {julia.veitl; lisa.kissmer}@bmw.de

¹Chair for Information Systems

²BMW Group

The Service Attributes

SAV Business Model

Towards a SAEV Pricing Model

Objective

Challenges Upcoming challenges & new requirements for new mobility service

providers (parking, charging time, social constraints, etc.)

Autonomous vehicles have a great disruptive & economic potential for

OEMs, however new entrants such as tech giants increase the

competitive pressure on established OEMs

Diverse and new customer needs require new business models such

as shared autonomous vehicle (SAV) systems

Research gap: Multicultural study about the willingness to pay for

shared autonomous electric vehicle systems (Krueger et al. 2016; Kockelman &

Quarles, 2018)

What are relevant service attributes for shared autonomous electric

vehicle systems (SAEVs) ?

Which customer-oriented pricing models for SAEVs could be

sustainable in the German as well as in the American market ?

Method

Qualitative in-depth interviews (Gläser & Laudel, 2010)

Quantitative customer survey using Adaptive-Choice Based Conjoint

Analysis (by Sawtooth Software)

Key Results*

Experts predict full acceptance of SAEV systems

Most important attributes

German market: reliability & safety

American market: safety & service quality

Willingness to pay for SAEVs will decrease compared to todays

mobility services

Coexistence of subscription models & pay-per-use options

Politics & regulations will play a major role in the future SAEV market

* Based on qualitative interviews, conjoint analysis is still in progress

Characteristics

Current on-demand mobility services (carsharing, ridesharing, taxi

services) as a foundation for first implementation of SAEVs

Service Attributes– The case of SAEVs

Current business models of mobility services

Research studies about

Sharing concepts

Electric and/or autonomous vehicles

Transportation choice

In-depth interviews with heavy users & mobility providers

Derivation of final service attributes for Conjoint Analysis

MOBILITY ON DEMAND SYSTEM WITH AUTONOMOUS VEHICLES

BookingVia

App, Call,

SMS

Users

Verification

Fleet

Management

System

Autonomous Vehicles

SERVICE ATTRIBUTES INFLUENCING WTP FOR SAEV SYSTEMS.

Willingness

to pay

Price (P)

Incentives

(Loyalty, Priority, Refer-a-friend) (P)

Availability (FC)

Cleanliness (FC)

Data Privacy (FC)

Safety (FC)

Support (FC)

Reliability (FC)

Ease of Use )EE)

Invoicing (EE)

Brand (HM)

Exterior (HM)

Engine (HM)

Interior (HM)

Vehicle Features (HM)

Use Case (H)

Convenience (PE)

ETA (PE)

Flexibility (PE)

Multimodality (PE)

Parking (PE)

Pooling (PE)

Waiting time (PE)

Image (SI)

Human interaction (A)

Politics & Regulations(A)

This work is part of the TUM Living Lab Connected Mobility (TUM LLCM) project and has been funded by the Bavarian Ministry of EconomicAffairs and Media, Energy and Technology (StMWi) through the Center Digitisation.Bavaria, an initiative of the Bavarian State Government.

ACKNOWLEDGMENTS

RoomR: Kick-starting Indoor NavigationNikolaos Tsiamitros, Efdal Ustaoglu, Georgios Pipelidis and Christian Prehofer{nikos.Tsiamitros, efdal.ustaoglu, georgios.pipelidis, christian.prehofer}@tum.deSoftware Engineering for Business Information Systems

Big Picture

Objective

Challenges

Approach

Results

Constructing accurate indoor maps to enable infrastructure

independent precise localization.

Devising a method to dynamically generate particles for the

particle filter to be used for localization.

Existing methods for localization cannot be used with the

available open map data.

Adjusting and expanding existing algorithms according to the needs

of our use case.

Use the geometry and other characteristics of indoor places to

deduce the location.

Crowd-source WiFi signal strength signals of the access points and

reason on them.

Provide a mapping framework that works transparently to create

high precision maps from unreliable sensor data.

I. Retrieve the indoor OSM

model and extract the

relevant map data.

II. Enhance the map with

particles and import it to

the particle filter.

III. Calculate the initial

direction of the user.

IV. Use an enhanced particle

filter with dead reckoning

to localize the user

I. Classify incoming data

based in their unique

properties.

II. Perform cluster analysis

to identify the number of

clusters.

III. Fuse all the data that

have been extracted

from the same regions.

IV. Train a classifier to

predict those locations.

We created an indoor

navigation app for the MI

building to demonstrate our

idea.

The user can find the location

of any room in the building on

an accurate map.

The route to the room from

the entrance is also

displayed.

The user can start navigation

at the entrance of the building

and localize himself during

the entire route.

Grammars

ParticleGeneration

OSMModel

InitialDirection

CurrentDirection

StepCounter

Localization

QuantifyConfidence

VisualizeLocation Classification

Cluster Analysis

WiFiAnalysis

GSM Analysis

Geom. Analysis

Fusion

Clustering

Labeled WiFi

Labeled GSM

Labeled Geom.

Labeling

WiFi Clustering

GSM Clustering

Geom. Clustering

Training

ClusteredData

RawData

This work is part of the TUM Living Lab Connected Mobility (TUM LLCM) project and has been funded by the Bavarian Ministry of Economic

Affairs and Media, Energy and Technology (StMWi) through the Center Digitisation.Bavaria, an initiative of the Bavarian State Government.

ACKNOWLEDGMENTS

SMART CAMERA APP FOR ASSISTING VISUALLY IMPAIRED (SCAVI)Santhanakrishnan Narayanan, Georgios Pipelidis and Christian Prehofer

{Santhanakrishnan.Narayanan, georgios.pipelidis; christian.Prehofer}@tum.de

Masters Student – Transportation Systems

Objective A brief on Google Tango

TOF based infrared camera (IRS1645C 3D image sensor chip) to

perceive depth

Fish eye camera along with inertial measurement unit (combination

of gyroscopes and accelerometers) to track location (visual

odometry)

RGB camera to capture details like color and as the viewfinder for

augmented reality

Develop a mobile application using Google Tango API for

visually impaired humans to detect obstacles in the path of

the user and notify him/her

Advantages of our Method Point Cloud Example

Realtime depth estimation

Better accuracy (1% of the distance measured)

Good working range (indoors – 0.1 to 4m)

Usability possible in low or no light conditions (only

depth estimation)

Point cloud data

Pixel Matrix

Depth Image

Camera

Intrinsics

Depth Image

Aggregate pixels

based on depth

Estimate Obstacle

size

Constructing depth image from point cloud data for

better visualisation and obstacle detection

Detecting the obstacle type (static objects in same

depth plane and objects with varying depth like

staircase)

Estimating obstacle size

To be done

Following pixels not to be considered from the point cloud

Pixels corresponding to depth > 3m

Pixels at a distance > 0.5m to the left and right of the focal centre

of depth camera

Pixels 1m above the focal centre of depth camera

Tango point cloud dataCurrent Plan

X – Distance of the points along top-bottom plane

Y – Distance of the points along left-right plane

Z – Depth value perpendicular to the plane of the camera

C - Confidence value in the range of [0, 1] where 1 corresponds to

full confidence

* When the phone is held in portrait mode