2nd International Data Science Conference 2019 - Conference Guide
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2019
2nd International Data
Science Conference 2019
Conference Guide
Technology
Health
Media
Inhalt
Preface 4 –5
Committees 6
The iDSC Idea 7
Program Overview 8 –11
Social Event 12
Keynote Speakers 14 –15
Workshops 16 –17
Abstracts – Research Track 18 –22
Conference Dinner 23
Venue & Travel Information 24 –25
Notes, Imprint 26 –27
2 3
Ladies and gentlemen,
dear colleagues,
welcome to the 2nd International Data Science Conference.
Welcome to With the general topic "Data Science – Analytics and Applications", this
year’s conference refers to the fact that companies have already moved
Salzburg University
to apply data science, adding sustainable value to the potential benefits
of digitalization in an agile environment.
The iDSC conference brings together researchers, scientists, engineers
of Applied Sciences! and entrepreneurs. We discuss new approaches in the fields of machine
learning, artificial intelligence, data mining and visualization to establish
efficient solutions to react flexibly and quickly to market conditions and
customer requests in order to take into account changes that can be
entrepreneurially decisive.
That’s why data science has established itself as the cornerstone of
institutions, organizations and companies and why it is crucial for
analytical processes as well as for the realization of essential applications
in business and research.
Take the chance to talk to scientists and business experts in the field of
data science to discuss new ways within their respective domains.
Participate in our workshops about state-of-the-art tools and topics and
get inspired by our keynotes and talks from science and industry.
Our gratitude goes to our keynotes and industry speakers as well as our
researchers, session chairs, review teams, to the program committee, to
all participants and colleagues who made this event possible.
We wish you exciting days and many stimulating discussions.
Your conference chairs,
Peter Haber Thomas J. Lampoltshammer Manfred Mayr
Salzburg University of Applied Sciences Danube University Krems Salzburg University of Applied Sciences
4 5
Committees
The iDSC Idea
Conference Chairs
National and The conference gives the participants the opportunity, over the course of
Salzburg University of Manolis Koubarakis Kapodistrian University
Peter Haber
Applied Sciences of Athens three days, to delve into the most current research and up-to-date
Danube University
practice in data science and data-driven business. The conference is split
Thomas J. Lampoltshammer Austrian Institute of
Krems Maria Leitner into two parallel tracks, a research track and an industry track.
Technology
Salzburg University of
Manfred Mayr Elena Lloret Pastor University of Alicante
Applied Sciences Research & Industry Track
Giuseppe Manco University of Calabria
The research track offers a series of short presentations from data
Local Organizers Robert Merz
Vorarlberg University of
science researchers regarding their current work in the fields of data
Applied Sciences
Nicole Siebenhandl
Salzburg University of mining, machine learning, data management and the entire spectrum of
Applied Sciences Federal University of
Edison Pignaton de Freitas
Rio Grande do Sul data science.
Salzburg University of
Maximilian Tschuchnig Vienna University of
Applied Sciences Florina Piroi
Technology In the industry track, practitioners demonstrate showcases of data-driven
Salzburg University of business and how they use data science to achieve organizational goals,
Dominik Vereno Vorarlberg University of
Applied Sciences Kathrin Plankensteiner
Applied Sciences with a focus on market & trends, energy, manufacturing, quality
assurance and health and sports.
Siegfried Reich Salzburg Research
Program Committee
Peter Reiter
Vorarlberg University of Workshops & Keynotes
San José State Applied Sciences
David Anastasiu
University
Michael Ruzicka Cockpit Our sponsors will have their own, special platform via workshops to
Arne Bathke University of Salzburg
Vienna University of
provide hands-on interaction with tools or to learn approaches towards
Marta Sabou
Markus Breunig
University of Applied Technology concrete solutions. In addition, there will be an exhibition of the
Science Rosenheim sponsors‘ products and services throughout the conference, with the
Graz University of
Frank Danielsen University of Agder Johannes Scholz Technology, Institute of opportunity for our participants to seek contact and advice.
Geodesy
Eric Davis Industrial Labs Axel Straschil pmIT Consult Rounding out the program, we are proud to present keynote
Salzburg University of presentations from leaders in data science and data-driven business,
Günther Eibl Danube University
Applied Sciences Lőrinc Thurnay
Krems both researchers and practitioners. These keynotes will be distributed
Süleyman Eken Kocaeli University Salzburg University of
over both conference days, providing times for all participants to come
Andreas Unterweger together and share views on challenges and trends in data science.
Applied Sciences
Salzburg University of
Karl Entacher Danube University
Applied Sciences Gabriela Viale Pereira
Krems
Luxembourg Institute
Mohammad Ghoniem of Science and Salzburg University of
Stefan Wegenkittl
Technology Applied Sciences
Vienna University of Vorarlberg University of
Elmar Kiesling Karl-Heinz Weidmann
Technology Applied Sciences
Salzburg University of Delft University of
Michael Gadermayr Anneke Zuiderwijk - van Eijk
Applied Sciences Technology
6 7
Program
22nd May 2019
Program
Overview WEDNESDAY
22nd May 2019
Research Track Industry Track Research Track Industry Track
Room 012 Room 013 Room 012 Room 013
08:00 am Registration (Foyer) 03:30 pm Data Analytics | NLP and Semantics Data Analytics | Energy
Chair: Andreas Unterweger Host: Michael Ruzicka / Axel Straschil
09:00 am Opening and Welcome
Room 013 03:30 Hakkı Yağız Erdinç (Donanim Haber): 03:30 David Steidl, Daniel Wagner
Combining Lexical and Semantic Similarity (Verbund): EDA & Anomaly Detection
09:15 am Keynote Methods for News Article Matching
Josef Waltl (Amazon Web Services): From Data to Value in Industrial Use Cases 04:15 Norbert Walchhofer (Cognify), Stefanie
Room 013 04:00 Martin Schnöll (Fact AI): Kritzner (Salzburg AG):
The Effectiveness of the Max Entropy Classifier No Space for Gaps - Forecasting Challenges in
10:00 am Keynote for Feature Selection the Control Energy Market
Bodo Hoppe (IBM Germany): What Lies Ahead of Us? IBM’s View on Technology Trends
Room 013 04:30 Lőrinc Thurnay (Danube University
Krems): Impact of Anonymization on Sentiment
Analysis of Twitter Postings
10:45 am Break
11:00 am Room 012 Room 013 05:00 pm Closing
Data Analytics | Complexity Data Analytics | Market & Trends
Chair: Lőrinc Thurnay Host: Michael Ruzicka / Axel Straschil
11:00 Jad Rayes (George Mason University): 11:00 Guido Harucksteiner (Skidata):
Exploring Insider Trading Within Hypernetworks Innovation & Trends with Special Regard to a 07:00 pm
Conference Dinner: Mozart Dinner Concert
FRUGAL Approach and Emerging Markets –
(only if booked in advance)
11:30 Abdel Aziz Taha 10:30 pm
(Research Studios Austria): 11:30 Robert Stubenrauch
Chance Influence in Datasets With Large (Business Upper Austria):
Number of Features From Industry 4.0 to Industrial Data - Accom-
panying Corporate Networks Through the
Digital Age
12:00 am Lunch
01:15 pm Keynote
Peter Parycek (Danube University Krems): Data-Driven Policy-Making
Room 013
02:00 pm Keynote
Christian Blakely (PricewaterhouseCoopers Zurich): Real-Time Learning and Prediction in
(Un)structured Data
Room 013
02:45 pm Coffee Break
8 9
Program
23rd May 2019
Program
Overview THURSDAY
23rd May 2019 Research Track
Room 012
Industry Track
Room 013
Workshops
Room 018
02:15 pm Data Analytics | Comprehensibility Data Analytics | Quality Assurance
Chair: Johannes Scholz Host: Michael Ruzicka / Axel Straschil
Research Track Industry Track Workshops 02:15 Maciej Skorski (Dell): 02:15 Oskar Preinfalk, Rene 02:15 Cognify:
Room 012 Room 013 Room 018 Probabilistic Approach to Web Leikermoser (Spar ICS): HADES – Profit from Prophet
Waterfall Charts Anomaly detection in retail processes
08:30 am Registration (Foyer)
02:45 Shefali Virkar (Danube 03:00 Bernhard Redl, Simon
University Krems): Facilitating Public Stiebellehner (craftworks):
09:30 am Keynote
Access to Legal Information - A CI/CD for Machine Learning
Stefan Wegenkittl (Salzburg University of Applied Sciences):
Conceptual Model for Developing an
Confronting the Small Data Challenge: Achieving Successful Digital Transformation in SMEs Requires
Agile Data-driven Decision Support
Transforming Processes, Roles and Technologies
System
Room 013
03:15 Wolfgang Kremser (Salzburg
10:15 am Break
Research): Do We Have a Data Culture?
10:30 am Data Analytics | Modelling Data Analytics | Manufacturing
Chair: Werner Pomwenger Host: Michael Ruzicka / Axel Straschil 03:45 pm Coffee Break
10:30 David Anastasiu (San José State 10:30 Dubravko Dolic (Continental): 10:30 Steadforce:
04:30 pm Short Papers Data Analytics | Health & Sports
University): A Data-Driven Approach for Predictive Maintenance for Tires Candy Consumption
Chair: Thomas J. Lampoltshammer Host: Michael Ruzicka / Axel Straschil
Detecting Autism Spectrum Disorders Forecasting
11:00 Neha Sehgal (University of
04:30 Dejan Radovanovic (Salzburg 04:30 Jonathan Boidol, Stephan 04:30 MathWorks:
11:00 Ioannis Gkioulekas (University Huddersfield & Valuechain Ltd.):
University of Applied Sciences): Neural Schiffner (Steadforce): Discovering Predictive Maintenance
College London): Optimal Regression ‘Champions’ & ‘Strugglers’ of UK
Machine Translation from Natural and Extracting Knowledge from large with MATLAB
Tree Models Through Mixed Integer Manufacturing Sector - The Tale from
Language into SQL with state-of-the- Text Collections - Introduction to Text
Programming Open Data
art Deep Learning Methods mining on the example of clinical trials
11:30 Lina Stanzel (AEE INTEC): A 11:30 Sven Ahlinder (Volvo):
05:00 Sebastian Malin (Fachhoch- 05:15 Richard Mohr (Techedge):
Spatial Data Analysis Approach for Visualization of many variables, by
schule Vorarlberg): Smart Recommen- Big Data and Real Time Analytics in
Public Policy Simulation in Thermal projection onto two of them
dation System to Simplify Projecting for US Football Sports. Implementing the
Energy Transition Scenarios
a HMI/SCADA Platform perfect match day with Arena
Analytics for the San Francisco 49ers.
12:00 am Lunch
05:30 Maximilian E. Tschuchnig
(Salzburg University of Applied 06:00 Konrad Linner, Philipp Lukas
01:15 pm Keynote Sciences): Adversarial Networks – A (Solvistas): Ice Hockey and Data
David Anastasiu (San José State University): The AI Data Revolution: Doing More With Less Data Labeling Technology for Image Augmentation Science - How the EHC Black Wings
Room 013 Linz wants to become a champion
06:00 Melanie Zumtobel (Fachhoch- with the help of Data Science
02:00 pm Break schule Vorarlberg): Using Supervised
Learning to Predict the Reliability of a
Welding Process
06:30 pm Networking
Room 017
08:00 pm Closing
10 11
Social Event
Friday, 24th May 2019
Half-Day 2nd International
Panorama To round off the conference, we will do a guided bus
tour through Salzburg’s lake disctrict »Salzkam-
Data Science
Tour mergut« on Friday, May 24th 2019. You can continue
the discussion in Austria’s picturesque scenery. Conference 2019
Meeting time: 8:45 am We spend half a day in the famous towns Fuschl and
Paris-Lodron-Straße, Salzburg St. Gilgen (30-45 min. stay) alongside some of the
Program Details
(opposite »Hotel am most beautiful lakes in Austria. You have the oppor-
Mirabellplatz«) tunity to reflect on presented topics and converse with
Departure: 9 am attendees and speakers alike.
Return to Salzburg: 1 pm
The tour is included in the conference ticket (only if
seperately booked in advance).
12 13Keynote Speakers Keynote Speakers
David C. Anastasiu David C. Anastasiu is an assistant professor in the Peter Parycek Peter Parycek is full professor for e-governance, head
San José State Danube University
University Department of Computer Engineering at San José Krems of the Department for E-Governance and Administra-
State University. His research interests fall broadly at tion at the Danube University Krems and head of the
the intersection of machine learning, data mining, Competence Centre Public IT at Fraunhofer Fokus
computational genomics, and high performance Berlin, funded by the Ministry of Interior. In August
computing. He was awarded the Next Generation Data 2018 he has become a member of the digital council to
Scientist (NGDS) Award at the 2016 IEEE International advise the German government on the important
Conference on Data Science and Advanced Analytics. matter of digitalisation.
Christian D. Blakely Christian started his career at NASA Goddard Space Josef Waltl Josef Waltl leads the global partner ecosystem for
Pricewaterhouse- Amazon Web
Coopers Zurich Flight Center in Washington DC as an atmospheric Services industrial software at Amazon Web Services (AWS).
physicist. After completing a Ph.D. in Computational Prior to AWS he worked in Siemens building on
Science at University of Maryland, he had a 3 year long software strategy and mergers & acquisitions for
deep dive into big data and machine learning while product lifecycle management, smart grid and mobility.
doing a post-doctoral fellowship with the United States He holds a Ph.D. and an MBA from the Technical
Department of Commerce. He moved to Switzerland to University Munich as well as a Dipl.-Ing. in Computer
pursue a career in FinTech, and now leads the machine Science from the University of Salzburg and a Dipl.-Ing.
learning team for PwC Switzerland, focusing on from the Salzburg University of Applied Sciences in
real-time machine learning technologies. He is also a Telecommunications Engineering and Systems.
part time classical concert pianist.
Bodo Hoppe Bodo Hoppe is a distinguished engineer in IBM Stefan Wegenkittl Stefan Wegenkittl is academic programme director of
IBM Germany Salzburg University
Research & Development GmbH located in Germany. of Applied Sciences the Applied Image and Signal Processing degree
He is responsible for the overall design quality and programme as well as senior lecturer and head of the
functionality of the IBM z Systems microprocessor and department of applied mathematics and data-mining
system ASICs. He is a thought leader in verification at Salzburg University of Applied Sciences. There, he
methodology and has introduced multiple innovations also heads the Applied Data Science Lab which
to verification approaches. He is leading the agile conducts research in the areas medical image
transformation enabling the user experience driving processing, biosignal processing and natural language
the hardware development from design thinking to processing. Current research is on various aspects of
feature based development. He is involved in multiple machine learning, representation learning and feature
activities and collaborations with IBM Research on new extraction in the aforementioned areas of application.
technologies.
14 15Program
23rd May 2019
Workshops
Workshop 1 Workshop 2
Thursday, 23rd May 2019 – 10:30 am Thursday, 23rd May 2019 – 02:15 pm
Steadforce: »Candy Consumption Forecasting« Cognify: »Profit from Prophet«
This workshop is targeted at out. Classical outlier detection and Providing high quality forcasts During the workshop we will give
students and professionals who filtering methods are applied, and timeseries like sales prices can be an introduction to timeseries
are interested in advanced data as prediction models we will use quite complex considering forecasting with Prophet and other
science methods and best survival regression. influential factors like holidays, timeseries approaches in theory
practices. The participants have te oppor- seasonality effects etc. and hands-on practice.
The aim of the workshop is to tunity to follow a complete data Prophet is an open source
develop a predictive model for the science use case on the basis of an forecasting tool developed by
demand for sweets in an office unusual data set. Facebook and an intuitive
environment. Using this in-house Development environment of the approach to modeling such
example, data cleaning, preproces- workshop: Python, Jupyter and the timeseries data.
sing and modelling will be carried Python Ecosystem.
Workshop 3
Thursday, 23rd May 2019 – 04:30 pm
MathWorks: »Predictive Maintenance with MATLAB «
In this workshop, an application schedule maintenance as soon as
from the field of predictive it is needed. The workshop covers
maintenance is used to demons- the complete workflow from
trate how data analytics turn data accessing and preprocessing data
to decisions. In contrast to through developing predictive
preventive maintenance, which models using machine learning
follows a set timeline, predictive techniques and visualizing results
maintenance schedules are to deploying the final algorithms in
determined by analytic algorithms production systems and embed-
and data from equipment sensors. ded devices.
With predictive maintenance,
organizations can identify issues
before equipment fails, pinpoint
the root cause of the failure, and
16 17Program
22nd May 2019
Abstracts – Research Track
Data Analytics | Complexity Data Analytics | NLP and Semantics
Jad Rayes and Priya Mani (George Mason University): Exploring Insider Trading Within Mehmet Umut Sen, Hakki Yagiz Erdinc, Burak Yavuzalp and Murat Can Ganiz (Donanim-
Hypernetworks Haber, Sabanci University; DonanimHaber, Dogus University; DonanimHaber, Istanbul
Insider trading can have crippling effects on the economy and its prevention is critical to the security and stability of global Technical University; VeriUs, Marmara University): Combining Lexical and Semantic
markets. It is hypothesized that insiders who trade at similar times share information. We analyze 400 companies and 2,000 Similarity Methods for News Article Matching
insiders, identifying interesting trading patterns in these networks that are suggestive of illegal activity. Insiders are classified
as either routine or opportunistic traders, allowing us to concentrate on well timed and highly profitable trades of the latter. Matching news articles from multiple different sources with different narratives is a crucial step towards advanced
Using trade classification and analyzing each trader’s role in a hypernetwork, reveals cliques of opportunistic and routine processing of online news flow. Although, there are studies about finding duplicate or near-duplicate documents in several
traders. This idea forms the basis of a graph based detection algorithm that seeks to identify traders belonging to opportuni- domains, none focus on grouping news texts based on their events or sources. A particular event can be narrated from very
stic cliques. The ideas of trade classification and trading cliques present interesting opportunities to develop more robust different perspectives with different words, concepts, and sentiment due to the different political views of publishers. We
policing systems which can automatically flag illegal activity in markets, and predict the likelihood that such activity will occur develop novel news document matching method which combines several different lexical matching scores with similarity
in the future. scores based on semantic representations of documents and words. Our experimental result show that this method is highly
successful in news matching. We also develop a supervised approach by labeling pairs of news documents as same or not,
then extracting structural and temporal features. The classification model learned using these features, especially temporal
Abdel Aziz Taha, Alexandros Bampoulidis and Mihai Lupu (Research Studios Austria): ones and train a classification model. Our results show that supervised model can achieve higher performance and thus
better suited for solving above mentioned difficulties of news matchingto opportunistic cliques. The ideas of trade
Chance influence in datasets with large number of features classification and trading cliques present interesting opportunities to develop more robust policing systems which can
Machine learning research, e.g. genomics research, is often based on sparse datasets that have very large numbers of automatically flag illegal activity in markets, and predict the likelihood that such activity will occur in the future.
features, but small samples sizes. Such configuration promotes the influence of chance on the learning process as well as on
the evaluation. Prior research underlined the problem of generalization of models obtained based on such data. In this paper, Martin Schnöll, Cornelia Ferner and Stefan Wegenkittl (Fact AI GmbH; Salzburg
we deeply investigate the influence of chance on classification and regression. We empirically show how considerable the
influence of chance such datasets is. This brings the conclusions drawn based on them into question. We relate the University of Applied Sciences): The Effectiveness of the Max Entropy Classifier for
observations of chance correlation to the problem of method generalization. Finally, we provide a discussion of chance Feature Selection
correlation and guidelines that mitigate the influence of chance.
Feature selection is the task of systematically reducing the number of input features for a classification task. In natural langua-
ge processing, basic feature selection is often achieved by removing common stop words. In order to more drastically reduce
the number of input features, actual feature selection methods such as Mutual Information or Chi-Squared are used on a
count-based input representation. We suggest a task-oriented approach to select features based on the weights as learned
by a Max Entropy classifier trained on the classification task. The remaining features can then be used by other classifiers to
do the actual classification. Experiments on different natural language processing tasks confirm that the weight-based
method is comparable to count-based methods. The number of input features can be reduced considerably while maintaining
the classification performance.
Thomas J. Lampoltshammer, Lőrinc Thurnay and Gregor Eibl (Danube University Krems):
Impact of Anonymization on Sentiment Analysis of Twitter Postings
The process of policy-modelling, and the overall field of policy-making are complex and put decision-makers in front of great
challenges. One of them is present in form of including citizens into the decision-making process. This can be done via various
forms of E-Participation, with active/passive citizen-sourcing as one way to tap into current discussions about topics and
issues of relevance towards the general public. An increased understanding of feelings behind certain topics and the resulting
behavior of citizens can provide great insight for public administrations. Yet at the same time, it is more important than ever to
respect the privacy of the citizens, act in a legally compliant way, and therefore foster public trust. While the introduction of
anonymization in order to guarantee privacy preservation represents a proper solution towards the challenges stated before,
it is still unclear, if and to what extent the anonymization of data will impact current data analytics technologies. Thus, this
research paper investigates the impact of anonymization on sentiment analysis of social media, in the context of smart
governance. Three anonymization algorithms are tested on Twitter data and the results are analyzed regarding changes within
the resulting sentiment. The results reveal that the proposed anonymization approaches indeed have a measurable impact on
the sentiment analysis, up to a point, where results become potentially problematic for further use within the policy-modelling
domain.
18 19Program
23rd May 2019
Data Analytics | Modelling Data Analytics | Comprehensibility
Manika Kapoor and David Anastasiu (San Jose State University): A Data-Driven Approach Maciej Skorski (DELL): Probabilistic Approach to Web Waterfall Charts
for Detecting Autism Spectrum Disorders The purpose of this paper is to propose an efficient and rigorous modeling approach for probabilistic waterfall charts
illustrating timings of web resources, with particular focus on fitting them on big data. An implementation on real-world data
Autism spectrum disorders (ASDs) are a group of conditions characterized by impairments in reciprocal social interaction and
is discussed, and illustrated on examples. The technique is based on non-parametric density estimation, and we discuss
by the presence of restricted and repetitive behaviors. Current ASD detection mechanisms are either subjective (survey-ba-
some subtle aspects of it, such as noisy inputs or singular data. We also investigate optimization techniques for numerical
sed) or focus only on responses to a single stimulus. In this work, we develop machine learning methods for predicting ASD
integration that arises as a part of modeling.
based on electrocardiogram (ECG) and skin conductance (SC) data collected during a sensory challenge protocol (SCP) in
which the reactions to eight stimuli were observed from 25 children with ASD and 25 typically developing children between 5
and 12 years of age. The length of the time series makes it difficult to utilize traditional machine learning algorithms to analyze
these types of data. Instead, we developed feature processing techniques which allow efficient analysis of the series without Shefali Virkar, Chibuzor Udokwu, Anna-Sophie Novak and Sofia Tsekeridou (Danube
loss of effectiveness. The results of our analysis of the protocol time series confirmed our hypothesis that autistic children are University Krems; INTRASOFT International S.A.): Facilitating Public Access to Legal
greatly affected by certain sensory stimulation. Moreover, our ensemble ASD prediction model achieved 93.33% accuracy,
which is 13.33% higher than the best of 8 different baseline models we tested. Information: A Conceptual Model for Developing an Agile Data-driven Decision Support
System
The European legal system is multi-layered and complex, and large quantities of legal documentation have been
Ioannis Gkioulekas and Lazaros Papageorgiou (University College London): Optimal produced since its inception. This has significant ramifications for European society, whose various constituent actors require
Regression Tree Models through Mixed Integer Programming regular access to accurate and timely legal information, and often struggle with basic comprehension of legalese. The project
focused on within this paper proposes to develop a suite of usercentric services that will ensure the real-time provision and
Regression analysis is a tool for predicting the output variables from a set of known independent variables. Through visualisation of legal information to citizens, businesses and administrations based on a platform supported by the proper
regression, a function that captures the relationship between the variables is fitted to the data. Tree regression models are environment for semantically annotated Big Open Legal Data (BOLD). The objective of this research paper is to critically
popular in the literature due to their ability to be computed quickly and their simple interpretations. However, creating complex explore how current user activity interacts with the components of the proposed project platform through the development of
tree structures can lead to overfitting the training data resulting in a poor predictive model. This work introduces a tree a conceptual model. Model Driven Design (MDD) is employed to describe the proposed project architecture, complemented
regression algorithm that employs mathematical programming to optimally split data into two sub regions, called nodes, and by the use of the Agent Oriented Modelling (AOM) technique based on UML (Unified Modelling Language) user activity
a statistical test to assess the quality of partitioning. A number of publicly available literature examples have been used to test diagrams to develop both the proposed platform’s user requirements and show the dependencies that exist between the
the performance of the method against others that are available in the literature. different components that make up the proposed system.
Lina Stanzel, Johannes Scholz and Franz Mauthner (AEE - Institut für Nachhaltige Wolfgang Kremser and Richard Brunauer (Salzburg Research Forschungsgesellschaft):
Technologien; Graz University of Technology, Institute of Geodesy): A Spatial Data Do we have a Data Culture?
Analysis Approach for Public Policy Simulation in Thermal Energy Transition Scenarios Nowadays, adopting a »data culture« or operating »data-driven« are desired goals for a number of managers.
The paper elaborates on an approach to simulate the effect of public policies regarding thermal energy transition pathways in However, what does it mean when an organization claims to have data culture? A clear definition is not available. This paper
urban communities. The paper discusses the underlying methodologies of calculating Heating Energy demand of buildings aims to sharpen the understanding of data culture in organizations by discussing recent usages of the term. It shows that data
and the rationale for potential zones for thermal energy systems. In order to simulate the effects of public policies on culture is a kind of organizational culture. A special form of data culture is a data-driven culture. We conclude that a
communities the authors developed a spatial Agentbased Model, where the buildings are the main objects that are subject to data-driven culture is defined by following a specific set of values, behaviors and norms that enable effective data analytics.
change, based on a number of both technically and socio-demographic parameters. In order to fill a spatial Agentbased Besides these values, behaviors and norms, this paper presents the job roles necessary for a datadriven culture. We include
Model with data a number of open source and commercially available datasets need to be spatially analyzed and merged. The the crucial role of the data steward that elevates a data culture to a data-driven culture by administering data governance.
initial results of the spatial Agent-based Model simulation show that public policies for thermal energy transition can be Finally, we propose a definition of data-driven culture that focuses on the commitment to data-based decision making and an
simulated accordingly. ever-improving data analytics process. This paper helps teams and organizations of any size that strive towards advancing
their – not necessarily big – data analytics capabilities by drawing their attention to the often neglected, non-technical
requirements: data governance and a suitable organizational culture.
20 21Program
23rd May 2019
Short Papers Side Event – only if booked in advance
Wednesday, 22nd May 2019
Conference Dinner
Maximilian Ernst Tschuchnig (Salzburg University of Applied Sciences):
Adversarial Networks — A Technology for Image Augmentation
A key application of data augmentation is to boost state-of-the-art machine learning for missing values and to generate more
data from a given dataset. Additional to transformations or patch extraction as augmentation methods, adversarial networks
can be used in order to learn the probability density function of the original data. Generative adversarial networks (GANs) are a 07:00 –10:30 pm,
adversarial method to generate new data from noise by pitting a generator against a discriminator and training in a zero-sum
game trying to find a Nash Equilibrium. This generator can then be used in order to convert noise into augmentations of the
St. Peter Stiftskulinarium Salzburg
original data. This short paper shows the usage of GANs in order to generate fake faces as well as tips to overcome the
notoriously hard training of GANs. Enjoy a unique culinary and musical experience in the baroque hall of
Europe’s oldest restaurant: A delicious three-course-meal combined with
Dejan Radovanovic (Salzburg University of Applied Sciences): Neural Machine Transla- some of Mozart’s most enchanting compositions.
tion from Natural Language into SQL with state-of-the-art Deep Learning methods
Reading text, identifying key ideas, summarizing, making connections and other tasks that require comprehension and St. Peter Stiftskulinarium
context are easy tasks for humans but training a computer to perform these tasks is a challenge. Recent advances in deep
learning make it possible to interpret text effectively and achieve high performance results across natural language tasks. Sankt-Peter-Bezirk 1/4
Interacting with relational databases trough natural language enables users of any background to query and analyze a huge 5020 Salzburg
amount of data in a user-friendly way. This paper summaries major challenges and different approaches in the context of
Natural Language Interfaces to Databases (NLIDB). A state-ofthe-art language translation model developed by Google
named Transformer is used to translate natural language queries into structured queries to simplify the interaction between Program:
users and relational database systems.
Doors open, drinks served
Melanie Zumtobel, Kathrin Plankensteiner (FH Vorarlberg): First course
Using supervised learning to predict the reliability of a welding process Arias & duets from »Don Giovanni«
In this paper, supervised learning is used to predict the reliability of manufacturing processes in industrial settings. As an Main course
example case, lifetime data has been collected from a special device made of sheet metal. It is known, that a welding Arias & duets from »Le Nozze di Figaro«
procedure is the critical step during production. To test the quality of the welded area, End-of-Life tests have been performed
on each of the devices. For the statistical analysis, not only the acquired lifetime, but also data specifying the device before Dessert
and after the welding process as well as measured curves from the welding step itself, e.g., current over time, are available. »A Little Night Music«
Typically, the Weibull and log-normal distributions are used to model lifetime. Also in our case, both are considered as an
appropriate candidate distribution. Although both distributions might fit the data well, the log-normal distribution is selected Arias & duets from »The Magic Flute«
because the ks-test and the Bayesian Factor indicate slightly better results. To model the lifetime depending on the welding
parameters, a multivariable linear regression model is used. To find the significant covariates, a mix of forward selection and
backward elimination is utilized. The t-test is used to determine each covariate’s importance while the adjusted coefficient of
determination is used as a global Goodness-of-Fit criterion. After the model that provides the best fit has been determined,
predictive power is evaluated with a non-exhaustive cross-validation and sum of squared errors. The results show that the
lifetime can be predicted based on the welding settings. For lifetime prediction, the model yields accurate results when
interpolation is used. However, an extrapolation beyond the range of available data shows the limits of a purely data-driven
model.
Sebastian Malin, Kathrin Plankensteiner, Robert Merz, Reinhard Mayr, Sebastian
Schöndorfer, Mike Thomas (FH Vorarlberg, COPA-DATA GmbH):
Smart recommendation system to simplify projecting for an HMI/SCADA platform
Modelling and connecting machines and hardware devices of manufacturing plants in HMI/SCADA software platforms is
considered time-consuming and requires expertise. A smart recommendation system could help to support and simplify the
tasks of the projecting process. In this paper, supervised learning methods are proposed to address this problem. Data
characteristics, modelling challenges, and two potential modelling approaches, one-hot encoding and probabilistic topic
modelling, are discussed.
22 23Organisation:
Nicole Siebenhandl,
FH Salzburg Information Technology &
Systems Management
T +43 50 2211-1330
Venue & Travel office@idsc.at
www.idsc.at
Information
Online Registration
Salzburg Stadt
The online registration for participating in the 2nd International Data Science Conference
A10 Tauernautobahn 2019 at Salzburg University of Applied Sciences is possible until May 15, 2019.
(Exit Puch-Urstein)
Urstein Sü
www.idsc.at/registration
d
ssallee
Schlo
Conference Venue Documentation of the iDSC 2019
P1 Zufahrt
FH Salzburg
Campus Urstein The responsible organizer of the event will take photos and make videos in the course of the
Salzach
conference (incl. audio tracks) in its prevailing interests of documentation and publication of
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Schloss
the event and its contents. Through participating in the event, the data protection policy
Hall
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eine
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Puch
Hallein
Tips for hotels in Salzburg
Arrival & Location plan Motel One – Salzburg Mirabell Hotel Lasserhof
A location plan as well as information on If possible use public transport (the train Elisabeth Kai 58-60, 5020 Salzburg Lasserstrasse 47, 5020 Salzburg
how to get to the conference venue (public »S3« directly stops at the campus! Get off T +43 662 885200 T +43 662 873388
transport, car) and parking sites can be at station »Puch Urstein«). The regional bus www.motel-one.com www.lasserhof.com
found at www.idsc.at/location/ lines 160 and 165 also stop directly at the salzburg-mirabell@motel-one.com info@lasserhof.com
arrival-information. campus.
Mercure Hotel – Salzburg City Wyndham Grand
Please note that there is only a limited Taxi & public transport Salzburg Bayerhamerstrasse 14 a, 5020 Salzburg Fanny-von-Lehnert-Straße 7, 5020 Salzburg
amount of parking sites available at Urstein Taxi: +43 662 8111 T +43 662 8814380 T +43 662 46880
Campus. When arriving by car, please only Public Transport Salzburg: www.mercure.com www.wyndhamgrandsalzburg.com
park in the appropriate sections. www.salzburg-verkehr.at H0984@accor.com info@wyndhamgrandsalzburg.com
24 25Notes Imprint
Salzburg University of Applied Sciences
Urstein Süd 1, 5412 Puch / Salzburg
T +43 50 2211-0
www.fh-salzburg.ac.at
Responsible for the content: Photocredits
Information Technology & Systems Title page: Salzburg Tourismus
Page 4: FH Salzburg/Neumayr/Leo
Management Page 5: FH Salzburg/LagS
Mag. Nicole Siebenhandl Page 12: WTG Hannes Peinsteiner, Wolfgang-Seifert, WTG
Page 14-15: Private
T +43 50 2211-1330 Page 16: Shutterstock
office@idsc.at Page 23: Salzburger Konzertgesellschaft m.b.H.
26 272nd International Data Science
Conference (iDSC) 2019
www.idsc.at
Sponsors
©FH Salzburg, Information Technology & Systems Management As of: May 2019, all errors and misprints reserved.
Cognify KG
Intelligent algorithms, complex
data analysis and technology
consulting
www.cognify.ai
The MathWorks GmbH
Mathematical computing software
www.mathworks.de
Supported by
ITG: innovative consulting and
location development
Salzburg's innovation centre
www.itg-salzburg.atYou can also read
