The following workshops will be conducted at i-KNOW 2017. As our Call for Workshops is still open to submission, this page will be updated with further workshops and information soon. Follow the links to find more information on each workshop as well as the issued Calls for Papers.
- DL-AAS – 1st International Workshop on Deep Learning for Autonomous and Assistance Systems
- Platform Economy & Business Models
- RS-BDA’17 – 2nd Workshop on Recommender Systems and Big Data Analytics
- Data-Driven Decision Support for Digitized Work Environments
- Human Computer Interaction (HCI) Perspectives on Industry 4.0
- SamI40 – 2nd International Workshop on Science, Application and Methods in Industry 4.0
- SnanDig – International Workshop on Social Network Analysis and Digital Humanities
- Visual Analytics
Organizers: Stefan Thalmann, Daniel Bachlechner, René Peinl, Michael Kohlegger, Sebastian Bader, Philipp Gölzer, Eric Schoop, Markus Vorderwinkler, Günter Schreier, Dieter Hayn
Work paradigms change significantly in the era of digitization. Due to the increasing complexity raised by personalization of products, shorter innovation cycles and more connected supply chains, requirements for employees are rising steadily. On the one hand, more knowledge needs to be learned in shorter time and on the other hand, the knowledge needed for work tasks is less predictable. As a consequence, increased decision support is needed during the execution of business processes. Further, trends such as the delimitation of employment and employee empowerment require more decentralized decision support. However, digitization also offers opportunities to address these challenges. Especially, the increasing availability of (sensor) data about work process offer manifold opportunities to provide suitable decision support in work processes.
This workshop discusses challenges in regard to digitization in industry as well as office environments and how data-driven solutions can address these challenges. Key questions are: How to make sense of the increasingly growing amount of (sensor) data available in production as well as office environments? How to provide meaningful decision support in work situations? Which data can be used for situation specific decision support and how can this context data be collected, especially with new sensors for retrofitting existing production equipment?
Workshop papers are considered for the special issue “Data-Driven Decision Support“ of the journal “Information Technology” to be published in 4/2018. The workshop is intended, among others, to give feedback on how to prepare/revise the paper for consideration by the journal.
Organizers: Eduardo Veas, Roman Kern
Artificial intelligence has once again become a topic of public interest. Predominantely, this is due to the recent advances in Deep Learning, with AlphaGo just being one example. This was made possible in part by bigger data sets and more computational resources. Currently, researchers worldwide are working hard to improve Deep Learning algorithms, apply them on various scenarios and build better systems to make use of such algorithms.
The levels of autonomy that these systems aim for varies with the application scenario and also with the capabilities of each system: from assistive systems that interact with the human to reach decisions, to fully autonomous systems. For instance, a strong focus concentrated on interpreting sensory input, such as, autonomous classification of images, using images to decide the next move in a game. These scenarios open the field for autonomous systems that drive robots, cars, drones, etc. On the other hand, an increasingly important task is to assist the human in interpreting large amounts of data available. As the volumes of data pushes the limits of exploratory data analysis, the role of personal assistants, offering intelligent processing and advice, becomes increasingly important. Intelligent assistance is also sought in tasks were ultimately autonomous system is desired, but cannot be realized currently, for example ADAS (Advanced Driving Assistance Systems) help detect lane changes or braking distance from other cars.
The DL-AAS workshop aims to support the scientific progress by bridging multiple application areas, where Deep Learning has been applied successfully. These three focus areas are: Natural Language Processing, Manufacturing and Autonomous Driving. The workshop strives to foster a vivid knowledge exchange between these very different fields and thus serve as a source for inspirations. Therefore we welcome contributions that allow for such a lively discussion.
Organizers: Mario Aehnelt, Ralf Klamma, Tobias Ley, Ronald Maier, Thomas Meneweger, Viktoria Pammer-Schindler, Manfred Tscheligi, Daniela Wurhofer
Today we are facing a new era of industrial automation and interconnection which drives the transition of human workplaces. New technologies but also novel business processes lead to a shift of worker related requirements at the data-intensive manufacturing workplace on the shop floor or in knowledge-intensive maintenance field operations. HCI research is already dealing with these new challenges by developing and providing practical assistance solutions which bring together again the power of industrial automation with the flexibility of human intelligence. This workshop aims to pick up and present examples of best practice and lessons learned from researching and rolling out novel methods and technologies for worker-focused assistance under industrial conditions.
Organizers: Gert Breitfuss, Romana Rauter, Christiana Müller
Enterprises that leverage the power of platform based business models have grown dramatically in the recent years and it is no longer a playground for the digital born organizations like Google, Uber or Airbnb. It is mainly the usage of (digital) technologies which allows companies to base their products and services on platforms facilitating the connections between different partners involved in such supply chains. Consequently, business models based on platforms create additional value by facilitating exchanges between two or more independent groups, usually consumers and producers.
What can be observed is that platform based business models are already playing a strategic role in all types of business (like agriculture, healthcare, industrial equipment etc.) and hence are not only promising for large international and technology-driven companies but might enable value creation also for various other types of companies. Such platform-based business models fundamentally change how companies can do business and opens up entirely new paths to growth. However, all of these platforms are diverse in function, structure and in the underlying business model and so far little is known about success factors, business model patterns, benefits for various partners being involved etc.
In this context, we seek contributions that address questions such as: What are the key elements of a platform business model? What business model patterns are predominating in platform economies? What platform archetypes can be identified? Which partners are involved in such platform business models? How does value creation look like? Thereby, empirical as well as conceptual papers or best practice examples are welcomed.
Organizers: Alexander Felfernig, Denis Helic, Elisabeth Lex, Stefan Reiterer, Dominik Kowald, Emanuel Lacic
Recommender systems are acknowledged as an essential instrument to support users in finding relevant information in an overloaded information space. While they have been proven successful in e.g., e-commerce applications, they can also support organizations in better identifying competences, help engage users in a continuous and dynamic knowledge exchange, and customize dissemination of knowledge as much as possible. Moreover, the advent of the big data era has posed the need for high scalability and real-time processing of frequent data updates, and thus, has brought new challenges for the recommender systems’ research community. As in 2016 (http://socialcomputing.know-center.tugraz.at/rs-bda/), the objective of this workshop is to bring together researchers and practitioners involved in developing, testing, and maintaining (social) recommender systems, especially in the light of big data. The workshop focuses on all aspects of recommender systems and big data analytics and it will provide a forum for discussing current practices and recent research results.
Organizers: Roman Kern, Gerald Reiner, Olivia Bluder
Scientific and technological progress allowed for evolutionary and revolutionary progress in many fields – with manufacturing as a prime example. This process is often associated with the term “Industry 4.0”. In the SamI40 workshop we discuss the scientific methods and technological approaches which are the key enabler for this movement and includes application areas ranging from supply chain management to process optimisation. As such, the workshop aims to bring together researchers and practitioners in the field. These types of submissions are foreseen: i) novel scientific contributions, ii) application of methods and their performance, and ii) best practice and reports from the field.
Organizers: Denis Helic, Christoph Trattner, Elisabeth Lex, Christian Gütl
Online social networks and social media sites allow numerous users to participate in collaboratively creating online information by producing new content and establishing new links between people, information, and services. Users participating in such online endeavours leave digital traces on the Web, which provides unparalleled research opportunities for studying questions in sociology, psychology, behavioral science, or political and economic science. While studying human digital traces have received attention from different communities, understanding human social behavior via non-invasive methods remains a challenging task and an open problem. Tackling these challenges requires the development of new methods, instruments and techniques as well as an interdisciplinary effort from researchers across disciplines such as computer science, humanities, social sciences and others.
Organizers: Vedran Sabol, Eduardo Veas, Tobias Schreck, Jörn Kohlhammer
More information coming soon…