Call for Papers 2017

i-KNOW 2017 – International Conference on Knowledge Technologies and Data-driven Business
October 11-12, 2017 in Graz, Austria

Important Dates

  • Workshop Paper Submission Deadline: July 7, 2017 – EXTENDED
  • Notification of Acceptance: July 31, 2017 – EXTENDED
  • Camera-ready Version of Workshop Papers: August 21, 2017
  • i-KNOW Conference in Graz: October 11-12, 2017

We offer workshops the opportunity to publish the best accepted workshop contributions as additional conference proceedings in the ACM digital library. Therefore acceptance of the papers will be either as full (regular) or short/demo papers with the corresponding presentation slots. Papers accepted as full will be published in the ACM Digital Library and papers accepted as short/demo can be optionally published in special online workshop proceedings. Please see the individual Calls for Workshops or contact the organizers of the workshop you want to submit to for details on publication opportunities.

Please submit your contribution using this link: iknow-submission
Do not forget to select the workshop you would like to contribute to.

Conference Theme: Knowledge Technologies and Data-driven Business

The International Conference on Knowledge Technologies and Data-driven Business – i-KNOW – has a 16-year history of bringing together the best minds from science and industry. Together we have successfully shaped research and practice on how knowledge and data are best leveraged in business and industry, and how knowledge technologies shape this relationship. Knowledge Technologies have dramatically changed over this period. i-KNOW 2017 aims at advancing research at the intersection of disciplines such as knowledge discovery, semantics, information visualization, visual analytics, social (semantic) and ubiquitous computing. The goal of integrating these approaches is to create cognitive computing systems that will interact naturally with humans, learn from their experiences and generate and evaluate evidence-based hypotheses. That is, we interpret cognitive computing as the convergence of various knowledge technologies research fields. On the other hand, data-driven business represents the business perspective on cognitive computing and takes application specific knowledge (such as semantics) into account.

In order to pick up recent trends, and to allow i-KNOW to always be at the cutting edge of this development, the scientific community will organize workshops dedicated to a specific topic connected to Data-driven Futures at the i-KNOW 2017. These workshops focus on particular technological approaches, on new applications of data-driven techniques, or specific domains in which they are applied. Find the Calls for Papers for the individual workshops together with their submission guidelines below.

Topics include (but are not limited to):

Human Computer Interaction (HCI) Perspectives on Industry 4.0

  • Cognitive support and cognitive automation for human sense-making and decision making processes
  • Visual assistance with augmented or virtual reality for complex data or knowledge-intensive work tasks
  • Smart and situated learning for a professional and self-directed work life on the shop floor
  • Data usability for enabling the worker to deal intuitively with complex and heterogeneous data
  • Division of labour between humans and machines in Industry 4.0: What can computers do? What do humans want to do? What are unique capabilities of humans and machines?
  • Mobile assistance to support humans on the shop floor. Potentials, challenges and visions for flexible, mobile assistance systems
  • Experiences of workers with novel technologies in smart industrial environments

More information and Call for Participation

Data-Driven Decision Support for Digitized Work Environments

  • 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?

More information and Call for Participation

SamI40 – 2nd International Workshop on Science, Application and Methods in Industry 4.0

  • Machine learning and artificial intelligence
  • Secure data exchange and communication
  • Decision making systems
  • Smart manufacturing & smart sensors
  • Swarm intelligence
  • (Big) data analytics & data science
  • Operations research
  • Simulation & virtualisation
  • Robust planning
  • Anomaly detection
  • Data-driven systems

More information and Call for Participation

Visual Analytics

More information and Call for Participation coming soon here.

RS-BDA’17 – 2nd Workshop on Recommender Systems and Big Data Analytics

  • Personalization and recommendations in data-intensive environments
  • Big data analytics for recommender systems
  • Cross-domain recommender systems
  • (Near) Real-time recommender systems
  • Context-aware recommender systems
  • Group recommenders
  • Social recommenders
  • Serendipity & diversity in recommender systems
  • Recommendations for long tail content
  • Cognitive aspects in recommender systems
  • Large, unstructured and social data for recommendations
  • Trust and reputation in recommender systems
  • Novel recommendation algorithms
  • Case studies of real-world implementations
  • Evaluation metrics and studies
  • Field and user studies
  • Recommendations in Technology Enhanced Learning
  • User interfaces for recommender systems
  • User modelling

More information and Call for Participation

SnanDig – International Workshop on Social Network Analysis and Digital Humanities

  • Studies of online communities
  • Communities of practice, online participation
  • Graph partitioning and community discovery
  • Link prediction
  • Location-aware social networks and mobility
  • Expertise and trust in online social networks
  • Crowdsourcing and social media
  • Information diffusion in social networks
  • Social influence
  • Opinion formation in social networks
  • Social bots and their role in shaping of public opinion
  • Studies of human culture based on online social networks and encyclopediae, computational methods to assess human culture along different dimensions (such as language, food, music, literature etc.)
  • Using social media to study psychological and social phenomena and corresponding methods to assess such phenomena via web-based investigations
  • Biases in social media, such as population biases, channel bias, self-selection bias, etc. and computational methods for assessment and correction
  • The study of limitations of tools used in digital humanities, extension of traditional source criticism into the development of systematic “tool criticism”
  • Social and individual inequalities on the web, e.g. gender or race disparities, computational methods and instruments to assess and track inequalities
  • Studies of political processes and dynamics on the web, such as elections, political unrest, grassroots movements
  • Predictions with social media, e.g. stock markets, spread of language, culture, memes or diseases
  • Longitudinal studies of social phenomena and social change on the web
  • Computational methods to assess and improve social and/or individual well-being via the web
  • Studies investigating the role and causal impact of algorithms in facilitating social interactions on the web

More information and Call for Participation

DL-AAS – 1st International Workshop on Deep Learning for Autonomous and Assistance Systems

  • Deep Learning for ADAS & Autonomous Driving
  • Deep Learning for Natural Language Processing
  • Deep Learning applied on manufacturing processes
  • Methods to support Exploratory Data Analysis
  • Open Information and Relation Extraction
  • Sensor fusion and data pre-processing
  • Reinforcement learning
  • Understanding learning of deep neural networks
  • Best practice in tuning of hyperparameters
  • Representation learning for feature engineering
  • Inverse (black-box) modelling with Deep Learning
  • Deep Learning – where did it fail?

More information and Call for Participation

Platform Economy & Business Models

  • 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?

More information and Call for Participation