Topics include:

• Big data in knowledge discovery
• Machine learning (e.g. unsupervised, supervised, semi-supervised…)
• Natural language analysis and information extraction
• Data and information retrieval
• Data and pattern mining
• Time series analysis and prediction (e.g. outlier/trend/… detection)
• Open information and relation extraction (e.g. knowledge base population, fact extraction)
• Enterprise information retrieval (e.g. cross-modal, interactive, roles & rights)
• Online methods (e.g. stream processing)
• Text mining (e.g. text summarisation, authorship identification …)
• Diversity & serendipity & privacy (e.g. in recommender systems)
• Deep learning approaches & application
• Text/web/social/user behaviour mining (e.g. sentiment analysis, intent mining)
• Data mining with taxonomies/ontologies/ linked open data
• Data and information quality

• Intelligent user interfaces for data analytics
• Human-computer interfaces for knowledge and information visualization
• Scalability of visual analytics and knowledge discovery techniques
• Interactive knowledge discovery
• Visual representations and metaphors
• Natural interaction techniques for visualization
• Visualization of knowledge, semantic information and linked data
• Visualization of search results, text and narratives
• Visualization of multimedia corpora
• Visualization of temporal and spatial information
• Visualization of large scale sensor data
• Visualization of structures and relationships
• Process and workflow visualization
• Visual support for reasoning and decision making
• Discourse and collaborative visualization
• Visual representations supporting knowledge transfer
• Cognitive and perceptual factors in visualization
• Evaluation and empirical studies of visualization interfaces
• Application reports and success stories
• Data structures, frameworks, and models underlying visualization

• Social media, social web, and social network analysis
• Web 2.0, future internet, and web science
• Collaborative knowledge creation and crowdsourcing
• Information quality and knowledge maturing
• Community evolution and user engagement
• Social information seeking and recommender systems
• Social search and retrieval systems
• Temporal and spatial analysis of social and information networks
• Social-semantic-content networks and their analysis
• Semantic uplifting in social networks
• Spam, misinformation and malicious activity discovery in social systems
• Social gaming and human computing
• Privacy & trust in social computing

• Ubiquitous (collaborative) work, learning, creativitiy
• Ubiquitous computing architectures and infrastructures
• Data management in ubiquitous computing systems
• Bridging the digital and physical worlds
• Ubiquitous sensors and sensor analyticsv
• Usage and usage data analytics
• User interaction and usability in ubicomp systems, especially in business and industry settings
• Augmented reality and augmentation interfaces
• Visual interfaces for collaboration
• User profiles and user models
• Context-awareness in ubicomp systems
• Adaptive systems, applications, interfaces and visualizations
• Evaluation and measurement approaches
• Security and privacy aspects of (mobile) sensing applications

• New publication and research processes and new paradigms for scientific communication
• Opportunities and challenges for researchers and research organizations
• Quality control in Science 2.0/ Open Science (e.g., metadata)
• New indicator systems to measure scientific quality (e.g., altmetrics)
• Epistemology and meaning of Science 2.0- and Open Science-related concepts
• Awareness-support for Science 2.0/ Open Science activities
• New feedback mechanisms among researchers and between science and society
• Empirical studies on the use of social media for Science 2.0/ Open Science as well as use cases
• Marketplaces for scientific data and publications
• Recommender systems in Science 2.0/ Open Science
• Virtual research environments and e-Infrastructures
• Digital research libraries and their role in Science 2.0/ Open Science
• Applications in and for Science 2.0/ Open Science
• Crowd-sourcing in science and citizen science
• Social mining and metadata extraction in academic resources
• Design and architecture of data sharing facilities
• Semantic web standards, data schemes and interoperability formats for Science 2.0/ Open Science
• Challenges in and reservations towards opening up scientific practices and using social media
• Legal dimensions in Science 2.0/ Open Science