15th International Conference on
Knowledge Technologies and Data-driven Business
October 21-22, 2015, Graz, Austria
There is a text file you can use for distribution.
Use the proceedings template provided by ACM for your camera ready submission.
Please submit your contribution using this link: iknow-submission
Do not forget to select the topic you would like to contribute to. Topics can either be one of the special workshops or one of the main tracks.
Full Paper Submissions extended (8 pages)
- Abstract Submission Deadline: June 8, 2015
- Paper Submission Deadline: June 22, 2015
- Notification of Acceptance: July 24, 2015
- Camera-Ready Paper: August 10, 2015
Poster & Demonstration Submissions (4 pages)
- Submission Deadline: June 22, 2015
- Notification of Acceptance: July 24, 2015
- Camera-Ready Version: August 10, 2015
i-KNOW proceedings will be published by ACM ICPS
Cognitive Computing and Data-Driven Business
We are specifically interested in the integration of data-centric and user-centric approaches and welcome contributions from both ends of the spectrum.
Topics of the Main Conference Track include
(but are not limited to):
The growing popularity of big data fostered a new type of researcher, the data scientist. Typical responsibilities of this role are associated with the area of knowledge discovery and data analytics. Algorithmic approaches often focus on machine learning, information retrieval and for textual data on natural language processing. In this call for paper we therefore invite all data scientists to submit your exciting work in the area of knowledge discovery and data analytics. In addition, we also invite (big data) research on application oriented work featuring solid evaluations.
- 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
The fields of Visual Analytics, Information Visualization and Knowledge Visualization involve the visual presentation of and interaction with complex knowledge structures, abstract information spaces and large data repositories to facilitate their rapid assimilation and understanding. The objective of this topical block is to bring together researchers and developers as well as practitioners and providers in the field of visualization, to provide an interdisciplinary forum for discussing theoretical and practical results, and to promote research and development in the field. We invite submission of original research papers reporting on theoretical advances, evaluation results or practical applications of Visual Analytics, Information and Knowledge Visualization in relevant real-world scenarios.
- 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 networks and social media have profoundly shaped how people interact whilst pushing the boundaries of Web technologies. Social Computing research aims to generate added value from social interactions by harvesting the collective knowledge of groups of people. The Social Computing track at i-Know 2015 particularly invites researchers from multiple disciplines (e.g. Computer Science, Social Sciences) to discuss the following topics:
- 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 personal computing devices are key gateways to the world of digital information and to communication; they enable working and learning in times and places where this was previously impossible. This track intends to explore: What has changed due to affordances of these ubiquitous personal technologies? How do interaction concepts need to be re-thought? How to deal with privacy issues or the constant availability of professionals?
We are looking for contributions on interaction experiences, application features, theories for designing ubiquitous computing systems, and evaluations of such systems in the context of business and industry.
- 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 analytics
- 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
Today scientists are provided with a variety of web-based tools and activities which influence – and may fundamentally change – the way research is carried out. The practice of incorporating such tools and activities in research and scholarly communication is referred to as “Science 2.0” or, broadly spoken, as “Open Science”. The Science 2.0 & Open Science track at the iKnow 2015 particularly invites Web researchers from multiple disciplines (e.g. information science, computer science, sociology, communication and media studies, linguistics, educations studies, legal studies, etc.) to discuss the following topics:
- 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
The i-KNOW conference is celebrating its 15th anniversary! We think this is a great opportunity to reflect how research in the area of Knowledge Management and Knowledge Technologies has changed and developed further as a reaction to new challenges and opportunities.
For this purpose, you are invited to contribute submissions for the i-KNOW anniversary special track series. All tracks have in common that they combine a look at the past with an outlook into future developments. We are delighted to have engaged renowned experts to chair our special track series focusing on the following topics:
Chairs: Martin Wolpers, Ralf Klamma
The introduction of smart factories, internet of things and cyber-physical systems (in Germany coined “Industry 4.0″) changes the industrial manufacturing and production workplaces dramatically.
Companies and employees are faced with new requirements regarding the workplace. It is questionable if existing pedagogical and technological concepts address the new emands on the workforce sufficiently and adequately. Today, and in spite of political wishes, industry 4.0 related workplaces are still at the verge of emergence.
This track aims to foster discussions on new learning and training challenges in smart industries where products will „know“ the workforce. Papers are invited that discuss Industry
4.0 related learning scenarios and how they are being addressed.
Topics of interest include, but are not limited to, the following:
- Collaborative learning
- Micro-format learning
- Just-in-time learning
- Education with/cyber-physical systems
- Wearables for learning purposes
- Smart factory learning scenarios
- Personalization and adaptation
- Learning resource creation and management
Social Knowledge Management: from collections of documents to connections of people and physical objects
Chairs: Ronald Maier, Andreas Schmidt
Although knowledge has always been considered as inherently situated in the heads of people, knowledge management has started out with a technocratic focus on representations of knowledge in documents. The manifold developments of knowledge management since then include aspects of semantics and ontologies, activities and processes, assessment and evaluation, integration and visualization and a focus on the dynamics of knowledge maturing from emergent knowledge created by individuals to standardized knowledge applied by societies at large. With the advent of social software and the recent developments in networking of physical objects, knowledge management has shifted its attention more recently to collaboration and social relationships in collectives of people, from small teams to large crowds, termed social knowledge management, as well as to the inclusion of representations of and the interaction with physical objects, termed the Internet of things.
We solicit submissions that address the following topics:
- Knowledge creation and knowledge maturing – from teams to crowds
- Social collaboration, social networks, social spaces
- Boundary spanning and the boundaryless organization
- Connectivity in teams and organizations
- Role of physical objects in knowledge management
- Motivational aspects of social knowledge management
Chair: Barbara Kump
In increasingly dynamic operating environments, organizations not only have to manage and distribute their strategically relevant knowledge, but they are facing the need for continuous organizational learning and change, in order to remain competitive. Therefore, organizations need to develop certain strategic change capabilities, which all build on organizational knowledge processes. Organizational change capabilities include (i) organizational capabilities to search the environment (e.g., customer needs, technological innovations…), (ii) organizational capabilities to reflect upon existing practices (e.g., inefficient processes or structures), (iii) organizational capabilities to develop and share ideas for business opportunities (e.g., through innovation, absorption), and (iv) organizational capabilities to implement planned changes (e.g., by training their workforce, establishing effective communication structures). While large firms often build such capabilities by establishing dedicated divisions for market research and organization development, small and medium-sized enterprises (SMEs) usually cannot afford such divisions. Novel technologies, such as search and recommendation technologies, sensor technologies, or informal learning technologies, bear great potential to support organizations in general, and SMEs in particular, with developing organizational change capabilities.
This track invites both conceptual and empirical contributions around the question of how novel technologies can support organizational learning and change processes.
Topics of interest include, but are not limited to, the following:
- Concepts for supporting organizational learning, continuous change, or the development of dynamic capabilities
- Search and retrieval technologies to track customer needs, market trends etc.
- Technologies which support reflection/reflective learning and changes in organizational processes and routines
- Technologies which support the development of ideas for business opportunities
- Technologies which support change communication
- Technologies which support learning at work, especially with regard to planned changes
Chairs: Dieter Schmalstieg, Denis Kalkofen and Eduardo Veas
Technological innovations continuously transform the way we reach for and use knowledge. With augmented reality (AR), interfaces are associated with physical objects and the physical world turns into a user interface, which results in extended possibilities to naturally interact with complex systems. This workshop concentrates on how novel presentation metaphors in AR change the way we experience digital augmentations in the context of the real world for a more immersive, human acceptable experience. The goal is to explore novel techniques that make the perception of AR a more human experience, that foster continuous use without hindering the activities of the user, that put knowledge within reach, so that users, while accessing, gathering, consuming digital knowledge, are not required to interrupt their activities.
We welcome work in progress and contributions on the following general topics:
- novel visualization metaphors in practice,
- new unique interfaces and display devices that transform our perception of AR,
- new multimodal experiences of augmentation (auditory, olfactory, haptics),
- studies and evaluations of long term use of AR,
- usability evaluations of AR applications and technology.
The workshop will increase awareness of the state-of-the art in augmented reality as an interface that can be used continuously, as well as open new avenues of research in multimodal interfaces for a more immersive AR experience.
Chairs: Jörn Kohlhammer, Tobias Schreck and Vedran Sabol
Over the past few decades, the fields of Information Visualisation and Scientific Visualisation have gone a long way in devising how data and information are presented to and interacted with by humans. A variety of general-purpose visualisation applications, such as Spotfire or Tableau, have emerged on the market bringing visualisation to the masses and, in connection with data analytics back-ends, transforming the way people view, explore and analyse data.
This i-Know Special Track invites scientists and practitioners to discuss theoretical and practical aspects of Visual Analytics, explore recent developments, and investigate trends from the following three perspectives: (i) reflecting on the achievements of traditional InfoVis and SciVis approaches and pointing out the advantages arising from advancing them to full-fledged Visual Analytics methods; (ii) reporting on latest results and advancements in Visual Analytics techniques, and on benefits arising from their practical applications; (iii) debating upcoming trends in Visual Analytics and the impact of related hot topics such as augmented reality, mobile computing and big data analytics.
We welcome novel contributions, surveys and work in progress on Visual Analytics including, but not limited to, the following topics:
- From user-configured charts and algorithms to adaptive, personalised, context-sensitive visual analytics
- From loosely coupled visualisation and data mining tools to integrated visual analytics applications
- Novel Visual Analytics methods and metaphors
- User studies and evaluation of Visual Analytics techniques
- Experiences and impact of practical applications of Visual Analytics
- Benefits of user feedback for enhancing analytical processes and workflows
- New client devices and multimodal interaction in Visual Analytics
- The role of augmented reality and immersive technologies in Visual Analytics
- Addressing the big data “V’s” with Visual Analytics
- Achieving scalability with visualisation
- Visualising change and dynamics
- Seamless presentation of heterogeneous data
- Conveying data quality, accuracy and trustworthiness
Chairs: Michael Granitzer, Benno Stein
The field of Information Retrieval (IR) shaped the last decade on how humans search and work with information. Global players such as Google demonstrate the enormous impact of retrieval technology on the society its use of information. With the I-Know Special Track on “From Boolean Retrieval to Knowledge Discovery and Big Data” we want to reflect the achievements in Information Retrieval in terms of research and industrial uptake. We aim to analyse their impact and influence on today’s world and probably extrapolate upcoming trends. We invite researchers to contribute by submitting research papers analysing past developments and an outlook on how they might impact future trends. Following the IR methodology, contributions should be based on a thorough analysis of particular sub-fields in Information Retrieval in terms of already published research or empirical analyses. See the following examples for possible topics to get an idea of a special and interesting contribution that you could provide:
- From ad-hoc retrieval to question-and-answering
- From TFIDF to Learning-to-Rank
- From known-item search to personalisation
- From mono-lingual to cross-lingual retrieval
- From Alta-Vista to Google
- From ELIZA to Watson
- From LSI to Deep Learning
- From keywords to Word2Vec
- From SERPs to straight answers
- From forming to cluster labeling
- From Cranfield to living labs
Chairs: Matthias Hagen and Roman Kern
Big Data started out as a buzz word being used to describe a lot of different aspects. In the mean time Big Data has established itself and has been recognised as a field of Computer Science. This i-KNOW special track tries to bring together researchers working on Big Data problems and applications that go beyond the mere buzz word. We invite papers that contribute to the knowledge in three different areas of Big Data research i) the algorithmic infrastructure, ii) the data management and integration and iii) the analytical part. We particularly encourage papers that will serve as a stepping stone for future research and hint the direction of this young and fast moving research field. We also invite papers that demonstrate the added benefit by combining multiple data sources, especially for real-time applications. Thus topics of this special track focused on Big Data are:
- Analytics and Data Mining
- Data Management and Data Integration
- Scalable and Distributed Architectures
- Evaluation Methodologies
- Real World Applications
- (Near) Real-Time Methods
Chairs: Alexander Felfernig, Elisabeth Lex
Recommender systems combine historical data on user preferences, (user) similarities and past behavior to suggest and predict items a user might be looking for. 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.
The objective of this special track is to bring together researchers and practitioners involved in developing, testing, and fielding recommender systems, especially in the area of knowledge management. The special track focuses on all aspects of recommender systems and it will provide a forum for discussing current practice and recent research results
Topics include but are not limited to:
- Personalization and recommendations in knowledge management
- Recommender algorithms
- Case studies of real-world implementations
- Evaluation methodologies for recommender systems
- Field and user studies of recommender systems
- Context-aware recommenders
- Cold-start problem
- Expert recommenders
- New trends and challenges in recommender systems
- Machine learning for recommendation
- Social recommenders
- Semantic technologies for recommendations
- Recommendations in TEL
- Trust and reputation in recommender systems
- User modelling for recommendations
- User interfaces for recommender systems
- Scalability of recommender systems
Chairs: Claudia Müller-Birn, Denis Helic
The scale and the rate of the Web’s growth as well as the Web’s impact on our society is unparalleled by any other technology. The Web’s rapid development resulted in a socio-technological system that is increasingly challenging to analyze, understand, or modify. Over the last years, a number of crowdsourcing systems emerged in which humans and machines collaborative to build for example structured and unstructured knowledge bases. Understanding the dynamics and evolution of knowledge flows within these systems, as they depend on inherent social and informational structures, is of particular interest, because it is the dynamics of human and algorithmic participation that determines their final success or failure. In order to analyze such socio-technological systems an interplay between aspects of computer science with social science and psychology is required. Various lines of research lead to a new interdisciplinary field, called Web Science which promises to answer existing scientific and engineering challenges in this context. .Although Web Science studies people, organizations, and policies that shape and are shaped by the Web, it is also concerned with the Web’s engineering aspects such as the analysis and design of the architecture, protocols, data and applications, or algorithms. Thus, Web Science adopts approaches from network science to study the Web as a network of people, and it also adopts methods from the statistics, machine learning and data mining to examine the digital traces of user interactions with the Web’s content.
This special track focuses on all aspects of Web Science. As Web Science is an interdisciplinary field combining research from disciplines such as mathematics, computer science, economics, social science, sociology, history, or psychology papers should preferably demonstrate interdisciplinary work contributing new models, algorithms, data analysis, frameworks and applications relevance to any aspect of the Web Science.
Topics include but are not limited to:
- Theory and methodology of Web Science
- Analysis of human behavior using digital traces
- Network analysis of the Web and of Web-based data
- Web Mining
- Web architecture
- Web Data
- Web practices and communities
- Knowledge and education on the Web
- Humanities on the Web
- Collective intelligence and collaborative production
- Social Media analytics
- Web economics, social entrepreneurship and innovation
- Intellectual property and the commons
- Case studies of crowdsourcing systems such as Wikipedia, Facebook, Twitter, World of Warcraft, open source software as well as empirical findings in related applications
Chairs: Herbert Leitold, Reinhard Posch
Data-driven business carries great potential and opportunities, but also risks: Invasion of privacy and security are concerns when utilising increasing amounts of data. With paradigms like cloud computing or big data this has an international dimension, spanning various legislations and a multitude of organisations providing or processing data. Such an environment asks for novel security paradigms, as well as a proper legal framework. The session aims to bring to together researchers to discuss security and privacy challenges in data-driven business. Both legal and technical contributions are sought to discuss on areas like:
- Privacy-preserving technologies
- Anonymity and pseudonymity
- Auditing, assessment, and compliance
- Authentication and Identification
- Context-aware encryption or data masking
- Legal barriers in a global, data-driven economy
- Legislation-aware computing
- Mobile and smart devices security
- Searchable, attribute-based, identity based, or homomorphic encryption
- Security and Trust Management
- Security policy enforcement in multi-organisational environments
- Trust-enhancing and trust-enabling technologies
Chair: Klaus North
The ever increasing amount and variety of data and information can be a source of inspiration or confusion. As data are on the lowest level of the knowledge ladder they require to be associated with meaning to become information, which needs skillful interpretation in a specific context as a basis for decision making. This track invites contributors to explore and discuss critical challenges, developments and solutions for management in general and knowledge management in particular in data-driven businesses. We invite conceptional as well as empirical papers on issues such as:
- Can data drive businesses or is it knowledge and competence that drives business?
- What are the mental models behind “data-driven” businesses and how do they differ regarding “knowledge-driven” businesses?
- Which knowledge is needed to interpret huge amounts of data?
- Which role does domain knowledge play in the interpretation of data?
- What are the implications for decision making based on data or based on intuition?
- How will work and leadership of knowledge workers change?
- How does innovation happen in data-driven businesses?
- What will be the role of explicit data/information and tacit knowledge?
- What will be the role of communities and networks?
- How do organizations learn based on big data?
- Stefanie Lindstaedt, Know-Center Graz & Graz University of Technology, Austria
- Harald Sack, Hasso-Platter Institute for IT Systems Engineering, Germany
- Tobias Ley, Tallinn University, Estonia
Organization & Dissemination Chair
- Nina Simon, Know-Center Graz, Austria
Knowledge Discovery, Analytics & Information Visualization
- Jörn Kohlhammer, FHG IGD, Germany
- Roman Kern, Know-Center Graz, Austria
- Vedran Sabol, Know-Center Graz, Austria
- Wolfgang Kienreich, Know-Center Graz, Austria
- Christin Seifert, University of Passau, Germany
Social & Ubiquitous Context-aware Computing
- Denis Helic, Graz University of Technology
- Viktoria Pammer, Know-Center & Graz University of Technology, Austria
- Elisabeth Lex, Know-Center & Graz University of Technology, Austria
- Christoph Trattner, Know-Center Graz, Austria
Science 2.0 & Open Science
- Klaus Tochtermann, ZBW – Leibniz Information Center for Economics, Germany
- Isabella Peters, ZBW – Leibniz Information Center for Economics, Germany
- Peter Kraker, Know-Center Graz, Austria
- Elisabeth Lex, Know-Center & Graz University of Technology, Austria
Posters & Demonstrations
- Jörg Simon, Know-Center Graz, Austria