Open Innovation in the Big Data Era With the MOVING Platform

Iacopo Vagliano, Franziska Gunther, Matthias Heinz, Aitor Apaolaza, Irina Bienia, Gert Breitfuss, Till Blume, Chrysa Collyda, Angela Fessl, Sebastian Gottfried, Peter Hasitschka, Jasmin Kellermann (+10 others)
2018 IEEE Multimedia  
In the Big Data era, people can access vast amounts of information, but often lack the time, strategies and tools to efficiently extract the necessary knowledge from it. Research and innovation staff needs to effectively obtain an overview of publications, patents, funding opportunities, etc., to derive an innovation strategy. The MOVING platform enables its users to improve their information literacy by training how to exploit data mining methods in their daily research tasks. Through a novel
more » ... ntegrated working and training environment, the platform supports the education of data-savvy information professionals and enables them to deal with the challenges of Big Data and open innovation. Open innovation is a distributed innovation process based on knowledge flows across organizational boundaries 1 which involves various actors, from researchers, to entrepreneurs, to users, to governments, and civil society. Many Open Innovation Systems (OIS) already exist, e.g., Innocentive ( and Hypios ( They mainly support collaborative idea generation and problem solving. However, the generation of ideas is not the biggest challenge of open innovation. Research and innovation staff in academia and industry needs to effectively obtain an overview of publications, patents, products, funding opportunities, etc., to derive appropriate innovation strategies. For instance, researchers and students need to find, understand, and build on top of a large and steadily increasing number of previous publications and other online educational resources (video lectures, tutorials, etc.). Similarly, financial auditors need to monitor a constantly evolving set of regulations pertinent to their daily work. In the Big Data era, such information is usually available and freely accessible in digital resources (text and media). However, students and professionals typically lack the time, strategies and tools to efficiently extract useful knowledge from all these resources. The MOVING project ( is developing a platform to enable people from all societal sectors (companies, universities, public administration) to improve their information literacy by training how to exploit data and text mining methods in their daily research tasks. Thus, the MOVING platform's users can more efficiency identify and process relevant information by knowing how to deal with data and text mining methods, and then use this information to contribute to open innovation, as any innovation is based on previous knowledge. SECTION TITLE HERE resources: unstructured data in the form of documents, structured data in the form of metadata, as well as video material and social media data; some of these resources are automatically selected and collected from the Web and social networks. In terms of tools, the platform supports actions such as cross-media search on these resources, and exploits video processing techniques that enable automatic concept annotation and search on the videos. Through an integrated training and working environment, the MOVING platform provides two main contributions: 1. The working environment provides tools for the analysis of large amounts of structured, semi-structured, and unstructured data, notably text and other media. Two aspects of Big Data are addressed, volume and variety. 2. The training environment supplies a training program to use these tools and boost open innovation processes. Finally, the combination of the working and training environment with a community of practice (currently being formed) will allow users to share ideas and challenges, as well as communicate their experiences and learn from them. MOVING is an interdisciplinary project bringing together unique expertise from computer science and media didactics. We conducted an extensive literature research and identified different fields of research related to OIS, based on existing classifications like Hrastinski et al. 2 These include OIS, Expert Search Systems (ESS), Recommender Systems (RS), Adaptive Hypermedia Systems (AHS), Decision Support Systems (DSS) and Technology-Enhanced Learning (TEL). We briefly discuss each of these related fields of research and show how they relate to MOVING. OIS are concerned with the facilitation of open innovation processes and the transfer of knowledge from the crowd into organisations. 1, 2 Typically, an organisation describes a problem to be solved and provides a tool that allows individuals to submit proposed solutions. Hrastinski et al. identified typical OIS features: idea submission (users submit an idea, often within predefined categories), problem submission (organisations submit a problem, users suggest solutions), proposal evaluation (users assess the quality of proposed solutions), expert directory (describing and locating experts), and marketplace (connecting innovators with innovation seekers). 2 In contrast, MOVING addresses the question of how managers, researchers and employees can be trained to initiate, maintain and support open innovation. Furthermore, the existing OIS mainly support collaborative idea generation. However, the time, strategies and tools to efficiently extract the necessary knowledge from existing, background information is usually missing. MOVING addresses this challenge by providing tools for analysing large amounts of text and media (working environment -Sections Data Acquisition to Data Visualization) and training programs for these tools (training environment, notably the Adaptive Training Support -Section Adaptive Training Support). Related to the expert directory of OIS, ESS identify people with relevant expertise on a topic of interest. Balog et al. reviewed this area. 3 Typically, ESS create profiles of candidate experts by associating a set of documents to them, to represent their expertise. In the context of open innovation, ESS are used by companies to search in the profiles and invite experts to submit solutions. The companies can then select the best contribution and acquire the rights to use it. Thus, ESS can boost a company's problem-solving activity. 2 However, finding experts in a given topic is often not sufficient since more diverse solutions in terms of domains of knowledge and perspectives on the problem are needed. Innovation often comes from experts in topics not directly related to the problem who can transfer the knowledge from one domain to another. In MOVING, advanced search and visualisation functions such as network graphs (Sections Data Indexing and Search and Data Visualization) enable users to find key literature and experts on a topic and potentially related topics. MAGAZINE NAME HERE RS are other related tools which suggest interesting items, e.g., movies, news, scientific papers. Typically, RS are classified into content-based, collaborative-filtering, knowledge-based, or hybrid. 4 Content-based RS make suggestions that take into account the items a user liked in the past. Collaborative-filtering RS generate recommendations to a user based on the items that similar users liked. Knowledge-based RS infer similarities between user requirements and item features described in a knowledge base. Hybrid RS combine one or more of these techniques. With the evolution of the Web toward a global data space known as the Linked Open Data cloud (, Linked-Data-based RS have emerged. They suggest items by exploiting knowledge on the LOD cloud. 5 In MOVING, recommendations go beyond suggesting items to the user: the Adaptive Training Support (ATS), described in Section Adaptive Training Support, recommends platform features based on the users' behaviour. In line with providing recommendations and personalized information, AHS aims to automatically adapt the organisation, presentation and interaction of personalized hypermedia content to its users. 6 To this end, AHS observe the users' interactions with the system and react to it. They maintain three interconnected models: diagnosis, educational and expert. The diagnosis model comprises assumptions and information about the level of knowledge of the user in a specific domain. The educational model provides a didactic concept of how to convey and present the content to users. The expert model contains relevant domain-specific knowledge. In MOVING, the ATS is based on the concepts underlying to AHS. It gives feedback regarding the user's context and activities on the MOVING platform (stored in the corresponding user profile). Based on this, it provides reflective questions to users, increasing their awareness of how they use the platform. Furthermore, it recommends new features to improve their search behaviour and train them to use the platform more effectively. DSS are another field of research related to MOVING, specifically to the ATS. Their goal is to provide decisional advice to enable faster, better, and easier decision-making. 7 Central to open innovation systems and open educational systems, and thus to MOVING, are the two dimensions of invocation and timing. Invocation refers to how guidance is invoked, 7 i.e., whether users are automatically notified by the system based on predefined events, receive feedback only when users actively request it, or based on some context. In MOVING, the ATS considers all three previously mentioned forms of guidance. We analyse the users' behaviour on the MOVING platform to automatically provide reflective questions, which are informed by the users' behaviour. For example, if one often uses a specific feature then the ATS assumes that one likes such feature and asks why, in contrast, when a feature is not used, its use is suggested. Finally, users can also actively request guidance. Timing refers to when guidance is invoked. 7 Guidance can be triggered during the actual user activity, before a user actually conducts an activity, and after a user performed an activity. MOVING focuses on triggering training support during and after user activities. Finally, TEL 8 is highly relevant to MOVING since using technological tools to support learning and knowledge acquisition is the central feature of our training environment. From TEL, MOVING borrows computer-supported reflective learning, i.e., the mechanism to learn from experience. 9 Reflective learning happens both directly within a work process (reflection-inaction) and more systematically outside operative work processes (reflection-before-action, reflection-on-action). 10 In the social context of an organisation, reflective learning must be understood not only as a cognitive process of the individual worker (individual reflective learning) but also as a social process (collaborative learning). Regarding computer-support for workrelated reflective learning, activity logging supports reflection by providing accurate data. A transfer of these results to work settings is often not easy to implement for multiple reasons. First, it is often not obvious what data that constitutes relevant aspects of work can be automatically captured. Second, these data needs to be closely related to relevant entities in the work domain (e.g., customers or artefacts). Finally, even the best-educated users have difficulties in gaining actionable knowledge out of those data. Our key insight from the previous work is that reflection guidance needs to be designed into computer-mediated reflection tools, and embedding reflective learning into business processes is crucial.
doi:10.1109/mmul.2018.2873495 fatcat:dvgpkpahtfcc3lpgwxhxf7k2ku