Publikationen

Given the importance of an organisation’s human capital to business success, aligning training and competencydevelopment with business needs is a key challenge. Many companies did initiate in the pastknowledge management activities or founded corporate universities as the organization intended to helpcompanies to face this challenge. In this deliverable, we talk about knowledge work and learning managementas a concept to “increase business performance” through a better short- and long-term learningapproach for people at management level. The aim is to provide a guideline for corporate users based onour and others' experiences of implementing solutions for knowledge work and learning. This is connectedto many forms and methods of learning: formal learning processes, informal learning, team learning,collaboration, social networking, community building etc. In many companies, managers think thatknowledge work can be supported solely by offering courses and enabling to access content on demand.In this deliverable this aspect (ACQUIRING knowledge) will not be in focus as it is more the job of atraining department to manage courses and catalogues. Instead we focus on APPLYING knowledge. Theconcept of knowledge work management comes into place, when companies see the ability of their employeesto APPLY their education and knowledge as a strategic instrument to create competitiveness andlook for tools to provide learning and knowledge at workplace on demand and fitting to the individualneeds. And this objective is very actual as the globalization creates pressure on companies and theknowledge and experience of the employees gets the most important differentiator to competitors – leadingto better innovation, faster processes, higher productivity and lower costs. In this deliverable, anoverall approach and guideline for companies will be provided on how to implement knowledge workmanagement and provide learning according to the needs in business and resulting from business processes.

Evaluation of information retrieval systems is a critical aspect of information retrieval research. New retrieval paradigms, as retrieval in the Semantic Web, present an additional challenge for system evaluation as no off-the-shelf test corpora for evaluation exist. This paper describes the approach taken to evaluate an information retrieval system built for the Semantic Desktop and demonstrates how standard measures from information retrieval research are employed for evaluation.

Is Web 2.0 just hype or just a buzzword, which might disappear in the near future? One way to find answers to these questions is to investigate the actual benefit of the Web 2.0 for real use cases. Within this contribution we study a very special aspect of the Web 2.0 ? the folksonomy ? and its use within self-directed learning. Guided by conceptual principles of emergent computing we point out methods, which might be able to let semantics emerge from folksonomies and discuss the effect of the results in self-directed learning.

Purpose: The purpose of this contribution is to motivate a new, rapid approachto modeling knowledge work in organizational settings and to introducea software tool that demonstrates the viability of the envisioned concept.Approach: Based on existing modeling structures, the KnowFlowr Toolsetthat aids knowledge analysts in rapidly conducting interviews and in conductingmulti-perspective analysis of organizational knowledge work is introduced.Findings: It is demonstrated how rapid knowledge work visualization can beconducted largely without human modelers by developing an interview structurethat allows for self-service interviews. Two application scenarios illustrate thepressing need for and the potentials of rapid knowledge work visualizations inorganizational settings.Research Implications: The efforts necessary for traditional modeling approachesin the area of knowledge management are often prohibitive. Thiscontribution argues that future research needs to take economical constraintsof organizational settings into account in order to be able to realize the fullpotential of knowledge work management.Practical Implications: This work picks up a problem identified in practiceand proposes the novel concept of rapid knowledge work visualization for makingknowledge work modeling in organizations more feasible.Value: This work develops a vision of rapid knowledge work visualization andintroduces a tool-supported approach that addresses some of the identified challenges.

While there is a lot of hype around various concepts associated with the term Web 2.0 in industry, little academic research has so far been conducted on the implications of this new approach for the domain of education. Much of what goes by the name of Web 2.0 can, in fact, be regarded as new kinds of learning technologies, and can be utilised as such. This paper explains the background of Web 2.0, investigates the implications for knowledge transfer in general, and then discusses its particular use in eLearning contexts with the help of short scenarios. The main challenge in the future will be to maintain essential Web 2.0 attributes, such as trust, openness, voluntariness and self-organisation, when applying Web 2.0 tools in institutional contexts.

Knowledge-intensive work plays an increasingly important role in organisations of all types. Knowledge workers contribute their effort to achieve a common purpose; they are part of (business) processes. Workflow Management Systems support them during their daily work, featuring guidance and providing intelligent resource delivery. However, the emergence of richly structured, heterogeneous datasets requires a reassessment of existing mining techniques which do not take possible relations between individual instances into account. Neglecting these relations might lead to inappropriate conclusions about the data. In order to uphold the support quality of knowledge workers, the application of mining methods, that consider structure information rather than content information, is necessary. In the scope of the research project DYONIPOS, user interaction patterns, e.g., relations between users, resources and tasks, are mapped in the form of graphs. We utilize graph kernels to exploit structural information and apply Support Vector Machines to classify task instances to task models

The prediction of time series is an important task in finance, economy, object tracking, state estimation and robotics. Prediction is in general either based on a well-known mathematical description of the system behind the time series or learned from previously collected time series. In this work we introduce a novel approach to learn predictions of real world time series like object trajectories in robotics. In a sequence of experiments we evaluate whether a liquid state machine in combination with a supervised learning algorithm can be used to predict ball trajectories with input data coming from a video camera mounted on a robot participating in the RoboCup. The pre-processed video data is fed into a recurrent spiking neural network. Connections to some output neurons are trained by linear regression to predict the position of a ball in various time steps ahead. The main advantages of this approach are that due to the nonlinear projection of the input data to a high-dimensional space simple learning algorithms can be used, that the liquid state machine provides temporal memory capabilities and that this kind of computation appears biologically more plausible than conventional methods for prediction. Our results support the idea that learning with a liquid state machine is a generic powerful tool for prediction.

Knowledge intensive organizations demand a rethinking of business process awareness. Their employees are knowledge workers, who are performing their tasks in a weakly structured way. Stiff organizational processes have to be relaxed, adopted and flexibilized to be able to provide the essential freedom requested by knowledge workers. For effectively and efficiently supporting this type of creative worker the hidden patterns, i.e. how they reach their goals, have to be discovered. This paper focuses on perceiving the knowledge workers work habits in an automatic way for bringing their work patterns to the surface. Capturing low level operating system events, observing user interactions on a fine granular level and doing in deep application inspection, give the opportunity to interrelate the received data. In the scope of the research project DYONIPOS these interrelation abilities are utilized to semantically relate and enrich this captured data to picture the actual task of a knowledge worker. Once the goal of a knowledge worker is clear, intelligent information delivery can be applied

Any software development project is based on assumptions about the state of the world that probably will hold when it is fielded. Investigating whether they are true can be seen as an important task. This paper describes how an empirical investigation was designed and conducted for the EU funded APOSDLE project. This project aims at supporting informal learning during work. Four basic assumptions are derived from the project plan and subsequently investigated in a two-phase study using several methods, including workplace observations and a survey. The results show that most of the assumptions are valid in the current work context of knowledge workers. In addition more specific suggestions for the design of the prospective APOSDLE application could be derived. Though requiring a substantial effort, carrying out studies like this can be seen as important for longer term software development projects.