Kröll Mark, Rath Andreas S., Weber Nicolas, Lindstaedt Stefanie , Granitzer Michael
2007
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
Rath Andreas S., Kröll Mark, Lindstaedt Stefanie , Granitzer Michael
2007
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