Kröll Mark, Rath Andreas S., Weber Nicolas, Lindstaedt Stefanie , Granitzer Michael
2007
Task Instance Classification via Graph Kernels
Mining and Learning with Graphs (MLG 07), Florenz, Italy, August 1-3, 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