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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(More)
Since more and more people use the micro-blogging platform Twitter to convey their needs and desires, it has become a particularly interesting medium for the task of identifying commercial activities. Potential buyers and sellers can be contacted directly thereby opening up novel perspectives and economic possibilities. By detecting commercial intent in(More)
Knowledge-intensive work plays an increasingly important role in organisations of all types. This work is characterized by a defined input and a defined output but not the way how to transform the input to an output. Within this context, the research project DYONIPOS aims at encouraging the two crucial roles in a knowledge-intensive organization the process(More)
A better understanding of what motivates humans to perform certain actions is relevant for a range of research challenges including generating action sequences that implement goals (planning). A first step in this direction is the task of acquiring knowledge about human goals. In this work, we investigate whether Search Query Logs are a viable source for(More)
Annotations represent an increasingly popular means for organizing, categorizing and finding resources on the "social" web. Yet, only a small portion of the total resources available on the web are annotated. In this paper, we describe a prototype - iTAG - for automatically annotating textual resources with human intent, a novel dimension of tagging. We(More)
Our system for the PAN 2015 authorship verification challenge is based upon a two step pre-processing pipeline. In the first step we extract different features that observe stylometric properties, grammatical characteristics and pure statistical features. In the second step of our pre-processing we merge all those features into a single meta feature space.(More)
In a knowledge-intensive business environment, knowledge workers perform their tasks in highly creative ways. This essential freedom required by knowledge workers often conflicts with their organization’s need for standardization, control, and transparency. Within this context, the research project DYONIPOS aims to mitigate this contradiction by supporting(More)
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(More)