Of course we share! Testing Assumptions about Social Tagging Systems

Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior

Supporting Researchers: Analyzing the Scholarly Publication Life Cycle and Social Bookmarking Systems

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Online Prediction of Molded Part Quality in the Injection Molding Process Using High-Resolution Time Series

Process-data-supported process monitoring in injection molding plays an important role in compensating for disturbances in the process. Until now, scalar process data from machine controls have been used to predict part quality. In this paper, we investigated the feasibility of incorporating time series of sensor measurements directly as features for machine learning models, as a suitable method of improving the online prediction of part quality. We present a comparison of several state-of-the-art algorithms, using extensive and realistic data sets. Our comparison demonstrates that time series data allow significantly better predictions of part quality than scalar data alone. In future studies, and in production-use cases, such time series should be taken into account in online quality prediction for injection molding

Summary of the 15th Discovery Challenge Recommending Given Names

The 15th ECML PKDD Discovery Challenge centered around the recommendation of given names. Participants of the challenge implemented algorithms that were tested both offline – on data collected by the name search engine Nameling – and online within Nameling. Here, we describe both tasks in detail and discuss the publicly available datasets. We motivate and explain the chosen evaluation of the challenge, and we summarize the different approaches applied to the name recommendation tasks. Finally, we present the rankings and winners of the offline and the online phase.

How social is social tagging?

Social tagging systems have established themselves as an important part in today’s web and have attracted the interest of our research community in a variety of investigations. This has led to several assumptions about tagging, such as that tagging systems exhibit a social component. In this work we overcome the previous absence of data for testing such an assumption. We thoroughly study the hypothesis of social interaction, leveraging for the first time live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that sharing of resources constitutes an important and indeed social aspect of tagging