Modeling and publishing Linked Open Data (LOD) involves the choice of which vocabulary to use. This choice is far from trivial and poses a challenge to a Linked Data engineer. It covers the search for appropriate vocabulary terms, making decisions regarding the number of vocabularies to consider in the design process, as well as the way of selecting and combining vocabularies. Until today, there is no study that investigates the different strategies of reusing vocabularies for LOD modeling and publishing. In this paper, we present the results of a survey with 79 participants that examines the most preferred vocabulary reuse strategies of LOD modeling. Participants of our survey are LOD publishers and practitioners. Their task was to assess different vocabulary reuse strategies and explain their ranking decision. We found significant differences between the modeling strategies that range from reusing popular vocabularies, minimizing the number of vocabularies, and staying within one domain vocabulary. A very interesting insight is that the popularity in the meaning of how frequent a vocabulary is used in a data source is more important than how often individual classes and properties arernused in the LOD cloud. Overall, the results of this survey help in understanding the strategies how data engineers reuse vocabularies, and theyrnmay also be used to develop future vocabulary engineering tools.

Social networking platforms are enabling users to create their own content, share this content with anyone they invite and organize connections with existing or new online contacts. Within these electronic environments users voluntarily add comments on virtual boards, distribute their search results or add information about their expertise areas to their social networking profiles and thereby share it with acquaintances, friends and increasingly even with colleagues in the corporate world. As a result, it is most likely that the underlying knowledge sharing processes result in many new and creative ideas. The objective of our research therefore is to understand if and how social social networking platforms can enforce creativity. In addition, we look at how these processes could be embedded within the organizational structures that influence innovative knowledge sharing behavior. The basis for our research is a framework which focuses on the relations between intrinsic motivation, creativity and social networking platforms. First results of our empirical investigation of a social software platform called "StudiVZ.net" proved that our two propositions are valid.

Information systems research has started to use crowdsourcing platforms such as Amazon Mechanical Turks (MTurk) for scientific research, recently. In particular, MTurk provides a scalable, cheap work-force that can also be used as a pool of potential respondents for online survey research. In light of the increasing use of crowdsourcing platforms for survey research, the authors aim to contribute to the understanding of its appropriate usage. Therefore, they assess if samples drawn from MTurk deviate from those drawn via conventional online surveys (COS) in terms of answers in relation to relevant e-commerce variables and test the data in a nomological network for assessing differences in effects. The authors compare responses from 138 MTurk workers with those of 150 German shoppers recruited via COS. The findings indicate, inter alia, that MTurk workers tend to exhibit more positive word-of mouth, perceived risk, customer orientation and commitment to the focal company. The authors discuss the study- results, point to limitations, and provide avenues for further research.

In dieser Arbeit wird ein Datenmodell für Shared Annotations vorgestellt. Basierend auf einem bereits existierenden Datenmodell für Annotationen, wird dieses erweitert um die Möglichkeit der Modellierung von Shared Annotations. Daraufhin werden Funktionen von Shared Annotations erläutert, die über das neue Annotationsmodell möglich sind.

The processing of data is often restricted by contractual and legal requirements for protecting privacy and IPRs. Policies provide means to control how and by whom data is processed. Conditions of policies may depend on the previous processing of the data. However, existing policy languages do not provide means to express such conditions. In this work we present a formal model and language allowing for specifying conditions based on the history of data processing. We base the model and language on XACML.