NoSQL and especially graph databases are constantly gaining popularity among developers of Web 2.0 applications as they promise to deliver superior performance when handling highly interconnected data compared to traditional relational databases. Apache Shindig is the reference implementation for OpenSocial with its highly interconnected data model. However, the default back-end is based on a relational database. In this paper we describe our experiences with a different back-end based on the graph database Neo4j and compare the alternatives for querying data with each other and the JPA-based sample back-end running on MySQL. Moreover, we analyze why the different approaches often may yield such diverging results concerning throughput. The results show that the graph-based back-end can match and even outperform the traditional JPA implementation and that Cypher is a promising candidate for a standard graph query language, but still leaves room for improvements.

Knowledge management is more and more happening in social environments, supported by social software. This directly changes the way knowledge workers interact and the way information and communication technology is used. Recent studies, striving to provide a more appropriate support for knowledge work, face challenges when eliciting knowledge from user activities and maintaining its situatedness in context. Corresponding solutions in such social environments are not interoperable due to a lack of appropriate standards. To bridge this gap, we propose and validate a first specification of an anticipatory standard in this field. We illustrate its application and utility analyzing three scenarios. As main result we analyze the lessons learned and provide insights into further research and development of our approach. By that we reach out to stimulate discussion and raise support for this initiative towards establishing standards in the domain of knowledge management.

In this paper, we discuss the work in progress of our current project focusing on opinion mining in the field of insurance business. The main purpose of this project is to improve Opinion Mining methods for the German language and optimize them with special regard to the insurance business. These improved methods make it possible to extract opinions from user-generated texts (in the insurance domain) in a better quality than today. We fetch the required text data for this study from a huge online community website for customers. There, we find a sufficient number of user reviews about insurance companies, which is necessary for our research. Besides the main purpose of this study, another aim is the development of a prototype. This could then be used to monitor the current "crowd’s opinion" about insurance products and services. For this reason and in order to understand key aspects of the domain, we collaborate with the nobisCum Deutschland GmbH, a German company offering consulting and software development services for the insurance industry. Using one data source and limited evaluation sets, we obtained first results which look promising.

After the hype around Web 2.0, organizations have revisited their efforts on knowledge management in order to create beneficial effects from their employees’ activities in social environments. While there is not much doubt about the positive effects on crea-tivity and flexibility, handling the variety of knowledge work environments that are in use at different organizational units or business partners can negatively affect productivity. While stand-ards have generally mushroomed in the IT domain, there is a lack of standards in knowledge management. This paper argues for a standard on exchanging knowledge between social environments. We revisit related work, particularly standards which can be reused for knowledge exchange, define a set of concepts that help to address the problem of exchanging contextualized knowledge as represented in social environments and present a first step towards formalizing the set of elements in an ontology.

Knowledge management (KM) instruments increasingly utilize social software applications which provide an opportunity to connect human and technology orientation to improve knowledge activities. Social systems, such as wikis, micro-blogs or organizational social networks, support several knowledge purposes, e.g., to create and develop knowledge. Even though knowing the context of knowledge creation is considered beneficial for re-using and developing knowledge, it is currently not well represented in such systems. So far, there is no standard to exchange information between diverse systems. To address this gap, we introduce a specification which allows for an activity stream-oriented perspective on knowledge to represent the creation context of knowledge as well as the exchange of information on knowledge activities between social systems. Our approach is based on existing standards to capture information in KM settings for the main entities knowledge activities, objects, context and people. We suggest extensions to take on the challenge of representing the vast variety of context resulting from the usage of many different kinds of (social) software and services.

We present a new phrase-based generated list of opinion bearing words and phrases for the German language. The list contains adjectives and nouns as well as adjectiveand noun-based phrases and their opinion values on a continuous range between -1 and +1. For each word or phrase two additional quality measures are given. The list was produced using a large number of product review titles providing a textual assessment and numerical star ratings from Amazon.de. As both, review titles and star ratings, can be regarded as a summary of the writers opinion concerning a product, they are strongly correlated. Thus, the opinion value for a given word or phrase is derived from the mean star rating of review titles which contain the word or phrase. The paper describes the calculation of the opinion values and the corrections which were necessary due to the so-called “J-shaped distribution” of online reviews. The opinion values obtained are amazingly accurate.

Many factors trigger migraine attacks. Weather is often reported to be one of the most common migraine triggers. However, there is little scientific evidence about the underlying mechanisms and causes. In our pilot study, we used smartphone apps and a web form to collect around 4,700 migraine messages in Germany between June 2011 and February 2012. Taking interdiurnal temperature changes as an indicator for changes in the prevailing meteorological conditions, our analyses were focused on the relationship between temperature changes and the frequency of occurrence of migraine attacks. Linear trends were fitted to the total number of migraine messages with respect to temperature changes. Statistical and systematic errors were estimated. Both increases and decreases in temperature lead to a significant increase in the number of migraine messages. A temperature increase (decrease) of 5 °C resulted in an increase of 19 ± 7 % (24 ± 8 %) in the number of migraine messages.

In this paper we present an approach to generate lists of opinion bearing phrases with their opinion values in a continuous range between -- 1 and 1. Opinion phrases that are considered include single adjectives as well as adjective-based phrases with an arbitrary number of words. The opinion values are derived from user review titles and star ratings, as both can be regarded as summaries of the user's opinion about the product under review. Phrases are organized in trees with the opinion bearing adjective as tree root. For trees with missing branches, opinion values then can be calculated using trees with similar branches but different roots. An example list is produced and compared to existing opinion lists.