Zusammenfassung

Weltweit leiden ca. 10% der Bevölkerung an Migräne. Vor allem das Wetter wird oft als Auslöser genannt – wissenschaftlich nachgewiesen ist ein Zusammenhang jedoch noch nicht. Ziel des Projektes ist es daher, Auslöser von Migräneattacken besser zu verstehen. Dazu wurden in vier Jahren ca. 20 000 Attacken mit den zugehörigen Ortsdaten erfasst. Erste Analysen belegen, dass die Häufigkeit von Attacken statistisch signifikant zunimmt, wenn es in den drei Tagen vor einer Attacke zu Änderungen der absoluten Temperatur kam. In der ersten Ausbaustufe des Projektes gab es jedoch noch einige Limitationen. Da die Teilnahme vollständig anonym stattfand, war es nicht möglich, differenzierte Auslösemuster in Subgruppen von Patienten zu erkennen. Dies zu untersuchen, ist ein Ziel des Folgeprojektes. Durch eine anonyme Registrierung der Patienten sollen genauere und individualisierte Analysen möglich sein.

Ein verbessertes Verständnis von Migräne- triggern kann positive Auswirkungen auf die Edukation von Patienten haben, hierzu ist eine individuelle Analyse der Situation des Patienten hilfreich. Aktives Attackenmanagement könnte das bei Migräne-Patienten häufige Meiden von Triggern ablösen und zum erfolgreicheren Umgang mit der Erkrankung führen.

Summary

Approximately 10% of the human population suffers from migraine. In particular, weather is one of the most prominent triggers. However, a correlation between weather and migraine attacks is not proven yet. For this reason, our project aims to get a better understanding of migraine attack triggers. In order to achieve this, we collected about 20 000 attacks with corresponding location information within the last four years. First analyses show a significant increase in migraine frequency in the case of a change in absolute temperature within the three days before an attack. In the first phase of the project were still a few limitations. Since the participation of users was completely anonymous, the detection of differentiated trigger patterns in subgroups of patients was not possible. Therefore, one aim of our follow-up project is the search for such patterns. Allowing patients to register anonymously will lead to more precise and individualized analyses. Furthermore, a better understanding in migraine triggers may have a positive effect on patient education.

For this, an individualized analysis of the patients’ situation will be quite helpful. An active attack management may be able to replace the systematic avoidance of trigger factors, practiced by migraine patients, and thus can lead to a successful handling of the disease.

Das Schlagwort „Big Data“ verspricht die Gewinnung relevanter Informationen durch die automatisierte Erfassung und Analyse sehr großer Datenmengen für unterschiedliche Anwendungsbereiche. Damit lassen sich praktisch alle wesentlichen Informationen zur Bewertung einer komplexen Situation erfassen. Big-Data-Technologien können dann mit Hilfe geeigneter Indikatoren Situationen in Echtzeit bewerten und genaue Prognosen ermöglichen. Wesentlich für diese Technologien ist die Verarbeitung großer Datenmengen unter engen zeitlichen Rahmenbedingungen, um die Aktualität der Ergebnisse sicherzustellen. Existierende betriebliche Informationssysteme auf der Basis relationaler Datenbankmanagementsysteme erreichen dabei ihre Grenzen und können in der Regel die geforderten Antwortzeiten nicht mehr erfüllen. Neuere Datenbanktechnologien versprechen hier einen deutlichen Effizienzgewinn, so dass auch sehr große Datenmengen im Rahmen interaktiver Anwendungen verarbeitet und analysiert werden können. Dieses Kapitel gibt anhand eines Beispiels zur Erfassung und Auswertung von Daten einer Betriebsdatenerfassung einen Überblick über diese Technologien.

Mit der zunehmenden Menge textueller Daten im Web 2.0 wächst auch die Notwendigkeit der maschinellen Auswertung dieser Daten, beispielsweise um in Texten geäußerte Meinungen aufzuspüren (Opinion Mining). Im vorliegenden Beitrag wird das Aspect-based Opinion Mining – ein Verfahren mit sehr hohem Detaillierungsgrad – für deutschsprachige Texte anhand eines Projekts für die Versicherungswirtschaft vorgestellt. Es wird gezeigt, dass in Bewertungsplattformen geäußerte Meinungen zu Produkten und Services von Versicherungen mit einer Genauigkeit von etwa 90% und einer Vollständigkeit von ca. 80% für positive und ca. 60% für negative Meinungen erkannt werden können.

In this paper, we report about benchmark experiments and results from optimizing database connectivity for querying social networking data from Apache Shindig in a Neo4j database. We built on our experiments from [1] and tried to improve performance of the current RESTful http connection in comparison to JDBC in order to fully utilize performance benefits of the graph database compared to relational database management systems. We implemented a database driver based on WebSockets. We found that BSON is a better data transfer format than JSON and compression increases performance in some settings while decreasing it in others. Multiple WebSocket connections are needed to scale to a high number of client requests and fully utilize database performance. Multi-threading is another key factor for scalability. Implementing a kind of stored procedure, we were able to further increase throughput and decrease response times.

Hybrid index structures support access to heterogeneous data types in multiple columns. Several experiments confirm the improved efficiency of these hybrid access structures. Yet, very little is known about the worst case time and space complexity of them. This paper aims to close this gap by introducing a theoretical framework supporting the analysis of hybrid index structures. This framework then is used to derive the constraints for an access structure which is both time and space efficient. An access structure based on a B+-Tree augmented with bit lists representing sets of terms from texts is the outcome of the analysis which is then validated experimentally together with a hybrid R-Tree variant to show a logarithmic search time complexity.

Knowledge management represents a key issue for both information systems’ academics and practitioners, including those who have become disillusioned by actual results that fail to deliver on exaggerated promises and idealistic visions. Social software, a tremendous global success story, has prompted similarly high expectations regarding the ways in which organizations can improve their knowledge handling. But can these expectations be met, whether in academic research or the real world? This article seeks to identify current research trends and gaps, with a focus on social knowledge environments. The proposed research agenda features four focal challenges: semi-permeable organizations, social software in professional work settings, crowd knowledge, and cross-border knowledge management. Three solutions emerge as likely methods to address these challenges: design-oriented solutions, analytical solutions, and interdisciplinary dialogue.

In this work, we present a system called PoliTwi, which was designed to detect emerging political topics (Top Topics) in Twitter sooner than other standard information channels. The recognized Top Topics are shared via different channels with the wider public. For the analysis, we have collected about 4,000,000 tweets before and during the parliamentary election 2013 in Germany, from April until September 2013. It is shown, that new topics appearing in Twitter can be detected right after their occurrence. Moreover, we have compared our results to Google Trends. We observed that the topics emerged earlier in Twitter than in Google Trends.

Finally, we show how these topics can be used to extend existing knowledge bases (web ontologies or semantic networks) which are required for concept-level sentiment analysis. For this, we utilized special Twitter hashtags, called sentiment hashtags, used by the German community during the parliamentary election.

In this paper we describe our experiences with setting up a single signon enabled intranet with open source software and then making it accessible over the internet using a reverse proxy. During this process, we encounter several issues. We describe those, discuss possible solutions and present our final setup.

In this paper, we present a study of aspect-based opinion mining using a lexicon-based approach. We use a phrase-based opinion lexicon for the German language to investigate, how good strong positive and strong negative expressions of opinions, concerning products and services in the insurance domain, can be detected. We perform experiments on hand-tagged statements expressing opinions retrieved from the Ciao platform. The initial corpus contained about 14,000 sentences from 1,600 reviews. For both, positive and negative statements, more than 100 sentences were tagged. We show, that the algorithm can reach an accuracy of 62.2% for positive, but only 14.8% for negative utterances of opinions. We examine the cases, in which the opinion could not correctly be detected or in which the linking between the opinion statement and the aspect fails. Especially, the large gap in accuracy between positive and negative utterances is analysed.

As multitouch phones and tablets become more popular, multitouch technologies receive increasing attention. The underlying interaction paradigm of such devices is the space on which objects are manipulated by the user's fingertips. It is natural that hypertext narratives find their way from primarily mouse-driven interaction to spatial structures and visually rich presentations. In this article we propose three features for multitouch hypertext narrative applications: (i) Native multitouch support and direct manipulations of fictive objects; (ii) using the space as a structuring mechanism rather than a means for presentation; and (iii) supporting presentation of visually rich objects. Our prototype, TouchStory, is a novel tool specialized for authoring and reading hypertext narratives that integrates these features.

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.