Usually, knowledge workers are said to not benefit from business process management (BPM) systems, since their main tasks are weakly structured and not representable by a workflow. However, not all of their tasks are equally weak structured, and with adaptive case management (ACM) solutions, a new category of tools came up to support those processes, even if they are weakly structured. This paper introduces an approach to support the creation of cases for ACM engines by mining activities from an activity stream and suggesting tasks that a knowledge worker can use to create a case. Furthermore, the approach supports maturing of the case towards a workflow by detecting repeating sequences in the execution of tasks and suggesting sub processes for the case which is possible with the case management model and notation (CMMN) together with the business process model and notation (BPMN). To allow for further enhancement of the cases, the ACM solution is extended with social collaboration features, so that people working on the case can comment and rate single tasks. The goal is to show that it is possible to establish ties from social activities and Web 2.0 to ACM and BPM. The presented solution uses a graph database as a basis for activity mining.

This paper presents a concept for integrating social media functionality into existing collaboration systems and using this functionality to also integrate the collaboration systems themselves. Social media functionality is used in the areas contents, contacts and communication to support knowledge sharing and knowledge maturing. Using a micro-service architecture, our concept allows for social media functionality in every collaboration system. Users benefit from a better overview due to a central activity stream with entries of all systems, as well as a consistent UI. They don’t have to chose a certain tool but can use the desired functionality like rating, tagging or commenting everywhere. We further provide information about a technical implementation of the concept with popular open source software like Liferay and OpenXchange.

After the human-oriented, technology-oriented (documents), process-oriented and social KM phases, KM support now needs integration of those beneficial technologies instead of hyping one and neglecting the other. This paper proposes a highly modular yet integrated approach to support KM instruments and shows how this approach is implemented based on open source collaboration systems in the SCHub project which represents an enterprise knowledge infrastructure (EKI). The applicability is shown with three KM instruments in the areas people, contents and processes. Social media and search functionality serve as an integration factor for the EKI.

Docker provides a good basis to run composite applications in the cloud, especially if those are not cloud- aware, or cloud-native. However, Docker concentrates on managing containers on one host, but SaaS provi- ders need a container management solution for multiple hosts. Therefore, a number of tools emerged that claim to solve the problem. This paper classifies the solutions, maps them to requirements from a case study and identifies gaps and integration requirements. We conclude that the Docker ecosystem could help moving from IaaS and PaaS solutions towards a runtime environment for SaaS applications, but needs consolidation.

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.