Die Anwendung von Business Intelligence in der unternehmerischen Praxis ist vielseitig und erstreckt sich von der Unterstützung des Top-Managements bei der Unternehmenssteuerung hin zu branchenspezifischen Lösungen wie der Risikoanalyse durch analytische Verfahren bei Banken und Versicherungen.

This paper develops and evaluates a BPMN-based process model which identifies and extracts blog content from the web and stores its textual data in a data warehouse for further analyses. Depending on the characteristics of the technologies used to create the weblogs, the process has to perform specific tasks in order to extract blog content correctly. The paper describes three phases: extraction, transformation and loading of data in a repository specifically adapted for blog content extraction. It highlights the objectives in these phases which must be achieved to ensure the correct extraction. The authors integrate the described process in a previously developed framework for blog mining. The authors' process model closes the conceptual gap in this framework as well as the gap in current research of blog mining process models. Furthermore, it can easily be adapted for other web extraction proposals.

Following the emergence of Social Media and the increasing willingness of customers to share thoughts, ideas, and experi-ences companies are trying to capitalize on such activities. Due to the vast amount of user-generated content, manual analysis and interpretation will not meet the demands of companies in highly competitive environments. Based on an integrative process model, which describes the process of idea generation, we outline a BPMN-based path that allows companies to steer user participation and the application of Text Mining methods to gain valuable ideas for innovative products. Our approach also illustrates the Knowledge Management perspective supporting the customers during idea generation. In order to demonstrate the applicability of our model we finally depict the whole process utilizing Dell’s IdeaStorm.

Ausgehend von der steigenden Anzahl an Produktbewertungen in Online-Shops fokussiert sich dieser Beitrag auf die Identifikation von Ironie in den Bewertungen, um eine korrekte, computer-gestutzte Interpretation sicherzustellen. Es werden bestehende Ansatze zur Erkennung von Ironie in Kundenrezensionen untersucht und bewertet. Daruber hinaus analysiert die Arbeit Erkenntnisse aus dem Bereich der Linguistik und entwickelt auf Grundlage der gewonnenen Einblicke ein Konzept fur ein verbessertes Identifikationssystem. Dieses wird implementiert und schlieslich anhand der Ergebnisse evaluiert.

In Social Commerce customers evolve to be an important information source for companies. Customers use the communication platforms of Web 2.0, for example Twitter, in order to express their sentiments about products or discuss their experiences with them. These sentiments can be very important for the development of products or the enhancement of marketing strategies. The research goal is to analyze customer sentiments in Twitter. The first step in the research is the detection of topics in Twitter entries which contain patterns of interest. For the topic detection, the authors use Latent Dirichlet Allocation for topic modeling. The authors found event based topics in the exemplary context of Sony's 3D TV sets. In future work, the authors will implement sentiment analysis algorithms in order to determine sentiments in the entries corresponding to the detected topics.

In Social Commerce customers evolve to be an important information source for companies. Customers use the communication platforms of Web 2.0, for example Twitter, in order to express their sentiments about products or discuss their experiences with them. These sentiments can be very important for the development of products or the enhancement of marketing strategies. The research goal is to analyze customer sentiments in Twitter. The first step in the research is the detection of topics in Twitter entries which contain patterns of interest. For the topic detection, the authors use Latent Dirichlet Allocation for topic modeling. The authors found event based topics in the exemplary context of Sony's 3D TV sets. In future work, the authors will implement sentiment analysis algorithms in order to determine sentiments in the entries corresponding to the detected topics.

In Social Commerce customers evolve to be an important information source for companies. Customers use the communication platforms of Web 2.0, for example Twitter, in order to express their sentiments about products or discuss their experiences with them. These sentiments can be very important for the development of products or the enhancement of marketing strategies. The research goal is to analyze customer sentiments in Twitter. The first step in the research is the detection of topics in Twitter entries which contain patterns of interest. For the topic detection, the authors use Latent Dirichlet Allocation for topic modeling. The authors found event based topics in the exemplary context of Sony's 3D TV sets. In future work, the authors will implement sentiment analysis algorithms in order to determine sentiments in the entries corresponding to the detected topics.

3m5. Media GmbH, Dresden Communardo Software GmbH, Dresden GI-Regionalgruppe, Dresden FERCHAU Engineering GmbH, Dresden IBM, Dresden itsax.de | pludoni GmbH, Dresden Kontext E GmbH, Dresden objectFab GmbH, Dresden queo GmbH, Dresden Robotron Datenbank-Software GmbH, Dresden SALT Solutions GmbH, Dresden SAP AG, Resarch Center Dresden Saxonia Systems AG, Dresden T-Systems Multimedia Solutions GmbH, Dresden Transinsight GmbH, Dresden xima media GmbH, Dresden

In the Social Commerce customers evolve to an important information source for companies. The customers use communication platforms of the Web 2.0, for example Twitter, in order to express their opinions about products or discuss their experiences with them. These opinions can be very important for the development of products or the product range of a company. Our approach enables a company viewing opinions about its products which are published using the microblogging service Twitter. A first step in our research progress is detecting topics in a specific context. In a further step the entries corresponding to these topics has to be analyzed for opinions. For topic detection we use topic modeling with the Latent Dirichlet Allocation. In our paper we found event-based topics in the context of Sony’s 3D TV sets. In future work we are able to implement Opinion Mining algorithms to determine sentiments in the entries corresponding to the detected topics.

Unstructured tetrahedral grids are a common data representation of three-dimensional scalar elds. For convex unstructured meshes efcient rendering methods are known. For concave or cyclic meshes, however, a signicant overhead is required to sort the grid cells in back to front order. In this paper we apply methods known from computational geometry to transform concave into convex grids. While this issue has been studied in theory it has not yet been applied to the specic area of unstructured volume rendering. This is mainly due to the complexity of the required geometrical operations. We demonstrate that the convexication of concave grids can be achieved by a combination of simple operations on triangle meshes. For convexied meshes the experimental results show that the performance penalty is only about 70% in comparison to approximately 300% for the fastest known concave sorting algorithm. In order to achieve high-quality visualizations we also adapt the preintegrated lighting technique to cell projection.

For the visualization of volume data the application of transfer functions is used widely. In this area the pre-integration technique allows high quality visualizations and the application of arbitrary transfer functions. For regular grids, this approach leads to a two-dimensional pre-integration table which easily fits into texture memory. In contrast to this, unstructured meshes require a three-dimensional pre-integration table. As a consequence the available texture memory limits the resolution of the pre-integration table and the maximum local derivative of the transfer function. Discontinuity artifacts arise if the resolution of the pre-integration table is too low. This paper presents a novel approach for accurate rendering of unstructured grids using the multi-texturing capabilities of commodity PC graphics hardware. Our approach achieves high quality by reconstructing the colors and opacities of the pre-integration table using the high internal precision of the pixel shader. Since we are using standard 2D multi-texturing we are not limited in the size of the pre-integration table. By combining this approach with a hardware-accelerated calculation of the pre-integration table, we achieve both high quality visualizations and interactive classification updates.