This paper outlines a novel approach for user navigation in complex virtual 3D city models on mobile devices. Users navigate within the virtual 3D city model by sketching navigation commands in the perspective view on the mobile client. The sketches are sent to the server, which reprojects the sketches onto the 3D scene, interprets these sketches in terms of navigation commands, and sends the resulting video-encoded image stream to the mobile client. This approach allows us to provide interactivity for complex virtual 3D city models on resource and bandwidth limited mobile clients. A high degree of usability is achieved because users can trigger complex navigation commands in a task and goal oriented way taking advantage of the navigation properties and affordances inherent to elements of geovirtual environments.

The increasing possibilities to collect vast amounts of data—whether in science, commerce, social networking, or government—have led to the “big data” phenomenon. The amount, rate, and variety of data that are assembled—for almost any application domain—is necessitating a re-examination of old technologies and development of new technologies to get value from the data, in a timely fashion. With increasing adoption and penetration of mobile technologies, and increasing ubiquitous use of sensors and small devices in the so-called Internet of Things, the big data phenomenon will only create more pressures on data collection and processing for transforming data into knowledge for discovery and action. A vibrant industry has been created around the big data phenomena, leading also to an energetic research agenda in this area. With the proliferation of big data hardware and software solutions in industry and research, there is a pressing need for benchmarks that can provide objective evaluations of alternative technologies and solution approaches to a given big data problem. This chapter gives an introduction to big data benchmarking and presents different proposals and standardization efforts.

In todays computer graphics applications, large 3D scenes are rendered which consist of polygonal geometries such as triangle meshes. Using state- of-the-art techniques, this geometry is often represented on the GPU using vertex and index buffers, as well as additional auxiliary data such as tex- tures or uniform buffers. For polygonal meshes of arbitrary complexity, the described approach is indispensable. However, there are several types of simpler geometries (e.g., cuboids, spheres, tubes, or splats) that can be generated procedurally. We present an efficient data representation and render- ing concept for such geometries, denoted as attributed vertex clouds (AVCs). Using this approach, geometry is generated on the GPU during execution of the programmable rendering pipeline. Each vertex is used as the argument for a function that procedurally generates the target geometry. This function is called a transfer function, and it is implemented using shader programs and therefore executed as part of the rendering process. This approach allows for compact geometry representation and results in reduced memory footprints in comparison to traditional representations. By shifting geometry generation to the GPU, the resulting volatile geometry can be controlled flexibly, i.e., its position, parameteri- zation, and even the type of geometry can be modified without requiring suggests improved rendering times and reduced memory transmission through the rendering pipeline.

This paper presents a mixed computational and manual procedure to systematically probe for distinct low coherent turns in design conversations. Existing studies indicate that focus shifts and their linguistic equivalent, low coherent turns, positively influence ideational productivity. Because coherence is a versatile phenomenon, we contribute a classification of low coherent turns to enable future research to further investigate the influence of low coherence turns on creativity. We analyze the DTRS11 corpus, comprising 16 sessions of design conversation that contain 9830 sentences, with automated Latent Semantic Analysis (LSA) to identify potential low coherent turns. We argue that an additional manual coherence analysis with the Topic Markup Scheme (TMS) further qualifies preselected turns. This mixed method procedure constitutes a promising pragmatic instrument for locating low coherent turns in large corpora. 297 distinct low coherent turns out of a total amount of 6072 turns were successfully retained. The selected data contains twice as much turns that shift the focus of attention within an existing design issue as turns that interrupt and introduce a new design issue. Based on an interpretative analysis of low coherent turns, we suggest distinguishing between turns that interrupt the focus of attention and turns that shift the focus of attention through either diversifying, reframing, or selective tendencies.

One of the main benefits of component-based architectures is their support for reuse. The port and interface definitions of architectural components facilitate the construction of complex functionality by composition of existing components. For sucha composition means for a sufficient verification either by testing or formal verification are necessary. However, the overwhelmingcomplexity of the interaction of distributed real-time components usually excludes that testing alone can provide the requiredcoverage when integrating a legacy component. In this paper we present a scheme on how embedded legacy components can be tackled.For the embedded legacy components initially a behavioral model is derived from the interface description of the architecturalmodel. This is in the subsequent steps enriched by an incremental synthesis using formal verification techniques for the systematicgeneration of component tests. The proposed scheme results in an effective combination of testing and formal verification.While verification is employed to tackle the inherently subtle interaction of the distributed real-time components which couldnot be covered by testing, local testing of the components guided by the verification results is employed to derive refinedbehavioral models. The approach further has two outstanding benefits. It can pin-point real failures without false negativesright from the beginning. It can also prove the correctness of the integration without learning the whole legacy component(using the restrictions of the integration context).

Nowadays, social networks produce a huge amount of spatial and spatiotemporal data that provide interesting knowledge. This knowledge can be discovered by clustering algorithms and the result of that can be used for different applications. One of such applications is the geospatial event detection based on data from social networks. Many of such detection methods rely on clustering algorithms that should provide clusters with the high level of density in space and intensity in time. Meanwhile, traditional clustering methods are not always practical for spatial and spatiotemporal data because of the specific of such data. Therefore, in this paper, we present the density and intensity-based spatiotemporal clustering algorithm with fixed distance and time radius. This approach produces the clusters that have the density-based center in space and intensity-based center in time. In the paper, we provide the description of the method from the perspective of 2 aspects: spatial and temporal. We complete the paper with the full description of the algorithm methods and detailed explanation of the pseudo code.

In our modern society, almost all events, processes, and decisions in a corporation are documented by internal written communication, legal filings, or business and financial news. The valuable knowledge in such collections is not directly accessible by computers as they mostly consist of unstructured text. This chapter provides an overview of corpora commonly used in research and highlights related work and state-of-the-art approaches to extract and represent financial entities and relations.The second part of this chapter considers applications based on knowledge graphs of automatically extracted facts. Traditional information retrieval systems typically require the user to have prior knowledge of the data. Suitable visualization techniques can overcome this requirement and enable users to explore large sets of documents. Furthermore, data mining techniques can be used to enrich or filter knowledge graphs. This information can augment source documents and guide exploration processes. Systems for document exploration are tailored to specific tasks, such as investigative work in audits or legal discovery, monitoring compliance, or providing information in a retrieval system to support decisions.