Visual analytics enables us to analyze huge information spaces in order to support complex decision making and data exploration. Humans play a central role in generating knowledge from the snippets of evidence emerging from visual data analysis. Although prior research provides frameworks that generalize this process, their scope is often narrowly focused so they do not encompass different perspectives at different levels. This paper proposes a knowledge generation model for visual analytics that ties together these diverse frameworks, yet retains previously developed models (e.g., KDD process) to describe individual segments of the overall visual analytic processes. To test its utility, a real world visual analytics system is compared against the model, demonstrating that the knowledge generation process model provides a useful guideline when developing and evaluating such systems. The model is used to effectively compare different data analysis systems. Furthermore, the model provides a common language and description of visual analytic processes, which can be used for communication between researchers. At the end, our model reflects areas of research that future researchers can embark on.

Figure 1: The image at the top represents a part of the data stream using our sliding slices visualization, which summarizes the stream using sliding windows to provide a summary timeline. The colored histogram at the bottom highlights major events based on extracted keywords and insights of interesting events which were identified by the analyst in real-time. ABSTRACTTo solve the VAST Challenge 2014 MC3 we use NStreamAware, which is our real-time visual analytics system to analyze data streams. We make use of various modern technologies like Apache Spark and others to provide high scalability and incorporate new technologies and show their use within visual analytics applications. Furthermore, we developed a web application, called NVisAware, to analyze and visualize data streams to help the analyst to focus on the most important time segments. We extracted socalled sliding slices, which are aggregated summaries calculated on a sliding window and represent them in a small-multiple like visualization containing various small visualizations (e.g., word clouds) to present an overview of the current time segment. We show how these techniques can be used to successfully solve the given tasks.

Five years after the first state-of-the-art report on Commercial Visual Analytics Systems we present a reevaluation of the Big Data Analytics field. We build on the success of the 2012 survey, which was influential even beyond the boundaries of the InfoVis and Visual Analytics (VA) community. While the field has matured significantly since the original survey, we find that innovation and research-driven development are increasingly sacrificed to satisfy a wide range of user groups. We evaluate new product versions on established evaluation criteria, such as available features, performance, and usability, to extend on and assure comparability with the previous survey. We also investigate previously unavailable products to paint a more complete picture of the commercial VA landscape. Furthermore, we introduce novel measures, like suitability for specific user groups and the ability to handle complex data types, and undertake a new case study to highlight innovative features. We explore the achievements in the commercial sector in addressing VA challenges and propose novel developments that should be on systems' roadmaps in the coming years.

Commercial buildings are significant consumers of electrical power. Also, energy expenses are an increasing cost factor. Many companies therefore want to save money and reduce their power usage. Building administrators have to first understand the power consumption behavior, before they can devise strategies to save energy. Second, sudden unexpected changes in power consumption may hint at device failures of critical technical infrastructure. The goal of our research is to enable the analyst to understand the power consumption behavior and to be aware of unexpected power consumption values. In this paper, we introduce a novel unsupervised anomaly detection algorithm and visualize the resulting anomaly scores to guide the analyst to important time points. Different possibilities for visualizing the power usage time series are presented, combined with a discussion of the design choices to encode the anomaly values. Our methods are applied to real world time series of power consumption, logged in a hierarchical sensor network.

measures, and maps customer feedback; (2) a novel way of determining term associations that identify attributes, verbs, and adjectives frequently occurring together; (3) a self-organizing term association map and a pixel cellbased sentiment calendar to identify co-occurring and influential opinion; and (4) a new geo-based term association technique providing a key term geo map to enable the user to inspect the statistical significance and the sentiment distribution of individual key terms. We have used and evaluated these techniques and combined them into a well-fitted solution for an effective analysis of large customer feedback streams such as web surveys (from product buyers) and Twitter (e.g., from Kung-Fu Panda movie reviewers).Keywords: Customer Sentiment Visual Analytics, Term Association, Geo-Term Association, Pixel Geo Map, Key Term Geo Map, Pixel Calendar. Introduction MotivationWith the rapid growth of social media, the number of customer comments available to corporations, business owners, and service managers interested in obtaining customer feedback is larger than ever. In addition to the traditional web survey, Twitter is a relatively new phenomenon that has the potential to generate massive amounts of customer comments. However, the language of the tweets is more casual than that of web reviews. Tweets are by definition short (maximum 140 characters) and tend to contain a significant number of abbreviations. The enormous size of the customer feedback data stream, the diversity of the comments, and the uneven distribution of feedback over time make sentiment analysis of this data very challenging.A set of common questions arises in the analysis of customer comments from surveys and other online data streams. Are there aspects of location or geography that impact how a product or service is received by customers? Does a product or service work better for people on the coasts compared to people living in the interior of the country? Does it make a difference if the customer lives in a remote area rather than in a high density urban setting? Is the product or service more appreciated in certain states or cities? What are the important features, attributes, and associated context terms, such as products, timely delivery, channel vendors, product quality, or past experiences that our customers want? How significant terms (content-bearing words, e.g., compound nouns, adjectives, and verbs) are best extracted and presented to business managers so they can understand the results (positive versus negative)? Business managers want to see the sentiment value of the review, but they also want to know the important terms in the context of a review. Furthermore, how to visualize reviews in a dense area without overlap (e.g., Los Angeles, New York), is also a challenge required to be resolved.To meet the above challenges, we propose a pipeline combining feature-based sentiment analysis and geo-term associations to enable store managers to analyze web survey feedback. As an example, Figure c. Figure 1...