Abstract: Motivation: Genome browsers that support fast navigation through vast datasets and provide interactive visual analytics functions can help scientists achieve deeper insight into biological systems. Toward this end, we developed Integrated Genome Browser (IGB), a highly configurable, interactive and fast open source desktop genome browser. Results: Here we describe multiple updates to IGB, including all-new capabilities to display and interact with data from high-throughput sequencing experiments. To demonstrate, we describe example visualizations and analyses of datasets from RNA-Seq, ChIP-Seq and bisulfite sequencing experiments. Understanding results from genome-scale experiments requires viewing the data in the context of reference genome annotations and other related datasets. To facilitate this, we enhanced IGB’s ability to consume data from diverse sources, including Galaxy, Distributed Annotation and IGB-specific Quickload servers. To support future visualization needs as new genome-scale assays enter wide use, we transformed the IGB codebase into a modular, extensible platform for developers to create and deploy all-new visualizations of genomic data. Availability and implementation: IGB is open source and is freely available from http://bioviz.org/igb. Contact: aloraine@uncc.edu

Abstract: Visual analytics supports humans in generating knowledge from large and often complex datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the process, a myriad of analytical and visualisation techniques are employed to generate a visual representation of the data. These often introduce their own uncertainties, in addition to the ones inherent in the data, and these propagated and compounded uncertainties can result in impaired decision making. The user's confidence or trust in the results depends on the extent of user's awareness of the underlying uncertainties generated on the system side. This paper unpacks the uncertainties that propagate through visual analytics systems, illustrates how human's perceptual and cognitive biases influence the user's awareness of such uncertainties, and how this affects the user's trust building. The knowledge generation model for visual analytics is used to provide a terminology and framework to discuss the consequences of these aspects in knowledge construction and though examples, machine uncertainty is compared to human trust measures with provenance. Furthermore, guidelines for the design of uncertainty-aware systems are presented that can aid the user in better decision making.

Abstract: To empower experimentalists with a means for fast and comprehensive chromatin immunoprecipitation sequencing (ChIP-seq) data analyses, we introduce an integrated computational environment, EaSeq. The software combines the exploratory power of genome browsers with an extensive set of interactive and user-friendly tools for genome-wide abstraction and visualization. It enables experimentalists to easily extract information and generate hypotheses from their own data and public genome-wide datasets. For demonstration purposes, we performed meta-analyses of public Polycomb ChIP-seq data and established a new screening approach to analyze more than 900 datasets from mouse embryonic stem cells for factors potentially associated with Polycomb recruitment. EaSeq, which is freely available and works on a standard personal computer, can substantially increase the throughput of many analysis workflows, facilitate transparency and reproducibility by automatically documenting and organizing analyses, and enable a broader group of scientists to gain insights from ChIP-seq data.

Abstract: While the primary goal of visual analytics research is to improve the quality of insights and findings, a substantial amount of research in provenance has focused on the history of changes and advances throughout the analysis process. The term, provenance, has been used in a variety of ways to describe different types of records and histories related to visualization. The existing body of provenance research has grown to a point where the consolidation of design knowledge requires cross-referencing a variety of projects and studies spanning multiple domain areas. We present an organizational framework of the different types of provenance information and purposes for why they are desired in the field of visual analytics. Our organization is intended to serve as a framework to help researchers specify types of provenance and coordinate design knowledge across projects. We also discuss the relationships between these factors and the methods used to capture provenance information. In addition, our organization can be used to guide the selection of evaluation methodology and the comparison of study outcomes in provenance research.

Abstract: Natural language interfaces for visualizations have emerged as a promising new way of interacting with data and performing analytics. Many of these systems have fundamental limitations. Most return minimally interactive visualizations in response to queries and often require experts to perform modeling for a set of predicted user queries before the systems are effective. Eviza provides a natural language interface for an interactive query dialog with an existing visualization rather than starting from a blank sheet and asking closed-ended questions that return a single text answer or static visualization. The system employs a probabilistic grammar based approach with predefined rules that are dynamically updated based on the data from the visualization, as opposed to computationally intensive deep learning or knowledge based approaches.The result of an interaction is a change to the view (e.g., filtering, navigation, selection) providing graphical answers and ambiguity widgets to handle ambiguous queries and system defaults. There is also rich domain awareness of time, space, and quantitative reasoning built in, and linking into existing knowledge bases for additional semantics. Eviza also supports pragmatics and exploring multi-modal interactions to help enhance the expressiveness of how users can ask questions about their data during the flow of visual analysis.

Abstract: edexcel igcse physics text answers, motor speech disorders diagnosis and treatment, zetor tractor manual pdf, achieving an aids transition preventing infections to sustain treatment, presario cq62 manual, craftsman leaf blower manual pdf, prepare for war rebecca brown, ecosystem sustainability and global change oceanography and marine biology series seas and oceans, chevrolet tracker 2015 manual, mercedes w204 om651 repair manual, civic learning through agricultural improvement bringing the loom and the anvil into proximity with the plow studies in the history of education, maternal newborn nursing care best evidence based practices, datadriven marketing the 15 metrics everyone in marketing should know, introductory english grammar stageberg, goldstein classical mechanics solutions manual, manual for refrigeration service technicians, cnml review course 2014, solution manual engineering economy 7th edition leland scribd, calculus third edition robert smith roland minton, ford explorer 2002 thru 2010 includes mercury mountineer haynes repair manual, advanced strength and applied elasticity solution manual 4th edition, i superorganism learning to love your inner ecosystem, mcculloch cs 370 operators manual, 2002 harley davidson dyna fxd models service manual set wide glide low rider super glide, 1995 toyota mr2 system wiring diagrams, virtual enterprises and collaborative networks ifip 18th world computer congress tc5wg55 5th working conference on virtual enterprises 22 27 in information and communication technology, 84 1201 carolinas solution preparation manual 134222, 1st grade math textbook 100 lessons 420 pages printed bw curriculum for homeschooling or classroom, tissue type plasminogen activity volume i t pa physiological and clinical aspects, space and time in language and literature, only nangi girl photo, assisting with patient care 2e, table tennis coaching manual

Abstract: We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis.

Abstract: We propose a visual analytics approach for the exploration and analysis of dynamic networks. We consider snapshots of the network as points in high-dimensional space and project these to two dimensions for visualization and interaction using two juxtaposed views: one for showing a snapshot and one for showing the evolution of the network. With this approach users are enabled to detect stable states, recurring states, outlier topologies, and gain knowledge about the transitions between states and the network evolution in general. The components of our approach are discretization, vectorization and normalization, dimensionality reduction , and visualization and interaction , which are discussed in detail. The effectiveness of the approach is shown by applying it to artificial and real-world dynamic networks.

Abstract: Data visualization is by far the most commonly used mechanism to explore and extract insights from datasets, especially by novice data scientists. And yet, current visual analytics tools are rather limited in their ability to operate on collections of visualizations---by composing, filtering, comparing, and sorting them---to find those that depict desired trends or patterns. The process of visual data exploration remains a tedious process of trial-and-error. We propose zenvisage, a visual analytics platform for effortlessly finding desired visual patterns from large datasets. We introduce zenvisage's general purpose visual exploration language, ZQL ("zee-quel") for specifying the desired visual patterns, drawing from use-cases in a variety of domains, including biology, mechanical engineering, climate science, and commerce. We formalize the expressiveness of ZQL via a visual exploration algebra---an algebra on collections of visualizations---and demonstrate that ZQL is as expressive as that algebra. zenvisage exposes an interactive front-end that supports the issuing of ZQL queries, and also supports interactions that are "short-cuts" to certain commonly used ZQL queries. To execute these queries, zenvisage uses a novel ZQL graph-based query optimizer that leverages a suite of optimizations tailored to the goal of processing collections of visualizations in certain pre-defined ways. Lastly, a user survey and study demonstrates that data scientists are able to effectively use zenvisage to eliminate error-prone and tedious exploration and directly identify desired visualizations.

Abstract: In today's world where everything is recorded digitally, right from our web surfing patterns to our medical records, we are generating and processing petabytes of data every day. Big data will be transformative in every sphere of life. But just to process and analyze those data is not enough, human brain tends to find pattern more efficiently when data is represented visually. Data Visualization and Analytics plays important role in decision making in various sectors. It also leads to new opportunities in the visualization domain representing the innovative ideation for solving the big-data problem via visual means. It is quite a challenge to visualize such a mammoth amount of data in real time or in static form. In this paper, we discuss why big data visualization is of utmost importance, what are the challenges related to it and review some big data visualization tools.

Abstract: We provide an overview of the recent trends toward digitalization and large-scale data analytics in healthcare. It is expected that these trends are instrumental in the dramatic changes in the way healthcare will be organized in the future. We discuss the recent political initiatives designed to shift care delivery processes from paper to electronic, with the goals of more effective treatments with better outcomes; cost pressure is a major driver of innovation. We describe newly developed networks of healthcare providers, research organizations, and commercial vendors to jointly analyze data for the development of decision support systems. We address the trend toward continuous healthcare where health is monitored by wearable and stationary devices; a related development is that patients increasingly assume responsibility for their own health data. Finally, we discuss recent initiatives toward a personalized medicine, based on advances in molecular medicine, data management, and data analytics.

Abstract: We present the design and implementation of iVoLVER, a tool that allows users to create visualizations without textual programming. iVoLVER is designed to enable flexible acquisition of many types of data (text, colors, shapes, quantities, dates) from multiple source types (bitmap charts, webpages, photographs, SVGs, CSV files) and, within the same canvas, supports transformation of that data through simple widgets to construct interactive animated visuals. Aside from the tool, which is web-based and designed for pen and touch, we contribute the design of the interactive visual language and widgets for extraction, transformation, and representation of data. We demonstrate the flexibility and expressive power of the tool through a set of scenarios, and discuss some of the challenges encountered and how the tool fits within the current infovis tool landscape.

Abstract: The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, has received considerable attention in recent years. Many visual analytics methods have been proposed across disciplines to understand large-scale structured and unstructured social media data. This objective, however, also poses significant challenges for researchers to obtain a comprehensive picture of the area, understand research challenges, and develop new techniques. In this paper, we present a comprehensive survey to characterize this fast-growing area and summarize the state-of-the-art techniques for analyzing social media data. In particular, we classify existing techniques into two categories: gathering information and understanding user behaviors. We aim to provide a clear overview of the research area through the established taxonomy. We then explore the design space and identify the research trends. Finally, we discuss challenges and open questions for future studies.

Abstract: This paper introduces the use of Augmented Reality as an immersive analytical tool in the physical world. We present Situated Analytics, a novel combination of real-time interaction and visualization techniques that allows exploration and analysis of information about objects in the user's physical environment. Situated Analytics presents both situated and abstract summary and contextual information to a user. We conducted a user study to evaluate its use in three shopping analytics tasks, comparing the use of a Situated Analytics prototype with manual analysis. The results showed that users preferred the Situated Analytics prototype over the manual method, and that tasks were performed more quickly and accurately using the prototype.

Abstract: Current analytical approaches in computational social science can be characterized by four dominant paradigms: text analysis (information extraction and classification), social network analysis (graph theory), social complexity analysis (complex systems science), and social simulations (cellular automata and agent-based modeling). However, when it comes to organizational and societal units of analysis, there exists no approach to conceptualize, model, analyze, explain, and predict social media interactions as individuals’ associations with ideas, values, identities, and so on. To address this limitation, based on the sociology of associations and the mathematics of set theory, this paper presents a new approach to big data analytics called social set analysis. Social set analysis consists of a generative framework for the philosophies of computational social science, theory of social data, conceptual and formal models of social data, and an analytical framework for combining big social data sets with organizational and societal data sets. Three empirical studies of big social data are presented to illustrate and demonstrate social set analysis in terms of fuzzy set-theoretical sentiment analysis, crisp set-theoretical interaction analysis, and event-studies-oriented set-theoretical visualizations. Implications for big data analytics, current limitations of the set-theoretical approach, and future directions are outlined.

Abstract: Visualization provides a powerful means for data analysis. But to be practical, visual analytics tools must support smooth and flexible use of visualizations at a fast rate. This becomes increasingly onerous with the ever-increasing size of real-world datasets. First, large databases make interaction more difficult once query response time exceeds several seconds. Second, any attempt to show all data points will overload the visualization, resulting in chaos that will only confuse the user. Over the last few years, substantial effort has been put into addressing both of these issues and many innovative solutions have been proposed. Indeed, data visualization is a topic that is too large to be addressed in a single survey paper. Thus, we restrict our attention here to interactive visualization of large data sets. Our focus then is skewed in a natural way towards query processing problem—provided by an underlying database system—rather than to the actual data visualization problem.

Abstract: Seeking better understanding of digital transformation.

Abstract: Scientific data repositories have historically made data widely accessible to the scientific community, and have led to better research through comparisons, reproducibility, as well as further discoveries and insights. Despite the growing importance and utilization of data repositories in many scientific disciplines, the design of existing data repositories has not changed for decades. In this paper, we revisit the current design and envision interactive data repositories, which not only make data accessible, but also provide techniques for interactive data exploration, mining, and visualization in an easy, intuitive, and free-flowing manner.

Abstract: We introduce a novel visual analytics approach to analyze speaker behavior patterns in multi-party conversations. We propose Topic-Space Views to track the movement of speakers across the thematic landscape of a conversation. Our tool is designed to assist political science scholars in exploring the dynamics of a conversation over time to generate and prove hypotheses about speaker interactions and behavior patterns. Moreover, we introduce a glyph-based representation for each speaker turn based on linguistic and statistical cues to abstract relevant text features. We present animated views for exploring the general behavior and interactions of speakers over time and interactive steady visualizations for the detailed analysis of a selection of speakers. Using a visual sedimentation metaphor we enable the analysts to track subtle changes in the flow of a conversation over time while keeping an overview of all past speaker turns. We evaluate our approach on real-world datasets and the results have been insightful to our domain experts.

Abstract: We present an online visual analytics approach to helping users explore and understand hierarchical topic evolution in high-volume text streams. The key idea behind this approach is to identify representative topics in incoming documents and align them with the existing representative topics that they immediately follow (in time). To this end, we learn a set of streaming tree cuts from topic trees based on user-selected focus nodes. A dynamic Bayesian network model has been developed to derive the tree cuts in the incoming topic trees to balance the fitness of each tree cut and the smoothness between adjacent tree cuts. By connecting the corresponding topics at different times, we are able to provide an overview of the evolving hierarchical topics. A sedimentation-based visualization has been designed to enable the interactive analysis of streaming text data from global patterns to local details. We evaluated our method on real-world datasets and the results are generally favorable.

Abstract: Massive open online courses (MOOCs) aim to facilitate open-access and massive-participation education. These courses have attracted millions of learners recently. At present, most MOOC platforms record the web log data of learner interactions with course videos. Such large amounts of multivariate data pose a new challenge in terms of analyzing online learning behaviors. Previous studies have mainly focused on the aggregate behaviors of learners from a summative view; however, few attempts have been made to conduct a detailed analysis of such behaviors. To determine complex learning patterns in MOOC video interactions, this paper introduces a comprehensive visualization system called PeakVizor. This system enables course instructors and education experts to analyze the “peaks” or the video segments that generate numerous clickstreams. The system features three views at different levels: the overview with glyphs to display valuable statistics regarding the peaks detected; the flow view to present spatio-temporal information regarding the peaks; and the correlation view to show the correlation between different learner groups and the peaks. Case studies and interviews conducted with domain experts have demonstrated the usefulness and effectiveness of PeakVizor, and new findings about learning behaviors in MOOC platforms have been reported.

Abstract: Recommender systems are being widely used to assist people in making decisions, for example, recommending films to watch or books to buy. Despite its ubiquity, the problem of presenting the recommendations of temporal event sequences has not been studied. We propose EventAction, which to our knowledge, is the first attempt at a prescriptive analytics interface designed to present and explain recommendations of temporal event sequences. EventAction provides a visual analytics approach to (1) identify similar records, (2) explore potential outcomes, (3) review recommended temporal event sequences that might help achieve the users' goals, and (4) interactively assist users as they define a personalized action plan associated with a probability of success. Following the design study framework, we designed and deployed EventAction in the context of student advising and reported on the evaluation with a student review manager and three graduate students.

Abstract: Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision-making processes based on financial data have been a popular topic in industries. However, analyzing financial data is a non-trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for financial data exploration. Often, the development of such systems requires researchers to collaborate with financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in financial domains with respect to real-world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.

Abstract: We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities. Supply network scale, scope, and complexity strain managers' cognitive capacity.Visual analytics augments and amplifies managers' supply network intelligence.We propose a visual analytic framework for supply network management.System incorporates multiple interactive visualizations and predictive analytics.Multi-phase evaluations reveal significant utility and usefulness of our system.

Abstract: This book provides a systematic review of many advanced techniques to support the analysis of large collections of documents, ranging from the elementary to the profound, covering all the aspects of the visualization of text documents. Particularly, we start by introducing the fundamental concept of information visualization and visual analysis, followed by a brief survey of the field of text visualization and commonly used data models for converting document into a structured form for visualization. Then we introduce the key visualization techniques including visualizing document similarity, content, sentiments, as well as text corpus exploration system in details with concrete examples in the rest of the book.

Abstract: Although there has been a great deal of interest in analyzing customer opinions and breaking news in microblogs, progress has been hampered by the lack of an effective mechanism to discover and retrieve data of interest from microblogs. To address this problem, we have developed an uncertainty-aware visual analytics approach to retrieve salient posts, users, and hashtags. We extend an existing ranking technique to compute a multifaceted retrieval result: the mutual reinforcement rank of a graph node, the uncertainty of each rank, and the propagation of uncertainty among different graph nodes. To illustrate the three facets, we have also designed a composite visualization with three visual components: a graph visualization, an uncertainty glyph, and a flow map. The graph visualization with glyphs, the flow map, and the uncertainty analysis together enable analysts to effectively find the most uncertain results and interactively refine them. We have applied our approach to several Twitter datasets. Qualitative evaluation and two real-world case studies demonstrate the promise of our approach for retrieving high-quality microblog data.

Abstract: In this article, we investigate methods for suggesting the interactivity of online visualizations embedded with text. We first assess the need for such methods by conducting three initial experiments on Amazon's Mechanical Turk. We then present a design space for Suggested Interactivity (i. e., visual cues used as perceived affordances—SI), based on a survey of 382 HTML5 and visualization websites. Finally, we assess the effectiveness of three SI cues we designed for suggesting the interactivity of bar charts embedded with text. Our results show that only one cue (SI3) was successful in inciting participants to interact with the visualizations, and we hypothesize this is because this particular cue provided feedforward.

Abstract: User tasks play a pivotal role in visualization design and evaluation. However, the term ‘task’ is used ambiguously within the visualization community. In this article, we critically analyze the re...