GeoLens: enabling interactive visual analytics over large-scale, multidimensional geospatial datasets

2015 Spring.Includes bibliographical references.With the rapid increase of scientific data volumes, interactive tools that enable effective visual representation for scientists are needed. This is critical when scientists are manipulating voluminous datasets and especially when they need to explore datasets interactively to develop their hypotheses. In this paper, we present an interactive visual analytics framework, GeoLens. GeoLens provides fast and expressive interactions with voluminous geospatial datasets. We provide an expressive visual query evaluation scheme to support advanced interactive visual analytics technique, such as brushing and linking. To achieve this, we designed and developed the geohash based image tile generation algorithm that automatically adjusts the range of data to access based on the minimum acceptable size of the image tile. In addition, we have also designed an autonomous histogram generation algorithm that generates histograms of user-defined data subsets that do not have pre-computed data properties. Using our approach, applications can generate histograms of datasets containing millions of data points with sub-second latency. The work builds on our visual query coordinating scheme that evaluates geospatial query and orchestrates data aggregation in a distributed storage environment while preserving data locality and minimizing data movements. This paper includes empirical benchmarks of our framework encompassing a billion-file dataset published by the National Climactic Data Center

Iterative Visual Analytics and its Applications in Bioinformatics

Indiana University-Purdue University Indianapolis (IUPUI)You, Qian. Ph.D., Purdue University, December, 2010. Iterative Visual Analytics and its Applications in Bioinformatics. Major Professors: Shiaofen Fang and Luo Si. Visual Analytics is a new and developing field that addresses the challenges of knowledge discoveries from the massive amount of available data. It facilitates humans‘ reasoning capabilities with interactive visual interfaces for exploratory data analysis tasks, where automatic data mining methods fall short due to the lack of the pre-defined objective functions. Analyzing the large volume of data sets for biological discoveries raises similar challenges. The domain knowledge of biologists and bioinformaticians is critical in the hypothesis-driven discovery tasks. Yet developing visual analytics frameworks for bioinformatic applications is still in its infancy. In this dissertation, we propose a general visual analytics framework – Iterative Visual Analytics (IVA) – to address some of the challenges in the current research. The framework consists of three progressive steps to explore data sets with the increased complexity: Terrain Surface Multi-dimensional Data Visualization, a new multi-dimensional technique that highlights the global patterns from the profile of a large scale network. It can lead users‘ attention to characteristic regions for discovering otherwise hidden knowledge; Correlative Multi-level Terrain Surface Visualization, a new visual platform that provides the overview and boosts the major signals of the numeric correlations among nodes in interconnected networks of different contexts. It enables users to gain critical insights and perform data analytical tasks in the context of multiple correlated networks; and the Iterative Visual Refinement Model, an innovative process that treats users‘ perceptions as the objective functions, and guides the users to form the optimal hypothesis by improving the desired visual patterns. It is a formalized model for interactive explorations to converge to optimal solutions. We also showcase our approach with bio-molecular data sets and demonstrate its effectiveness in several biomarker discovery applications

Visual Event Cueing in Linked Spatiotemporal Data

abstract: The media disperses a large amount of information daily pertaining to political events social movements, and societal conflicts. Media pertaining to these topics, no matter the format of publication used, are framed a particular way. Framing is used not for just guiding audiences to desired beliefs, but also to fuel societal change or legitimize/delegitimize social movements. For this reason, tools that can help to clarify when changes in social discourse occur and identify their causes are of great use. This thesis presents a visual analytics framework that allows for the exploration and visualization of changes that occur in social climate with respect to space and time. Focusing on the links between data from the Armed Conflict Location and Event Data Project (ACLED) and a streaming RSS news data set, users can be cued into interesting events enabling them to form and explore hypothesis. This visual analytics framework also focuses on improving intervention detection, allowing users to hypothesize about correlations between events and happiness levels, and supports collaborative analysis.Dissertation/ThesisMasters Thesis Computer Science 201

Visualizing the Motion Flow of Crowds

In modern cities, massive population causes problems, like congestion, accident, violence and crime everywhere. Video surveillance system such as closed-circuit television cameras is widely used by security guards to monitor human behaviors and activities to manage, direct, or protect people. With the quantity and prolonged duration of the recorded videos, it requires a huge amount of human resources to examine these video recordings and keep track of activities and events. In recent years, new techniques in computer vision field reduce the barrier of entry, allowing developers to experiment more with intelligent surveillance video system. Different from previous research, this dissertation does not address any algorithm design concerns related to object detection or object tracking. This study will put efforts on the technological side and executing methodologies in data visualization to find the model of detecting anomalies. It would like to provide an understanding of how to detect the behavior of the pedestrians in the video and find out anomalies or abnormal cases by using techniques of data visualization

Visual Analytics of Co-Occurrences to Discover Subspaces in Structured Data

We present an approach that shows all relevant subspaces of categorical data condensed in a single picture. We model the categorical values of the attributes as co-occurrences with data partitions generated from structured data using pattern mining. We show that these co-occurrences are a-priori, allowing us to greatly reduce the search space, effectively generating the condensed picture where conventional approaches filter out several subspaces as these are deemed insignificant. The task of identifying interesting subspaces is common but difficult due to exponential search spaces and the curse of dimensionality. One application of such a task might be identifying a cohort of patients defined by attributes such as gender, age, and diabetes type that share a common patient history, which is modeled as event sequences. Filtering the data by these attributes is common but cumbersome and often does not allow a comparison of subspaces. We contribute a powerful multi-dimensional pattern exploration approach (MDPE-approach) agnostic to the structured data type that models multiple attributes and their characteristics as co-occurrences, allowing the user to identify and compare thousands of subspaces of interest in a single picture. In our MDPE-approach, we introduce two methods to dramatically reduce the search space, outputting only the boundaries of the search space in the form of two tables. We implement the MDPE-approach in an interactive visual interface (MDPE-vis) that provides a scalable, pixel-based visualization design allowing the identification, comparison, and sense-making of subspaces in structured data. Our case studies using a gold-standard dataset and external domain experts confirm our approach’s and implementation’s applicability. A third use case sheds light on the scalability of our approach and a user study with 15 participants underlines its usefulness and power

HUMAN-DATA INTERACTION IN LARGE AND HIGH-DIMENSIONAL DATA

Human-Data Interaction (HDI) is an emerging field which studies how humans make sense of large and complex data. Visual analytics tools are a central component of this sensemaking process. However, the growth of big data has affected their performance, resulting in latency in interactivity or long query-response times, both of which degrade one's ability to do knowledge discovery. To address these challenges, a new paradigm of data exploration has appeared in which a rapid but inaccurate result is followed by a succession of gradually more accurate answers. As the primary objective of this thesis, we investigated how this incremental latency affects the quantity and quality of knowledge discovery in an HDI system. We have developed a big data visualization tool and studied 40 participants in a think-aloud experiment, using this tool to explore a large and high-dimensional data. Our findings indicate that although incremental latency reduces the rate of discovery generation, it does not affect one's chance of making a discovery per each generated visualization, and it does not affect the correctness of those discoveries. However, in the presence of latency, utilizing contextual layers such as a map result in fewer mistakes while exploring higher-dimensional visualizations lead to more incorrect discoveries. As the secondary objective, we investigated what strategies improved a subject's performance. Our observations suggest that successful participants explore the data methodically, by first examining simple and familiar concepts and then gradually adding complexity to the visualizations, until they build a correct mental model of the inner workings of the tool. With this model, they generate several discovery patterns, each acting as a blueprint for forming new insights. Ultimately, some participants combined their discovery patterns to create multifaceted data-driven stories. Based on these observations, we propose design guidelines for developing HDI platforms for large and high-dimensional data