Visual analytics for large-scale bioinformatic data sets

Rapid advances in sequencing technologies (Next Generation Sequencing or NGS) have led to a vast increase in the quantity of bioinformatics data available, with this increasing scale presenting enormous challenges to researchers seeking to identify complex interactions. This paper is concerned with the domain of transcriptional regulation, and the use of visualisation to identify relationships between specific regulatory proteins (the transcription factors or TFs) and their associated target genes (TGs). We present preliminary work from an ongoing study which aims to determine the effectiveness of different visual representations and large scale displays in supporting discovery. Following an iterative process of implementation and evaluation, representations were tested by potential users in the bioinformatics domain to determine their efficacy, and to understand better the range of ad hoc practices among bioinformatics literate users. Results from two rounds of small scale user studies are considered with initial findings suggesting that bioinformaticians require richly detailed views of TF data, features to compare TF layouts between organisms quickly, and ways to keep track of interesting data points.

A Visual Analytics Framework for Strategic Airlift Decision Making

The foundational concept of Network Enabled Capability relies on effective, timely information sharing. This information is used in analysis, trade and scenario studies, and ultimately decision-making. In this paper, the concept of visual analytics is explored as an enabler to facilitate rapid, defensible, and superior decision-making. By coupling analytical reasoning with the exceptional human capability to rapidly internalize and understand visual data, visual analytics allows individual and collaborative decision-making to occur in the face of vast and disparate data, time pressures, and uncertainty. An example visual analytics framework is presented in the form of a decision-making environment centered on the Lockheed C-5A and C-5M aircraft. This environment allows rapid trade studies to be conducted on design, logistics, and capability within the aircraft?s operational roles. Through this example, the use of a visual analytics decision-making environment within a military environment is demonstrated.

Exploração e análise de dados coletados pelo sistema integrado de monitoramento ambiental utilizando técnicas de visual analytics

Pós-graduação em Ciências Cartográficas - FCT

Topographic visual analytics of multibeam dynamic SONAR data

This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point relative similarity measures) appropriate to the below-water maritime domain, we are investigating a preliminary prototype topographic visualisation model. The focus of the current paper is on the mathematical problem of exploiting a relative dissimilarity representation of signals in a visual informatics mapping model, driven by real-world sonar systems. A realistic noise model is explored and incorporated into non-linear and topographic visualisation algorithms building on the approach of [9]. Concepts are illustrated using a real world dataset of 32 hydrophones monitoring a shallow-water environment in which targets are present and dynamic.

A Visual Analytics Approach to Comparing Cohorts of Event Sequences

Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the structure of the event sequences (e.g., event order, co-occurring events, or frequencies of events), the attributes about the events and records (e.g., gender of a patient), or metrics about the timestamps themselves (e.g., duration of an event). Running statistical tests to cover all these cases and determining which results are significant becomes cumbersome. Current visual analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. Visual analytics tools leverage humans' ability to easily see patterns and anomalies that they were not expecting, but is limited by uncertainty in findings. Statistical tools emphasize finding significant differences in the data, but often requires researchers have a concrete question and doesn't facilitate more general exploration of the data. Combining visual analytics tools with statistical methods leverages the benefits of both approaches for quicker and easier insight discovery. Integrating statistics into a visualization tool presents many challenges on the frontend (e.g., displaying the results of many different metrics concisely) and in the backend (e.g., scalability challenges with running various metrics on multi-dimensional data at once). I begin by exploring the problem of comparing cohorts of event sequences and understanding the questions that analysts commonly ask in this task. From there, I demonstrate that combining automated statistics with an interactive user interface amplifies the benefits of both types of tools, thereby enabling analysts to conduct quicker and easier data exploration, hypothesis generation, and insight discovery. The direct contributions of this dissertation are: (1) a taxonomy of metrics for comparing cohorts of temporal event sequences, (2) a statistical framework for exploratory data analysis with a method I refer to as high-volume hypothesis testing (HVHT), (3) a family of visualizations and guidelines for interaction techniques that are useful for understanding and parsing the results, and (4) a user study, five long-term case studies, and five short-term case studies which demonstrate the utility and impact of these methods in various domains: four in the medical domain, one in web log analysis, two in education, and one each in social networks, sports analytics, and security. My dissertation contributes an understanding of how cohorts of temporal event sequences are commonly compared and the difficulties associated with applying and parsing the results of these metrics. It also contributes a set of visualizations, algorithms, and design guidelines for balancing automated statistics with user-driven analysis to guide users to significant, distinguishing features between cohorts. This work opens avenues for future research in comparing two or more groups of temporal event sequences, opening traditional machine learning and data mining techniques to user interaction, and extending the principles found in this dissertation to data types beyond temporal event sequences.

Visual analytics of medical and biological data

Otto-von-Guericke-Universität Magdeburg, Fakultät für Informatik, Habilitationsschrift, 2016

Visual fingerprints of the acoustic environment: The use of acoustic indices to characterise natural habitats

Acoustic recordings play an increasingly important role in monitoring terrestrial environments. However, due to rapid advances in technology, ecologists are accumulating more audio than they can listen to. Our approach to this big-data challenge is to visualize the content of long-duration audio recordings by calculating acoustic indices. These are statistics which describe the temporal-spectral distribution of acoustic energy and reflect content of ecological interest. We combine spectral indices to produce false-color spectrogram images. These not only reveal acoustic content but also facilitate navigation. An additional analytic challenge is to find appropriate descriptors to summarize the content of 24-hour recordings, so that it becomes possible to monitor long-term changes in the acoustic environment at a single location and to compare the acoustic environments of different locations. We describe a 24-hour ‘acoustic-fingerprint’ which shows some preliminary promise.

VisArchive: a time and relevance based visual interface for searching, browsing, and exploring project archives

Background Project archives are becoming increasingly large and complex. On construction projects in particular, the increasing amount of information and the increasing complexity of its structure make searching and exploring information in the project archive challenging and time-consuming. Methods This research investigates a query-driven approach that represents new forms of contextual information to help users understand the set of documents resulting from queries of construction project archives. Specifically, this research extends query-driven interface research by representing three types of contextual information: (1) the temporal context is represented in the form of a timeline to show when each document was created; (2) the search-relevance context shows exactly which of the entered keywords matched each document; and (3) the usage context shows which project participants have accessed or modified a file. Results We implemented and tested these ideas within a prototype query-driven interface we call VisArchive. VisArchive employs a combination of multi-scale and multi-dimensional timelines, color-coded stacked bar charts, additional supporting visual cues and filters to support searching and exploring historical project archives. The timeline-based interface integrates three interactive timelines as focus + context visualizations. Conclusions The feasibility of using these visual design principles is tested in two types of project archives: searching construction project archives of an educational building project and tracking of software defects in the Mozilla Thunderbird project. These case studies demonstrate the applicability, usefulness and generality of the design principles implemented.

An overview of interactive visual data mining techniques for knowledge discovery

n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.