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...

Task Cube: A three-dimensional conceptual space of user tasks in visualization design and evaluation:

The analysis of data streams is important in many security-related domains to gain situational awareness. To provide monitoring and visual analysis of such data streams, we propose a system, called NStreamAware, that uses modern distributed processing technologies to analyze streams using stream slices, which are presented to analysts in a web-based visual analytics application, called NVisAware. Furthermore, we visually guide the user in the feature selection process to summarize the slices to focus on the most interesting parts of the stream based on introduced expert knowledge of the analyst. We show through case studies, how the system can be used to gain situational awareness and eventually enhance network security. Furthermore, we apply the system to a social media data stream to compete in an international challenge to evaluate the applicability of our approach to other domains.

/pdf/nstreamaware-real-time-visual-analytics-for-data-streams-to-58g2midg7m.pdf

NStreamAware: real-time visual analytics for data streams to enhance situational awareness

Visual data analysis is composed of a collection of cognitive actions and tasks to decompose, internalize, and recombine data to produce knowledge and insight. Visual analytic tools provide interactive visual interfaces to data to support discovery and sensemaking tasks, including forming hypotheses, asking questions, and evaluating and organizing evidence. Myriad analytic models can be incorporated into visual analytic systems at the cost of increasing complexity in the analytic discourse between user and system. Techniques exist to increase the usability of interacting with analytic models, such as inferring data models from user interactions to steer the underlying models of the system via semantic interaction, shielding users from having to do so explicitly. Such approaches are often also referred to as mixed-initiative systems. Sensemaking researchers have called for development of tools that facilitate analytic sensemaking through a combination of human and automated activities. However, design guidelines do not exist for mixed-initiative visual analytic systems to support iterative sensemaking. In this paper, we present candidate design guidelines and introduce the Active Data Environment (ADE) prototype, a spatial workspace supporting the analytic process via task recommendations invoked by inferences about user interactions within the workspace. ADE recommends data and relationships based on a task model, enabling users to co-reason with the system about their data in a single, spatial workspace. This paper provides an illustrative use case, a technical description of ADE, and a discussion of the strengths and limitations of the approach.

Mixed-initiative visual analytics using task-driven recommendations

As the scale of unlabeled data rises, it becomes increasingly valuable to perform scalable, unsupervised data analysis. Tensor decompositions, which have been empirically successful at finding meaningful cross-dimensional patterns in multidimensional data, are a natural candidate to test for scalability and meaningful pattern discovery in these massive real-world datasets. Furthermore, the production of big data of different types necessitates the ability to mine patterns across disparate sources. The coupled tensor decomposition framework captures this idea by supporting the decomposition of several tensors from different data sources together. We present a scalable implementation of coupled tensor decomposition on Apache Spark. We introduce nonnegativity and sparsity constraints, and perform all-at-once quasi-Newton optimization of all factor matrix parameters. We present results showing the billion-scale scalability of this novel implementation and also demonstrate the high level of interpretability in the components produced, suggesting that coupled, all-at-once tensor decompositions on Apache Spark represent a promising framework for large-scale, unsupervised pattern discovery.

All-at-once Decomposition of Coupled Billion-scale Tensors in Apache Spark

A new way to train a virtual assistant with unsupervised learning is presented in this thesis. Rather than integrating with a particular set of programs and interfaces, this new approach involves shallow integration between the virtual assistant and computer through machine vision. In effect the assistant interprets the computer screen in order to produce helpful recommendations to assist the computer user. In developing this new approach, called AVRA, the following methods are described: an unsupervised learning algorithm which enables the system to watch and learn from user behavior, a method for fast filtering of the text displayed on the computer screen, a deep learning classifier used to recognize key onscreen text in the presence of OCR translation errors, and a recommendation filtering algorithm to triage the many possible action recommendations. AVRA is compared to a similar commercial state-of-the-art system, to highlight how this work adds to the state of the art. AVRA is a deep learning image processing and recommender system that can collaborate with the computer user to accomplish various tasks. This document presents a comprehensive overview of the development and possible applications of this novel virtual assistant technology. It detects onscreen tasks based upon the context it perceives by analyzing successive computer screen images with neural networks. AVRA is a recommender system, as it assists the user by producing action recommendations regarding onscreen tasks. In order to simplify the interaction between the user and AVRA, the system was designed to only produce action recommendations that can be accepted with a single mouse click. These action recommendations are produced without integration into each individual application executing on the computer. Furthermore, the action recommendations are personalized to the user’s interests utilizing a history of the user’s interaction.

/pdf/composing-recommendations-using-computer-screen-images-a-2q9yicrinc.pdf

Composing Recommendations Using Computer Screen Images: A Deep Learning Recommender System for PC Users

The 2014 IEEE Visual Analytics Science and Technology (VAST) Challenge presented researchers with a single fictitious scenario: the disappearance of staff members of the GASTech oil and gas company on location on the island of Kronos. A group named the Protectors of Kronos (POK) was the prime suspect in the disappearance. Three mini-challenges and a grand challenge were offered. Mini-challenge 1 included multiple types of text data for participants to provide a timeline of key events and characterize the POK, mini-challenge 2 focused on individuals' movement and financial data for participants to provide patterns of daily life, and mini-challenge 3 featured real-time streaming social media and emergency service data for participants to provide hostage and kidnapper information. The grand challenge asked the participants to integrate results and generate a synopsis of events. The VAST Challenge received 73 submissions from 13 countries.

VAST challenge 2014: The Kronos incident

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VAST challenge 2014: The Kronos incident