skip to main content


Title: Three Perceptual Tools for Seeing and Understanding Visualized Data
The visual system evolved and develops to process the scenes, faces, and objects of the natural world, but people adapt this powerful system to process data within an artificial world of visualizations. To extract patterns in data from these artificial displays, viewers appear to use at least three perceptual tools, including a tool that extracts global statistics, one that extracts shapes within the data, and one that produces sentence-like comparisons. A better understanding of the power, limits, and deployment of these tools would lead to better guidelines for designing effective data displays.  more » « less
Award ID(s):
1901485
NSF-PAR ID:
10350293
Author(s) / Creator(s):
Date Published:
Journal Name:
Current Directions in Psychological Science
Volume:
30
Issue:
5
ISSN:
0963-7214
Page Range / eLocation ID:
367 to 375
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    What do football passes and financial transactions have in common? Both are networked walk processes that we can observe, where records take the form of timestamped events that move something tangible from one node to another. Here we propose an approach to analyze this type of data that extracts the actual trajectories taken by the tangible items involved. The main advantage of analyzing the resulting trajectories compared to using, e.g., existing temporal network analysis techniques, is that sequential, temporal, and domain-specific aspects of the process are respected and retained. As a result, the approach lets us produce contextually-relevant insights. Demonstrating the usefulness of this technique, we consider passing play within association football matches (an unweighted process) and e-money transacted within a mobile money system (a weighted process). Proponents and providers of mobile money care to know how these systems are used—using trajectory extraction we find that 73% of e-money was used for stand-alone tasks and only 21.7% of account holders built up substantial savings at some point during a 6-month period. Coaches of football teams and sports analysts are interested in strategies of play that are advantageous. Trajectory extraction allows us to replicate classic results from sports science on data from the 2018 FIFA World Cup. Moreover, we are able to distinguish teams that consistently exhibited complex, multi-player dynamics of play during the 2017–2018 club season using ball passing trajectories, coincidentally identifying the winners of the five most competitive first-tier domestic leagues in Europe.

     
    more » « less
  2. null (Ed.)
    The CS Education community has developed many educational tools in recent years, such as interactive exercises. Often the developer makes them freely available for use, hosted on their own server, and usually they are directly accessible within the instructor's LMS through the LTI protocol. As convenient as this can be, instructors using these third-party tools for their courses can experience issues related to data access and privacy concerns. The tools typically collect clickstream data on student use. But they might not make it easy for the instructor to access these data, and the institution might be concerned about privacy violations. While the developers might allow and even support local installation of the tool, this can be a difficult process unless the tool carefully designed for third-party installation. And integration of small tools within larger frameworks (like a type of interactive exercise within an eTextbook framework) is also difficult without proper design. This paper describes an ongoing containerization effort for the OpenDSA eTextbook project. Our goal is both to serve our needs by creating an easier-to-manage decomposition of the many tools and sub-servers required by this complex system, and also to provide an easily installable production environment that instructors can run locally. This new system provides better access to developer-level data analysis tools and potentially removes many FERPA-related privacy concerns. We also describe our efforts to integrate Caliper Analytics into OpenDSA to expand the data collection and analysis services. We hope that our containerization architecture can help provide a roadmap for similar projects to follow 
    more » « less
  3. Sudeepa Roy and Jun Yang (Ed.)
    Data we encounter in the real-world such as printed menus, business documents, and nutrition labels, are often ad-hoc. Valuable insights can be gathered from this data when combined with additional information. Recent advances in computer vision and augmented reality have made it possible to understand and enrich such data. Joining real-world data with remote data stores and surfacing those enhanced results in place, within an augmented reality interface can lead to better and more informed decision-making capabilities. However, building end-user applications that perform these joins with minimal human effort is not straightforward. It requires a diverse set of expertise, including machine learning, database systems, computer vision, and data visualization. To address this complexity, we present Quill – a framework to develop end-to-end applications that model augmented reality applications as a join between real- world data and remote data stores. Using an intuitive domain-specific language, Quill accelerates the development of end-user applications that join real-world data with remote data stores. Through experiments on applications from multiple different domains, we show that Quill not only expedites the process of development, but also allows developers to build applications that are more performant than those built using standard developer tools, thanks to the ability to optimize declarative specifications. We also perform a user-focused study to investigate how easy (or difficult) it is to use Quill for developing augmented reality applications than other existing tools. Our results show that Quill allows developers to build and deploy applications with a lower technical background than building the same application using existing developer tools. 
    more » « less
  4. Memory Forensics is one of the most important emerging areas in computer forensics. In memory forensics, analysis of userland memory is a technique that analyses per-process runtime data structures and extracts significant evidence for application-specific investigations. In this research, our focus is to examine the critical challenges faced by process memory acquisition that can impact object and data recovery. Particularly, this research work seeks to address the issues of consistency and reliability in userland memory forensics on Android. In real-world investigations, memory acquisition tools record the information when the device is running. In such scenarios, each application’s memory content may be in flux due to updates that are in progress, garbage collection activities, changes in process states, etc. In this paper we focus on various runtime activities such as garbage collection and process states and the impact they have on object recovery in userland memory forensics. The outcome of the research objective is to assess the reliability of Android userland memory forensic tools by providing new research directions for efficiently developing a metric study to measure the reliability. We evaluated our research objective by analysing memory dumps acquired from 30 apps in different Process Acquisition Modes. The Process Acquisition Mode (PAM) is the memory dump of a process that is extracted while external runtime factors are triggered. Our research identified an inconsistency in the number of objects recovered from analysing the process memory dumps with runtime factors included. Particularly, the evaluation results revealed differences in the count of objects recovered in different acquisition modes. We utilized Euclidean distance and covariance as the metrics for our study. These two metrics enabled the authors to identify how the change in the number of recovered objects in PAM impact forensic analysis. Our conclusion revealed that runtime factors could on average result in about 20% data loss, thus revealing these factors can have an obvious impact on object recovery. 
    more » « less
  5. Logging is a universal approach to recording important events in system workflows of distributed systems. Current log analysis tools ignore the semantic knowledge that is key to workflow construction and analysis. In addition, they focus on infrastructure-level distributed systems. Because of fundamental differences in log features, they are ineffective in distributed data analytics systems. This paper proposes IntelLog, a semantic-aware non-intrusive workflow reconstruction tool for distributed data analytics systems. It is capable of building hierarchical relationships between components and events from logs generated by the targeted systems with little or even no domain knowledge. Leveraging natural language processing, IntelLog automatically extracts and formats semantic information in each log message, including system events, identifiers, locality information, and metrics values. It builds a graph to represent the hierarchical relationship of components in the targeted system via nomenclature conventions. We implement IntelLog for Hadoop MapReduce, Spark and Tez. Evaluation results show that IntelLog provides a fine-grained view of the system workflows with semantics. It outperforms existing tools in automatically detecting anomalies caused by real-world problems, misconfigurations and system bugs. Users can query the formatted semantic knowledge to understand and further troubleshoot the systems. 
    more » « less