VAST Challenge 2012: Visual analytics for big data

Abstract

The 2012 Visual Analytics Science and Technology (VAST) Challenge posed two challenge problems for participants to solve using a combination of visual analytics software and their own analytic reasoning abilities. Challenge 1 (C1) involved visualizing the network health of the fictitious Bank of Money to provide situation awareness and identify emerging trends that could signify network issues. Challenge 2 (C2) involved identifying the issues of concern within a region of the Bank of Money network experiencing operational difficulties utilizing the provided network logs. Participants were asked to analyze the data and provide solutions and explanations for both challenges. The data sets were downloaded by nearly 1100 people by the close of submissions. The VAST Challenge received 40 submissions with participants from 12 different countries, and 14 awards were given.

Geospatial big data handling theory and methods: A review and research challenges

“…There are many strong reasons for this with the primary one being that some of our computational and statistical approaches do not scale -there is simply too much data (Keim et al 2013, Shneiderman 2014. Even smaller amounts of data in forms and tables are not really human readable, thus the interactive and exploratory visualization environments help at the very early 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 9 stages in dealing with big data in making sense of what the data actually contains (Cook et al 2012, Frankel & Reid 2008, Hoffer 2014. In other words, visualizations essentially enable humans to deal with big data where machines along might fall short.…”

Fluency of visualizations: linking spatiotemporal visualizations to improve cybersecurity visual analytics

“…It is difficult to have a joint integral modeling approach. We have observed such sequential analysis processes in our own practice [22] while solving the VAST 2012 challenge MC2 [17], and in other winning entries [23,24] when they tried to solve the VAST 2013 challenge MC3 [25]. Many times when looking for issues, the user first examined the temporal aspect by looking at the time series curves to find out the anomalies (e.g., huge peak in the curve), then checked out other detailed visualizations to allocate the affecting hosts (IP addresses).…”

“…Alan M. MacEachren's GeoVISTA Center [9] uses highlighting, brushing, and linking, and filtered and linked selections to help users analyze georeferenced time-varying multivariate data. IEEE VAST 2012 Mini-Challenge 1 (MC1) asked researchers to analyze a high-dimensional spatiotemporal dataset [17]. Most of the challenge entries used maps and statistical graphs.…”

A Visualization Method Based on Graph Database in Security Logs Analysis

“…Through cluster analysis, we can find the abnormal data, classify the abnormal data, and produce the evidence [4][5][6] . In visualization, Richar A. Becker first proposed the concept of visualization of network data [7] . Girardind used a variety of visualization techniques to analyze firewall logs [8] .…”