Live Reorderable Accordion Drawing (LiveRAC) Peter McLachlan MSc Thesis Presentation September, 2006

Live Reorderable Accordion Drawing (LiveRAC)

Peter McLachlanMSc Thesis Presentation

September, 2006

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Presentation Overview

• Motivation

• Related Work

• LiveRAC Overview and Implementation

• Discussion

LiveRAC

Motivation Overview Problem Domain Devices & Data Domain Tools Requirements Our Solution

Related Work

LiveRAC

Discussion

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Problem Domain

• “Managed Hosting Services”, data center operations staff


Related Work

LiveRAC

Discussion

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Network Devices

• Network device: any electronic device that connects to a computer network

• Most network devices can be monitored

• Network Operation Centre (NOC): facility for monitoring large numbers of network devices


Related Work

LiveRAC

Discussion

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Monitored Data

• Most data collected from network devices is time-series data – time stamp and value

• Two types of time-series objects collected:– performance metrics

• [ 10 AUG 2006 9:52:37, CPU, 95% ]

– alarm data• [ 10 AUG 2006 9:52:37, MAJOR, “HIGH TEMP” ]

• Key difference for visualization:– performance metrics quantitative– alarms categorical


Related Work

LiveRAC

Discussion

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Detail Overload


Related Work

LiveRAC

Discussion

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RRDTool

• Database system with statistical graphics

• Monitors individual hosts– 6-inch view

• Basis for many related applications

http://oss.oetiker.ch/rrdtool/


Related Work

LiveRAC

Discussion

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Ganglia

• Cluster monitoring tool, uses RRDtool back-end

• Provides aggregate charts, 100-mile high overview

http://ganglia.sourceforge.net/


Related Work

LiveRAC

Discussion

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OpenNMS

• Aggregates SNMP data from multiple hosts, uses RRDtool back-end

• Alarm management• 1000-mile high overviews

http://opennms.org


Related Work

LiveRAC

Discussion

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• Scale to large, dynamic datasets – thousands of devices– dozens of data channels– multiple time scales

• Three levels of activity

Visualization Solution Requirements


Related Work

LiveRAC

DiscussionActive region Time scale # of items

Total database Days to years Billions

In memory Several seconds Millions

On screen Sub-second ThousandsLiveRAC

Back-end DB

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Our solution: LiveRAC


Related Work

LiveRAC

Discussion

•Interactive user-directed exploration of overview & detail

–Second to sub-second timeframe

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Our solution: LiveRAC

• Reorderable matrix– rows of network devices– columns of time-series objects

• Semantic zooming and aggregation for cells– large cells show time-series charts– compact representations in reduced

areas– aggregate spatial representation shown

in highly compressed regions


Related Work

LiveRAC

Discussion

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Visual Introduction to LiveRAC

QuickTime™ and aTechSmith EnSharpen decompressor

are needed to see this picture.

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Research Contributions

• A scalable visualization system called LiveRAC using real world data

• Algorithms and code to support dynamic and reorderable data elements in accordion drawing

• Infrastructure and algorithms for user-definable semantic zoom in accordion drawing


Related Work

LiveRAC

Discussion

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Information Visualization

• Human visual channel is highest-bandwidth perceptual system [Norretranders, 1999]

• Information visualization: field of study whose object is to aid cognition through the graphic representation of abstract data– displays relevant information graphically to

assist in memory tasks– supports data exploration through direct

interaction – assists in pattern finding through the display

of overview and detail, search, and user-directed reordering

Motivation

Related Work InfoVis Time-series Statistical Graphics Reorderable Vis Semantic Zoom Accordion Drawing SWIFT

LiveRAC

Discussion

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Information Visualization Techniques - A few examples

Small multiples Pre-attentivevisual cues

Motivation


LiveRAC

Discussion

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Information Visualization Techniques - A few examples

Small multiples Pre-attentivevisual cues

Motivation


LiveRAC

Discussion

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Time-Series Data

• Extensively explored in information visualization

• Many techniques cluster similar time-series data points together, e.g. work by van Wijk et al.

• LiveRAC provides many small-multiples views of time-series data

J. van Wijk, E. Van Selow. Cluster and calendar based visualization of time series data. Proc. IEEE Symposium on Information Visualization, pp 4.

J. Lin, E. Keogh, S. Lonardi, J. Lankford, D. Nystrom. Visually mining and monitoring massive time series. SIGKDD 2004, 460-469.

Motivation


LiveRAC

Discussion

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Statistical Graphics

• Statistical graphics:

– projection of abstract shapes representing observed quantitative data

– in use for centuries in various forms [Beniger, 1978]

– used throughout science and industry in commonly available tools like Excel™

Motivation


LiveRAC

Discussion

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Reorderable Visualizations

• Supports user-directed pattern finding when patterns are not known a priori [Bertin, 1981]

– allows users to group and sort data to identify or confirm patterns– supports an intuition-driven model for interacting with data

• LiveRAC adds reordering to accordion drawing

E.Mäkinen, H. Siirtola. Reordering the Reorderable Matrix as an Algorithmic Problem. Theory and Application of Diagrams. 2000, 453–467.

Motivation


LiveRAC

Discussion

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Semantic zooming

• Semantic zooming represents data differently at different zoom levels [Perlin, 1993]

• Optimize representation for available space

• Allow multiple levels of detail

R. Bade, S. Schlechtweg, S. Miksch. Connecting Time-Oriented Data and Information to a Coherent Interactive Visualization. CHI 2004, pp 105-112.

Motivation


LiveRAC

Discussion

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Accordion Drawing

• Information visualization technique

• Stretch-and-squish navigation– enlarge some areas while

retaining surrounding context

• Guaranteed visibility– important landmarks remain

visible

Munzner, Guimbretiere, Tasiran, Zhou, and Zhang. TreeJuxtaposer: Scalable Tree Comparison using Focus+Context with Guaranteed Visibility. SIGGRAPH 2003, 453-462.

Motivation


LiveRAC

Discussion

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PowerSetViewer

• Dynamic accordion drawing– insert and remove data at run time

• Limitations:– domain-specific solution

– not user-reorderable

Qiang Kong. Visual mining of power sets with large alphabets. UBC CS, Master’s thesis, May 2006.

Motivation


LiveRAC

Discussion

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PRISAD

• Introduces the concept of per-frame partitioning of data into screen-visible regions

• Reduces n data nodes to p where p is bounded by the number of display pixels

• Provides an API for developing accordion drawing applications

• Limitation: – static data structures

Slack, Hildebrand, and Munzner.Partitioned Rendering Infrastructure for Scalable Accordion Drawing (Extended Version) Information Visualization, 5(2), p. 137-151, 2006.

Motivation


LiveRAC

Discussion

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SWIFT

• SWIFT is a set of data storage, aggregation and visualization tools that integrate multiple data sources [Koutsofios, 1999]

• Developed at AT&T Labs, fully deployed in a production role

• Data sources include SNMP, intrusion detection systems, Windows system monitors, and custom written daemons

• Highly scalable• Optimized for streaming data

Motivation


LiveRAC

Discussion

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SWIFT Front-ends

Koutsofios, North, Truscott, and Keim. Visualizing large-scale telecommunication networks and services.IEEE Visualization 1999, 457-461

• Existing views:– Geographic views– Node-link diagrams– Raw data text

• Limitations: – Cannot compare

between large numbers of time-series objects

• LiveRAC: reorderable matrix visualization for SWIFT

Motivation


LiveRAC

Discussion

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LiveRAC Architecture

Motivation

Related Work

LiveRAC Architecture Semantic Zoom Reordering Playback

Discussion

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LiveRAC Architecture - PRISAD• Uses and enhances the PRISAD

accordion drawing API– PRISAD provides:

• well-established scalability• pixel-bounded rendering performance• extensive infrastructure

• Contributions: – fully dynamic generic data structures

• add, remove and reorder

Motivation

Related Work


Discussion

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LiveRAC Architecture - SWIFT• SWIFT back-end provides:

– unified interface for multiple collected data sources

– temporal aggregation

• Separate rendering and data-service threads allow interaction during data retrieval

• Time window can be selected to display historical or live data

Motivation

Related Work


Discussion

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Semantic Zooming

• CPU usage at several levels of detail Motivation

Related Work


Discussion

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Variable LOD Charts

• jGLChartUtil: High-performance OpenGL statistical graphics library

• Several data representations: – line charts– scatter charts– bar charts– histograms– sparklines

Motivation

Related Work


Discussion

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Variable LOD Charts

• Optimizes chart representation for best use of available space– scales fonts– ‘best fit’ axis

labeling– modifies chart grid

• Representation and size selected by application, not library

Motivation

Related Work


Discussion

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Defining Semantic Zoom Levels

• Bundle: specification of how to draw cells in a column– defines graphical representation at

different cell sizes– can contain single or multiple time-

series objects– pre-defined in configuration file

• Generic bundles provide defaults

Motivation

Related Work


Discussion

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Aggregation

• At lowest level of detail, multiple rows are aggregated to single visual representation– convey single useful value for large

quantity of information

• Aggregated representation is colored box with varying saturation– for alarms, color indicates highest

severity alarm – gray indicates metric data– saturation is a function of density

Motivation

Related Work


Discussion

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LiveRAC Semantic Zooming

Semantic Zooming Video



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Reordering

• LiveRAC allows rows and columns to be reordered– rows sorted by device name, or by customer

identifier and sub-sorted by device name, or ordered arbitrarily

– columns ordered arbitrarily, locations specified by user

• Required significant extensions to PRISAD to provide dynamic data structures

Motivation

Related Work


Discussion

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Reordering Rows and Columns - Visual Example

Motivation

Related Work

LiveRAC Overview Architecture Dynamic Structure Reordering Aggregation Semantic Zoom Performance

Discussion

Future Work

ConclusionReordering Video



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LiveRAC Data Playback

• Playback consists of advancing the time window by a configurable duration at regular intervals

• Historical data can be viewed faster than real-time

• Current data can be viewed in real-time

Motivation

Related Work


Discussion

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Discussion & Results

• LiveRAC was deployed using real world data at AT&T Labs

• LiveRAC proved to be scalable– interactive frame rates

• 4000+ device rows• dozens of performance/alarm columns

Motivation

Related Work

LiveRAC

Discussion Results Future Work Conclusion

Active region Time scale # of items

Total database 3 Months Millions

In memory <5 seconds 80,000

On screen 10-15 FPS Thousands

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LiveRAC Case Study

Case study video



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User Feedback

• Demo feedback was positive:– users familiar with the old system were able to

quickly recognize customers based on familiarity with the data

– LiveRAC identified by domain managers as possible next-generation tool for data center usage

– users had numerous suggestions for the system, a good indication that they were excited by the possibilities

Motivation

Related Work

LiveRAC


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Future Work

• Interaction:– field study LiveRAC in real-world environment– support alternative navigation options

• auto-expanding search region

• hot-keying groups of devices or metrics

– expand data representation library

• Data processing:– computational correlation of alarm and metric

data

• Performance:– lazy evaluation during reordering

Motivation

Related Work

LiveRAC


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Conclusion

• Contributions: – working system for interactive

visualization of large real-world time-series data sets

– algorithms for reorderable accordion drawing

– infrastructure for semantic zoom in accordion drawing

Motivation

Related Work

LiveRAC


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Targeted User Activities

• Overview: – Situational awareness– Critical alarm notification

• Detail:– Incident investigation – Capacity planning – Trend analysis

Motivation

Related Work

LiveRAC Architecture Visual Encoding Aggregation Semantic Zoom Reordering Playback

Discussion

Future Work

Conclusion

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Monitoring Large-scale Systems: A Difficult Problem

• Many disparate data sources– Different platforms– Different protocols– Different services– Lack of integrated solutions

• Lack of context in standard tools

• Shortage of integrated visualization solutions

Motivation

Related Work


Discussion

Future Work

Conclusion

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SWIFT Limitations

• Visualizations for SWIFT only provide node-link and geographic views

• These views are good for mapping physical relationships

Motivation

Related Work


Discussion

Future Work

Conclusion

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SWIFT Architecture

Motivation

Related Work


Discussion

Future Work

Conclusion

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LiveRAC handles rows and columns differently

• Large number of rows in a typical data view (thousands)

• Comparatively small number of columns (dozens)

Motivation

Related Work


Discussion

Future Work

Conclusion

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Performance Requirements

• LiveRAC must maintain interactive frame rates while modifications to the grid are taking place

• We need to draw guaranteed visible zones first to provide landmarks

• The system needs to scale to thousands of devices, and tens of categories of monitored alarms & metrics

• The system must support a large number of data points for alarms & metrics

Motivation

Related Work


Discussion

Future Work

Conclusion

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Split Line Performance

Split Line Tree Operation Complexity

Find split line bounds O(1) (cached)

Find split line by index O(log n)

Find index of node O(log n)

Insert split line O(9 log n)

Delete split line O(12 log n)

Motivation

Related Work


Discussion

Future Work

Conclusion

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Charting Performance

• System: 3Ghz Pentium-IV

• Chart: 3 data series, 100 points each series– First draw: ~50ms– Subsequent redraws after

modification: <5ms– Redraw from cached OpenGL

display list: <1ms

Motivation

Related Work


Discussion

Future Work

Conclusion

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Statistical Graphics

Line chart Bar chart

Scatter plot Pie chart

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LiveRAC Visual Encoding• Matrix view

– rows of devices– columns of metrics and alarms:

time-series objects– a cell contains a representation for

a set of values of any time-series object

• at highest density, cells are colored boxes

• at lower densities cells can contain text, or graphical representations

Motivation

Related Work


Discussion

Future Work

Conclusion

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Accordion Drawing Split Line Structure

Slack. Partitioned Rendering Infrastructure For Stable Accordion NavigationM.Sc. Thesis

Split lines

Motivation

Related Work

LiveRAC Overview Architecture Dynamic Structure Reordering Aggregation Semantic Zoom

Future Work

Conclusion

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Accordion Drawing Split Line Structure

Slack. Partitioned Rendering Infrastructure For Stable Accordion NavigationM.Sc. Thesis

Motivation

Related Work

LiveRAC Overview Architecture Dynamic Structure Reordering Aggregation Semantic Zoom

Future Work

Conclusion

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Static Split Lines

• Previous accordion drawing implementations used static, ordered lists of split lines

• Insert, remove and reorder operations were O(n)

0 1 2 3 4 5 6 7

Motivation

Related Work

LiveRAC Overview Architecture Dynamic Structure

Reordering Aggregation Semantic Zoom Performance

Discussion

Future Work

Conclusion

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Dynamic Split Lines

• Dynamic split lines are required for maintaining interactive frame rates while adding/removing rows or columns to the matrix

• Client-server streaming architecture implies that new devices, alarms and metrics will be a common occurrence

• Dynamic split lines support reordering of data

• In the future, direct user modification of the data set can be supported

Motivation

Related Work



Discussion

Future Work

Conclusion

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Dynamic Split Line Requirements• Requirements:

– Worst case logarithmic insert and remove operations

– O(log n) worst case path to any node from the root

– Linear scalability in memory usage– Support for arbitrary ordering with

enumeration

• Can we use a red-black tree? – Red-black trees address the first three

requirements– How can we avoid re-enumeration of keys if

we allow nodes to be manipulated arbitrarily?

Motivation

Related Work



Discussion

Future Work

Conclusion

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Dynamic Split Lines: Solution

Solution: Maintain sub-tree sizes

Find: node index 3 3 < (3+1), descend left

3 > (1 + 1), descend right

found index 3

Motivation

Related Work



Discussion

Future Work

Conclusion

1 2 3 4 5

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Reordering Rows and Columns: Implementation• For rows, we can swap pointers from split

lines to devices without changing the split line structure– O(1)– Preserves layout topology

• For columns, we use a global ordering list, mapping a bundle/metric/alarm name to an index number, changes to this list affects rendering of all columns– A hash map is maintained to back map column

name to index numbers for O(1) lookups during rendering, this must also be fixed during a reorder

– Swapping columns is O(c), where c is the number of columns, typically < 100

Motivation

Related Work


Discussion

Future Work

Conclusion

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Documents

Live Reorderable Accordion Drawing (LiveRAC) Peter McLachlan MSc Thesis Presentation September, 2006