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Spreadsheet Visualisation to Improve End-user Understanding

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Spreadsheet Visualisation to Improve End-user Understanding Daniel Ballinger, Robert Biddle and James Noble School of Mathematical and Computing Sciences – PowerPoint PPT presentation

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Title: Spreadsheet Visualisation to Improve End-user Understanding


1
Spreadsheet Visualisation to Improve End-user
Understanding
  • Daniel Ballinger, Robert Biddle and James Noble
  • School of Mathematical and Computing Sciences
  • Email db, robert, kjx_at_mcs.vuw.ac.nz
  • http//www.mcs.vuw.ac.nz/db/honours.html

2
Motivation
  • Spreadsheets are a common form of end-user
    programming.
  • Unfamiliar spreadsheets can contain daunting
    amounts of information in the layout and
    inter-cell dependencies.
  • Visualisation can be used to aid in end-user
    understanding of spreadsheets.
  • Working outside the spreadsheet application
    allows for greater flexibility in visualisation.
  • We focused on Microsoft Excel due to its large
    market share.

3
Excels Current Visualisation Support Range
Finder
  • Invoked by clicking in a cell and then in the
    formula bar.
  • Components are coloured in the bar and sheet.
  • Allows for visual manipulation.
  • Mainly only useful for spatially close cells.

Range Finder
4
Excels Current Visualisation Support Formula
Auditing Tools
Auditing Tools
  • Invoked using Formula Auditing Toolbar.
  • Trace dependents or precedents.
  • Arrows always point to referenced cell.
  • Users may navigate spatially disjoint cells.
    (semantic navigation)
  • Complicated spreadsheets can create a tangle of
    arrows.

5
Related Work
  • Takeo Igarashi
  • Spreadsheets augment a visible tabular layout
    with invisible formulas.
  • Created visualisations to help reveal the hidden
    dataflow graphs and superficial tabular layouts
    of spreadsheets.
  • Markus Clermont
  • Most end-users are not trained programmers.
  • Many spreadsheets exist beyond being simple
    scratch pads.
  • Raymond Panko
  • Studies of empirical data into spreadsheet
    errors.
  • Found error rates can be disturbingly high.
  • Errors attributed to over confidence and lack of
    formal checking.
  • Margaret Burnett
  • The importance of scalability in visualisations.
  • Forms/3 and an embedded testing methodology.

6
Spreadsheet Application Toolkit
  • Find and store spreadsheets from the Internet.
  • Extract low level structures. E.g. Cell values
    and formulas.
  • Analyse spreadsheet structures. Either individual
    or corpus.
  • Conveying the findings through visualisation.

7
Visualisations
  • Spreadsheet layout
  • Clustering
  • Data Dependency Flow
  • Data Dependency Direction
  • Graph Structure
  • Fisheye view
  • Formula Inspection
  • Corpus Analysis

8
Spreadsheet layout Real-estate Utilisation 2D
  • Understanding layout is an important first step
    in learning about a new spreadsheet.
  • Actual values and formulas are only shown as
    occupied cells.
  • The visualisation layout mimics that of Excel,
    with columns along the top of the x-axis and rows
    running down the y-axis.
  • Cells with a higher occupancy level are coloured
    towards the red end of the colour spectrum.

9
Spreadsheet layout Real-estate Utilisation 3D
  • Occupancy data is projected into 3D to create a
    surface map.
  • Discrete to continuous data transformation helps
    smooth the effects of spikes.
  • Coloured to give a Topographical terrain effect.
  • Full benefit is seen with user interaction.

10
BIRCH Clustering
  • BIRCH clustering partitions records into clusters
    that are similar according to two or more
    attributes.
  • Current visualisations use the Euclidean distance
    between cells as the similarity metric.

11
Data Dependency Flow
  • Visualising just average unit vectors for each
    cell can reduce the visual clutter.
  • 3D can be used to separate vectors that occur at
    different sheet levels.
  • Note the curvature back towards the origin for
    this workbook.

12
Data Dependency Direction
Radar Graph for Outgoing Dependencies
  • Concentrate purely on the directions of data flow
    relative to cells.
  • Angles are sorted into 36 buckets then feed to
    Excel to create the graph.
  • After the four main axis the next significant
    measure occurs between 300 and 360º.

13
Graph Structure
  • Disregarding spatial bounds allows some
    structures to become clearer.

Spring view
Source Data
14
Fisheye view - Focus Context
  • Addresses formula dependencies that span large
    distances or are many cells deep.
  • Trees are warped over a hyperbolic lens to
    achieve both focus in the centre and context.
  • An artificial red root node is introduced to
    connect disjoint trees.

15
Formula Inspection Data Flow
  • Visualising formula components and flow
    direction.
  • Fully trace worksheets in one view.

Basic Referencing Components
16
Formula Inspection - Dependency Types
Row Absolute
  • Excel allows for combinations of relative and
    absolute positioning.
  • Understanding the referencing type is important
    when replicating formula and identifying regular
    patterns.

Fully Absolute
Column Absolute
Relative
17
Corpus Analysis of 259 Workbooks
Spatial Centre
  • Demonstrations of visualisations created from a
    corpus.
  • With this sample corpus the average worksheet
    centre is more column centric.
  • Function utilisation suggests that the logical
    functions, such as IF, actually outnumber simpler
    math functions like SUM.

Number of non-empty Worksheets 227 Number of
empty Worksheets 195 Average Row 1.348 Average
Column 18.098 Max Row 1384 Max Col 82 Total
Occupied cells 55491 Orphans 51570 Root Cells
2105 Leafs 1031 Nodes in Cyclic References
29 Local Formula 108 Family Trees 509 Max Tree
Depth 22 Max Tree Breadth 150
Function Utilisation
18
Summary
  • Spreadsheets are significant examples of end-user
    programming
  • Visualisation can assist end-users in better
    understanding the structure of spreadsheets
  • In particular, the hidden structures created by
    formula
  • Reviewed literature to investigate the
    implications of the hidden structures.
  • Developed a toolkit to externally access the
    spreadsheet structure and generate
    visualisations.
  • Created several sample visualisations to help
    improve end-user understanding.

19
Current and Future Work
  • Detailed user studies, including usability
    evaluations
  • Domain specific visualisations
  • Spreadsheet corpus analysis to find large
    patterns
  • Visualisation scalability to larger more complex
    spreadsheets

http//www.mcs.vuw.ac.nz/db/honours.html
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