visual analytics meets process mining: challenges and...

Visual Analytics Meets Process Mining: Challenges and OpportunitiesTheresia Gschwandtner and Silvia Miksch

Information Overload[Howson, 2008]

[Aigner - presentation 2015]

Image: Brett Ryder (The Economist, 2010)

Information Overload

Electronic health records

Real-time sensors

Communication logs

Transactions

Motivation: An Information Gap

Somewhere in the data there is valuable information.

S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S SS S S S S S S S



[Card et al. 1999]

One Approach: Machine Learning & Data Mining

Tap the power of computersStatistical analysisReports

$




[Card et al. 1999]

well grounded field

Descriptive statistics

Confirmatory data analysis

main challenge: find a model

Bayesian statistics

Exploratory data analysis [Tukey, 1977]

visual exploration methods

insights about what the data looks like find a model

Statistics

[Fekete et al, 2008]

Another Approach: Visualization

Tap the power of human perceptionComplex view of the dataInteractive controls to explore data and see patterns




[Card et al. 1999]

WHY VISUALIZATION?

Anscombe's Quartet

[http://en.wikipedia.org/wiki/Anscombe's_quartet; Anscombe, 1973]

Goal: automatically find interesting facts in the data

Hertzsprung Russel Diagram

[Fekete et al., 2008]

X-axis : temperature of stars and the

Y-axis: their magnitude.

Let's play a game: the game of 15. The ‘pieces’ for the game are the nine digits - 1, 2, 3, 4, 5, 6, 7, 8, 9. Each player takes a digit in turn. Once a digit is taken, it cannot be used by the other player. The first player to get three digits that sum to 15 wins.

Player A takes 8Player B takes 2 Player A takes 4 Player B takes 3 Player A takes 5

Question 1: Suppose you are now to step in and play for B. What move would you make?

Game of 15

[Norman, 1993]

Tic-Tac-Toe

xox o

x

[Norman, 1993]

Game of 15

[Norman, 1993]

[Few, 2006]

high bandwidth fast, parallel pattern recognition pre-attentive expand human working memory

“The eye... the window of the soul, is the principal means by which the central sense can most completely and abundantly appreciate the infinite works of nature.”

Leonardo da Vinci (1452 – 1519)

Human Vision

EXTERNAL COGNITION

»The power of the unaided mind is highly overrated. […] How have we increased memory, thought, andreasoning? By the invention of external aids: It isthings that make us smart.«

[Norman, 1993, p. 43]

External Cognition

34x 72

0

20

40

60

80

100

120

Mental Paper & Pencil

Tim

e to

Mul

tiply

(sec

)

6823802448

2

1

[Card et al., 1999]

Visualization Success Story

Mystery: What is causing a cholera epidemic in London in 1854?

[Tufte, 1997]adapted from [Hearst , 2004]

London 1854

Analytical Reasoning Process

[Thomas & Cook 2005]

Why Visualization?

Increasing cognitive resources

such as by using a visual resource to expand human working memory

Reducing search

such as by representing a large amount of data in a small space

Enhancing the recognition of patterns

such as when information is organized in space by its time relationships

Supporting the easy perceptual inference of relationships

that are otherwise more difficult to induce

Perceptual monitoring of a large number of potential events

Providing a manipulable medium

that, unlike static diagrams, enables the exploration of a space of parameter values

[Card et al., 1999]

Method

[Aigner - presentation 2015]

MACHINE LEARNING & DATA MINING

Machine Learning & Data Mining

[ Aigner et al., 2011]

Visualization vs. Computation

[http://infoproc.blogspot.co.at/2013/06/spy-vs-spy.html]

VISUAL ANALYTICSCOMBINATION OF VISUAL AND ANALYTICA METHODS

[http://infoproc.blogspot.co.at/2013/06/spy-vs-spy.html]

Screen resolution: 1600 * 900 = 1.440.000Yearly measurements of water level in Low.Austria:1 5.256.000Number of cellular phones in Austria (2005):2 8.160.000Transmitted emails every hours (world-wide):3 35.388.000

Whole data often not presentableStatistics, Machine Learning & Data Mining

Most important data and information

Results

Huge Amounts of Data vs. Screen Resolution

1 ... Amt der NÖ Landesregierung, Abt. WA5 - Hydrologie, http://www.noel.gv.at/SERVICE/WA/WA5/htm/wnd.htm2 ... CIA Factbook, https://www.cia.gov/cia/publications/factbook/3 ... How Much Information?, UC Berkeley, http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/

Visual Analytics

“Visual analytics isthe science of analytical reasoning

facilitated by interactive visual interfaces.”

[Thomas and Cook, 2005]

Visual Analytics – Process

[Keim, et al. 2008]

Visual Analytics

Data Mining vs InfoVis Analytic Process

[Bertini and Lalanne, 2010]

INTERACTIVITY

„Interaction between human and computer is at the heart ofmodern information visualization and for a single overridingreason: the enormous benefit that can accrue from beingable to change one's view of a corpus of data. […]

Those who wish to acquire insight must explore, interactively subsets of that corpus to find their way towardsthe view that triggers an 'a ha!' experience.“

[Spence, 2007]

InteractivityPast

Only passive observationsRepresentation not changeable“one fits all”

TodayActive examination with visualizationsDynamically adaptable and modifiable

→ Different users, goals, and data

Interactivity

[http://www.sxc.hu]

[http://www.google.com]

[http://www.google.com]

Interaction Taxonomy

[Yi et al., 2007] and [Raskin, 2000]

IndicateSelectExploreReconfigureEncodeAbstract/ElaborateFilterConnectActivateModify

InfoScope

Indicate: show me where I am pointing at

[Brodbeck and Girardin, 2003]

Select: mark something as interesting


InfoScope

Explore: show me something else

Overview + Detail,Zooming + Panning

[Aigner and Miksch, 2006]

[Bade et al., 2004]

Gravi++

Reconfigure: show me a different arrangement

[Hinum et al., 2006]

Encode: show me a different representation

Multiple Views: Brushing & Linking

[Baldonado et al., 2000]

Displaying detailed information about data case(s)

Abstract/Elaborate: show me more or less detail

[Weishapl and Aigner, 2007]

Filter: show me something conditionally

[Shneiderman, 1994 ff]

Connect: show me related items


Activate: trigger actions

[Rind et al., 2010]

VisuExplore

generate

delete

move

transform

copy

Modify: manipulate elements

Value of Interaction

Reduction of distanceReducing the gulfs of execution and evaluation

[Norman, 1988]

Value of Interaction

Reduction of distanceReducing the gulfs of execution and evaluation

Reduction of cognitive load Cognitive offloading, external anchoring, information foraging

Higher engagement Feeling of being in control / first person-ness

Higher expressiveness of the user interface language Richer possibilities for input and output

VISUAL APROACHES FORPROCESS MINING

C9: Combining Process Mining With Other Types of Analysis

„By combining automated process mining techniques with interactive visual analytics, it is possible to extract more insights from event data.”

[van der Aalst et al., 2011]

Process Mining Tasks

[van der Aalst et al., 2011]

Process Discovery

[Günther and van der Aalst, 2007]

Fuzzy Miner

Outflow: aggregated temporal event data

Process Discovery

[Wongsuphasawa, 2011]

Process Discovery

Glyph for a reoccurring graph pattern [Maguire et al., 2013]

Process Discovery

Different views for a process model: logical and time based

[Hipp et al., 2012]

Process Discovery[Vrotsou et al., 2009]

ActiviTree

Process Conformance

[Adriansyahet et al., 2011]

Process Conformance

[http://www.processmining.org/online/conformance_checker]

Process Enhancement [Rozinat, 2009]

Disco

ProM Performance Analysis Plugin

Color: waiting time

Labels: transition probabilities

Process Enhancement

[http://www.processmining.org]

Process Enhancement

[van der Aalst, 2011]

Potential of Visual Analytics

Visualizations, interactions & mining techniques to

Explore the event log data beforehand

Understand data

Identify interesting behaviour

Investigate and finetune process model

EXAMPLES

Guideline Conformance

Actions scheduled by Clinical Practice Guideline vs. actions executed by caregiver

Valid Action:

Applied correctly (conditions fulfilled)

Invalid Action:

Applied by caregiver, but not according to guideline

Missing Action/Missing Action Interval:

Missing in the execution (although scheduled)


[Bodesinsky et al., 2013]


Temporal view of executed actions

Valid Actions as diamond

Invalid Actions marked with “X”

Time spans (missing action execution): connected upper and lower parallel bars



Highlighting of time points

Vertical line for quick identification


Plan Strips[Seyfang et al., 2012]

EventExplorer

books – sports & wellnesssportssportswear


EventExplorer

viewrelated itembuy


EventExplorer


EventExplorer: Temporal Scale


EventExplorer


EventExplorer: Pattern Mining

Automatic

Select minimum frequency and length of patterns of interest

Overview of occuring patterns

Interactive

Select sequence of interest and count re-occurences

Search for a specific pattern

Use of wildcards

Other FeaturesGrouping events

Grouping similar events

E.g., ‚indian restaurant‘ and ‚vietnamese restaurant‘ into ‚exoticrestaurant‘

Sorting sessions

Time

Pattern count

Sequence length

Display other attributes

Filtering

Open Issues

Visualization

Scalability and aggregation

Better visual pattern detection

Interactions

Ordering sessions by similarity

Different vertical alignments (sequential and temporal)

Pattern mining

Fuzzy pattern mining

Pattern mining with temporal distances

FURTHER CHALLENGES

Data quality and uncertainty

Complex and less structured logs

Case heterogeneity

Event granularity

Concept drift

Missing, incorrect, imprecise, uncertain, irrelevant data, noise

van der Aalst et al.: finding, merging, and cleaning event data

[Bose et al.,2013] [van der Aalst et al., 2011]

Time has a Complex Structure

Modelling Time

[Aigner et al.,2011]

Callenges – Summary

Intertwining Process Mining with Visual Analytics

Interaction to properly support process discovery and enhancement

Scalable analysis from single event sequences to multiple event logs

Data quality and uncertainty

Complexity of time-oriented data

Evaluation

Thanks to…

Wolfgang Aigner,Peter Bodesisnsky,Paolo Federico,Silvia Miksch,Wil van der Aalst,and many more

...for I reused and adapted some of their slides, pictures, and ideas

visual analytics meets process mining: challenges and...

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