Interactive Poster: Visual Mining of Business Process Data

Interactive Poster: Visual Mining of Business Process Data

MingC.Hao,DanielA. UmeshwarDayal, Schneidewind Hewlett ResearchLaboratories

1.

^{analyses,partialmatching}techniques,aswellascluster,

andclassificationanalysis.

Business 'datais largeand mast a triple-parameter symmetric circular graph layout to

to be directly Usually the represent the source, and destination

operationsconsistofmany and and attributes

every data may take a path the a process flow matrix to link multiple circulargraphs

In weshowa processschema. _{together showthe important ofthebusiness}

this

processisaverysimple realistic processoperations.

Inthecreditcard analysisexampleshowninFigure we business _atleast10times

mayuseaclusteringalgorithmtoidentifycustomerpurchase getpurchase

data

fraud issuer and

sales type; and present them in to the fraud amountasshowninFigure2.

Our

_techniquehasbeen_{designedto forlargevolumesof} complex business process data. The automatic analysis determines the importantrelationships and the visualization shows the detected relationships. contrast to Parallel Coordinatedisplays[I], werestrictthevisualizationtothree in one circulardisplayand multipledisplaysare linkedtoshowmorethan3parameters.Inaddition,weadapta newlayouttogivemoreweighttoimportantdatavalues.

Figure AFraudAnalysisProcess 3.TheWeightedCircularGraphLayout

In order to give important informationmore attention, we.

&fineaweightfunctionforthenodes.Thisweightfunction allocatesmorespaceforimportantnodesandisrealizedbya weightedradian asshowninFigure3.Theweight ofanode dependsonafourthattributeA.

We&finetheweightbytheratioof attribute Aandthe s u mofallattributes

Our Approach

In this poster, we introduce a new interactivevisualization techniquetoreducedatacomplexitybyabstractingthemost critical which influencebusinessoperations.

Our_{techniqueusesthreebasic}components:

abusinessparametercorrelationmatrixtodetermine the

are a

(on

red, andburgundy;lessgreen)

of

thatthe of is

This might

Figure

2:

Fraud Business Analysis

First publ. in: InfoVis 2004, IEEE Symposium on Information Visualization , Austin, Texas, USA , May/jun, 2004

Konstanzer Online-Publikations-System (KOPS) URL: http://www.ub.uni-konstanz.de/kops/volltexte/2008/6959/

URN: http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-69591

4.2 IT Service I Analysis

4. Applications

3: processflows,-andthecause_ofthe

5.

InITserviceanalysis,analysts interestedinthe_causeof

unfulfilled or the impact of on

Many research efforts have focused on how to thebusiness data,asloggedby services, intovaluable Inthisapplication,the

LevelObjectives)statusindicatestheprobabilityofaservice becoming (violated),

with

0beingthemostprobable and⁴beingtheleastprobable. data contains^10,061

servicetransactionswith 50 most

criticalparametersare time,searchtime,month, day, andhour. showsITsearchtimedistribution,

Inthis wereducethecomplexityofbusinessprocess analysis by abstractingthe most critical which influencebusinessoperations.

We

them_inmultiple circulargraphs.Ourreal world applications show significant of techniques in business processanalysis.

A., B.: a Toolfor

Multi-Dimemiom1 CA,1990.

showa ofIT

4Ais thethreecritical andSearchtime)fromthecorrelation and are

correlatedas bynearlyparallellinesexceptsomeoutliers low to Lintswiththe

arecoloredred. 4hasthehighestsearchtime(morered,pink,burgundy)m4A.

4E

-

4H the sequenceofsearchtime (month,day,andhour)

4E a circular toshowthetimedependency.The in arelinkedto bytheflowprocessmatrix. analyst

on ₄in _{4B 4F}aregenerated,showingthat 4 with times, seenbytheblue

theanalystclickson 0in 4Cand4 0aregenerated,showingthat 0associateswithlower times,as bythe and greencolors.

4Dand4Hshowthecauseofoutliers

4D 4Haregeneratedwhentheanalystclicksonday2 searchtime-morered inFigure4E.All are out.Theanalyst onthe linesendnodes(connected day21tomonth9,and -14)todrill tothe record

as availability.

Figure4 ServiceProcess