Interface Model Elicitation from Textual Scenarios

1 HCIS’2008 – Milan, September 8-9th, 2008, during IFIP World Congress 2008

Interface Model Elicitationfrom Textual Scenarios

Christophe Lemaigre, Josefina Guerrero, Jean Vanderdonckt

Université catholique de Louvain (UCL)Louvain School of Management (LSM)

Information Systems Unit (ISYS)Place des Doyens, 1 – B-1348 Louvain-la-Neuve (Belgium)

http://www.isys.ucl.ac.be/


Introduction and motivations

• Model Elicitation– Consists of

• The identification of model elements• From Textual scenario

– First step of a model-driven engineering process– Selection of several models : user, task, domain, organization,

resource and job• Characterizing the concepts used in the development life cycle

of user interfaces for Worfklow Information Systems


The underlying ontology

• Reduced view

– Task : piece of work (same resource, location, time period)– Organizational unit : physical location, equipped with resources – User stereotype : human being

Organizational Unit Job Task1..* 1..* 1..* 1..*

Task Resource

User Stereotype Material Immaterial

Process Workflow1..*1..*

*0..1 0..1 * 0..1 *

1..*

1..*

1..* 1..*

isOrganizedInto ► isOrderedIn ►

1..* 1..*

Object Method

Manipulates ►

Invokes ►

*

* * *


The underlying ontology

• Expanded view

decompositiontemporal

precondition : event

unaryRelationshipbinaryRelationship

independentConcurrencyconcurrencyWithInformationPassing

orderIndependencedisabling suspendResumeenabling

enablingWithInformationPassing

iterationfiniteIteration

iterationNumber : integeroptional

deterministicChoice undeterministicChoice

1..n

1..n

1..n 1..n

0..n

0..n

1..n

0..n 0..n

software servicesmachine

meansMaterials

hardwareM

immaterial

1..n

1..n

disablingWithInformationPassinginclusiveChoice

uiModelcreationDate : stringschemaVersion : string...

1

1..n

1..n

2..n

1..n

1..n

11

0..n 11..n

11..n

sequential parallelSplit exclusiveChoice simpleMerge multiChoicesynchronization

0..n

0..1

cooperation

1

0..n

0..n

1

1

1

targettargetId : string

sourcesourceId : string

userStereotypeid : stringstereotypeName : stringtaskExperience : stringsystemExperience : stringdeviceExperience : stringtaskMotivation : string

targetProcesstargetProcessId : String...

sourceProcesssourceProcessId : String...

logEntryid : Stringname : StringtimeStamp : Datedescription : StringlogType : String

taskRelationshipid : stringname : string

1..n

1

1..n

1

1..n1 1..n1

organizationalUnitid : Stringname : StringorganizationObjectives : StringgroupType : StringphisicalLocation : StringhierarchyLevel : Stringrelationship : StringorganizationRules : String

agendaid : stringname : string

processOperatorid : Stringname : String

taskResourceid : Stringname : Stringcapability : Stringfeatures : Stringcost : Stringtaskload : Stringavailability : String

taskModel

0..n

1

0..n

1

jobid : Stringname : StringjobSpecifications : StringjobFamily : StringjobGrade : StringjobPrivileges : String

agendaItemidWorkflow : stringidProcess : stringidTask : stringidUserStereotype : stringidOrganizationalUnit : stringstatus : stringdateBegan : datedateDue : datedateDelegation : datedateFinished : datereminder : datecompleted : integerdelegation : stringdateAllocation : date

workflowid : Stringname : String

processModel

taskid : stringname : stringtype : stringfrequency : integerimportance : integerstructurationLevel : integercomplexityLevel : integercriticity : integercentrality : integerterminationValue : stringuserAction : stringtaskItem : stringpreCondition : StringpostCondition : String

1..n1

1..n1

processid : Stringname : Stringfrequency : Integerimportance : Integercategory : Integer

workItemidWorkflow : stringidProcess : stringidTask : stringidTaskResource : stringidOrganizationalUnit : stringstatus : stringdateBegan : datedateDue : datedateAllocation : datedateDelegation : datedateFinished : date

workListid : stringname : string0..n 1

1

0..n


Related work

• Some other tools use model elicitation at some level – U-Tel, ConcurTaskTress, T2T, Garland et al. Brasser &

vanderLinden• Shortcomings :

– Focused on a single model– No attribute elicitation– Result that can hardly be exploited


Methodology and tool support

• We developped an elicitation methodology based on three levels– Manual classification– Dictionary-based classification– Semantic understanding

• And implemented the first and second one in a tool, made of– A text edition part, with syntactic coloration– Trees in which model elements are dispatched


Tool support

• Model Elicitation Tool


Level 1: manual classification

• Definition :– Program user does the elicitation job– Without the help of an automated process

• Method :– Selection of a piece of text from the scenario– Choose the appropriate model and object type



• Tool : elicitation of a task

1 2

3



• Advantages : – Accurate result– Easier to implement than automated elicitation– No need of classification datas

• Inconvenients : – Fastidious for the user– Time costly


Level 2: dictionary-based classification

• Definition :– Underlies on a set of predefined terms that will be automatically

extracted and identified as model objects– Two kinds of dictionaries :

• Generic dictionary, which is domain-independant • Specific dictionary, linked with a definite domain

• Method :– Improved pattern-matching process :

• Based on the recognition of phrases• That are associated with their model definition• Plural forms and conjugation are taken into account (e.g. to

provide // providing)



• Tool : elicitation of jobs using a dictionary



• Advantages : – Processing speed– No human intervention needed

• Inconvenients : – Lack of precision, some elements being poorely classified due to

the fact it is context-independant– No relations between elements (e.g. hierarchy)


Level 3: toward semantic understanding

• Definition :– Try to approximate natural language understanding

• Method :– Using syntactic tagging, semantic tagging and chunk parsing.– Detection of

• Concepts such as task types or attribute types• Relationships between model elements

– No tool support currently



• Concrete example– “An accountant receives taxes complaints, but he is also in

charge of receipts perception”– Model elements :

• Task : receive taxes complaints• Task : charge of receipts perception• Job : accountant• Relation “performed by” between those tasks an the job • Temporal operator : concurrency for the tasks, used by default



• Advantages : – Expressivity, being able to deduce relationships between model

elements– Automatic treatement

• Inconvenients : – Difficult to implement– Natural language understanding is a field of informatics research

that needs a lot of work and improvement


After model elicitation

• Once elicitation job is done, some treatments can be performed– Use of syntactical coloration allowing the user to check its work– Verification of the compliance with some desirable quality

properties– UsiXML export, allowing to use tools like IdealXML or FlowiXML to

edit models and derivate user interfaces


Conclusion and future work

• Methodology and tool support– Combination of three complementary methods– Allowing elicitation of elements from several models and relations

between those elements– Oriented towards user-interfaces generation for workflow

information systems– Implemented in a tool, using Usi-XML standard to export its result

• Future works :– Advanced visualisation (e.g carrousel)– Take into account inter-model relationships– Refine the third level towards a more natural language

understanding


Thank you very much for your attention

For more information and downloading,http://www.isys.ucl.ac.be/bchi

http://www.usixml.orgUser Interface eXtensible Markup Language

http://www.similar.ccEuropean network on Multimodal UIs

Special thanks to all members of the team!

http://www.isys.ucl.ac.be/bchi

http://www.usixml.org/

http://www.similar.cc/

Interface Model Elicitation from Textual Scenarios

Technology

Transcript of Interface Model Elicitation from Textual Scenarios