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Kiem Verbindt Software Architectuur11 Nov 2018 17:41:04
Purpose
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Views
MappingNo viewpoint
DocumentationThis view describes how based upon events, a User journey map can be constructedto be interpreted by a User journey manager.
Our story starts with the eventlog. How this log came to be is not the concern of thisarchitecture. The log contains different events and is possibly enhanced torepresent STEM aspects.
S: SentimentT: TopicE: EmotionM: MotivationOn top of that extra events can be deduced from text.
The STEM enhanced eventlog in turn is used to build a model that can be viewed.
ElementsElement TypeContext data Data ObjectEvent Data ObjectEvent data Data ObjectProcess miner Application ComponentRaw user event log Data ObjectSTEM enhanced user eventlog Data ObjectSTEM eventlog classifier Application ServiceText data Data ObjectUser journey manager Business ActorUser journey map Business ObjectUser story miner Application ServiceUser story model Data Object
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ParameterizationNo viewpoint
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DocumentationTo enable Mapping and Prediction the STEM eventlog classifier needs classificationmodels. These models describe how to classify a text for one of the aspects.
The models are built by using text mining. For this to succeed, annotated text isneeded. This can be created manually by adding annotations to domain relevanttext quotations.
ElementsElement TypeAccount data Business ObjectContext data Data ObjectEvent Data ObjectEvent data Data ObjectMail conversation Business ObjectOpen data Business ObjectRaw user event log Data ObjectSTEM annotated text Data ObjectSTEM Annotation Business ServiceSTEM classification models Data ObjectSTEM event classifiers Application ComponentSTEM eventlog classifier Application ServiceText data Data ObjectText mining algorithm Application ComponentText mining service Application ServiceUser web trail Business Object
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PredictionNo viewpoint
DocumentationPrediction is used to inform agents in the organization real time about specific userstory cases that are not going as intended or to even suggest ways to improve theremainder of the case.
For a large part prediction builds on functionality also used for Mapping the userjourney. You can see this on the left. The same User story model is created but fromthere something else follows.
The User story model itself is used to build rules that enable to predict userbehavior based on past behavior. This can be done directly based on the User storymodel which results in a User prediction model. It is also possible to enhance theUser story model manually so that a distinction between different persona is made.This results in a Persona prediction model.
Both models can be used to predict user behavior based on already recordedbehavior in the form of a user story case log. Based on these predictionsintervention alerts and intervention suggestions can be created.
ElementsElement TypeAction Business ProcessContext data Data ObjectEvent Data ObjectEvent data Data ObjectIntervention alert Business ObjectIntervention suggestion Business ObjectPersona coupling Application ServicePersona enriched User story model Data ObjectPersona Prediction Classifier Application ServicePersona prediction model Data Object
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Element TypeProcess miner Application ComponentRaw user event log Data ObjectRaw user story case log Data ObjectRule builder Application ComponentSTEM annotated text Data ObjectSTEM classification models Data ObjectSTEM enhanced user eventlog Data ObjectSTEM enhanced user story case log Data ObjectSTEM event classifiers Application ComponentSTEM eventlog classifier Application ServiceSTEM user case log classifier Application ServiceText data Data ObjectText mining algorithm Application ComponentText mining service Application ServiceUser advisor Business ActorUser interaction Business ProcessUser prediction Data ObjectUser prediction classifier Application ServiceUser prediction model Data ObjectUser predictor Application ServiceUser relations manager Business ActorUser story miner Application ServiceUser story model Data Object
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Account dataType Business Object
Information about a specific user or account that can be useful to interpret actionsof the user or agents of the organization.
ActionType Business Process
Actions taken by the User advisor.
Intervention alertType Business Object
An intervention alert, alerts a User relations manager so that he can intervene.
Intervention suggestionType Business Object
Suggestion to the User advisor on how to act towards the user.
Mail conversationType Business Object
Event log where successive mails are events.
Open dataType Business Object
Event data like tweeds on twitter or other information that might explain behaviorof users like the weather on a specific day and location.
STEM AnnotationType Business Service
Annotating text with STEM specific annotations.
S: SentimentT: TopicE: EmotionM: Motivation
User advisorType Business Actor
Business Layer
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User interactionType Business Process
Users interacting through touch points creates events.
User journey managerType Business Actor
Manages the user journey for the users
User journey mapType Business Object
Visualization of the User story model. Of course multiple visualizations of the samemodel are possible because of the multi dimensionality of the User story model.
User relations managerType Business Actor
User web trailType Business Object
Event log that shows relevant click events from the user.
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Context dataType Data Object
Attributes data that is relevant to the event or to a whole case but is not directlyrelated to the event itself.
EventType Data Object
Refers to one activity instance related to one process instance (case) and onetimestamp; event refers to a case, an activity instance, and a point in time; eventshave attributes; events have a name (the classifier of the event), which default isthe activity it refers to, but that is not mandatory;
Event dataType Data Object
Attributes structured data directly related to the event
Persona couplingType Application Service
Using the user story model, specific paths are associated with specific persona.
Persona enriched User story modelType Data Object
A User Story Model where certain paths are annotated with persona information.
Persona Prediction ClassifierType Application Service
A classifier that builds rules that enable to detect what kind of persona is involvedin a specific user story.
Persona prediction modelType Data Object
A set of rules that enable automatic prediction of the persona involved in a specificuser story.
Process minerType Application Component
Process mining solutions like ProM or BupaR or their commercial counterparts.
Application Layer
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Raw user event logType Data Object
The raw user event log contains all data relating to events in the user stories thatwas obtained from various sources. Creating the event log is not part of thisarchitecture because this is specific to the organizations providing the log.
Raw user story case logType Data Object
The Raw user story case log contains all data relating to events in a specific userstory that was obtained from various sources. Creating the Raw user story case logis not part of this architecture because this is specific to the organizations providingthe log.
Rule builderType Application Component
STEM annotated textType Data Object
To create text classification models annotated text is used.
STEM classification modelsType Data Object
There are different types of classification hat are used to enhance the eventlog withadditional knowledge that can be mined from text:S: SentimentT: TopicE: EmotionM: MotivationOn top of that extra events can be deduced from text.
The STEM event classification models each support specific classifications. Forreadability of the model these are modeled as one.
STEM enhanced user eventlogType Data Object
This eventlog contains the structured information from the Raw user event log andin addition the now structured data obtained from Text data through the use of theSTEM eventlog classifier.
STEM enhanced user story case logType Data Object
This case log contains the structured information from the Raw user story case logand in addition the now structured data obtained from Text data through the use ofthe STEM eventlog classifier.
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STEM event classifiersType Application Component
There are different types of classification hat are used to enhance the eventlog withadditional knowledge that can be mined from text:S: SentimentT: TopicE: EmotionM: MotivationOn top of that extra events can be deduced from text.
The STEM event classifiers each perform specific classifications. For readability ofthe model these different classifiers are modeled as one.
STEM eventlog classifierType Application Service
There are different types of classification hat are used to enhance the eventlog withadditional knowledge that can be mined from text:S: SentimentT: TopicE: EmotionM: MotivationOn top of that extra events can be deduced from text.
The STEM eventlog classifier bundles the functionality of the different underlyingclassifiers.
STEM user case log classifierType Application Service
There are different types of classification hat are used to enhance the eventlog withadditional knowledge that can be mined from text:S: SentimentT: TopicE: EmotionM: MotivationOn top of that extra events can be deduced from text.
The STEM user case log classifier bundles the functionality of the differentunderlying classifiers.
Text dataType Data Object
Text data usually relates directly to a single event and in that way it can be viewedupon as unstructured event data. It can however relate to multiple events and evento events that are not in the raw user eventlog.
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Text mining algorithmType Application Component
Text mining as a broad concept is used for different aspects of STEM classification.Different algorithms will be used but are modeled as one to maintain readability ofthe model. Usually supported learning algorithms are used.
Text mining serviceType Application Service
Text mining as a broad concept is used for different aspects of STEM classification.Different services will be used but are modeled as one to maintain readability of themodel.
User predictionType Data Object
Prediction of the most likely next steps. Based on this an advice or an interventionalert can be given.
User prediction classifierType Application Service
A classifier that builds rules to classify the expected next step from the user.
User prediction modelType Data Object
A set of rules that enable automatic prediction of the next step in a specificcustomer story.
User predictorType Application Service
A prediction algorithm that uses the User prediction model and / or the Personaprediction model to predict the most likely next steps. Based on this an advice or anintervention alert can be given.
User story minerType Application Service
Mines eventlogs and creates models / a model that represent the historic userstories.
User story modelType Data Object
The user story model is a model that "shows" the different user stories as a process.
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Access relationType Access relation
Text mining serviceSTEM classification models
SourceTarget
Access relationType Access relation
Text mining serviceSTEM annotated text
SourceTarget
Realization relationType Realization relation
Text mining algorithmText mining service
SourceTarget
Access relationType Access relation
STEM eventlog classifierRaw user event log
SourceTarget
Access relationType Access relation
STEM eventlog classifierSTEM enhanced user eventlog
SourceTarget
Access relationType Access relation
STEM event classifiersSTEM classification models
SourceTarget
Realization relationType Realization relation
STEM event classifiersSTEM eventlog classifier
SourceTarget
Realization relationType Realization relation
STEM event classifiersSTEM user case log classifier
SourceTarget
Access relationType Access relation
Relations
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STEM user case log classifierRaw user story case log
SourceTarget
Access relationType Access relation
User story minerSTEM enhanced user eventlog
SourceTarget
Realization relationType Realization relation
Process minerUser story miner
SourceTarget
Access relationType Access relation
User story minerUser story model
SourceTarget
Access relationType Access relation
User prediction classifierUser story model
SourceTarget
Composition relationType Composition relation
EventEvent data
SourceTarget
Composition relationType Composition relation
EventText data
SourceTarget
Composition relationType Composition relation
EventContext data
SourceTarget
Realization relationType Realization relation
User story modelUser journey map
SourceTarget
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Access relationType Access relation
User journey managerUser journey map
SourceTarget
Realization relationType Realization relation
Rule builderUser prediction classifier
SourceTarget
Access relationType Access relation
User prediction classifierUser prediction model
SourceTarget
Access relationType Access relation
STEM user case log classifierSTEM enhanced user story case log
SourceTarget
Access relationType Access relation
User predictorUser prediction model
SourceTarget
Access relationType Access relation
User predictorSTEM enhanced user story case log
SourceTarget
Access relationType Access relation
User predictorUser prediction
SourceTarget
Access relationType Access relation
User relations managerIntervention alert
SourceTarget
Access relationType Access relation
User relations managerIntervention suggestion
SourceTarget
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Access relationType Access relation
User advisorIntervention suggestion
SourceTarget
Realization relationType Realization relation
User predictionIntervention alert
SourceTarget
Realization relationType Realization relation
User predictionIntervention suggestion
SourceTarget
Realization relationType Realization relation
Context dataOpen data
SourceTarget
Realization relationType Realization relation
Context dataAccount data
SourceTarget
Realization relationType Realization relation
Text dataMail conversation
SourceTarget
Realization relationType Realization relation
Event dataUser web trail
SourceTarget
Realization relationType Realization relation
Text dataAccount data
SourceTarget
Realization relationType Realization relation
Event dataSource
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Mail conversationTarget
Access relationType Access relation
STEM AnnotationAccount data
SourceTarget
Access relationType Access relation
STEM AnnotationMail conversation
SourceTarget
Access relationType Access relation
STEM AnnotationUser web trail
SourceTarget
Access relationType Access relation
STEM AnnotationSTEM annotated text
SourceTarget
Access relationType Access relation
User interactionEvent
SourceTarget
Realization relationType Realization relation
Rule builderPersona Prediction Classifier
SourceTarget
Access relationType Access relation
User story minerSTEM enhanced user eventlog
SourceTarget
Access relationType Access relation
Persona Prediction ClassifierPersona prediction model
SourceTarget
Access relationType Access relation
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User predictorPersona prediction model
SourceTarget
Serving relationType Serving relation
User advisorAction
SourceTarget
Access relationType Access relation
Persona couplingUser story model
SourceTarget
Access relationType Access relation
Persona couplingPersona enriched User story model
SourceTarget
Access relationType Access relation
Persona Prediction ClassifierPersona enriched User story model
SourceTarget
Composition relationType Composition relation
Raw user event logEvent
SourceTarget
Composition relationType Composition relation
Raw user story case logEvent
SourceTarget
Realization relationType Realization relation
Event dataOpen data
SourceTarget
Comment on intervention suggestionType Access relation
User advisorEvent data
SourceTarget
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Feed backType Association relation
Event dataSTEM annotated text
SourceTarget
Comment on intervention suggestions is used as annotation to improveclassification models. This requires a standardized set of possible replies from theUser advisor.
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