Evolving Mashup Interfaces using a Distributed Machine Learning and Model Transformation Methodology

Sixth International Workshop on Information Systems in Distributed Environment (ISDE’2015)
Rhodes, Greece, 26–30 October 2015
Evolving Mashup Interfaces using a Distributed
Machine Learning and Model Transformation
Methodology
Antonio Jesús Fernández-García1, Luis Iribarne1, Antonio Corral1
and James Z. Wang2
1 Applied Computing Group, University of Almería, Spain
2 The Pennsylvania State University, USA
Sixth International Workshop on Information Systems
in Distributed Environment (ISDE’2015)
TIN2013-41576-R P10-TIC-6114

Index
•  Context and Motivation
•  Methodology Proposal
•  Case Study
•  Conclusions
•  Future Work
2

3
Background & ContextMain problem: how to store and analyze the user interaction, which is provided,
in many cases, by different kinds of users.
Hot topic on big data & machine
learning in which we are involved
Background, Context and Motivation

4
Concrete Model 1 Concrete Model 2
Abstract Model 1 Abstract Model 2
Rules
Model Transformation

•  Iribarne et al. and Criado et al.
–  component-based architectures are able to compose both dynamically
and autonomously
•  Adjustments are based on a number of adaptation rules which are
defined statically in a rule repository (Design Time)
•  Changes in requirements. Changes in context. New users. New
components.
•  Dynamic adaptive appears to be insufficient
5

•  Dynamic adaptation EVOLVE over time based on
–  Changes in users and components
–  Changes in the information system
–  Changes in the context
6
Discovery of
behavioural
patterns
Create
Prediction
Models
Update Set
of Adaptation
Rules
Methodology Proposal
Machine Learning
Model Transformation
Cloud Computing
Big Data

7
Let’s save it:
-  Operations Performed
-  Workspace Situation
-  Internal System Information
-  Context Information
#1 Distributed Storage of User Behaviour

WorkSpaceServices
Menu
Services
Component
Component Menu
Operations
Mashup UI

Mashup UI
Mashup UI
{Services, Components, Operations}
{Services Menu, WorkSpace, Component Menu}
Services = {Service 1, Service 2, …, Service N}
Components = {Component 1, Component 2, …, Component M}
Component X = {PosX, PosY, Width, Height}
Operations = {Add, Delete, Move, Resize}

10
Information of:
- Users Categories
- Users Subcategories
- Users Sessions
- Users Interactions
- Time of the Day
- How components are used
- When components are
used
- Where components are
used
- How components are
positioned
Context & System Information

11
#2 Creating Distributed Data Views
•  Data views are composed with information
spread in the whole infrastructure
•  The intented objective is to organise data
•  The structure given to data tries to optimize
the results of the execution of Machine
Learning Experiments

12
#3 Application of Machine Learning Algorithms
•  Study and Analyze Machine Learning Algorithms
and apply them to the “Data Views”.
•  Objective: Generates new rules capable of
improving the user interface adaptation
•  Clustering, Association Learning, Classication,
Similarity Matching, Neural Networks, Bayesian
Networks,Genetic Algorithms…

13
#4 Conversion Rules
HOT = Higher Order Transformation
Inferred rules have their own format. Must be transformed to the format suited
in the interface where they will be applied. Model Transformation

14
#5 Evaluation of Rules
•  Should a new rule added to the repository or not?
•  Conflict Manager: Handle potential conflicts

15

16
ENIA
ENvironmental Information Agent
Case Study

Components Aggrupation
COTSget Component 2: Geo Parks
Component 1: Cuttle Roads
Base Map
Commercial
off-the-shell
+
Widgets

WorkSpaceServices
Menu
Services Component
COTSget Menu
COTSget
Operations
COScore Mashup UI

COScore Enia Mashup UI
{Services, CotsGET, Components, Operations}
{Services Menu, WorkSpace, CotsGET Menu}
Services = {Service 1, Service 2, …, Service N}
CotsGETs= {CotsGET 1, CotsGET 2, …, CotsGET L}
Components = {Component 1, Component 2, …, Component M}
CotsGET X = {PosX, PosY, Width, Height}
Operations = {Add, Delete, Move, ResizeBigger, ResizeSmaller} +
{Group, Ungroup, AddGroup, UngroupDelete, UngroupGroup} +
{Maximize, Minimize}

#1 Distributed Storage of user Behaviour
•  Operation carried out on the interface: Add Component, Delete
Component, Move Component, Resize Component…
•  User Information: Name, Category, Home Region, Personal data…
•  Temporal Information: Date, Time, Day, Month, Season…
•  Location Information: latitude, longitude, proximity to coast…
•  Other Information: Temperature, Humidity, Wind, Precipitation…
•  State of the Worspace: Location and size of all components
20
Case Study

Acquisition Data Web
Service
ENIA Core Web Services with
Context Information
Client (ENIA)
2) ENIA Core. Information about Users and Components à http://www.enia.dreamhosters.com/webservices
1) Azure Cloud Web App. Written in PHP à http://getinteraction.azurewebsites.net/service.php
3) Context Information. I.E: Temperature à http://www.webservicex.net/globalweather.asmx?op=GetWeather
4) SQL Azure Database à basedatosinteraccion.database.windows.net
Schema
Graphic JSON
Schema

#2 Creating Distributed DataViews.
22
Case Study
Case Study
User Category Time of day Date Localization
Tourist 15:00-17:00 Summer Near to Cost
User Category Date Operation Component
Farmer From January to March Insert Component Tomate Seeds Providers
Set of structured data so they can serve as entry elements to
machine learning algorithms
Insert “Beach Temperatures” Component
Insert “Tomate Seeds Prices” Component

idUser
userType
idSession
idInteraction
dateTime
operationPerformed
idComponent
idUser
userType
isNewUser : boolean
season : list
operationPerformed (filter = add)
idComponent (ObjectiveField)
Before FE After FE
Decission Trees --- Ensemble --- Random Decision Forest
#2 Creating Distributed DataViews (Feature Engineering)
#3 Application of Machine Learning Algorithms.

#2 Creating Distributed DataViews (Feature Engineering)
idUser
userType
idSession
idInteraction
dateTime
operationPerformed
idComponent
idUser
userType
isNewUser : boolean
season : list
operationPerformed (filter = add)
idComponent (ObjectiveField)
Before FE After FE
clusterFieldClustering
idUser (let’s keep it)
Clustering --- Decission Trees --- Ensemble --- Random Decision Forest

•  We need to identify the time of day when a “tourist” user will need to
find out the water temperature of a beach by using the “temperature of
beaches" component.
Supervised Regression Algorithms
•  Supervised à we already have data of previous tourists who have
used the “temperature of beaches" component.
•  Regression Algorithm à predict a continuous value output (time of
day) depending on a series of attributes (user type, season,
location…).
25
Case Study
Case Study

•  We need to identify when a component, after apply the “ResizeBigger”
operation, would be interesting to show it at FullScreen using the
“Maximize” operation.
Supervised Classification Algorithms
•  Supervised à we already have data of previous tourists who have
used the “temperature of beaches" component.
•  Classification Algorithm à predict a discrete value output (whether to
show the component full screen or not) depending on a series of
attributes (component size before resize, component size after resize,
…).
26
Case Study
Case Study

•  Divide users depending the component they manipulates and, based
on that segmentation, suggest them services for this new type of user
detected.
Unsupervised Clustering Algorithms
•  Unsupervised à A priori we have no idea what component are used
for the users (or what components they will use in the future).
•  Clustering Algorithm à We expect the algorithm to “group” users
based on the components they manipulate.
27
Case Study
Case Study

#4 Conversion Rules
•  The format of ENIA rules is ATL (a transformation model language
which also provides a number of tools for Model Driven Engineering)
•  We create a HOT (Higher Order Transformation) to transform the
output of the algorithm to ATL Rules
28
#5 Evaluation of Rules
•  In case of contradiction, the new rule is prioritized
•  Old rules are not deleted (just ignored). We keep track of the
historical set of rules

•  A methodology is proposed to allow mashup user interfaces
in distributed systems to adapt to user requirements at
runtime, be intelligent and evolve over time through the use
of machine learning and model transformations.
•  The methodology consists of 5 steps
–  Save User Behaviour
–  Create Data Views
–  Apply ML Algorithms
–  Rules Transformation using HOT
–  Evaluate new generated Rules
29
Conclusions
Conclusions

•  Define a Data Acquisition Process.
•  Creation of automatic learning systems (fed data
automatically view Web Services and apply ML algorithms
continuously updating the rules repository)
•  Statistical learning techniques will be used to classify or group
different user intentions for individualized interface treatment.
•  Optimization or improvement of algorithms to optimize results.
30
Future Work
Future Work

31
Contraportada
Thank you for your attention

Evolving Mashup Interfaces using a Distributed Machine Learning and Model Transformation Methodology

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