The document discusses bridging the gap between current operating systems and AI-integrated systems, including transforming from process-driven to data-driven enterprises and the challenges of big data science initiatives; it also provides two case studies on using artificial intelligence for subjective analytics on social media and developing chatbots.

1. Bridging the Gap between Current Operation
System and AI-Integrated System
Dr. Dickson Lukose
3 – 4 April, 2018
Kuala Lumpur, Malaysia

2. Market Pull
2
Transformation
from Process Driven Enterprise
to Data Driven Enterprise
PROCESS-
DRIVE
ENTERPRISE
Slow Reaction to Market Dynamics Fast Reaction to Market Dynamics
Evolution of Business Paradigm
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3. Artificial Intelligence for Data Science
3
Dr. Marcell Vollmer
Chief Digital Officer @ SAP Ariba
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4. Artificial Intelligence for Data Science
4
Source: https://whatsthebigdata.com/2016/10/17/visually-linking-ai-machine-learning-deep-learning-big-data-and-data-science/
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5. Challenges in Big Data Science Initiatives
5
Overcoming Silo
Mentality
Overcoming
Data Sharing
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6. Data Lake (Data Swamp) vs Semantic Data Lake
6
DATA LAKE
Enterprise Data
Sensor Web
Structured, Semi-Structured
& Unstructured
Unstructured (Structured) Structured & Semi-
Structured
Structured
Linked Open Data
CRAWLERS DATA HARVESTERS
WEB
KNOWLEDGE
HARVESTER
DATA
INGESTION ENGINE
Social Media
?DATA
SWAMP
SEMANTIC
DATA LAKE
HARMONIZATION
CLEANSING
FUSION
DEDUPLICATION
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7. When do we need Semantic Data
Lake (Enterprise Knowledge Graph)?
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8. Semantic Data Lake Design Goals
8
Data Ingestion
- User configurable tools to map, ingest and link
data from any source.
- Structured & Unstructured data processing,
NLP, Text Analytics, Image Analytics, Video
Analytics.
Data Representation
- Flexible, standard-based graph model,
accessible by informaticians and business
users.
- RDF & OWL
Data Consumption
- Semantic Search, Dashboards, Network
Analytics, Semantic Analytics
- Natural Language Question Answering
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9. Data Analytics (Building Models)
9
Classification & Regression
Clustering
Anomaly Detection
Topic Modeling
DeepNet
Timeseries
Association Discovery
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10. Case Studies: Applications of Artificial
Intelligence
Case Study One:
Subjective Analytics on Social Media and
Social Network
Case Study Two:
Chatbots
10

11. Social Media Global Popularity
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12. Social Media Intelligence Process
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13. Social Media Intelligence (Interactive Visualization)
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14. 14
Social Media Intelligence
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15. 15
Raw Data
Cleansed
Data
Relevant
Data
Network
Analytics
Geospatial
Analysis
Insights
Harvesting
Cleansing
Filtering
Social
Network
Analytics
Social Network Analytics Process
NETWORK
CLUSTERING ASSOCIATION
INFLUENCERS
CONNECTORS
POPULARITY
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16. Social Network Analytics (Visual Analytics)
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17. 17
Social Network Analytics
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18. Chatbot (Software Agent) Landscape
Chatbot is not new!!!!
ELIZA is an early natural language
processing computer
program created from 1964 to 1966
at the MIT Artificial Intelligence
Laboratory by Joseph
Weizenbaum.
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19. AIML: Artificial Intelligence Markup
Language
2001: AIML, or Artificial Intelligence Markup Language, is an XML dialect for creating
natural language software agents released.
Dr. Richard Wallace (Chief
Science Office of Pandorabots)
A.L.I.C.E. (Artificial Linguistic
Internet Computer Entity) is a free
software chatbot created in AIML
(Artificial Intelligence Markup
Language).
The Loebner Prize is an annual
competition in artificial
intelligence that awards prizes to
the computer
programs considered by the
judges to be the most human-
like.
Steve Worswick
is the creator
of Mitsuku.
Mitsuku chatbot
wins Loebner
Prize for most
humanlike A.I.,
yet again.
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20. Chatbot Conversation Framework
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AIML
Machine Learning
Deep Learning
Natural Language Processing
Question-Answering
Semantic Technology

21. Chatbot Life-Cycle
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22. Digital Advisor
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23. 23
Data Science for Managers
16-18 October 2017
Operationalisation
“A repeatable, efficient process for creating and effectively deploying
predictive analytics models into production”
Step 1: Move from cottage industry
to an industrial process for
building analytics models.
Step 2: Access to data is standardised.
Step 3: Definitions of this data are
shared and analytical
datasets are generated in a
repeatable (and where possible)
automated.
Step 4: Use BDA Workbench
(BDAW) to implement
(realize) systematic approach
to data management feeds
and defining modelling
workflow.
Step 5: Use BDAW to enable
analytic team to perform
ongoing management and monitoring
of models that are deployed in
production.
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24. 24
Data Science for Managers
16-18 October 2017
Key Operationalisation Challenges
• Analytic Model built by the Modelling
Group and Deployed by IT.
• Time required to deploy models and
to integrate models with other
applications can be long.
• Models are deployed in proprietary
formats.
• Models are application dependent.
• Models are system dependent.
• Models are architecture dependent.
• Model should be independent of the
environment (Dev/Stg/Prod).
• Model should be independent of the
application coding language.
Solution:
 Predictive Model Markup Language
(PMML) or JSON-PML or CUSTOM.
 Move deployment responsibility from
IT to Operations/Product-Team.
 Use BDAW.
Analytic Infrastructure
IT Organization
Storage
Compute
Network
Analytic Operation,
Security and Compliance
Deployed Models
Operations, Product Team
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25. 25
Data Science for Managers
16-18 October 2017
Predictive Model Markup Language (PMML)
• The Predictive Model Markup
Language (PMML) is an XML-
based predictive model interchange
format.
• PMML provides a way for analytic
applications to describe and
exchange predictive models
produced by data mining and
machine learning algorithms.
Ref: http://dmg.org/pmml/v4-1/GeneralStructure.html
• Since PMML is an XML-based
standard, the specification comes
in the form of an XML schema.
• The PMML XSD contains required
elements and attributes that must
be present for the PMML to be
valid
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26. 26
Data Science for Managers
16-18 October 2017
(IT/Data Engineer) (Data Science Team) (Product Owner)
Model Building and Deployment
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27. 27
Data Science for Managers
16-18 October 2017
Model Life Cycle – Model Revision Complexity
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28. 28
Data Science for Managers
16-18 October 2017
Model Life Cycle – Model Performance
Q: How to measure
Business Impact?
A: Measure in terms of:
- Economic Impact
- Social Impact
- KPI
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29. 29
Data Science for Managers
16-18 October 2017
Model Life Cycle – Acceptable Model Error
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30. 30
Data Science for Managers
16-18 October 2017
Model Life Cycle – When is the Right Time for Model Revision
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31. 31
Data Science for Managers
16-18 October 2017
Model Life Cycle – Model Revision
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32. 32
Data Science for Managers
16-18 October 2017
Model Life Cycle – Model Revision Complexity
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33. 33
Data Science for Managers
16-18 October 2017
Model Life Cycle – Model Revision Complexity
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34. 34
Data Science for Managers
16-18 October 2017
Model Life Cycle – Model Revision Complexity
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35. 35
Data Science for Managers
16-18 October 2017
Model Life Cycle – Model Revision Complexity
Process Data
Exploratory Data Analysis
Build Models in
Development/Modeling
Environment
Deploy Models in
Operational System
PMML
Model
Revision
Retire Old Model
and Deploy
Revised Model
PMMLDATA LOG
DATA LOG
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36. 36
Data Science for Managers
16-18 October 2017
Model Building, Deployment and Revision
(IT/Data Engineer) (Data Science Team) (Product Owner)
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37. Dr. Dickson Lukose (PhD)
GCS Agile Pty. Ltd.
Level 10, 461 Bourke Street
Melbourne, VIC 3000
Australia
Email: dlukose@gcsagile.com.au
Phone: +61408510817