https://doi.org/10.1007/978-981-13-8323-6_32 Conventional data analytics process uses dashboard with tables, charts, summaries, search tool in projecting its analysis outcome to its user with the goal of enabling discovery of useful information or suggesting conclusions to support decision-making. Such decision-making mechanisms can be improved further by using natural language interface in the dashboard components, e.g. using natural language keywords to search the sales performance of a product. Motivated by the needs to enable a user friendlier interaction with analytics outcome, this paper proposes a chatbot, called analytics bot who can assist in the role of decision making by delivering information of dashboard components with human like conversational pattern.

1. Interfacing Chatbot with Data
Retrieval and Analytics Queries for
Decision Making
Gan Keng Hoon, Loo Ji Yong and Goh Kau Yang
School of Computer Sciences
Universiti Sains Malaysia
The 6th International Conference on
Robot Intelligence Technology and
Applications 2018
Pullman Lakeside Hotel,
Putrajaya, Malaysia
Dec 16, 2018 - Dec 18, 2018

2. The Softer Side of A Bot

3. A Chatbot

6. Chatbot for Decision Makings
• Ordering Food
• Book a Ticket
• Get Health Tips
• Get Makeup Tips
& …

7. Task Oriented Chatbot
Master a single domain, e.g. knowledge
about a company, a domain, a skill scope
etc.
Able to have conversational interactions
and context awareness.
Can have some element of personality.

8. Chatbot ModellingTask-basedChatbot
Modelling
Conversation
Interactions
Domain Specific Task
Conversation Contents
Static Contents
Classified Responses
Simple Retrieval
Dynamic Contents
API/Functions
Structured Query

9. Data Retrieval

10. Decision Making

11. Analytics Queries
What is the total articles published in the 1st quarter?
Who published the most?
What are the popular research areas?
Please compare the citations for year 2017 and the year before.
Is there a correlation between the
number of articles published and
the number of supervised students?

12. Analytics
Chatbot

13. “total articles published in the 1st quarter?"
// generate a corresponding intermediate object with
the following contents
intent: count (from keyword “total")
target entity: article
constraint entity: date
constraint entity values: january, february, march
// from the object, SQL processing engine will
generate a query as following
SELECT article’, COUNT(article') FROM
PUBLICATIONS
WHERE ‘date' = “jan” or ‘date' = “feb” or
‘date’=“mar”;
Interface

14. Conversational-based Natural Language
Understanding
“total articles published in the 1st quarter?"
// generate a corresponding intermediate object with the
following contents
intent: count (from keyword “total")
target entity: article
constraint entity: date
constraint entity values: january, february, march
Text Preprocessing
Natural Language
Processing
Text Normalization
Information
Extraction

15. Intent to Business Logic Translation
// generate a corresponding intermediate object
with the following contents
intent: count (from keyword “total")
target entity: article
constraint entity: date
constraint entity values: january, february, march
// from the object, SQL processing engine
will generate a query as following
SELECT article’, COUNT(article') FROM
PUBLICATIONS
WHERE ‘date' = “jan” or ‘date' = “feb” or
‘date’=“mar”;
Actionable Data
Interface Mapping
Structured Query
Formulation
Function/API
Calls
Natural Language
Generation

16. Data Retrieval & Analytics based
Conversational Framework

19. Nybble The Robotic Kitten

20. Questions/Discussions
THANK YOU
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