As organizations move aggressively to understand the kind of content that drives chatbots, there should be an equally aggressive investigation of the systems for managing chatbot content. Effective management of such content requires two capabilities: 1) searching for content quickly and with pinpoint accuracy, and 2) expanding the storage system without impacting retrieval times. A component content management system has a better architecture than a document- or file-level content management system for delivering these two capabilities. The differences between these two types of content management systems are not noticeable when a proof-of-concept chatbot is driven by 10, 100, or even 1,000 strings. However, as chatbots become increasingly sophisticated, the underlying content expands to more than 10,000 strings. Within a few years, chatbots will be driven by 100,000 or even 1,000,000 distinct strings of content, and the CCMS architecture will outperform the traditional CMS architecture at these levels of scale. In this session, Eric Kuhnen examines the mathematics of chatbot content management and explains the expected performance profiles of content management systems optimized for XML and for non-XML content. Attendees will understand how CCMS technology delivers superior performance in both Search and Scale versus CMS technology. November 29, 2017

3. CHATBOT CONTENT MANAGEMENT
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4. CHATBOT: AN OPERATIONAL DESCRIPTION
• From Wikipedia…
• Some chatterbots use sophisticated natural language processing systems, but many simpler
systems scan for keywords within the input, then pull a reply with the most matching keywords,
or the most similar wording pattern, from a database.
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5. #idw2017

6. MODELING CHATBOT STRING MANAGEMENT
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Query
May I have a hamburger?
Chatbot
Would you like fries with that?
Chatbot
How about a drink?

7. MODELING CHATBOT STRING MANAGEMENT
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Chatbot
How about a juicy hamburger?
Chatbot
Would you like fries with that?
Query
May I have a soft-drink?

8. MODELING CHATBOT STRING MANAGEMENT
#idw2017
Chatbot
How about a hamburger to wash it down?
Chatbot
Would you like fries with that?
Query
May I have a soft-drink?

9. MODELING CHATBOT STRING MANAGEMENT
• Description
• For every pair of Chatbot strings, there are two sequences: A à B and B à A
• Formula
• 𝑃 𝑛, 𝑟 =
&!
&() !
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Chatbot Strings Sequences
2 2
3 6
4 12
5 20
6 30
7 42
8 56
9 72
10 90
Chatbot Strings Sequences
101 90
102 9,900
103 999,000
104 ~100 million
105 ~10 billion
106 ~1 trillion

10. MODELING CHATBOT-STRING SEQUENCE RETRIEVALS
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Chatbot-string Sequences in a
“Component” CMS
Chatbot-string Sequences in a
“Document” CMS

11. “Get Next String in Sequence” in Document CMS
• SELECT NextString
• FROM Chatbot_Strings
• WHERE StringID = <some ID>
“Get Next String in Sequence” Component CMS
• Get(<some ID>)
MODELING CHATBOT-STRING SEQUENCE RETRIEVALS
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12. MODELING CHATBOT-STRING SEQUENCE RETRIEVALS
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Chatbot Strings Unindexed CMS Indexed CMS CCMS
4 1 1 1
5 1 1 1
6 2 2 1
7 3 3 1
8 3 3 1
9 4 3 1
10 5 4 1
100 495 10 1
1,000 49,950 17 1
10,000 4,999,500 24 1
100,000 499,995,000 30 1
1,000,000 49,999,950,000 37 1

13. MODELING CHATBOT-STRING SEQUENCE RETRIEVALS
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1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
1,000,000,000
10,000,000,000
100,000,000,000
2 3 4 5 6 7 8 9 10 100 1000 10000 100000 1000000
Disk Retrievals
Unindexed Document CMS Indexed Document CMS Component CMS
Assumptions
• 10 records per disk block

14. Document CMS
• Storage
• Add Disk à Tablespace Expansion and Rebalancing
• Query Optimization
• Add Hints à Can Override Query Optimizer
“Get Next String in Sequence” Component CMS
• Storage
• Add Disk
CMS SCALING TECHNIQUES
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15. FINAL THOUGHTS
• Chatbot Performance: Proof-of-Concept versus Production
• Chatbot Scaling: Simplicity
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