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On the 
personalization of 
event-based 
systems 
Speaker: Opher Etzion 
opher.etzion@gmail.com 
Joint work with Fabiana F...
2 
Example: 
Personalized aides for elderly to maintain independent life 
Alerts example: 
Door was not locked within 2 mi...
3 
On Personalization 
The industrial revolution opened the era of 
mass production, variety depends on the 
economy of sc...
4 
The term “Internet of Things” was coined by Kevin 
Ashton in 1999. 
His observation was that all the data on the 
Inter...
5 
The value of sensors 
Kevin Ashton: “track and count everything, and 
greatly reduce waste, loss, and cost. We could 
k...
6 
Differences between the traditional Internet to 
the Internet of Everything 
Topic Traditional Internet Internet of Eve...
7 
“How does Event Processing get into the 
picture?” 
While the weakest link is now considered the data 
integration issu...
8 
A major difference between traditional Internet 
and the IoE – usability 
The success of the Internet is attributed to ...
For situational awareness…. 
Languages are actually more complex than 
SQL 
9 
// Large cash deposit 
insert into LargeCas...
10 
12 Hurdles Hampering The Internet of Things 
1. Basic Infrastructure Immaturity 
2. Few Standards 
3. Security Immatur...
11 
Democratization of use in Internet of 
Everything 
Challenges: 
Integration of sensors and actuators 
Personalization ...
12 
Personalization of situation detection
Eliminating noise from the model 
Current models are close to the 
implementation models – and from pure 
logic view conta...
14 
The Event Model 
Research project developed by IBM Haifa Research Lab and 
Knowledge Partners International that dealt...
TEM Concepts 
Facts 
Glossary Logic 
Actors 
Events 
States 
Event Derivation 
Logic Transitions 
IT elements Goals 
Compu...
Simple example: 
Top down design of event model for suspicious 
account derivation 
Suspicious Account Compliance officer ...
Specification for deriving Suspicious 
Account 
Suspicious account Logic 
Row # 
When 
Expression 
When 
Start 
When 
End ...
Pattern on events 
Suspicious customer logic 
Row # Context Conditions 
When Partition by Event filter Pattern on events F...
19 
My main motivation is to use the experience and 
knowledge I have accumulated over the years to make a 
better world
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On the personalization of event-based systems

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Talk given in ACM Multimedia conference on Human Centered Event Understanding from Multimedia

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On the personalization of event-based systems

  1. 1. On the personalization of event-based systems Speaker: Opher Etzion opher.etzion@gmail.com Joint work with Fabiana Fournier from IBM
  2. 2. 2 Example: Personalized aides for elderly to maintain independent life Alerts example: Door was not locked within 2 minutes after entrance Falling event detected Vocal distress detected No motion for certain time period detected While much technology exists, it is not widely used. It needs to be more personalized, more affordable, and much simpler… Motion sensor Chair Sensor Door sensor Voice Sensor Alert family member The research required is multi-disciplinary: Technology oriented, human oriented, economic oriented and particular domain oriented
  3. 3. 3 On Personalization The industrial revolution opened the era of mass production, variety depends on the economy of scale. Current technology such as Internet of Things provides the opportunity to enable everybody to create their own systems. This requires multi-disciplinary effort.
  4. 4. 4 The term “Internet of Things” was coined by Kevin Ashton in 1999. His observation was that all the data on the Internet has been created by a human. His vision was: “we need to empower computers with their own means of gathering information, so they can see, hear, and smell the world by themselves”.
  5. 5. 5 The value of sensors Kevin Ashton: “track and count everything, and greatly reduce waste, loss, and cost. We could know when things needs replacing, repairing or recalling, and whether they were fresh or past their best” The value is in the ability to know and react in a timely manner to situations that are detected by sensors
  6. 6. 6 Differences between the traditional Internet to the Internet of Everything Topic Traditional Internet Internet of Everything Who creates content? Human Machine How is the content consumed? By request By pushing information and triggering actions How content is combined? Using explicitly defined links Through explicitly defined operators What is the value? Answer questions Action and timely knowledge What was done so far? Both content creation (HTML…) and content consumption (search engines) Mainly content creation
  7. 7. 7 “How does Event Processing get into the picture?” While the weakest link is now considered the data integration issue – looking beyond that we can find event processing Combining data from multi-sensors to get observations, alerts, and actions in real-time gets us to the issue of detecting patterns in event streams However much of the IoT world has not realized it yet…
  8. 8. 8 A major difference between traditional Internet and the IoE – usability The success of the Internet is attributed to its relative simplicity: to connect to create content to search Imagine that any search in the Internet would have been done using SQL queries… How pervasive do you think the Internet would have been?
  9. 9. For situational awareness…. Languages are actually more complex than SQL 9 // Large cash deposit insert into LargeCashDeposit select * from Cash deposit where amount > 100,000 // Frequent (At least three) large cash deposits create context AccountID partition by accountId on Cash deposit; Context AccountID Insert into FrequentLargeCashDeposits select count(*) from LargeCashDeposit having count(*)>3; // Frequent cash deposits followed by transfer abroad Context AccountID insert into SuspiciousAccount select * from pattern [ every f=FrequentCashDeposit -> t=TransferAbroad where timer.within(10 days)]
  10. 10. 10 12 Hurdles Hampering The Internet of Things 1. Basic Infrastructure Immaturity 2. Few Standards 3. Security Immaturity 4. Physical Security Tampering 5. Privacy Pitfalls 6. Data Islands 7. Information, but Not Insights 8. Power Consumption and Batteries 9. New Platforms with New Languages and Technologies 10.Enterprise Network Incompatibility 11.Device Overload 12.New Communications and Data Architectures Chris Curran, October 30, 2014 https://www.linkedin.com/ pulse/article/20141030181 835-509139-12-hurdles-hampering- the-internet-of-
  11. 11. 11 Democratization of use in Internet of Everything Challenges: Integration of sensors and actuators Personalization of situation detection Pervasive use
  12. 12. 12 Personalization of situation detection
  13. 13. Eliminating noise from the model Current models are close to the implementation models – and from pure logic view contain “noise”. Bringing data from current state Query Enrichment Inclusion in events Examples: Determine what food-type the container carries Fetch the temperature regulations for a specific food type Other noise : workarounds 13 For simplification we need to clean the noise
  14. 14. 14 The Event Model Research project developed by IBM Haifa Research Lab and Knowledge Partners International that dealt with simplification of event processing using model driven engineering approach The Event Model design goals Short video can be found in: https://www.youtube.com/watch?v =9zjy8wngy5Y&feature=youtu.be
  15. 15. TEM Concepts Facts Glossary Logic Actors Events States Event Derivation Logic Transitions IT elements Goals Computation Logic
  16. 16. Simple example: Top down design of event model for suspicious account derivation Suspicious Account Compliance officer Bank transaction system Frequent large cash deposits Frequent large cash deposits Large cash deposit Large cash deposit cash amount <Cash deposit> customer threshold
  17. 17. Specification for deriving Suspicious Account Suspicious account Logic Row # When Expression When Start When End Partition by Filter on event Pattern Filter on pattern Account ID Frequent large cash deposits 1 always same is Detected Frequent large cash deposits Logic Row # When Expression When Start When End Partition by Filter on event Pattern Filter on pattern Account ID Count(Large cash deposit) 1 every 10 days same > 3 Large cash deposit Logic Row # When Expression When Start When End Partition by Filter on event Pattern Filter on pattern Customer ID cash amount <Cash deposit> 1 always same >= customer threshold
  18. 18. Pattern on events Suspicious customer logic Row # Context Conditions When Partition by Event filter Pattern on events Filter on patterned events Expressi on Start End Customer ID Amount <Cash deposit> Amount <Transfer Abroad> Cash deposit Account <Cash Deposit> 1 Every week same >= 150K >= 100K OCCURS BEFORE Transfer Abroad IS NOT Account <Transfer Abroad> A B C D Pattern on events designates what the relationship between events is. In this case conditions C states that an event should occur before another.
  19. 19. 19 My main motivation is to use the experience and knowledge I have accumulated over the years to make a better world

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