The document discusses personalizing event-based systems to better assist elderly individuals. It proposes sensors around the home that can detect events like an unlocked door, falling, or vocal distress to alert family members. While the technology exists, it needs to be more personalized, affordable, and simple to use. The research requires a multi-disciplinary effort across technology, human factors, economics, and specific domains. Personalizing such systems to individuals and simplifying the technology is seen as key to widespread adoption.

1. On the
personalization of
event-based
systems
Speaker: Opher Etzion
opher.etzion@gmail.com
Joint work with Fabiana Fournier from IBM

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
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
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
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
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
“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
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. 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
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
Democratization of use in Internet of
Everything
Challenges:
Integration of sensors and actuators
Personalization of situation detection
Pervasive use

12. 12
Personalization of situation detection

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
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. TEM Concepts
Facts
Glossary Logic
Actors
Events
States
Event Derivation
Logic Transitions
IT elements Goals
Computation
Logic

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. 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. 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
My main motivation is to use the experience and
knowledge I have accumulated over the years to make a
better world