on the personalization of event-based systems
DESCRIPTION
Talk given in ACM Multimedia conference on Human Centered Event Understanding from MultimediaTRANSCRIPT
Speaker: Opher [email protected]
Joint work with Fabiana Fournier from IBM
On the personalization of event-based systems
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Example:
Personalized aides for elderly to maintain independent life
Motion sensor
Door sensor
ChairSensor
Voice Sensor
Alert family member
Alerts example:Door was not locked within 2 minutes after entranceFalling event detectedVocal distress detectedNo 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…
The research required is multi-disciplinary:
Technology oriented, human oriented, economic oriented and particular domain oriented
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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.
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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”.
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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
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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
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“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…
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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 contentto 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?
For situational awareness….Languages are actually more complex than SQL
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// 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)]
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12 Hurdles Hampering The Internet of Things
Chris Curran, October 30, 2014https://www.linkedin.com/pulse/article/20141030181835-509139-12-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
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Democratization of use in Internet of Everything
Challenges:
Integration of sensors and actuators Personalization of situation detection Pervasive use
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Personalization of situation detection
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 EnrichmentInclusion in
events
Examples:
Determine what food-type
the container carries
Fetch the temperature
regulations for a specific
food type
Other noise : workarounds
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For simplification we need to clean the noise
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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
TEM Concepts
Facts
Actors
EventsStates
Event Derivation
Logic Transitions
Goals IT elements
Glossary Logic
Computation
Logic
Simple example:Top down design of event model for suspicious account derivation
Bank transaction system
Compliance officerSuspicious Account
Frequent large cash
deposits
Frequent large cash
deposits
Large cash deposit
Large cash deposit
cash amount
<Cash deposit>
customer threshold
Simple example: TEM Logic Specification for deriving Suspicious Account
Suspicious account Logic
Row #When
ExpressionWhen Start
When End
Partition by Filter on event Pattern Filter on pattern
Account ID Frequent large cash deposits
1always same is Detected
Frequent large cash deposits Logic
Row #When
ExpressionWhen Start
When End
Partition by Filter on event Pattern Filter on pattern
Account ID Count(Large cash deposit)
1every 10
dayssame > 3
Large cash deposit Logic
Row #When
ExpressionWhen Start
When End
Partition by Filter on event Pattern Filter on pattern
Customer ID cash amount <Cash deposit>
1always same >= customer
threshold
Pattern on events
Pattern on events designates what the relationship between events is. In this case conditions C states that an event should occur before another.
Suspicious customer logic
Row # Context Conditions
When Partition by Event filter Pattern on events Filter on patterned events
Expression
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
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My main motivation is to use the experience and
knowledge I have accumulated over the years to make a
better world