EBMIP 2013
Workshop on Event-based Media Integration and Processing
co-located with ACM Multimedia 2013 – October 21-22, B...
Example to kickoff the discussion:
context driven scenario- detecting car theft

A specific car is
moving

detect ->

All ...
Outline of this talk

The players and semantics

Situation

Context

3
3

© 2013, IBM Corporation
In daily life we often react to events..

4
4

© 2013, IBM Corporation
Many times we react to situations that are combination
of events within a context
The house sensor detects that the child
...
Event pattern

Event
Patterns

The house sensor detects that the child
did not arrive home within 2 hours from
the schedul...
Event Driven Applications follow the 4D paradigm
I want to know about it
immediately and react in the
best possible way

A...
Detect

Swim Lane

The act of bringing into a system’s sphere of understanding knowledge about an event.

Activity

Trigge...
Derive
The act of becoming aware of events that are not directly detectable by bringing together
events with other events,...
Decide
The act of determining the course of action to do in response to the situation. This
includes the background inform...
Do
The act of performing the course of action that was decided upon.

Notification: Sending a signal of sort to either a p...
The evolution of analytics
Analytics 1.0 – business intelligence / reporting
over data warehouse

Analytics 2.0 --- statis...
Event Processing is being used for various reasons
EP Solution Segments – Business Value

BUSINESS
IT

CONSUMERS
13
13

© ...
The Event Processing Value
In a moderate-sized financial institution, over a billion transactions occur each
and every wor...
Outline of this talk

What is event processing ?

Situation

Context

15
15

© 2013, IBM Corporation
Getting back to the car theft example:

A specific car is
moving

detect ->

All authorized
drivers for this car
are not i...
Players in the story

Sensors:
Car GPS sensor
Car camera
Person’s location sensor

Situation:
Person enters car and then
C...
Concepts
Player

Processing
Element

Fact

Context

Domain

Situation

Event

Actor

18
18

© 2013, IBM Corporation
Fact
What?
How?

Piece of information about
actor or event

Examples:
Car-id of Car
Car-id of Moving Car
Authorized-driver...
Domain
What?

Describes an entity type in
the real world

How?

An abstract term, associated
with data type and

Examples:...
Event
What?

Something that happens

How?

Represented as a aggregation
of characteristics and
associated facts

Name:
Per...
ACTOR
What?
How?

Car GPS

Any entity, person, or organization
that has a relevant role
Represented through associated
Fac...
Roles of actor with respect to events
Event producer

Car GPS

Event consumer

Security officer

Actor

Actuator

Car stop...
Outline of this talk

What is event processing ?

The players and semantics

Context

24
24

© 2013, IBM Corporation
The notion of pattern

Event
Patterns

Partitioned by

Car

Filter

Picture <Person enters
car>

Is not similar to

Any pi...
Pattern example
Partitioned
by

Car

Filter

Picture
<Person
enters car>

Is not similar
to

Any picture
<driver>

Pattern...
Sample of pattern types
all pattern is satisfied when the relevant
event set contains at least one instance of
each event...
Pattern detection

Pattern detect EPA
Output
Input terminal
filter expression

Pattern
matching set

Derivation
Instance
s...
Outline of this talk

What is event processing ?

The players and semantics

Situation

29
29

© 2013, IBM Corporation
Our entire culture is context sensitive

In the play “The Tea house of the August Moon” one of the
characters says: Pornog...
Context has three distinct roles (which may be combined)

The events that relate to each
customer are processed
separately...
Context types
Segmentation Oriented

Temporal

Spatial

Fixed interval

Fixed location

Event interval

Entity distance lo...
Context type examples
Segmentation Oriented
“All Children 2-5 years old”
“All platinum customers”
Temporal

Spatial

“Ever...
Fixed Interval
In a fixed interval context each window is an interval that has a fixed
time length; there may be just one ...
Event Interval
In an event interval context each window is an interval that starts
when the associated EPA receives an eve...
Sliding fixed interval
In a sliding fixed interval context each window is an interval with fixed
temporal size. New window...
Sliding event interval
A sliding event interval is an interval of fixed size (events number)
that continuously slides on t...
Segmentation oriented context
.
 Unrestricted number of partitions:
 Average of customer’s deposits over last month

Joh...
Segmentation context –(II)
 Fixed number of partitions
 Distribution of alcohol consumption by age

18-25

26-50

50-

3...
Spatial context
Within th e house
Fixed Location

Withi n 2 KM from the
motel

Enti ty di stance location

Event distance
...
Spatial properties of events

Area
Point

Line

The green neighborhood

Point (X1, Y1)
Road 62

Car C

Car H

Car F

Car G...
Fixed location
A fixed location context has predefined context partitions based on specific spatial entities.
An event is ...
43
43

© 2013, IBM Corporation
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Event semantics and model - multimedia events workshop

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  • Event semantics and model - multimedia events workshop

    1. 1. EBMIP 2013 Workshop on Event-based Media Integration and Processing co-located with ACM Multimedia 2013 – October 21-22, Barcelona, Spain
    2. 2. Example to kickoff the discussion: context driven scenario- detecting car theft A specific car is moving detect -> All authorized drivers for this car are not in the car: theft is concluded derive -> 2 Either notify police or chase the car by private agency 2 decide -> Stop the car by police or by car built-in stopper do © 2013, IBM Corporation
    3. 3. Outline of this talk The players and semantics Situation Context 3 3 © 2013, IBM Corporation
    4. 4. In daily life we often react to events.. 4 4 © 2013, IBM Corporation
    5. 5. Many times we react to situations that are combination of events within a context The house sensor detects that the child did not arrive home within 2 hours from the scheduled end of classes for the day I want to be notified when my own investment portfolio is down 5% since the start of the trading Day; have an agent call me when I am available, send SMS when I am in a meeting, and Email when I am out of office. 5 5 © 2013, IBM Corporation
    6. 6. Event pattern Event Patterns The house sensor detects that the child did not arrive home within 2 hours from the scheduled end of classes for the day 6 6 © 2013, IBM Corporation
    7. 7. Event Driven Applications follow the 4D paradigm I want to know about it immediately and react in the best possible way Awareness Detect Situation Derive Reaction Decide Do 7 7 © 2013, IBM Corporation
    8. 8. Detect Swim Lane The act of bringing into a system’s sphere of understanding knowledge about an event. Activity Trigger Event State Change Something of interest happened Detection can be by : human report, sensor, instrumentation, publisher… 8 8 © 2013, IBM Corporation
    9. 9. Derive The act of becoming aware of events that are not directly detectable by bringing together events with other events, data, patterns and publishing the observation as a derived event. Raw Raw events Raw events events 9 9 © 2013, IBM Corporation
    10. 10. Decide The act of determining the course of action to do in response to the situation. This includes the background information needed to be collected to make the decision. Decision by human Decision by rule based systems Decision by optimization Pass through: Sometimes there no decision is required; only course10 action. of 10 © 2013, IBM Corporation
    11. 11. Do The act of performing the course of action that was decided upon. Notification: Sending a signal of sort to either a person or system. This 11 would include calling a web-service or subscription to alerts. 11 © 2013, IBM Corporation
    12. 12. The evolution of analytics Analytics 1.0 – business intelligence / reporting over data warehouse Analytics 2.0 --- statistical reasoning based on big data . Volume is the key driver Event processing is key technology Analytics 3.0 – real-time operational on streaming data. Velocity is the key driver. 12 12 © 2013, IBM Corporation
    13. 13. Event Processing is being used for various reasons EP Solution Segments – Business Value BUSINESS IT CONSUMERS 13 13 © 2013, IBM Corporation
    14. 14. The Event Processing Value In a moderate-sized financial institution, over a billion transactions occur each and every working day; there are multiple events associated with each transaction We don’t know if any specific event will happen We don’t know when any specific event will happen Within all these events, hidden situations can be deduced. These situations indicate business opportunities or threats; for some of them, there is a short time to exploit or contain. Event Processing gives organizations the awareness into 14 these situations to build competitive edge 14 © 2013, IBM Corporation
    15. 15. Outline of this talk What is event processing ? Situation Context 15 15 © 2013, IBM Corporation
    16. 16. Getting back to the car theft example: A specific car is moving detect -> All authorized drivers for this car are not in the car: theft is concluded derive -> 16 Either notify police or chase the car by private agency 16 decide -> Stop the car by police do © 2013, IBM Corporation
    17. 17. Players in the story Sensors: Car GPS sensor Car camera Person’s location sensor Situation: Person enters car and then Car moving Person location for all eligible drivers is not near car location Entering person does not look like any eligible driver Events: Car moving Person changed location Person enters car Actuators: Car stopper Security enforcers 17 17 © 2013, IBM Corporation
    18. 18. Concepts Player Processing Element Fact Context Domain Situation Event Actor 18 18 © 2013, IBM Corporation
    19. 19. Fact What? How? Piece of information about actor or event Examples: Car-id of Car Car-id of Moving Car Authorized-driver of Car A function of the actor/event to a domain Provides information about Car-id of Car Fact M 1 Actor Car Provides information about Car-id of Moving Car Fact M 1 Event Moving Car Is a classified to Car-id of Car Fact 1 M Domain 19 19 Car-id © 2013, IBM Corporation
    20. 20. Domain What? Describes an entity type in the real world How? An abstract term, associated with data type and Examples: Car Driver Name: Car Synonyms: Data type: Vehicle 2 char + 8 digits 20 20 © 2013, IBM Corporation
    21. 21. Event What? Something that happens How? Represented as a aggregation of characteristics and associated facts Name: Person enters car Characteristics: Time: 22/10/2013 22:24 Location: Barcelona, Balmes 132 Certainty: 1 Source : car camera 453430 Car id: 14321313 Examples: Moving Car Person enters car Person changes location Picture: 21 21 © 2013, IBM Corporation
    22. 22. ACTOR What? How? Car GPS Any entity, person, or organization that has a relevant role Represented through associated Fact-types and event related roles Actor M Role n Event Examples: Car Driver Car GPS Mobile phone Moving Car 22 22 © 2013, IBM Corporation
    23. 23. Roles of actor with respect to events Event producer Car GPS Event consumer Security officer Actor Actuator Car stopper Event Car Event subject Eligible driver Event descriptor Data provider 23 23 Driving authorization store © 2013, IBM Corporation
    24. 24. Outline of this talk What is event processing ? The players and semantics Context 24 24 © 2013, IBM Corporation
    25. 25. The notion of pattern Event Patterns Partitioned by Car Filter Picture <Person enters car> Is not similar to Any picture <driver> Pattern Person enters car Occurs before Moving Car Pattern Location <Car> Is not near 25 Any 25 Last location <driver> © 2013, IBM Corporation
    26. 26. Pattern example Partitioned by Car Filter Picture <Person enters car> Is not similar to Any picture <driver> Pattern Person enters car Occurs before Moving Car Pattern Location <Car> Is not near Any Last location <driver> 26 26 © 2013, IBM Corporation
    27. 27. Sample of pattern types all pattern is satisfied when the relevant event set contains at least one instance of each event type in the participant set any pattern is satisfied if the relevant event set contains an instance of any of the event types in the participant set  absence pattern is satisfied when there are no relevant events Top/bottom K values pattern is satisfied by the events which have the N highest value of a specific attribute over all the relevant events, where N is an argument  value average pattern is satisfied when the value of a specific attribute, averaged over all the relevant events, satisfies the value average threshold assertion. always pattern is satisfied when all the relevant events satisfy the always pattern assertion 27 sequence pattern is satisfied when the relevant event set contains at least one event instance for each event type in the participant set, and the order of the event instances is identical to the order of the event types in the participant set. increasing pattern is satisfied by an attribute A if for all the relevant events, e1 << e2  e1.A < e2.A  relative max distance pattern is satisfied when the maximal distance between any two relevant events satisfies the max threshold assertion moving toward pattern is satisfied when for any pair of relevant events e1, e2 we have e1 << e2  the 27 location of e2 is closer to a certain object then the location of e1. © 2013, IBM Corporation
    28. 28. Pattern detection Pattern detect EPA Output Input terminal filter expression Pattern matching set Derivation Instance selection Derivation expression Matching Context expression Pattern signature: Pattern type Pattern parameters Relevant event types Pattern policies Relevance filtering Relevant event types Participant events Not selected 28 28 © 2013, IBM Corporation
    29. 29. Outline of this talk What is event processing ? The players and semantics Situation 29 29 © 2013, IBM Corporation
    30. 30. Our entire culture is context sensitive In the play “The Tea house of the August Moon” one of the characters says: Pornography question of geography •This says that in different geographical contexts people view things differently •Furthermore, the syntax of the language (no verbs) is typical to the way that the people of Okinawa are talking When hearing concert people are not talking, eating, and keep their mobile phone on “silent”. 30 30 © 2013, IBM Corporation
    31. 31. Context has three distinct roles (which may be combined) The events that relate to each customer are processed separately Partition the incoming events Grouping together events that happened in the same hour at the same location Grouping events together Different processing for Different context partitions Determining the processing 31 31 © 2013, IBM Corporation
    32. 32. Context types Segmentation Oriented Temporal Spatial Fixed interval Fixed location Event interval Entity distance location Context Sliding fixed interval Event distance location Sliding event interval State Oriented 32 32 © 2013, IBM Corporation
    33. 33. Context type examples Segmentation Oriented “All Children 2-5 years old” “All platinum customers” Temporal Spatial “Every day between 08:00 and 10:00 AM” Context “A week after borrowing a disk” “3 miles from the traffic accident location” “Within an authorized zone in a manufactory” “A time window bounded by TradingDayStart and TradingDayEnd events” State Oriented “Airport security level is red” “Weather is stormy” 33 33 © 2013, IBM Corporation
    34. 34. Fixed Interval In a fixed interval context each window is an interval that has a fixed time length; there may be just one single window or a periodically repeating sequence of windows. Fixed interval Interval start Interval end July 12, 2010, 2:30 PM + 3 hours Recurrence Temporal ordering 08:00 10:00 08:00 10:00 08:00 10:00 34 34 © 2013, IBM Corporation
    35. 35. Event Interval In an event interval context each window is an interval that starts when the associated EPA receives an event that satisfies a specified predicate. It ends when it receives an event that satisfies a second predicate, or when a given period has elapsed. Event interval From patient’s admittance to patient’s release Initiator event list Terminator event list Patient’s admittance Patient’s release Expiration time offset Expiration event count Within 3 days from an earthquake Initiator policy Terminator policy Earthquake Temporal ordering + 3 days 35 35 © 2013, IBM Corporation
    36. 36. Sliding fixed interval In a sliding fixed interval context each window is an interval with fixed temporal size. New windows are opened at regular intervals relative to one another. Sliding fixed interval Interval period Interval duration 2 hours 2 hours Interval size (events) 2 hours Temporal ordering 1 hour 1 hour 1 hour 36 36 © 2013, IBM Corporation
    37. 37. Sliding event interval A sliding event interval is an interval of fixed size (events number) that continuously slides on the time axis. Sliding event interval Every 3 blood pressure measurements Event list Interval size (events) Event period Temporal ordering 37 37 © 2013, IBM Corporation
    38. 38. Segmentation oriented context .  Unrestricted number of partitions:  Average of customer’s deposits over last month John Tim Helen David 38 38 © 2013, IBM Corporation
    39. 39. Segmentation context –(II)  Fixed number of partitions  Distribution of alcohol consumption by age 18-25 26-50 50- 39 39 © 2013, IBM Corporation
    40. 40. Spatial context Within th e house Fixed Location Withi n 2 KM from the motel Enti ty di stance location Event distance location Within 10 K M fro m the accid ent 40 40 © 2013, IBM Corporation
    41. 41. Spatial properties of events Area Point Line The green neighborhood Point (X1, Y1) Road 62 Car C Car H Car F Car G Car E Car A 41 41 © 2013, IBM Corporation
    42. 42. Fixed location A fixed location context has predefined context partitions based on specific spatial entities. An event is included in a partition if its location attribute indicates that it is correlated with the partition’s spatial entity. e ve n t e n tity C o n ta in e d In e ve n t entity event entity e ve n t entity Relations between the event’s location and the context e nt ity ’s l ocatio n event entity e n tity C o n ta in s e n tity e n tity ev ent event ev ent e ve n t entity entity event e n tity e n tity entity e n tity event e ve n t o ve rla p s e ve n t e n tity event ev ent e n tity ev ent d isjo in t ev ent entity ev ent e q u a ls entity ev ent entity entity, event e1 e n tity , e ve n t entity , ev ent event e n tity e ve n t e n tity ev ent e n tity ev ent entity to u ch e s An event is classified to a context partition if satisfy a spatial relationship with fixed entity 42 entity ev ent 42 © 2013, IBM Corporation
    43. 43. 43 43 © 2013, IBM Corporation
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