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EvIM: a real time complex event discovery platform for CPSS
1. EvIM: Event Information Management
A Real-time Complex Event Discovery Platform for
Cyber-Physical-Social Systems
Minh-Son Dao*, Siripen Pongpaichet+, Laleh Jalali+,
Kyoungsook Kim*, Ramesh Jain+, and Koiji Zettsu*
*National Institute of Information and Communications Technology,
+University of California, Irvine, USA
2nd April, 2014
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2. • Event Discovery Platform for CPS
• Integrated EventWarehouse & EventShop
INTRODUCTION
• Sensors, Processors, ActuatorsCHALLENGE
• Situation Abstraction
• Situation Model Discovery
PROPOSED
SOLUTIONS
• From Trillions of Information Objects to
Focused Situations
ENRICH SITUATION
AWARENESS
• Asthma Risk Situations ModelSCENARIO
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3. INTRODUCTION
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Web
Location Based
Mobile Applications
Ongoing Archived
Database System
satellite
Cloud
resources
Environmental
Sensor Devices
Internet of Things
Social Media
Billions of
geo-location and
time based devices
Cyber Physical
Social Systems
Real-time
Information sharing
&
decision making
Experts
People
Governmental
Agencies
Situations
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7. CHALLENGES
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Processors
ActuatorsSensors
How to detect or predict
situations that can make
human life better?
How to communicate
successfully and instantly
with potential actuators?
How to discover useful
information from
heterogeneous and large
scale sensors?
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8. INFORMATION OBJECT
• A new concept for manipulate information, called
“Information Object”
• Encapsulate major characteristics of event: what,
where, when, who.
General case:
(data, operators) = Information Object Model
Specific case:
(sensors' data, operators) = Event
(events, operators) = Complex Event Model
(events/complex events, actions) = Situation Model
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Definition:
Situation is “actionable abstraction of observed spatio-temporal descriptors”
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9. SITUATION MODELS
Heterogeneous Sensor Data
Homogeneous
Information
Object
Event:“wearing
mask” topic (i.o3)
Event:Abnormally
increasing of
PM2.5 (i.o1)
Event:Abnormally
decreasing of
wind speed (i.o2)
Send Alert
Longitude,
latitude, time
“Aggregate:Sum, Filter:Value, Logic:AND”
(((#i.o1 + #i.o2) > b) ^ #i.o4)
space time
objects
topic
I.O. model
Ops
“Filter:Value”
(#i.o3 > a)
op
i.o
Actuator S
Act
TOPIC
detection ABNORMAL
detection
PM2.5 sensorTwitter sensor Wind speed sensor
d d d
op op op
i.o i.o i.o
ComplexEvent:
High Air Pollution
(i.o5)
ComplexEvent:
Unusual wearing
mask event (i.o4)
op
i.o
Situation:
Send alert to people
in high air pollution
(i.o6)
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10. • Target users:
– Application designers and developers in any expert
domain who want to create situation-aware
applications in the large scale and dynamic fashion
• Functionalities:
– Discover hidden patterns and generate semi-
automated situation recognition models
– Easy to build applications and require less technical or
computer science expertise
– Improve the quality of the defined situation model
and support dynamic and personalized action
DESIGN GOALS
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11. SITUATION AWARENESS APP
• From Trillions of Information Objects to
Focused Situations
• The process of building any situation
awareness application including 4 steps
1. Application Requirement and Specification
2. Model Retrieval and Discovery
3. Model Alteration and Design
4. Application Execution and Monitoring
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20. CONCLUSION & FUTURE
• EvIM is designed to harvest and analyze data
streams coming from heterogeneous sensors
• Introduce a unique Information Object (IO)
concept to harmonize data streams
• EvIM provides benefits in building situation
awareness application across domains
• At the designing state, the existing model, list of
IO candidates are provided to application
developers. However, users can modify the model
to serve their own purposes.
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20
Internet of Things (IoT) era
Trillions heterogeneous data streams
BUT data is not information!
The Web now has enormous volume of heterogeneous data being continuously reported by different sensors and humans from different locations.
The web is become a universal medium or data, information, and knowledge exchange
Real world phenomena are now being observed by multiple media streams which are available in real-time over the web, and increasingly the majority of these has space and time semantic .
We believe:
A significant fraction of the data regularly created is location-sensitive data streams.
Many emerging applications are related to taking actions in real time and depend on emerging ‘situations’ and contexts.
Lots of data about emerging situations and contexts is already available and more is becoming available.
Examples: Disaster Management detect effected area
Health environment hazard
What is the point to create generic framework?????
Definition of Situation and Events????
Situation is actionable abstraction of observed spatio-temporal descriptor
Internet of things
Data base system
Global sensor (fields sensors)
Satellite
Social network
Mobile application
IaaS is the most basic level of the cloud computing service models. It offers the virtual (as well as physical) machines, servers, storage options, load balancers, networks, and more.
PaaS is next in line, focusing more on operating systems, databases, web servers, development tools, etc. This is where IT development happens.
Differ from conventional systems that use data-driven or
pre-defined event-based approaches, the proposed platform
can alleviate the burden of human intervention meanwhile
increase the scalability, robustness, feasibility, and applicability by offering series of services
that not only automatically discover correlations among sensors' data to extract useful information
but also can help users design and monitor situations visually, efficiently and effectively, under on-fly mode.
EvIM is designed under context-awareness perspective.
EventWarehouse: is in charge of harvesting and analyzing data streams coming from heterogeneous sensors networks. This component can homogenize data format into unique Information Object (IO). In addition, it can discover correlations among information objects to automatically generate situation model based on a giving context
EventShop: a spatio-temporal-theme complex event processing system, can detect and predict situation and send actionable information to actuators.
How to discover useful information from heterogeneous and large scale sensors?
- several sensors can report the same data for one information,
- one information can only be achieved by a set of sensors not only by one sensor
- data streams may be of different media types (e.g. free text, numeric value, categorical, or rich media types like images, audio and videos),
- the format of observation streams can be different such as structure(records, XML, array), semi-structure, and unstructured.
How to detect or predict situations that can make human life better?
How to communicate successfully and instantly with potential actuators?
1 Using NLP techniques to extract as much as possible “context information” – find the list of candidate
2 water-shed clustering – started from the set of candidate sensors, within a certain distance
3 Using STL (Seasonal Trend Decomposition Procedure based on loess) to detect outlier peaks, then we marked and symbolized by special symboics.
4 Find the most frequent pattern by using multiple sequential alignment techniques