3. BACKGROUND
Context is any information that can be used to
characterize the situation of a person, a device or a
non-computing physical object. To enable context
awareness, system developers must provide agents
with the access to context. The process of acquiring
context from the physical environment is called
context acquisition.
Contexts are acquired by directly accessing low-level
context sensors.
Contexts are acquired from some kind of middle-ware
infrastructures that in turn interact with low-level context
sensors.
Contexts are acquired from servers that maintain
situational knowledge about the environment.
4. CONTEXT BROKER
Platforms that provide context in realtime are “Context Brokers”
“A context broker is a service that is designed to gather reachable
context data of a variety of types, sources and velocity. It then
applies conditioning, integration, rules and analytics to derive the
reduced prepared context data, actionable at a point of business
decision by a system or a human.”
“Emerging patterns of digital business and the Internet of Things
challenge organizations to be aware of current and historical context.
IT strategy leaders are adopting context brokers, joining enterprise
databases and big data with business analytics for smarter business
decisions.”
Context Brokers for Smarter Business Decisions by Yefim V. Natis and W. Roy Schulte
5. CHARACTERISTICS AND USE CASES
Activities to accomplish Context Broker Functionality
Collection of data
Deduction of (Actionable) Context
Storage of context data
Triggering context events
Sharing (expose) Context Data
Use Cases
Customer Service: Identify concerning events or virality and notify
Logistics: Identify Optimal Route for Supply vehicles
Retail: Locate customer in store and offer discounts based on previous buying/online
search history
Finance: Loan and Claim processing decision making
Healthcare: Recommend treatment based on medical history of Patient and Family, Life
Style etc
8. ARCHITECTURE KEY POINTS
Context Collection
Data collection Adapter. Can be custom standalone components or based on
Frameworks
Context Derivation
CEP frameworks like Apache Storm can process incoming streams
Batch Analytics can be achieved by Hadoop platform components (Map Reduce or
Spark)
Custom processing components (simple message listeners), in case existing brokers not
supported by CEP frameworks, can be deployed as containers and managed by Mesos
Context Sharing
These can be Micro services exposing specific APIs
Data communication can leverage standard format like NGSI (
http://technical.openmobilealliance.org/Technical/release_program/docs/NGSI/V1_0-20120529-A/OMA-TS-NGS
)
Can be plugged into any existing Middle ware using supporting
components from Architecture
Supports addition of Adhoc or new analysis by ability deploying or un
deploying components without disturbing existing deployment
9. MODES OF CONTEXT BROKERS
Mode Description
Simple Collects and exposes Data
Smart Collects Data, Performs Analysis and exposes Data
Adaptive Collects Data, Performs Analysis including on demand
Analysis and exposes Data
Active Collects Data, Performs Analysis, Identifies events
and Alerts
http://www.gartner.com//it/content/2960500/2960535/february_11_context_brokers_ynatis.pdf?userId=57589440