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ARCADE - Abstraction and Realization of Complex Event Scenarios Using Dynamic Rule
Creation
Ashish A [email protected]
Motivation
The Industry Scenarios
Scenario 1: Measurement value from equipment goes beyond the threshold.
Scenario 2: Measurement values from multiple equipment exceed their respective thresholds.
Scenario 3: At a given instant of time, 'n' out of 'm' equipment in a group are above their respective threshold values for certain period of time.
Scenario 4: The case of equipment going down: Absence of data for a certain period of time.
Scenario 5: Case of frozen equipment: In a realistic scenario, it is expected to have white noise around mean data points. Non existence of it or in other words a flat curve of values from equipment should raise an alarm.
Scenario 6: Case of bad equipment: The data values not meeting the expected quality is an indicator of the equipment going bad.
Scenario 7: The rate of rise or fall of data points over a period of time is higher than expected.
Challenge – Bridging the IT-Business Gap
IT DeveloperBusiness User
Where existing CEP tools lag
Require too much work
Can’t be used by Business users
What we observed
• In the industry, new type of event scenario definition is an infrequent activity.
• There is a frequent need to reuse the existing event scenarios and create new similar event scenarios.
• It is also fairly common to update the complex event scenarios or remove them if they are no longer required.
• The available complex event tools approach the problem of complex event definition from a technical domain. They do not capture the business domain of the event definition.
• The existing tools therefore require trained personnel and significant effort to achieve the industry requirements around complex event definition.
The Solution
Reusable Complex Event Templates
• A Complex Event Template is a complex event definition with variables.
• We call these variables Scenario Parameters
Industry Scenarios redefined
• Scenario 1: Measurement value M1 from equipment E1 goes beyond the threshold T1.
• Scenario 2: Measurement values [M1, M2, …, Mn] from multiple equipment [E1, E2, …, En] exceed their respective thresholds [T1, T2, …, Tn].
• Scenario 3 : At a given instant of time ‘t’, 'n' out of 'm' equipment in a group [E1, E2, …, Em] are above their respective threshold values [T1, T2, …, Tn] for certain period of time t-t0 where [E1, E2, …, Em] is a subset of all equipment [E1, E2, …, Em, ..., En].
• Scenario 4 : The case of equipment E1 going down: Absence of data for a certain period of time t-t0.
• Scenario 5 : Case of frozen equipment E1: In a realistic scenario, it is expected to have white noise around mean data points. Non existence of it or in other words a flat curve of values from equipment E1 for duration t-t0 should raise an alarm.
• Scenario 6 : Case of bad equipment E1: The data values not meeting the expected quality Q is an indicator of the equipment E1 going bad.
• Scenario 7 : The rate R of rise or fall of data points over a period of time t-t0 is higher than expected Tr.
Dynamic Realization of templates
Measurement value M1 from equipment E1 goes beyond the threshold T1
Measurement value ‘temperature’ from an equipment ‘temperatureTransmitter’ goes beyond the threshold ‘100’
Measurement value ‘pH’ from an equipment ‘waterAcidity’ goes beyond the threshold ‘6.5’
As-is vs Modified
The as-is approach to realizing business event had a lot of IT dependence, was time consuming and tedious
The notion of complex event templates and instances significantly limits the IT intervention, simplifies business event realization and allows their dynamic life cycle management
Lifecycle
Create
Update
Delete
Read
Activate
Deactivate
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