Debs2011 ARCADE

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The document proposes a solution called ARCADE to bridge the gap between IT developers and business users in defining and managing complex event scenarios. It introduces the concept of reusable complex event templates with scenario parameters to simplify the definition of common scenarios. These templates can then be dynamically realized as instances by assigning specific values to the parameters. This reduces IT dependence and makes the process simpler for business users to define, update, and manage the lifecycle of complex event scenarios.

Technology Business

ARCADE - Abstraction and Realization of Complex Event Scenarios Using Dynamic Rule Creation Ashish A Kulkarni [email_address]

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 Developer Business User

Where existing CEP tools lag Require too much work Can’t be used by Business users

What we observed ,[object Object],[object Object],[object Object],[object Object],[object Object]

Reusable Complex Event Templates ,[object Object],[object Object]

Industry Scenarios redefined ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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

Lecture 3 Statistical Process Control (SPC) Data collection for Six SigmaData are simply facts and figures without context or interpretation.Information refers to useful or meaningful patterns found in the data.Knowledge represents information of sufficient quality and/or quantity that actions can be taken based on the information.If data are not collected and used wisely, their vary existence can lead to activities that are ineffective and possibly even counterproductive.An organization collects data & reacts whenever an out-of-specification condition occurs. “Common cause” & “ special cause” variation There are two causes of process variations: 1) Common cause variation: This variation is due to the process only. It may not tell you whether the process meets the needs of the customer unless it is compared with the specification. This can be improved by focusing on the process. 2) Special cause variation: This variation is due the individual employee, if the point is beyond specification limits. In this case the focus should be about what happened relative to the individual employee as though it were a “special” condition. Attribute versus Variable Data Attribute data: It is a data with yes or no decision such as:whether an iten passed or failed a testpass/fail, go/no go gaging, true/false, accept/reject. There are no quantifiable values Variable data: are related to measurements with quantifiable values such as:Diameter of a part which has been machinedlength or thickness of the machined part The success of Six SigmaThe success of Six Sigma depends upon knowing the difference between special & common cause variations and how the organization reacts to the data.If the management focuses on wrong cause of variation, it can lead to waste of time (firefighting).It can also effect employee motivation & morale.Reacting to one data point that do not meet the specification limit can be counterproductive and very expensive.Do not use “firefighting” actions just because the data point is out of specification limits. It must first be determined whether the condition is common or special cause. Example of variability due to common causeControl limits are calculated from the sample data.There are no data points outside the control limits therefore there are no special causes within the data.The source of variation in this case is “common cause” due to process. Type of firefighting done by management before evaluating the cause of variabilityProduction supervisors might constantly review production output by employee, machine, product line, work shift etc.An administrative assistant’s daily output & memo’s may be monitored.The average time per call may be monitored in a call center.The efficiency of computer programmers may be monitored by tracking “lines of code produced per day”. All of these actions would be a waste of time if the cause of variability is “common cause” and due to the process rather than individu ...

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Threat Mitigation Worksheet Form # TM01 Page ____ of _____ Business Name: Address: Facility # 001 Contact: Phone number: E-mail: Asset Threat Mitigation Techniques Threat Identification and Assessment Worksheet Form # TIDA01 Page ____ of _____ Business Name: Address: Facility # 001 Contact: Phone number: E-mail: Threat POC Assets Affected Consequence (C, S, M, I) Severity (C, S, M, I) Asset Identification Worksheet Form # AID01 Page ____ of _____ Business Name: Address: Facility # 001 Contact: Phone Number: E-mail: Asset Quantity Department or Location Value Priority C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A Business Process Identification Worksheet Form # BPID01 Page ____ of _____ Business Name: Address: Facility # 001 Contact: Phone number: E-mail: Business Process Priority Department Assets Used C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A C N D N/A Risk Assessment documentation templates are located within this section. Make additional copies as needed. In a real risk analysis process, one of the first steps is meeting with all department managers, upper management, employee representatives, and workers in the production environment, human resources staff, and other staff members to get their input. Without input from the people actually doing the work, you might not think of essential factors. That isn't possible here, so direct any questions you have to the instructor, or do independent research to find your answers. · First, identify the business processes that must continue for the organization to keep functioning—for example, collecting money from customers, receiving and processing sales, developing new products, and so on. Document major business processes that drive SunGrafix, using the Business Process column of the Business Process Identification Worksheet. (You need your imagination and some common sense for this step.) Assign a priority level to each process (using the priority rankings in the following list). Write down the department that performs the process, and leave the Assets Used column blank for now. Next, identify the organization's assets. Using the Asset Identification Worksheet that is provided in the Course Documents section on Blackboard, list each asset, its location, and approximate value, if known. (For multiple identical assets, describe the asset and list the quantity instead of listing each individual asset.) In organization-wide risk assessments, you would list all assets, including office furniture, industrial ...

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Debs2011 ARCADE

1. ARCADE - Abstraction and Realization of Complex Event Scenarios Using Dynamic Rule Creation Ashish A Kulkarni [email_address]

2. Motivation

3. 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.

4. Challenge – Bridging the IT-Business Gap IT Developer Business User

5. Where existing CEP tools lag Require too much work Can’t be used by Business users

7. The Solution

10. 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 ’

11. 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

12. Lifecycle Create Update Delete Read Activate Deactivate

13. Measurement Publication - Subscription

14.

Editor's Notes

The chemical and petroleum industry is facing a lot of challenges that are economical, environmental, and technical or operations related. There is overwhelming amounts of complex data from instrumented equipment. Need for near real time collaborative decision making, production costs, integrated operations, improved asset management are some of the main focus areas in this industry. RFID and sensors help in gathering various data points from the equipment. Continuous monitoring of this equipment for performance, and downtime is critical from operations perspective. This involves continuously monitoring the three phase flow of sediments (water, oil and gas) retrieved from reservoirs, calculation of gas to oil ratio (GOR) and comparing the well potential with actual output, detection of wells that are not performing properly, computing flow rates at multiple choke valves, identifying unacceptable drift during well performance monitoring. Water is increasingly becoming a scarce resource with no substitute. It is ridden with many issues mainly concerning ineffective usage, manual quality and quantity readings and close linkage with energy and carbon management as pumping water consumes power and generates greenhouse gases. In the water treatment facilities of the future, it will be imperative to apply advanced analytics to water quality in real time. Water quality analyzers collect many instrumented parameters like turbidity, conductivity, pH, chlorine residual, pressure, temperature, ammonia, oxidation -reduction potential and total organic carbon. It is desirable to monitor the pH variance for example and detect as it goes beyond acceptable thresholds. The input to such a system can even come from or the output can go to advanced analytics system, or to enterprise asset management systems, or real time control systems.
In the world of integrated operations, there is also a strong driver to bridge the gap between business and IT. The business would like to continuously monitor their business operations, and be able to change what they are looking at with minimal or no intervention from their IT. They want to be able to do this at real time. It is like looking through the kaleidoscope at the different patterns by varying the mirror angle. Of course it is not always a pretty picture that the patterns show. Sometimes the patterns reveal interesting trends that might help the business to cross-sell especially true of product sales, retail, supply-chain and similar businesses. At other times, the event patterns disclose potentially harmful trends, faulty equipment or an equipment about to go down. And at yet other times, they aid in identifying frauds or their likelihood. By observing these events, the business can control its inventory, change its manufacturing process, raise an asset maintenance order, alert key stake holders or take other appropriate action. The ability to control and vary what they are looking at not only saves them IT services cost but lets them take immediate advantage of business opportunities and react to impending breakdowns and failures. In the complex event scenarios that we identified above, we observed that the business was often interested in varying certain facets of the complex event scenario definition. Some wanted to monitor the pH of water against a threshold of 6.8 while others wanted to monitor it for a lesser threshold. Some wanted a level 1 alert on the temperature of a transmitter reaching certain value 1, a level 2 alert on the temperature breaching value 2 and be able to vary these thresholds on temporal and need basis.
A complex event definition is composed of a set of input events and a set of rules. The input event comes from an Event Source . If we look back at the business event scenario 1 that we identified, the measurement M1 from equipment E1 is the input event; equipment being the event source. The rule is to check if the measurement value exceeds the threshold. Similarly we can identify the measurements and rules for the other business event scenarios. Alternately we can also say that the scenario parameters are of two types- Measurement type and Rule type. This distinction is quite useful. This enables us to define a publication – subscription architecture for the measurement type parameters. When a new complex event instance is created and activated, we subscribe to the scenario parameter instances of the measurement type parameters. There is a corresponding publish model around the event source as well. Whenever the event source generates a measurement value, it is published for all the subscribing complex event instances to receive and act on it. There is also an intermediate step of Event Adaptation . Event adaptation involves creation of an event from the measurement value in a format expected by the underlying CEP runtime engine The other important blocks in the figure are the Event Information Management Services that map to the CRUD operations around the complex event templates and instances. The Query Services allow querying for the complex event templates, instances, scenario parameters, parameter instances and other artifacts. Event Orchestration Services map to the CEP tooling for creating complex event templates, and the Notification Services enable notification when a complex event is detected. There is an adaptation before the Event Sink to transform the complex event in a format expected by the sink. Event sink is a consumer of the business event. The Event Repository maps to the persistent store that we discussed. Together with the Event Topic/Channel Registry it helps in maintaining the measurement subscriptions. The subscriptions are live as long as there is at least one complex event instance receiving events through that subscription. When all the instances subscribing to a measurement are deactivated, the subscription is no longer needed and can be removed.

Debs2011 ARCADE

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Editor's Notes