Document the daily insurance decision service project.

Importable-Rule-Projects/DailyInsurance/README.md

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+# Introduction
+
+This sample shows a simplified daily car insurance price calculation.  
+The decision service computes the daily premium based on various attributes like driver's age, distance driven, risk of the zone driven, ect.
+
+# Demo Scenarios
+
+The project can be used to demo many Corticon concepts. It has simple rulesheets and ruleflows to show the basic constructs rule modelers work with.
+
+In "Calculate Daily Insurance Per Distance Driven.ers", rule 12 (column 12) can be used to show the power of conflict checker.
+
+"Adjust Daily Insurance Based On Zone Driven.ers" has a pre-condition filter: it will trigger only if the distance driven for the day is more than 0.
+
+The project contains multiple ruleflows to demo in various target environments like browser, Serverless and MarkLogic.
+Specifically:
+
+1) Calculate Daily Insurance.erf: is very generic and can be used with any target environment.  All data is passed to the Corticon decision service at once.
+   Data can contain a single or multiple drivers to process in a single call.
+2) Calculate Daily Insurance With SCO.erf: is specific to MarkLogic.  It is identical to the generic one, but it contains an additional step to retrieve the zone driven from an additional data document in MarkLogic
+
+## Demos with MarkLogic
+
+One can setup a MarkLogic trigger to execute the Corticon decision service on document change or create.
+See https://github.com/corticon/corticon.js-samples/blob/master/Importable-Rule-Projects/DailyInsurance/_MarkLogicTrigger/trigger.txt for an example on
+what the trigger code looks like.
+
+The next sections illustrate various patterns on how Corticon Decision Services can be used with MarkLogic
+
+### All Data Passed In
+
+Here the MarkLogic document causing the trigger to fire contains all the data necessary for the decision service to reach its conclusion.
+
+![allDataIn.PNG](allDataIn.PNG)
+
+### Data Retrieval on Demand
+
+In this pattern, we show how not all the data needs to be passed at once and how the Corticon decision service retrieves additional 
+data as part of its rules via a service callout.  This is particularly advantageous as the data retrieval can be triggered conditionally
+thus optimizing performance, network traffic and overall cost.
+
+For example, here we retrieve the risk level for zone driven during the day.
+
+![dataOnDemand.PNG](dataOnDemand.PNG)