decision-engine | decision engine which based on fact and rules | Rule Engine library

 by   anandrajneesh JavaScript Version: 1.2.0 License: MIT

kandi X-RAY | decision-engine Summary

kandi X-RAY | decision-engine Summary

decision-engine is a JavaScript library typically used in Server, Rule Engine, Ruby On Rails applications. decision-engine has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can install using 'npm i decision-engine' or download it from GitHub, npm.

A decision engine which based on fact and rules provided can deduce what decisions should be made. For example in an Ecommerce system, when users place orders there are multiple decisions to be made, whether the order qualifies for discount, what kind of shipping is applicable and so on. This calls for a business rule engine which decision-engine exactly is.
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              decision-engine has a low active ecosystem.
              It has 9 star(s) with 3 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 5 open issues and 8 have been closed. On average issues are closed in 37 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of decision-engine is 1.2.0

            kandi-Quality Quality

              decision-engine has no bugs reported.

            kandi-Security Security

              decision-engine has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              decision-engine is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              decision-engine releases are available to install and integrate.
              Deployable package is available in npm.
              Installation instructions, examples and code snippets are available.

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            decision-engine Key Features

            No Key Features are available at this moment for decision-engine.

            decision-engine Examples and Code Snippets

            No Code Snippets are available at this moment for decision-engine.

            Community Discussions

            Trending Discussions on decision-engine

            QUESTION

            Understanding Phreak algorithm's performance
            Asked 2020-May-26 at 18:41

            I'm trying to understand what is making Drools perform poorly in our use case. I read the Phreak documentation here: https://access.redhat.com/documentation/en-us/red_hat_decision_manager/7.4/html/decision_engine_in_red_hat_decision_manager/phreak-algorithm-con_decision-engine

            It doesn't seem to mention anything regarding how node to node jumps are done. Here's an example to explain what I mean by that:

            Let's say I have a Person object with three fields: name, lastName, SSN

            I define a large number of rules this way: when lastName = 'Smith' and SSN = 'XXXXXXXXXX' then name = "Jane". Assuming I have a large number of people with "Smith" as last name, say 10,000, what is the complexity to get a single name given a last name and an SSN? Would it take 10,000 comparisons, or does the "Smith" node keep some form of hash map with all the underlying SSN?

            What if instead of an SSN with an equality operator I used a range, like a date range for example, defining rules like this: "when lastName = 'Smith' and startedShool >= 2005 and finishedSchool <= 2010". Do nodes keep some fancy data structure to speed up the queries with range operators?

            EDIT: Per request I'm adding an example to explain how the rules are set up

            We have a single class called Evidence as both input and output. We build each Evidence instance in a different activation-group and add it to a set. We usually define a few catch-all, broad rules with low salience and add rules with more specific conditions and higher salience.

            This is a sample of two rules with increasing salience. We define ~10K of these.

            One can imagine a sort of tree structure where at each level the salience increases and the conditions become more stringent. The Evidence class functions as a sort of key-value pair, so many of them will have the same node in common (e.g. name = location).

            To execute the rules, we would add two evidences (e.g. [name= location, stringValue='BBB'] and [name = code, stringValue = theCode]) and fire the rules.

            ...

            ANSWER

            Answered 2020-May-26 at 18:41

            Here is the rule template

            Source https://stackoverflow.com/questions/60815511

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install decision-engine

            You can install using 'npm i decision-engine' or download it from GitHub, npm.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            Install
          • npm

            npm i decision-engine

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          • HTTPS

            https://github.com/anandrajneesh/decision-engine.git

          • CLI

            gh repo clone anandrajneesh/decision-engine

          • sshUrl

            git@github.com:anandrajneesh/decision-engine.git

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