�ݺ�ߣshows by User: JacobFeldman2

�ݺ�ߣshows by User: JacobFeldman2 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: JacobFeldman2 / Wed, 13 Jan 2016 21:11:09 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: JacobFeldman2 Virtual Loader - Truck Loading Software /slideshow/virtual-loader-truck-loading-software/57026083 virtualloader-160113211109
oldie but goodie developed in 1995]]>
oldie but goodie developed in 1995]]> Wed, 13 Jan 2016 21:11:09 GMT /slideshow/virtual-loader-truck-loading-software/57026083 JacobFeldman2@slideshare.net(JacobFeldman2) Virtual Loader - Truck Loading Software JacobFeldman2 oldie but goodie developed in 1995 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/virtualloader-160113211109-thumbnail.jpg?width=120&height=120&fit=bounds" /> oldie but goodie developed in 1995

Virtual Loader - Truck Loading Software from Jacob Feldman

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0 Good old u serv product derby in the brave new world of decision management /slideshow/good-old-u-serv-product-derby-in-the-brave-new-world-of-decision-management/57025884 goodolduservproductderbyinthebravenewworldofdecisionmanagement-160113210607
Presentations at BBC-2015]]>
Presentations at BBC-2015]]> Wed, 13 Jan 2016 21:06:07 GMT /slideshow/good-old-u-serv-product-derby-in-the-brave-new-world-of-decision-management/57025884 JacobFeldman2@slideshare.net(JacobFeldman2) Good old u serv product derby in the brave new world of decision management JacobFeldman2 Presentations at BBC-2015 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/goodolduservproductderbyinthebravenewworldofdecisionmanagement-160113210607-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentations at BBC-2015

Good old u serv product derby in the brave new world of decision management from Jacob Feldman

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0 Practical Techniques for Resolving Conflicts among Business Rules /slideshow/practical-techniques-for-resolving-conflicts-among-business-rules/41210903 brforum2014-141106091127-conversion-gate02
Modern rules and decisions management systems provide effective mechanisms for modeling, managing and execution of business decision models. However, building real-world decision models, business analysts frequently face complex issues related to diagnostic and resolution of business rule conflicts. Some systems can effectively verify decision model consistency and diagnose rule conflicts. However, contradictory rules occur in normal business situations, and maintaining rules with exceptions is a very typical example. Today it mainly remains a responsibility of users to represent rules in such a way that allows them to avoid conflicts. As a result, the number of rules grows exponentially making their maintenance a real problem. Is it possible to automatically resolve rule conflicts? In this presentation we discuss different techniques for automatic resolution of conflicts among business rules. We will consider classical “strict” rules and “defeasible” rules that actually can be defeated by other rules. We will consider different representations of superiority relations among rules when one rule may override the conclusion of another rule. We will describe practical examples of contradictory rules from financial and other business domains. And finally we will demonstrate how a well-known theory of the “defeasible reasoning” can be incorporated into the modern Business Rules Management systems delivering practical solutions for this important problem]]>
Modern rules and decisions management systems provide effective mechanisms for modeling, managing and execution of business decision models. However, building real-world decision models, business analysts frequently face complex issues related to diagnostic and resolution of business rule conflicts. Some systems can effectively verify decision model consistency and diagnose rule conflicts. However, contradictory rules occur in normal business situations, and maintaining rules with exceptions is a very typical example. Today it mainly remains a responsibility of users to represent rules in such a way that allows them to avoid conflicts. As a result, the number of rules grows exponentially making their maintenance a real problem. Is it possible to automatically resolve rule conflicts? In this presentation we discuss different techniques for automatic resolution of conflicts among business rules. We will consider classical “strict” rules and “defeasible” rules that actually can be defeated by other rules. We will consider different representations of superiority relations among rules when one rule may override the conclusion of another rule. We will describe practical examples of contradictory rules from financial and other business domains. And finally we will demonstrate how a well-known theory of the “defeasible reasoning” can be incorporated into the modern Business Rules Management systems delivering practical solutions for this important problem]]> Thu, 06 Nov 2014 09:11:27 GMT /slideshow/practical-techniques-for-resolving-conflicts-among-business-rules/41210903 JacobFeldman2@slideshare.net(JacobFeldman2) Practical Techniques for Resolving Conflicts among Business Rules JacobFeldman2 Modern rules and decisions management systems provide effective mechanisms for modeling, managing and execution of business decision models. However, building real-world decision models, business analysts frequently face complex issues related to diagnostic and resolution of business rule conflicts. Some systems can effectively verify decision model consistency and diagnose rule conflicts. However, contradictory rules occur in normal business situations, and maintaining rules with exceptions is a very typical example. Today it mainly remains a responsibility of users to represent rules in such a way that allows them to avoid conflicts. As a result, the number of rules grows exponentially making their maintenance a real problem. Is it possible to automatically resolve rule conflicts? In this presentation we discuss different techniques for automatic resolution of conflicts among business rules. We will consider classical “strict” rules and “defeasible” rules that actually can be defeated by other rules. We will consider different representations of superiority relations among rules when one rule may override the conclusion of another rule. We will describe practical examples of contradictory rules from financial and other business domains. And finally we will demonstrate how a well-known theory of the “defeasible reasoning” can be incorporated into the modern Business Rules Management systems delivering practical solutions for this important problem <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/brforum2014-141106091127-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Modern rules and decisions management systems provide effective mechanisms for modeling, managing and execution of business decision models. However, building real-world decision models, business analysts frequently face complex issues related to diagnostic and resolution of business rule conflicts. Some systems can effectively verify decision model consistency and diagnose rule conflicts. However, contradictory rules occur in normal business situations, and maintaining rules with exceptions is a very typical example. Today it mainly remains a responsibility of users to represent rules in such a way that allows them to avoid conflicts. As a result, the number of rules grows exponentially making their maintenance a real problem. Is it possible to automatically resolve rule conflicts? In this presentation we discuss different techniques for automatic resolution of conflicts among business rules. We will consider classical “strict” rules and “defeasible” rules that actually can be defeated by other rules. We will consider different representations of superiority relations among rules when one rule may override the conclusion of another rule. We will describe practical examples of contradictory rules from financial and other business domains. And finally we will demonstrate how a well-known theory of the “defeasible reasoning” can be incorporated into the modern Business Rules Management systems delivering practical solutions for this important problem

Practical Techniques for Resolving Conflicts among Business Rules from Jacob Feldman

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0 The Decision Table Template For Geospatial Business Rules /slideshow/decision-camp-2014alexkarman/40437438 decisioncamp2014alexkarman-141018134405-conversion-gate01
We explore a new system that allows business users to natively define and maintain complex spatial rules without becoming experts in specific Java APIs. We applied OpenRules BDMS to create a new Spatial Decision Table template that allows stakeholders with no GIS training to use plain English in familiar Decision Model spreadsheets to define spatially aware business rules without any additional software.]]>
We explore a new system that allows business users to natively define and maintain complex spatial rules without becoming experts in specific Java APIs. We applied OpenRules BDMS to create a new Spatial Decision Table template that allows stakeholders with no GIS training to use plain English in familiar Decision Model spreadsheets to define spatially aware business rules without any additional software.]]> Sat, 18 Oct 2014 13:44:05 GMT /slideshow/decision-camp-2014alexkarman/40437438 JacobFeldman2@slideshare.net(JacobFeldman2) The Decision Table Template For Geospatial Business Rules JacobFeldman2 We explore a new system that allows business users to natively define and maintain complex spatial rules without becoming experts in specific Java APIs. We applied OpenRules BDMS to create a new Spatial Decision Table template that allows stakeholders with no GIS training to use plain English in familiar Decision Model spreadsheets to define spatially aware business rules without any additional software. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/decisioncamp2014alexkarman-141018134405-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> We explore a new system that allows business users to natively define and maintain complex spatial rules without becoming experts in specific Java APIs. We applied OpenRules BDMS to create a new Spatial Decision Table template that allows stakeholders with no GIS training to use plain English in familiar Decision Model spreadsheets to define spatially aware business rules without any additional software.

The Decision Table Template For Geospatial Business Rules from Jacob Feldman

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0 Building Domain-Specific Decision Models /slideshow/decision-camp2014jacobfeldman/40432988 decisioncamp2014-141018100302-conversion-gate01
In this presentation at the DecisionCAMP-2014 (http://www.decision-camp.com) I shared real-world experience of building various domain-specific decisions and business rules using a general-purpose BRDMS “OpenRules”. Our approach to a “business DSL” is based on development of domain-specific templates for decisions and business rules that utilize business concepts and decision variables for a particular domain (business glossary). My presentation shows how to convert domain-specific Java APIs to business-oriented decision modeling constructs. It describes 3 real-world use cases.]]>
In this presentation at the DecisionCAMP-2014 (http://www.decision-camp.com) I shared real-world experience of building various domain-specific decisions and business rules using a general-purpose BRDMS “OpenRules”. Our approach to a “business DSL” is based on development of domain-specific templates for decisions and business rules that utilize business concepts and decision variables for a particular domain (business glossary). My presentation shows how to convert domain-specific Java APIs to business-oriented decision modeling constructs. It describes 3 real-world use cases.]]> Sat, 18 Oct 2014 10:03:02 GMT /slideshow/decision-camp2014jacobfeldman/40432988 JacobFeldman2@slideshare.net(JacobFeldman2) Building Domain-Specific Decision Models JacobFeldman2 In this presentation at the DecisionCAMP-2014 (http://www.decision-camp.com) I shared real-world experience of building various domain-specific decisions and business rules using a general-purpose BRDMS “OpenRules”. Our approach to a “business DSL” is based on development of domain-specific templates for decisions and business rules that utilize business concepts and decision variables for a particular domain (business glossary). My presentation shows how to convert domain-specific Java APIs to business-oriented decision modeling constructs. It describes 3 real-world use cases. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/decisioncamp2014-141018100302-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this presentation at the DecisionCAMP-2014 (http://www.decision-camp.com) I shared real-world experience of building various domain-specific decisions and business rules using a general-purpose BRDMS “OpenRules”. Our approach to a “business DSL” is based on development of domain-specific templates for decisions and business rules that utilize business concepts and decision variables for a particular domain (business glossary). My presentation shows how to convert domain-specific Java APIs to business-oriented decision modeling constructs. It describes 3 real-world use cases.

Building Domain-Specific Decision Models from Jacob Feldman

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0 Integrating Business Rules, Constraint Programming and Machine Learning Technologies /slideshow/br-forum2008eugene-freuderjacobfeldman/34126366 brforum2008-140430081324-phpapp02
This presentation describes how such technologies as BR (Business Rules), CP (Constraint Programming) and ML (Machine Learning and Predictive Analytics) can be used together for online decision support]]>
This presentation describes how such technologies as BR (Business Rules), CP (Constraint Programming) and ML (Machine Learning and Predictive Analytics) can be used together for online decision support]]> Wed, 30 Apr 2014 08:13:24 GMT /slideshow/br-forum2008eugene-freuderjacobfeldman/34126366 JacobFeldman2@slideshare.net(JacobFeldman2) Integrating Business Rules, Constraint Programming and Machine Learning Technologies JacobFeldman2 This presentation describes how such technologies as BR (Business Rules), CP (Constraint Programming) and ML (Machine Learning and Predictive Analytics) can be used together for online decision support <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/brforum2008-140430081324-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation describes how such technologies as BR (Business Rules), CP (Constraint Programming) and ML (Machine Learning and Predictive Analytics) can be used together for online decision support

Integrating Business Rules, Constraint Programming and Machine Learning Technologies from Jacob Feldman

]]> 887 4 https://cdn.slidesharecdn.com/ss_thumbnails/brforum2008-140430081324-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Rules-based Mobile Resource Learner for Field Scheduling Applications /slideshow/trimble-decisioncamp/29398303 trimbledecisioncamp-131220151005-phpapp02
In this presentation Dr. Evgeny Selensky, a scheduling specialist from OpenRules customer Trimble, describes a rules-based mobile resource learner that addresses these problems in conjunction with a highly popular field scheduling tool. The learner enables the system to learn and adjust information about multi-level technical skills and geographic areas of the customer field service workforce. It allows a new customer to start using the scheduler with zero-configuration by just analyzing an actual history of technician work assignments. The learner uses a relatively small set of easily configurable rules that encode how workforce skills and locations are learned dynamically. The learned information is then fed into the scheduler/optimizer tool to improve schedule quality. The usage of the learner is demonstrated on a few simple examples. ]]>
In this presentation Dr. Evgeny Selensky, a scheduling specialist from OpenRules customer Trimble, describes a rules-based mobile resource learner that addresses these problems in conjunction with a highly popular field scheduling tool. The learner enables the system to learn and adjust information about multi-level technical skills and geographic areas of the customer field service workforce. It allows a new customer to start using the scheduler with zero-configuration by just analyzing an actual history of technician work assignments. The learner uses a relatively small set of easily configurable rules that encode how workforce skills and locations are learned dynamically. The learned information is then fed into the scheduler/optimizer tool to improve schedule quality. The usage of the learner is demonstrated on a few simple examples. ]]> Fri, 20 Dec 2013 15:10:05 GMT /slideshow/trimble-decisioncamp/29398303 JacobFeldman2@slideshare.net(JacobFeldman2) Rules-based Mobile Resource Learner for Field Scheduling Applications JacobFeldman2 In this presentation Dr. Evgeny Selensky, a scheduling specialist from OpenRules customer Trimble, describes a rules-based mobile resource learner that addresses these problems in conjunction with a highly popular field scheduling tool. The learner enables the system to learn and adjust information about multi-level technical skills and geographic areas of the customer field service workforce. It allows a new customer to start using the scheduler with zero-configuration by just analyzing an actual history of technician work assignments. The learner uses a relatively small set of easily configurable rules that encode how workforce skills and locations are learned dynamically. The learned information is then fed into the scheduler/optimizer tool to improve schedule quality. The usage of the learner is demonstrated on a few simple examples. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/trimbledecisioncamp-131220151005-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this presentation Dr. Evgeny Selensky, a scheduling specialist from OpenRules customer Trimble, describes a rules-based mobile resource learner that addresses these problems in conjunction with a highly popular field scheduling tool. The learner enables the system to learn and adjust information about multi-level technical skills and geographic areas of the customer field service workforce. It allows a new customer to start using the scheduler with zero-configuration by just analyzing an actual history of technician work assignments. The learner uses a relatively small set of easily configurable rules that encode how workforce skills and locations are learned dynamically. The learned information is then fed into the scheduler/optimizer tool to improve schedule quality. The usage of the learner is demonstrated on a few simple examples.

Rules-based Mobile Resource Learner for Field Scheduling Applications from Jacob Feldman

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0 How to Build Smarter Decision Models Capable To Make Optimal Decisions /JacobFeldman2/decision-camp2013jacobfeldman decisioncamp2013-131220150512-phpapp02
In this presentation we describe how “Decision Optimization” utilizes off-the-shelf optimization tools to help a business user to create smarter decision models that are capable to find decisions that minimize/maximize certain optimization objectives. Instead of trying to specify all possible rules, a decision model creator uses decision tables to represent only major business constraints and relationships between different decision variables. An optimization objective may be represented as a special decision variable defined on other key variables. A decision optimization component does the rest of work by automatically considering multiple alternatives and selecting the best one within a time limit defined by a user. Contrary to traditional rule engines, the optimization component is specifically designed to solve constraint satisfaction and optimization problems using proven optimization techniques such as constraint and/or linear programming. ]]>
In this presentation we describe how “Decision Optimization” utilizes off-the-shelf optimization tools to help a business user to create smarter decision models that are capable to find decisions that minimize/maximize certain optimization objectives. Instead of trying to specify all possible rules, a decision model creator uses decision tables to represent only major business constraints and relationships between different decision variables. An optimization objective may be represented as a special decision variable defined on other key variables. A decision optimization component does the rest of work by automatically considering multiple alternatives and selecting the best one within a time limit defined by a user. Contrary to traditional rule engines, the optimization component is specifically designed to solve constraint satisfaction and optimization problems using proven optimization techniques such as constraint and/or linear programming. ]]> Fri, 20 Dec 2013 15:05:12 GMT /JacobFeldman2/decision-camp2013jacobfeldman JacobFeldman2@slideshare.net(JacobFeldman2) How to Build Smarter Decision Models Capable To Make Optimal Decisions JacobFeldman2 In this presentation we describe how “Decision Optimization” utilizes off-the-shelf optimization tools to help a business user to create smarter decision models that are capable to find decisions that minimize/maximize certain optimization objectives. Instead of trying to specify all possible rules, a decision model creator uses decision tables to represent only major business constraints and relationships between different decision variables. An optimization objective may be represented as a special decision variable defined on other key variables. A decision optimization component does the rest of work by automatically considering multiple alternatives and selecting the best one within a time limit defined by a user. Contrary to traditional rule engines, the optimization component is specifically designed to solve constraint satisfaction and optimization problems using proven optimization techniques such as constraint and/or linear programming. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/decisioncamp2013-131220150512-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this presentation we describe how “Decision Optimization” utilizes off-the-shelf optimization tools to help a business user to create smarter decision models that are capable to find decisions that minimize/maximize certain optimization objectives. Instead of trying to specify all possible rules, a decision model creator uses decision tables to represent only major business constraints and relationships between different decision variables. An optimization objective may be represented as a special decision variable defined on other key variables. A decision optimization component does the rest of work by automatically considering multiple alternatives and selecting the best one within a time limit defined by a user. Contrary to traditional rule engines, the optimization component is specifically designed to solve constraint satisfaction and optimization problems using proven optimization techniques such as constraint and/or linear programming.

How to Build Smarter Decision Models Capable To Make Optimal Decisions from Jacob Feldman

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0 Rule Violations and Over-Constrained Problems /slideshow/rule-violations-and-overconstrained-problems/16241797 rulesfest2009-jacobfeldman-130129122243-phpapp01
This presentation 1) introduces concepts of hard and soft rules (constraints); 2) shares experience of measuring, controlling, and minimizing rule violations; 3) provides concrete examples of real‐world problems along with Java code that shows different practical approaches of handling rule violations]]>
This presentation 1) introduces concepts of hard and soft rules (constraints); 2) shares experience of measuring, controlling, and minimizing rule violations; 3) provides concrete examples of real‐world problems along with Java code that shows different practical approaches of handling rule violations]]> Tue, 29 Jan 2013 12:22:43 GMT /slideshow/rule-violations-and-overconstrained-problems/16241797 JacobFeldman2@slideshare.net(JacobFeldman2) Rule Violations and Over-Constrained Problems JacobFeldman2 This presentation 1) introduces concepts of hard and soft rules (constraints); 2) shares experience of measuring, controlling, and minimizing rule violations; 3) provides concrete examples of real‐world problems along with Java code that shows different practical approaches of handling rule violations <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rulesfest2009-jacobfeldman-130129122243-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation 1) introduces concepts of hard and soft rules (constraints); 2) shares experience of measuring, controlling, and minimizing rule violations; 3) provides concrete examples of real‐world problems along with Java code that shows different practical approaches of handling rule violations

Rule Violations and Over-Constrained Problems from Jacob Feldman

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0 Using Decision Tables to Model and Solve Scheduling and Resource Allocation Problems /slideshow/br-forum2012feldmanv1/16021716 brforum2012-feldman-v1-130116091228-phpapp02
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]]> Wed, 16 Jan 2013 09:12:28 GMT /slideshow/br-forum2012feldmanv1/16021716 JacobFeldman2@slideshare.net(JacobFeldman2) Using Decision Tables to Model and Solve Scheduling and Resource Allocation Problems JacobFeldman2 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/brforum2012-feldman-v1-130116091228-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Using Decision Tables to Model and Solve Scheduling and Resource Allocation Problems from Jacob Feldman

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0 Connecting the Dots /slideshow/connecting-dotsjacobfeldmanv3/16021714 connectingdots-jacobfeldman-v3-130116091225-phpapp02
A Simple Inference Framework for Connecting the Dots]]>
A Simple Inference Framework for Connecting the Dots]]> Wed, 16 Jan 2013 09:12:25 GMT /slideshow/connecting-dotsjacobfeldmanv3/16021714 JacobFeldman2@slideshare.net(JacobFeldman2) Connecting the Dots JacobFeldman2 A Simple Inference Framework for Connecting the Dots <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/connectingdots-jacobfeldman-v3-130116091225-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> A Simple Inference Framework for Connecting the Dots

Connecting the Dots from Jacob Feldman

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0 Modeling and Solving Decision Optimization Problems /slideshow/intelli-fest2012jacobfeldman/16021713 intellifest2012-jacobfeldman-130116091223-phpapp02
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]]> Wed, 16 Jan 2013 09:12:23 GMT /slideshow/intelli-fest2012jacobfeldman/16021713 JacobFeldman2@slideshare.net(JacobFeldman2) Modeling and Solving Decision Optimization Problems JacobFeldman2 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/intellifest2012-jacobfeldman-130116091223-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Modeling and Solving Decision Optimization Problems from Jacob Feldman

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0 https://cdn.slidesharecdn.com/profile-photo-JacobFeldman2-48x48.jpg?cb=1640890475 OpenRules is a popular Open Source Business Decision Management System openrules.com https://cdn.slidesharecdn.com/ss_thumbnails/virtualloader-160113211109-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/virtual-loader-truck-loading-software/57026083 Virtual Loader - Truck... https://cdn.slidesharecdn.com/ss_thumbnails/goodolduservproductderbyinthebravenewworldofdecisionmanagement-160113210607-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/good-old-u-serv-product-derby-in-the-brave-new-world-of-decision-management/57025884 Good old u serv produc... https://cdn.slidesharecdn.com/ss_thumbnails/brforum2014-141106091127-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/practical-techniques-for-resolving-conflicts-among-business-rules/41210903 Practical Techniques f...