Karar Ağacı Algoritması ile E-ticaret Uygulaması

2021 ◽  
Author(s):  
Hilal Çelik ◽  
Ahmet Çınar
Keyword(s):  
Rule Engine

İnternet kullanımının büyük bir ivme ile arttığı son yıllarda internet sadece bir araştırma ve veri depolama ortamı olmaktan ziyade birer alış veriş merkezi haline dönüştü. E-ticaret olarak adlandırılan bu kavram iş dünyası pazarına farklı bir boyut kazandırdı. Özellikle sosyal medya siteleri son zamanlarda birden fazla e- ticarete dönüşmüştür. Gelişen e-ticaret dünyasında doğru müşteri kitlesine daha yüksek doğruluk payıyla ulaşmaya çalıştığımız bu çalışmada amaç sisteme yeni giriş yapan bir müşteriye eldeki mevcut ürünün satışı yapılabilir mi? Bu çalışma Knime platformu üzerinde KDD veri bilimi yöntemi adımları takip edilerek makine öğrenmesi algoritmalarından karar ağaçları kullanılmış ve öğrenme tiplerinden gözetimli öğrenme ile gerçekleştirildi. Bu çalışmanın scorer sonucu olarak %93,487 bir doğruluk payı elde edildi. Bu çalışmada veri setin üzerinde daha kolay çalışabilmek için Knime’nin düğümlerinden olan Rule Engine vasıtasıyla sözcük türünde aynı tür veriler sayısal veriye dönüştürülerek hem veri setinin daha kolay yorumlanması sağlanmış hem de veri seti üzerinde çalışılırken zamandan kazanç sağlanmıştır.

RUNES II: A Distributed Rule Engine Based on Rete Network in Cloud Computing

2014 ◽  
Vol 7 (6) ◽  
pp. 91-110 ◽  
Author(s):  
Rui Zhou ◽  
Guowei Wang ◽  
Jinghan Wang ◽  
Jing Li
Keyword(s):  
Cloud Computing ◽  
Rule Engine

SWRLx

2018 ◽  
pp. 348-367
Author(s):  
Souad Bouaicha ◽  
Zizette Boufaida
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Object Oriented ◽  
Web Ontology Language ◽  
Rule Engine ◽  
Ontology Language ◽  
New Knowledge ◽  
Semantic Web Rule Language

Although OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language) add considerable expressiveness to the Semantic Web, they do have expressive limitations. For some reasoning problems, it is necessary to modify existing knowledge in an ontology. This kind of problem cannot be fully resolved by OWL and SWRL, as they only support monotonic inference. In this paper, the authors propose SWRLx (Extended Semantic Web Rule Language) as an extension to the SWRL rules. The set of rules obtained with SWRLx are posted to the Jess engine using rewrite meta-rules. The reason for this combination is that it allows the inference of new knowledge and storing it in the knowledge base. The authors propose a formalism for SWRLx along with its implementation through an adaptation of different object-oriented techniques. The Jess rule engine is used to transform these techniques to the Jess model. The authors include a demonstration that demonstrates the importance of this kind of reasoning. In order to verify their proposal, they use a case study inherent to interpretation of a preventive medical check-up.

Extending IoTs Into the Cloud-Based Platform for Examining Amazon Web Services

2018 ◽  
pp. 216-227 ◽  
Author(s):  
Jagdeep Kaur ◽  
Meghna Sharma
Keyword(s):  
Web Services ◽  
Future Research ◽  
Rule Engine ◽  
Management Tools ◽  
The Public ◽  
Main Motive ◽  
Amazon Web Services ◽  
Future Research Directions ◽  
Iot Devices ◽  
Development And Management

The public cloud Amazon Web Service (AWS) provides a wide range of services like computation, networking, analytics, development and management tools, application services, mobile services, and management of Internet-of-Things (IoT) devices. The Amazon Web Services (AWS) IoT is an excellent IoT cloud platform and is exclusively responsible for connecting devices into various fields like healthcare, biology, municipal setup, smart homes, marketing, industrial, agriculture, education, automotive, etc. This chapter highlights many other initiatives promoted by AWS IoT. The main motive of this chapter is to present how AWS IoT works. The chapter starts with the design principles of AWS IoT services. Further, the authors present a detailed description of the AWS IoT components (e.g., Device SDK, Message Broker, Rule Engine, Security and Identity Service, Thing Registry, Thing Shadow, and Thing Shadow Service). The chapter concludes with a description of various challenges faced by AWS IoT and future research directions.

Rule Engines and Agent-Based Systems

2012 ◽  
pp. 211-218 ◽  
Author(s):  
Agostino Poggi ◽  
Michele Tomaiuolo
Keyword(s):  
Dynamic Environments ◽  
Multi Agent Systems ◽  
Rule Engine ◽  
Rule Based ◽  
Agent Based ◽  
Agent Systems ◽  
Loosely Coupled ◽  
Domain Specific ◽  
Multi Agent ◽  
Good Support

Expert systems are successfully applied to a number of domains. Often built on generic rule-based systems, they can also exploit optimized algorithms. On the other side, being based on loosely coupled components and peer to peer infrastructures for asynchronous messaging, multi-agent systems allow code mobility, adaptability, easy of deployment and reconfiguration, thus fitting distributed and dynamic environments. Also, they have good support for domain specific ontologies, an important feature when modelling human experts’ knowledge. The possibility of obtaining the best features of both technologies is concretely demonstrated by the integration of JBoss Rules, a rule engine efficiently implementing the Rete-OO algorithm, into JADE, a FIPA-compliant multi-agent system.

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