Design of Data Analysis Systems for Business Process Automation

Rymarczyk, Tomasz; Cieplak, Tomasz; Kłosowski, Grzegorz; Rymarczyk, Pawel

doi:10.5604/01.3001.0012.5283

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…In the age of global digitization, information growth is almost exponential. The accumulation of ever-larger databases and information resources requires the automation of sales and business systems (Rymarczyk et al, 2018). In a world of virtual assistants and automated recommendation systems, knowledge graph-based systems are gaining importance as a critical tool for data management and analysis.…”

Section: Introductionmentioning

confidence: 99%

Product Knowledge Graphs - Creating a Knowledge System for Customer Support

Przysucha,

Kaleta,

Dmowski

et al. 2024

ERSJ

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Purpose: This article explores developing and integrating a product knowledge graph within an e-commerce customer support system to improve product discovery and recommendation processes. Methodology:The methodology involves a structured development process for the knowledge graph, utilizing natural language processing (NLP) to extract relevant entities from product data and machine learning algorithms to establish and categorize relationships between products. The approach integrates data from multiple sources, including vendor catalogs, online reviews, and customer interactions, ensuring a comprehensive data set. Findings: The research resulted in the creation of a dynamic, scalable knowledge graph that significantly enhances the accuracy and personalization of product recommendations. The graph's ability to link seemingly disparate data points allows for a nuanced understanding of user behavior and preferences, improving customer satisfaction and sales performance. Practical Implications: The presented method has significant implications for retailers looking to enhance their online presence and customer interaction. By implementing this knowledge graph, retailers can expect to streamline their product recommendation processes and gain deeper insights into customer trends, which can inform broader marketing and inventory decisions. Value: This study's novelty lies in applying a comprehensive knowledge graph tailored explicitly for e-commerce systems. This graph integrates abstract and concrete entities to offer a richer, more interconnected dataset than traditional relational databases.

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Section: Introductionmentioning

confidence: 99%

Product Knowledge Graphs - Creating a Knowledge System for Customer Support

Przysucha,

Kaleta,

Dmowski

et al. 2024

ERSJ

View full text Add to dashboard Cite

show abstract

“…In a market economy characterized by a high degree of competitiveness, the consumer is at the center of attention of companies operating on the market (Rymarczyk, Cieplak, Kłosowski, Rymarczyk, 2018;Polakowski, Filipowicz, Sikora, and Rymarczyk, 2008). Getting to know its needs, preferences, and factors influencing how it behaves is one of the essential tasks of every company.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Consumer Behavior Using an Intelligent Multi-Source System

Mazurek,

Halas,

Sikora

et al. 2024

ERSJ

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Purpose: This work aims to develop an innovative system that analyzes multi-source data and human behavior, ultimately creating and sharing improved procedures and solutions. It focuses on building an IT system prototype for behavior analysis, optimizing the data mining process, and generating innovative business processes. Design/Methodology/Approach: The application aims to optimize processes, analyze data, and reveal relationships between data and processes. Business models will be created using external data, data warehouses (such as ERP systems), and data from online resources (web mining). A process database will support computational intelligence algorithms, with an agent responsible for gathering online data. New data management methods were developed and implemented, while algorithms were designed for efficient web data searching. The system will leverage artificial neural networks, statistical and stochastic methods, fuzzy sets, genetic algorithms, and combinations to build an intelligent computing system. Findings: The innovative system will contribute new data management methods and algorithms for web data searching and analysis. The algorithms will advance methods and concepts for capturing, transmitting, collecting, and extracting information while providing suitable data presentation formats. Practical Implications: The insights from this system have the potential to revolutionize the way businesses identify and optimize new processes, generate innovative business models, and strengthen their decision-making. By comprehensively analyzing multi-source data, this system can inspire and motivate professionals in the field of data analysis and process optimization. Originality/Value: This research is at the forefront of developing and implementing a system for analyzing multi-source data and human behavior. By using cutting-edge techniques such as artificial neural networks, statistical and stochastic methods, fuzzy sets, and genetic algorithms, this work provides an intelligent and robust framework for mining data and optimizing business processes, which is sure to intrigue and interest academic researchers, data analysts, and business professionals in the audience.

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Artificial Intelligence Application in Smart Warehousing Environment for Automated Logistics

Pandian¹

2019

JAICN

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The advancements in the technologies, the revolution in the business procedures and the entailment to modify the operation in the warehousing as the result of the accumulating orders along with the complications involved in it, and the shortage in the management skills has paved way for the emergence of the smart ware housing. More over as the warehousing takes a vital role in the supply chain and prevails as the key feature in the logistics, smart ware housing is very much necessitated to enhance the organization management and success. The application of the artificial intelligence in the warehousing operations enhances the potentials of the warehousing functioning in the logistics, management and the co-ordination. The application of the artificial intelligence in the warehousing to make it a smart environment for the automated logistics is proposed in the paper. The paper concentrates on the automated storage and the retrieval using the internet of things, artificial intelligence and the cloud computing to have an any time access of the stock available in the warehouse.

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Design of Data Analysis Systems for Business Process Automation

Cited by 3 publications

References 15 publications

Product Knowledge Graphs - Creating a Knowledge System for Customer Support

Product Knowledge Graphs - Creating a Knowledge System for Customer Support

Analysis of Consumer Behavior Using an Intelligent Multi-Source System

Artificial Intelligence Application in Smart Warehousing Environment for Automated Logistics

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