Title:
Business intelligence in retail sector using data cubes
/ By Hiba Hussein Issa Bloukh
; supervised by Dr. Dana Abdel-Karim al-Qudah.
ذكاء الأعمال في قطاع البيع بالتجزئة باستخدام مكعبات البيانات
ذكاء الأعمال في قطاع البيع بالتجزئة باستخدام مكعبات البيانات
ذكاء الأعمال في قطاع البيع بالتجزئة باستخدام مكعبات البيانات
Author:
Bloukh, Hiba Hussein Issa , author.
al-Qudah, Dana Abdel-Karim, supervisor.
The University of Jordan (Amman, Jordan). King Abdullah II School of Information Technology. Department of Information Technology.
General Notes:
Thesis (M.Sc in Web Intelligence )--The University of Jordan (Amman, Jordan), King Abdullah II School of Information Technology, Department of Information Technology
, 2024.
Includes bibliographical references and index.
This thesis introduces an innovative methodology by integrating data analysis and machine learning techniques in the retail sector. Leveraging advanced tools such as data cubes and machine learning algorithms, the research extracts actionable insights from retail data.
The dataset originates from a prominent retail company's website, encompassing diverse data sources. Utilizing machine learning, specifically market basket analysis, customer lifetime value estimation, and sales prediction, the study aims to monitor key performance indicators (KPIs) of an E-commerce website. The integration of these techniques occurs within Microsoft Power BI, utilizing data cubes for a comprehensive understanding of customer behavior.
The primary objective of this research is to develop a tailored analytical tool to meet the specific analysis requirements of retailers' websites. The goal is to enhance decision-making processes by providing a detailed scrutiny of e-commerce platform data.
The thesis culminates in an interactive Power BI report that empowers end-users to monitor KPIs, track sales and orders trends, and explore market basket analysis. Users can filter results based on item categories and examine customer segments along with their purchasing behavior.
The summary highlights key findings: the Customer Lifetime Value (CLTV) model achieves an overall accuracy of 81.29%, with the "Low" category exhibiting optimal precision, recall, and F1 score. Additionally, sales predictions are characterized by Mean Squared Error (MSE) of 14785, Root Mean Squared Error (RMSE) of 121, and Absolute Squared Error (ASE) of eleven. These metrics contribute to a comprehensive evaluation of the model's performance.
تقدم هذه الأطروحة نهجا مبتكرا يجمع بين تحليل البيانات وتعلم الآلة داخل قطاع التجزئة الإلكترونية، باستخدام تقنيات متقدمة مثل مكعبات البيانات وتعلم الآلة لاستخلاص رؤى قابلة للتنفيذ من بيانات موقع التجارة الإلكترونية. تجمع هذه الأطروحة مصادر بيانات متنوعة من البيانات الخام مع نتائج تعلم الآلة (تحليل سلة التسوق، قيمة عمر العميل وتوقع المبيعات) مراقبة مؤشرات الأداء الرئيسية (KPIs) لموقع التجارة الإلكترونية وفهم سلوك العملاء بعمق باستخدام مكعبات البيانات باستخدام Microsoft Power BI.
يهدف هذا البحث بشكل أساسي إلى تطوير أداة مصممة خصيصًا لتلبية متطلبات تحليل مواقع التجار الإلكترونيين للتدقيق في بيانات منصة التجارة الإلكترونية الخاصة بهم، وبالتالي تحسين عمليات صنع القرار.
ناتج الأطروحة هو تقرير Power BI تفاعلي يتيح للمستخدمين النهائيين مراقبة مؤشرات الأداء الرئيسية واتجاهات المبيعات والطلبات، واستكشاف تحليل سلة التسوق مع القدرة على تصفية النتائج بناءً على فئة العناصر وإظهار شرائح العملاء مع سلوك الشراء الخاص
The electronic version is available in theses database \\ University of Jordan.
Includes abstracts in Arabic and English.
Subject:
Artificial intelligence
Business intelligence
data cube
E -- commerce.
Machine learning
Dissertation Note:
Thesis (M.Sc in Web Intelligence )--The University of Jordan (Amman, Jordan), King Abdullah II School of Information Technology, Department of Information Technology
, 2024.
Physical Description:
1CD-ROM : PDF.
Publication Date:
2024.
There are no items available
Title:
Business Intelligence Second European Summer School, eBISS 2012, Brussels, Belgium, July 15-21, 2012, Tutorial Lectures / edited by Marie-Aude Aufaure, Esteban Zimányi.
Lecture Notes in Business Information Processing,
Lecture Notes in Business Information Processing,
Author:
Aufaure, Marie-Aude. editor.
Zimányi, Esteban. editor.
SpringerLink (Online service)
General Notes:
Managing Complex Multidimensional Data -- An Introduction to Business Process Modeling -- Machine Learning Strategies for Time Series Forecasting -- Knowledge Discovery from Constrained Relational Data: A Tutorial on Markov Logic Networks -- Large Graph Mining: Recent Developments, Challenges and Potential Solutions -- Big Data Analytics on Modern Hardware Architectures: A Technology Survey -- An Introduction to Multicriteria Decision Aid: The PROMETHEE and GAIA Methods -- Knowledge Harvesting for Business Intelligence -- Business Semantics as an Interface between Enterprise Information Management and the Web of Data: A Case Study in the Flemish Public Administration.
To large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels. BI is now impacted by the “Big Data” phenomena and the evolution of society and users. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications. In addition, they must be able to provide their results also on mobile devices, taking into account location-based or time-based environmental data. The lectures held at the Second European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI and BPM technologies, but extend into innovative aspects that are important in this new environment and for novel applications, e.g., machine learning, logic networks, graph mining, business semantics, large-scale data management and analysis, and multicriteria and collaborative decision making. Combining papers by leading researchers in the field, this volume equips the reader with the state-of-the-art background necessary for creating the future of BI. It also provides the reader with an excellent basis and many pointers for further research in this growing field.
Publisher:
Springer Berlin Heidelberg : Imprint: Springer,
Publication Place:
Berlin, Heidelberg :
ISBN:
9783642363184
Subject:
Economics.
Computational complexity.
Computer science.
Database management.
Information storage and retrieval systems.
Information systems.
Management information systems.
Economics/Management Science.
Business information systems.
Computer Appl. in Administrative Data Processing.
Database management.
Information Storage and Retrieval.
Discrete Mathematics in Computer Science.
Probability and Statistics in Computer Science.
Series:
Lecture Notes in Business Information Processing, 138
Lecture Notes in Business Information Processing, 138
Contents:
Managing Complex Multidimensional Data -- An Introduction to Business Process Modeling -- Machine Learning Strategies for Time Series Forecasting -- Knowledge Discovery from Constrained Relational Data: A Tutorial on Markov Logic Networks -- Large Graph Mining: Recent Developments, Challenges and Potential Solutions -- Big Data Analytics on Modern Hardware Architectures: A Technology Survey -- An Introduction to Multicriteria Decision Aid: The PROMETHEE and GAIA Methods -- Knowledge Harvesting for Business Intelligence -- Business Semantics as an Interface between Enterprise Information Management and the Web of Data: A Case Study in the Flemish Public Administration.
Physical Description:
X, 235 p. 83 illus. online resource.
Electronic Location:
http://dx.doi.org/10.1007/978-3-642-36318-4
Publication Date:
2013.
Title:
Business Intelligence Tools for Small Companies A Guide to Free and Low-Cost Solutions / by Albert Nogués, Juan Valladares.
Author:
Nogués, Albert. author.
Valladares, Juan. author.
SpringerLink (Online service)
General Notes:
Chapter 1: Business Intelligence for Everybody -- Chapter 2: Agile Methodologies for BI Projects -- Chapter 3: SQL Basics -- Chapter 4: Project Initialization - Database and Source ERP Installation -- Chapter 5: Data Modeling for BI Solutions -- Chapter 6: ELT Basics -- Chapter 7: Performance Improvements -- Chapter 8: The BI Reporting Interface -- Chapter 9: MOLAP Tools for Budgeting -- Chapter 10: BI Process Scheduling: How to Orchestrate and Update Running Processes -- Chapter 11: Moving to a Production Environment -- Chapter 12: Moving BI Processes to the Cloud -- Chapter 13: Conclusions and Next Steps.
Learn how to transition from Excel-based business intelligence (BI) analysis to enterprise stacks of open-source BI tools. Select and implement the best free and freemium open-source BI tools for your company’s needs and design, implement, and integrate BI automation across the full stack using agile methodologies. Business Intelligence Tools for Small Companies provides hands-on demonstrations of open-source tools suitable for the BI requirements of small businesses. The authors draw on their deep experience as BI consultants, developers, and administrators to guide you through the extract-transform-load/data warehousing (ETL/DWH) sequence of extracting data from an enterprise resource planning (ERP) database freely available on the Internet, transforming the data, manipulating them, and loading them into a relational database. The authors demonstrate how to extract, report, and dashboard key performance indicators (KPIs) in a visually appealing format from the relational database management system (RDBMS). They model the selection and implementation of free and freemium tools such as Pentaho Data Integrator and Talend for ELT, Oracle XE and MySQL/MariaDB for RDBMS, and Qliksense, Power BI, and MicroStrategy Desktop for reporting. This richly illustrated guide models the deployment of a small company BI stack on an inexpensive cloud platform such as AWS. You will learn how to manage, integrate, and automate the processes of BI by selecting and implementing tools to: Implement and manage the business intelligence/data warehousing (BI/DWH) infrastructure Extract data from any enterprise resource planning (ERP) tool Process and integrate BI data using open-source extract-transform-load (ETL) tools Query, report, and analyze BI data using open-source visualization and dashboard tools Use a MOLAP tool to define next year's budget, integrating real data with target scenarios Deploy BI solutions and big data experiments inexpensively on cloud platforms.
Publisher:
Apress : Imprint: Apress,
Publication Place:
Berkeley, CA :
ISBN:
9781484225684
Subject:
Computer science.
Big data.
Database management.
Information storage and retrieval.
Computer science.
Open Source.
Database management.
Information Storage and Retrieval.
Big Data/Analytics.
Contents:
Chapter 1: Business Intelligence for Everybody -- Chapter 2: Agile Methodologies for BI Projects -- Chapter 3: SQL Basics -- Chapter 4: Project Initialization - Database and Source ERP Installation -- Chapter 5: Data Modeling for BI Solutions -- Chapter 6: ELT Basics -- Chapter 7: Performance Improvements -- Chapter 8: The BI Reporting Interface -- Chapter 9: MOLAP Tools for Budgeting -- Chapter 10: BI Process Scheduling: How to Orchestrate and Update Running Processes -- Chapter 11: Moving to a Production Environment -- Chapter 12: Moving BI Processes to the Cloud -- Chapter 13: Conclusions and Next Steps.
Physical Description:
XXIII, 326 p. 168 illus., 163 illus. in color. online resource.
Electronic Location:
http://dx.doi.org/10.1007/978-1-4842-2568-4
Publication Date:
2017.
Title:
Business Intelligence with SQL Server Reporting Services by Adam Aspin.
Author:
Aspin, Adam. author.
SpringerLink (Online service)
General Notes:
Business Intelligence with SQL Server Reporting Services helps you deliver business intelligence with panache. Harness the power of the Reporting Services toolkit to combine charts, gauges, sparklines, indicators, and maps into compelling dashboards and scorecards. Create compelling visualizations that seize your audience’s attention and help business users identify and react swiftly to changing business conditions. Best of all, you'll do all these things by creating new value from software that is already installed and paid for – SQL Server and the included SQL Server Reporting Services. Businesses run on numbers, and good business intelligence systems make the critical numbers immediately and conveniently accessible. Business users want access to key performance indicators in the office, at the beach, and while riding the subway home after a day's work. Business Intelligence with SQL Server Reporting Services helps you meet these need for anywhere/anytime access by including chapters specifically showing how to deliver on modern devices such as smart phones and tablets. You'll learn to deliver the same information, with similar look-and-feel, across the entire range of devices used in business today. Key performance indicators give fast notification of business unit performance Polished dashboards deliver essential metrics and strategic comparisons Visually arresting output on multiple devices focuses attention.
Publisher:
Apress : Imprint: Apress,
Publication Place:
Berkeley, CA :
ISBN:
9781484205327
Subject:
Computer science.
Data mining.
Computer science.
Computer Science, general.
Data Mining and Knowledge Discovery.
Physical Description:
XXII, 428 p. 152 illus. online resource.
Electronic Location:
http://dx.doi.org/10.1007/978-1-4842-0532-7
Publication Date:
2015.
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