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E-raamat: Artificial Intelligence for Business Analytics: Algorithms, Platforms and Application Scenarios

  • Formaat: PDF+DRM
  • Ilmumisaeg: 01-Mar-2023
  • Kirjastus: Springer Vieweg
  • Keel: eng
  • ISBN-13: 9783658375997
  • Formaat - PDF+DRM
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 01-Mar-2023
  • Kirjastus: Springer Vieweg
  • Keel: eng
  • ISBN-13: 9783658375997

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While methods of artificial intelligence (AI) were until a few years ago exclusively a topic of scientific discussions, today they are increasingly finding their way into products of everyday life. At the same time, the amount of data produced and available is growing due to increasing digitalization, the integration of digital measurement and control systems, and automatic exchange between devices (Internet of Things). In the future, the use of business intelligence (BI) and a look into the past will no longer be sufficient for most companies.Instead, business analytics, i.e., predictive and predictive analyses and automated decisions, will be needed to stay competitive in the future. The use of growing amounts of data is a significant challenge and one of the most important areas of data analysis is represented by artificial intelligence methods.This book provides a concise introduction to the essential aspects of using artificial intelligence methodsfor business analytics, presents machine learning and the most important algorithms in a comprehensible form using the business analytics technology framework, and shows application scenarios from various industries. In addition, it provides the Business Analytics Model for Artificial Intelligence, a reference procedure model for structuring BA and AI projects in the company. This book is a translation of the original German 1st edition Künstliche Intelligenz für Business Analytics by Felix Weber, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2020. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.
1 Business Analytics and Intelligence
1(32)
Need for Increasing Analytical Decision Support
1(4)
Distinction Between Business Intelligence and Business Analytics
5(2)
Categorisation of Analytical Methods and Models
7(4)
Descriptive Analytics
7(1)
Predictive Analytics
8(2)
Prescriptive Analytics
10(1)
Business Analytics Technology Framework (BA.TF)
11(8)
Data Sources
13(1)
Data Preparation
13(2)
Data Storage
15(1)
Analysis
16(1)
Access and Use
17(1)
(Big)-Data Management and Governance
18(1)
Procedure Model: Business Analytics Model for Artificial Intelligence (BAM.AI)
19(11)
Development Cycle
21(1)
Business Understanding
21(1)
Data Discovery
22(1)
Data Wrangling
23(1)
Analysis
24(1)
Validation
25(1)
New Data Acquisition
25(1)
Deployment Cycle
25(1)
Publish
26(1)
Analytic Deployment
27(2)
Application Integration
29(1)
Test
29(1)
Production/Operations
30(1)
Continuous Improvement
30(1)
References
30(3)
2 Artificial Intelligence
33(32)
Machine Learning
35(7)
Supervised Learning
36(1)
Unsupervised Learning
37(1)
Reinforcement Learning
38(1)
Overview of the Types of Machine Learning
39(1)
Neural Networks
39(3)
Types of Problems in Artificial Intelligence and Their Algorithms
42(21)
Classification
42(3)
Dependencies and Associations
45(3)
Clustering
48(2)
Regression, Prediction, or Forecasting
50(3)
Optimization
53(1)
Detection of Anomalies (Outliner)
54(2)
Recommendation or Recommender Systems
56(3)
When to Use Which Algorithm?
59(4)
References
63(2)
3 AI and BA Platforms
65(48)
Basic Concepts and Software Frameworks
65(16)
Data Management
65(1)
Data Warehouse
65(1)
Data Lake
66(2)
Data Streaming and Message Queuing
68(2)
Database Management System
70(1)
Apache Hadoop
71(2)
Data Analysis and Programming Languages
73(1)
Python
73(1)
R
74(1)
SQL
75(1)
Scala
75(1)
Julia
76(1)
AI Frameworks
77(1)
Tensorflow
77(1)
Theano
78(1)
Torch
79(1)
Scikit-Learn
79(1)
Jupyter Notebook
80(1)
Business Analytics and Machine Learning as a Service (Cloud Platforms)
81(29)
Amazon AWS
82(1)
Amazon AWS Data Services
83(4)
Amazon AWS ML Services
87(4)
Google Cloud Platform
91(1)
Data Services from Google
92(3)
ML Services from Google
95(1)
Google Prediction API and Cloud AutoML
96(1)
Google Cloud Machine Learning Engine (Cloud Machine Learning Engine)
96(1)
IBM Watson
96(1)
Microsoft Azure
97(1)
Data Services from Microsoft Azure
97(2)
ML Services from Microsoft Azure
99(1)
Overview of Other Microsoft Azure Services
100(1)
SAP Business Technology Platform (SAP BTP)
100(2)
Data Services from SAP
102(3)
ML Services from SAP
105(4)
SAP HANA Database Platform
109(1)
Build or Buy?
110(2)
References
112(1)
4 Case Studies on the Use of AI-Based Business Analytics
113(19)
Case Study: Analyzing Customer Sentiment in Real Time with Streaming Analytics
113(8)
Customer Satisfaction in the Retail Sector
113(1)
Technology Acceptance and Omnichannel for More Data
114(2)
Customer Satisfaction Streaming Index (CSSI)
116(1)
Implementation in a Retail Architecture
117(2)
Results
119(2)
Case Study: Market Segmentation and Automation in Retailing with Neural Networks
121(11)
The Location Decision in Stationary Trade
122(1)
Marketing Segmentation and Catchment Area
123(1)
Classical Clustering Approaches and Growing Neural Gas
124(3)
Project Structure
127(1)
The Data and Sources
127(3)
Implementation
130(2)
Results 132(2)
References 134
Felix Weber is a scientist at the University of Duisburg-Essen with a research focus on digitalization, artificial intelligence, price, promotion and assortment management, and transformation management. At the Chair of Information Systems and Integrated Information Systems, he is the founder of the Retail Artificial Intelligence Lab (retAIL) and at the same time a senior consultant for SAP systems in wholesale and retail. He thus combines current practice with scientific research in this subfield.