E-Commerce Big Data Mining and Analytics:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Singapore
Springer
2023
|
| Edition: | 1st ed |
| Series: | Advanced Studies in E-Commerce Series
|
| Links: | https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=30669056 |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (217 Seiten) |
| ISBN: | 9789819935888 |
Staff View
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| 505 | 8 | |a Intro -- Preface -- I. Why Write a Book -- II. Organization of the Book -- III. Target Readers -- IV. Study Suggestions -- Acknowledgements -- Contents -- About the Author -- 1 Introduction -- 1.1 Overview of Business Big Data Mining and Applications -- 1.2 Big Data Infrastructure -- 1.2.1 Infrastructure Layer -- 1.2.2 Big Data Layer -- 1.3 Overview of Big Data Research in Commerce -- 1.3.1 Big Data Fusion -- 1.3.2 Knowledge Fusion -- 1.3.3 Trajectory Big Data Mining -- 1.3.4 Knowledge Graphs -- 1.3.5 User Portraits -- 1.3.6 E-Commerce Recommendation System -- References -- 2 Data Collection in the Era of Big Data -- 2.1 Data Types of Business Big Data -- 2.1.1 Structured Data -- 2.1.2 Semi-structured Data -- 2.1.3 Unstructured Data -- 2.2 Online Business Big Data Collection Solution -- 2.2.1 Enterprise Data Collection -- 2.2.2 Web Crawler Data -- 2.2.3 Mobile Device Data -- 2.2.4 Database Data Collection -- 2.3 Offline Business Big Data Collection Solution -- 2.3.1 Physical Data Collection -- 2.3.2 Activity Data Collection -- 2.4 Cases of Business Big Data Collection -- 2.4.1 Precise User Portrait Description -- 2.4.2 Social Platform User Description -- 3 Pre-processing Big Data for Business -- 3.1 Business Big Data Pre-processing Techniques -- 3.1.1 Data Acquisition -- 3.1.2 Data Cleaning -- 3.1.3 Data Transformation -- 3.1.4 Data Integration -- 3.1.5 Data Imputation -- 3.2 Inconsistency Elimination Strategies for Multi-source Heterogeneous Commerce Big Data -- 3.3 Semantic Extraction and Analysis of Business Big Data -- 3.3.1 What Is Semantics -- 3.3.2 Semantic Analysis in Big Data -- 3.4 Business Big Data Pre-processing Case -- 4 Big Data Database for Business -- 4.1 Key-Value Store -- 4.1.1 Background to the Development of Key-Value Store -- 4.1.2 Key-Value Database Versus Relational Databases -- 4.1.3 Key Value Database Advantages | |
| 505 | 8 | |a 4.1.4 Redis -- 4.2 Column Family Store -- 4.2.1 Column Family Database Storage Structure -- 4.2.2 Column Family Database Features -- 4.2.3 HBase -- 4.3 Graph Store -- 4.3.1 The Concept of a Graph -- 4.3.2 Property Graph -- 4.3.3 Graph Database -- 4.3.4 Neo4j -- 5 Security Management on Big Data of Business -- 5.1 Traceability Technology of Business Big Data -- 5.1.1 The Definition of Data Traceability -- 5.1.2 The Definition of PROV -- 5.1.3 The Constraint of PROV Traceability Graph -- 5.2 Privacy Protection of Business Big Data -- 5.2.1 Data Desensitization Technology -- 5.2.2 Differential Privacy Protection -- 5.2.3 K-anonymity -- 5.3 The Data Sharing of Commercial Big Data -- 5.3.1 Access Control -- 5.3.2 Zero Trust Architecture -- 5.3.3 Attribute Based Encryption -- 5.3.4 Homomorphic Encryption -- 5.4 Blockchain Technology -- 5.4.1 Peer to Peer Network -- 5.4.2 Digital Signature -- 5.4.3 Hash Function -- 5.4.4 SPV Lightweight Verification and Melkel Hash Tree -- 5.4.5 Application of Blockchain in Business Big Data -- 5.5 Business Big Data Management Case -- 5.5.1 Demand Analysis -- 5.5.2 Network Architecture Design -- 5.5.3 Data Storage Design -- 6 Big Commerce Data Knowledge Representation -- 6.1 Multi-granularity E-Commerce Entity Construction Model -- 6.1.1 Multi-granularity E-Commerce Entity Category -- 6.1.2 E-Commerce Entity Recognition -- 6.2 Multi-category E-Commerce Entity Relationship Extraction -- 6.2.1 Multi-category E-Commerce Entity Relationship Categories -- 6.2.2 Multi-category E-Commerce Entity Relationship Extraction Methods -- 6.3 Multi-level Knowledge Representation Model -- 6.3.1 Knowledge Representation Model Based on Natural Language Processing -- 6.3.2 Knowledge Representation Model Based on Relational Network -- 6.4 Case Studies of Big Commerce Data Knowledge Representation -- References | |
| 505 | 8 | |a 7 Business Big Data Knowledge Fusion -- 7.1 Semantic Extraction and Semantic Association -- 7.1.1 Subgraph Matching Algorithm for RDF -- 7.1.2 Knowledge Graph Keyword Search Algorithm -- 7.1.3 Semantic Association Ranking Techniques -- 7.2 User Profile Construction -- 7.2.1 User Data Collection -- 7.2.2 Segmentation of User Groups -- 7.2.3 Building a User Profile -- 7.2.4 Application of User Profiling -- 7.3 Knowledge Graph Construction -- 7.3.1 Knowledge Extraction -- 7.3.2 Knowledge Integration -- 7.3.3 Knowledge Storage and Graph Database Neo4j -- 7.4 Knowledge Reasoning and Interpretability -- 7.4.1 Knowledge Discovery and Reasoning -- 7.4.2 Rule-Based Knowledge Reasoning -- 7.4.3 Graph-Based Knowledge Reasoning -- 7.4.4 Neural Network-Based Knowledge Inference -- 7.4.5 Interpretability Analysis of Knowledge Reasoning -- 7.5 Business Big Data Knowledge Fusion Case -- 7.5.1 Introduction to Knowledge Fusion Tools -- 7.5.2 Technical Challenges of Knowledge Fusion -- 7.5.3 A Classic Case of Business Big Data Knowledge Fusion -- 8 Common Business Big Data Management and Decision Model -- 8.1 Robust Multi-task Learning for Clustering -- 8.1.1 Background -- 8.1.2 Problem Formalization -- 8.1.3 Cluster Multitasking Learning Based on Representative Tasks -- 8.2 Recommendations that Integrate User Interests -- 8.2.1 Background -- 8.2.2 Related to the Definition -- 8.2.3 Modeling Endogenous and Exogenous Interests of Users -- 8.2.4 Modeling Missing Data -- 8.2.5 A Recommendation Model that Incorporates User Interests -- 8.3 A Multi-objective Reinforcement Learning Framework for Community Deception -- 8.3.1 Introduction to Community Hiding Algorithms -- 8.3.2 Community Hiding Based on Multi-objective Reinforcement Learning -- 8.4 Mining of Periodic Coactive Populations in Trajectory Data -- 8.4.1 Background -- 8.4.2 Problem Formalization | |
| 505 | 8 | |a 8.4.3 Mining Algorithm for Periodic Populations in Trajectory Data -- 8.5 A Purchase Prediction Method Based on Semi-supervised Multi-view Learning -- 8.5.1 Feature Construction of co-EM-LR Model -- 8.5.2 Online Travel Customer Segmentation -- 8.5.3 Analysis of Online Travel Purchasing Patterns -- 8.5.4 Structure of the co-EM-LR Model -- 8.6 Recommendation Based on Probabilistic Matrix Decomposition and Feature Fusion -- 8.6.1 Application Scenarios of the PMF-MAI Model -- 8.6.2 Feature Construction of PMF-MAI Model -- 8.6.3 Structure of the PMF-MAI Model -- 8.7 Indoor Positioning Technology Based on Asynchronous Sensor -- 8.7.1 Indoor Positioning Technology Background -- 8.7.2 Asynchronous Sensing Method -- 8.7.3 Indoor Area Location Method for Asynchronous Sensing Data -- 8.8 Graph K-means Algorithm Based on Leader Recognition, Dynamic Game and Viewpoint Evolution -- 8.8.1 Study Scenarios, Motivations, and Meanings -- 8.8.2 Basic Knowledge and Problem Definition -- 8.8.3 Specific Framework -- 8.8.4 Experiment -- 8.8.5 Conclusion -- 9 Application of Business Big Data Management and Decision Making -- 9.1 Malicious User Fraud Detection -- 9.1.1 Malicious User Comment Detection -- 9.1.2 Recommended System Support Attack Detection -- 9.1.3 Credit Card Fraud Detection -- 9.2 Online Purchase Decision Model -- 9.2.1 Purchase Prediction Model -- 9.2.2 Personalized Recommendation Model -- 9.2.3 Sales Forecasting Model -- 9.3 Related Applications of Tourism E-Commerce -- 9.3.1 Point of Interest POI and Travel Package Recommendation -- 9.3.2 Travel Itinerary Planning -- 9.4 Business Applications of Location-Based Services -- 9.4.1 APP Takeaway Food -- 9.4.2 Car-Hailing Route Planning -- 9.4.3 Restaurant, Hotel and Gas Station Recommendation Based on Location Service -- References | |
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| author_facet | Cao, Jie |
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| contents | Intro -- Preface -- I. Why Write a Book -- II. Organization of the Book -- III. Target Readers -- IV. Study Suggestions -- Acknowledgements -- Contents -- About the Author -- 1 Introduction -- 1.1 Overview of Business Big Data Mining and Applications -- 1.2 Big Data Infrastructure -- 1.2.1 Infrastructure Layer -- 1.2.2 Big Data Layer -- 1.3 Overview of Big Data Research in Commerce -- 1.3.1 Big Data Fusion -- 1.3.2 Knowledge Fusion -- 1.3.3 Trajectory Big Data Mining -- 1.3.4 Knowledge Graphs -- 1.3.5 User Portraits -- 1.3.6 E-Commerce Recommendation System -- References -- 2 Data Collection in the Era of Big Data -- 2.1 Data Types of Business Big Data -- 2.1.1 Structured Data -- 2.1.2 Semi-structured Data -- 2.1.3 Unstructured Data -- 2.2 Online Business Big Data Collection Solution -- 2.2.1 Enterprise Data Collection -- 2.2.2 Web Crawler Data -- 2.2.3 Mobile Device Data -- 2.2.4 Database Data Collection -- 2.3 Offline Business Big Data Collection Solution -- 2.3.1 Physical Data Collection -- 2.3.2 Activity Data Collection -- 2.4 Cases of Business Big Data Collection -- 2.4.1 Precise User Portrait Description -- 2.4.2 Social Platform User Description -- 3 Pre-processing Big Data for Business -- 3.1 Business Big Data Pre-processing Techniques -- 3.1.1 Data Acquisition -- 3.1.2 Data Cleaning -- 3.1.3 Data Transformation -- 3.1.4 Data Integration -- 3.1.5 Data Imputation -- 3.2 Inconsistency Elimination Strategies for Multi-source Heterogeneous Commerce Big Data -- 3.3 Semantic Extraction and Analysis of Business Big Data -- 3.3.1 What Is Semantics -- 3.3.2 Semantic Analysis in Big Data -- 3.4 Business Big Data Pre-processing Case -- 4 Big Data Database for Business -- 4.1 Key-Value Store -- 4.1.1 Background to the Development of Key-Value Store -- 4.1.2 Key-Value Database Versus Relational Databases -- 4.1.3 Key Value Database Advantages 4.1.4 Redis -- 4.2 Column Family Store -- 4.2.1 Column Family Database Storage Structure -- 4.2.2 Column Family Database Features -- 4.2.3 HBase -- 4.3 Graph Store -- 4.3.1 The Concept of a Graph -- 4.3.2 Property Graph -- 4.3.3 Graph Database -- 4.3.4 Neo4j -- 5 Security Management on Big Data of Business -- 5.1 Traceability Technology of Business Big Data -- 5.1.1 The Definition of Data Traceability -- 5.1.2 The Definition of PROV -- 5.1.3 The Constraint of PROV Traceability Graph -- 5.2 Privacy Protection of Business Big Data -- 5.2.1 Data Desensitization Technology -- 5.2.2 Differential Privacy Protection -- 5.2.3 K-anonymity -- 5.3 The Data Sharing of Commercial Big Data -- 5.3.1 Access Control -- 5.3.2 Zero Trust Architecture -- 5.3.3 Attribute Based Encryption -- 5.3.4 Homomorphic Encryption -- 5.4 Blockchain Technology -- 5.4.1 Peer to Peer Network -- 5.4.2 Digital Signature -- 5.4.3 Hash Function -- 5.4.4 SPV Lightweight Verification and Melkel Hash Tree -- 5.4.5 Application of Blockchain in Business Big Data -- 5.5 Business Big Data Management Case -- 5.5.1 Demand Analysis -- 5.5.2 Network Architecture Design -- 5.5.3 Data Storage Design -- 6 Big Commerce Data Knowledge Representation -- 6.1 Multi-granularity E-Commerce Entity Construction Model -- 6.1.1 Multi-granularity E-Commerce Entity Category -- 6.1.2 E-Commerce Entity Recognition -- 6.2 Multi-category E-Commerce Entity Relationship Extraction -- 6.2.1 Multi-category E-Commerce Entity Relationship Categories -- 6.2.2 Multi-category E-Commerce Entity Relationship Extraction Methods -- 6.3 Multi-level Knowledge Representation Model -- 6.3.1 Knowledge Representation Model Based on Natural Language Processing -- 6.3.2 Knowledge Representation Model Based on Relational Network -- 6.4 Case Studies of Big Commerce Data Knowledge Representation -- References 7 Business Big Data Knowledge Fusion -- 7.1 Semantic Extraction and Semantic Association -- 7.1.1 Subgraph Matching Algorithm for RDF -- 7.1.2 Knowledge Graph Keyword Search Algorithm -- 7.1.3 Semantic Association Ranking Techniques -- 7.2 User Profile Construction -- 7.2.1 User Data Collection -- 7.2.2 Segmentation of User Groups -- 7.2.3 Building a User Profile -- 7.2.4 Application of User Profiling -- 7.3 Knowledge Graph Construction -- 7.3.1 Knowledge Extraction -- 7.3.2 Knowledge Integration -- 7.3.3 Knowledge Storage and Graph Database Neo4j -- 7.4 Knowledge Reasoning and Interpretability -- 7.4.1 Knowledge Discovery and Reasoning -- 7.4.2 Rule-Based Knowledge Reasoning -- 7.4.3 Graph-Based Knowledge Reasoning -- 7.4.4 Neural Network-Based Knowledge Inference -- 7.4.5 Interpretability Analysis of Knowledge Reasoning -- 7.5 Business Big Data Knowledge Fusion Case -- 7.5.1 Introduction to Knowledge Fusion Tools -- 7.5.2 Technical Challenges of Knowledge Fusion -- 7.5.3 A Classic Case of Business Big Data Knowledge Fusion -- 8 Common Business Big Data Management and Decision Model -- 8.1 Robust Multi-task Learning for Clustering -- 8.1.1 Background -- 8.1.2 Problem Formalization -- 8.1.3 Cluster Multitasking Learning Based on Representative Tasks -- 8.2 Recommendations that Integrate User Interests -- 8.2.1 Background -- 8.2.2 Related to the Definition -- 8.2.3 Modeling Endogenous and Exogenous Interests of Users -- 8.2.4 Modeling Missing Data -- 8.2.5 A Recommendation Model that Incorporates User Interests -- 8.3 A Multi-objective Reinforcement Learning Framework for Community Deception -- 8.3.1 Introduction to Community Hiding Algorithms -- 8.3.2 Community Hiding Based on Multi-objective Reinforcement Learning -- 8.4 Mining of Periodic Coactive Populations in Trajectory Data -- 8.4.1 Background -- 8.4.2 Problem Formalization 8.4.3 Mining Algorithm for Periodic Populations in Trajectory Data -- 8.5 A Purchase Prediction Method Based on Semi-supervised Multi-view Learning -- 8.5.1 Feature Construction of co-EM-LR Model -- 8.5.2 Online Travel Customer Segmentation -- 8.5.3 Analysis of Online Travel Purchasing Patterns -- 8.5.4 Structure of the co-EM-LR Model -- 8.6 Recommendation Based on Probabilistic Matrix Decomposition and Feature Fusion -- 8.6.1 Application Scenarios of the PMF-MAI Model -- 8.6.2 Feature Construction of PMF-MAI Model -- 8.6.3 Structure of the PMF-MAI Model -- 8.7 Indoor Positioning Technology Based on Asynchronous Sensor -- 8.7.1 Indoor Positioning Technology Background -- 8.7.2 Asynchronous Sensing Method -- 8.7.3 Indoor Area Location Method for Asynchronous Sensing Data -- 8.8 Graph K-means Algorithm Based on Leader Recognition, Dynamic Game and Viewpoint Evolution -- 8.8.1 Study Scenarios, Motivations, and Meanings -- 8.8.2 Basic Knowledge and Problem Definition -- 8.8.3 Specific Framework -- 8.8.4 Experiment -- 8.8.5 Conclusion -- 9 Application of Business Big Data Management and Decision Making -- 9.1 Malicious User Fraud Detection -- 9.1.1 Malicious User Comment Detection -- 9.1.2 Recommended System Support Attack Detection -- 9.1.3 Credit Card Fraud Detection -- 9.2 Online Purchase Decision Model -- 9.2.1 Purchase Prediction Model -- 9.2.2 Personalized Recommendation Model -- 9.2.3 Sales Forecasting Model -- 9.3 Related Applications of Tourism E-Commerce -- 9.3.1 Point of Interest POI and Travel Package Recommendation -- 9.3.2 Travel Itinerary Planning -- 9.4 Business Applications of Location-Based Services -- 9.4.1 APP Takeaway Food -- 9.4.2 Car-Hailing Route Planning -- 9.4.3 Restaurant, Hotel and Gas Station Recommendation Based on Location Service -- References |
| ctrlnum | (ZDB-30-PQE)EBC30669056 (ZDB-30-PAD)EBC30669056 (ZDB-89-EBL)EBL30669056 (OCoLC)1392444373 (DE-599)BVBBV049876369 |
| dewey-full | 658.87202856312 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.87202856312 |
| dewey-search | 658.87202856312 |
| dewey-sort | 3658.87202856312 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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Why Write a Book -- II. Organization of the Book -- III. Target Readers -- IV. Study Suggestions -- Acknowledgements -- Contents -- About the Author -- 1 Introduction -- 1.1 Overview of Business Big Data Mining and Applications -- 1.2 Big Data Infrastructure -- 1.2.1 Infrastructure Layer -- 1.2.2 Big Data Layer -- 1.3 Overview of Big Data Research in Commerce -- 1.3.1 Big Data Fusion -- 1.3.2 Knowledge Fusion -- 1.3.3 Trajectory Big Data Mining -- 1.3.4 Knowledge Graphs -- 1.3.5 User Portraits -- 1.3.6 E-Commerce Recommendation System -- References -- 2 Data Collection in the Era of Big Data -- 2.1 Data Types of Business Big Data -- 2.1.1 Structured Data -- 2.1.2 Semi-structured Data -- 2.1.3 Unstructured Data -- 2.2 Online Business Big Data Collection Solution -- 2.2.1 Enterprise Data Collection -- 2.2.2 Web Crawler Data -- 2.2.3 Mobile Device Data -- 2.2.4 Database Data Collection -- 2.3 Offline Business Big Data Collection Solution -- 2.3.1 Physical Data Collection -- 2.3.2 Activity Data Collection -- 2.4 Cases of Business Big Data Collection -- 2.4.1 Precise User Portrait Description -- 2.4.2 Social Platform User Description -- 3 Pre-processing Big Data for Business -- 3.1 Business Big Data Pre-processing Techniques -- 3.1.1 Data Acquisition -- 3.1.2 Data Cleaning -- 3.1.3 Data Transformation -- 3.1.4 Data Integration -- 3.1.5 Data Imputation -- 3.2 Inconsistency Elimination Strategies for Multi-source Heterogeneous Commerce Big Data -- 3.3 Semantic Extraction and Analysis of Business Big Data -- 3.3.1 What Is Semantics -- 3.3.2 Semantic Analysis in Big Data -- 3.4 Business Big Data Pre-processing Case -- 4 Big Data Database for Business -- 4.1 Key-Value Store -- 4.1.1 Background to the Development of Key-Value Store -- 4.1.2 Key-Value Database Versus Relational Databases -- 4.1.3 Key Value Database Advantages</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.1.4 Redis -- 4.2 Column Family Store -- 4.2.1 Column Family Database Storage Structure -- 4.2.2 Column Family Database Features -- 4.2.3 HBase -- 4.3 Graph Store -- 4.3.1 The Concept of a Graph -- 4.3.2 Property Graph -- 4.3.3 Graph Database -- 4.3.4 Neo4j -- 5 Security Management on Big Data of Business -- 5.1 Traceability Technology of Business Big Data -- 5.1.1 The Definition of Data Traceability -- 5.1.2 The Definition of PROV -- 5.1.3 The Constraint of PROV Traceability Graph -- 5.2 Privacy Protection of Business Big Data -- 5.2.1 Data Desensitization Technology -- 5.2.2 Differential Privacy Protection -- 5.2.3 K-anonymity -- 5.3 The Data Sharing of Commercial Big Data -- 5.3.1 Access Control -- 5.3.2 Zero Trust Architecture -- 5.3.3 Attribute Based Encryption -- 5.3.4 Homomorphic Encryption -- 5.4 Blockchain Technology -- 5.4.1 Peer to Peer Network -- 5.4.2 Digital Signature -- 5.4.3 Hash Function -- 5.4.4 SPV Lightweight Verification and Melkel Hash Tree -- 5.4.5 Application of Blockchain in Business Big Data -- 5.5 Business Big Data Management Case -- 5.5.1 Demand Analysis -- 5.5.2 Network Architecture Design -- 5.5.3 Data Storage Design -- 6 Big Commerce Data Knowledge Representation -- 6.1 Multi-granularity E-Commerce Entity Construction Model -- 6.1.1 Multi-granularity E-Commerce Entity Category -- 6.1.2 E-Commerce Entity Recognition -- 6.2 Multi-category E-Commerce Entity Relationship Extraction -- 6.2.1 Multi-category E-Commerce Entity Relationship Categories -- 6.2.2 Multi-category E-Commerce Entity Relationship Extraction Methods -- 6.3 Multi-level Knowledge Representation Model -- 6.3.1 Knowledge Representation Model Based on Natural Language Processing -- 6.3.2 Knowledge Representation Model Based on Relational Network -- 6.4 Case Studies of Big Commerce Data Knowledge Representation -- References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">7 Business Big Data Knowledge Fusion -- 7.1 Semantic Extraction and Semantic Association -- 7.1.1 Subgraph Matching Algorithm for RDF -- 7.1.2 Knowledge Graph Keyword Search Algorithm -- 7.1.3 Semantic Association Ranking 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Multitasking Learning Based on Representative Tasks -- 8.2 Recommendations that Integrate User Interests -- 8.2.1 Background -- 8.2.2 Related to the Definition -- 8.2.3 Modeling Endogenous and Exogenous Interests of Users -- 8.2.4 Modeling Missing Data -- 8.2.5 A Recommendation Model that Incorporates User Interests -- 8.3 A Multi-objective Reinforcement Learning Framework for Community Deception -- 8.3.1 Introduction to Community Hiding Algorithms -- 8.3.2 Community Hiding Based on Multi-objective Reinforcement Learning -- 8.4 Mining of Periodic Coactive Populations in Trajectory Data -- 8.4.1 Background -- 8.4.2 Problem Formalization</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8.4.3 Mining Algorithm for Periodic Populations in Trajectory Data -- 8.5 A Purchase Prediction Method Based on Semi-supervised Multi-view Learning -- 8.5.1 Feature Construction of co-EM-LR Model -- 8.5.2 Online Travel Customer Segmentation -- 8.5.3 Analysis of Online Travel Purchasing Patterns -- 8.5.4 Structure of the co-EM-LR Model -- 8.6 Recommendation Based on Probabilistic Matrix Decomposition and Feature Fusion -- 8.6.1 Application Scenarios of the PMF-MAI Model -- 8.6.2 Feature Construction of PMF-MAI Model -- 8.6.3 Structure of the PMF-MAI Model -- 8.7 Indoor Positioning Technology Based on Asynchronous Sensor -- 8.7.1 Indoor Positioning Technology Background -- 8.7.2 Asynchronous Sensing Method -- 8.7.3 Indoor Area Location Method for Asynchronous Sensing Data -- 8.8 Graph K-means Algorithm Based on Leader Recognition, Dynamic Game and Viewpoint Evolution -- 8.8.1 Study Scenarios, Motivations, and Meanings -- 8.8.2 Basic Knowledge and Problem Definition -- 8.8.3 Specific Framework -- 8.8.4 Experiment -- 8.8.5 Conclusion -- 9 Application of Business Big Data Management and Decision Making -- 9.1 Malicious User Fraud Detection -- 9.1.1 Malicious User Comment Detection -- 9.1.2 Recommended System Support Attack Detection -- 9.1.3 Credit Card Fraud 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| id | DE-604.BV049876369 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T20:24:22Z |
| institution | BVB |
| isbn | 9789819935888 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035215819 |
| oclc_num | 1392444373 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (217 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer |
| record_format | marc |
| series2 | Advanced Studies in E-Commerce Series |
| spelling | Cao, Jie Verfasser aut E-Commerce Big Data Mining and Analytics 1st ed Singapore Springer 2023 ©2023 1 Online-Ressource (217 Seiten) txt rdacontent c rdamedia cr rdacarrier Advanced Studies in E-Commerce Series Description based on publisher supplied metadata and other sources Intro -- Preface -- I. Why Write a Book -- II. Organization of the Book -- III. Target Readers -- IV. Study Suggestions -- Acknowledgements -- Contents -- About the Author -- 1 Introduction -- 1.1 Overview of Business Big Data Mining and Applications -- 1.2 Big Data Infrastructure -- 1.2.1 Infrastructure Layer -- 1.2.2 Big Data Layer -- 1.3 Overview of Big Data Research in Commerce -- 1.3.1 Big Data Fusion -- 1.3.2 Knowledge Fusion -- 1.3.3 Trajectory Big Data Mining -- 1.3.4 Knowledge Graphs -- 1.3.5 User Portraits -- 1.3.6 E-Commerce Recommendation System -- References -- 2 Data Collection in the Era of Big Data -- 2.1 Data Types of Business Big Data -- 2.1.1 Structured Data -- 2.1.2 Semi-structured Data -- 2.1.3 Unstructured Data -- 2.2 Online Business Big Data Collection Solution -- 2.2.1 Enterprise Data Collection -- 2.2.2 Web Crawler Data -- 2.2.3 Mobile Device Data -- 2.2.4 Database Data Collection -- 2.3 Offline Business Big Data Collection Solution -- 2.3.1 Physical Data Collection -- 2.3.2 Activity Data Collection -- 2.4 Cases of Business Big Data Collection -- 2.4.1 Precise User Portrait Description -- 2.4.2 Social Platform User Description -- 3 Pre-processing Big Data for Business -- 3.1 Business Big Data Pre-processing Techniques -- 3.1.1 Data Acquisition -- 3.1.2 Data Cleaning -- 3.1.3 Data Transformation -- 3.1.4 Data Integration -- 3.1.5 Data Imputation -- 3.2 Inconsistency Elimination Strategies for Multi-source Heterogeneous Commerce Big Data -- 3.3 Semantic Extraction and Analysis of Business Big Data -- 3.3.1 What Is Semantics -- 3.3.2 Semantic Analysis in Big Data -- 3.4 Business Big Data Pre-processing Case -- 4 Big Data Database for Business -- 4.1 Key-Value Store -- 4.1.1 Background to the Development of Key-Value Store -- 4.1.2 Key-Value Database Versus Relational Databases -- 4.1.3 Key Value Database Advantages 4.1.4 Redis -- 4.2 Column Family Store -- 4.2.1 Column Family Database Storage Structure -- 4.2.2 Column Family Database Features -- 4.2.3 HBase -- 4.3 Graph Store -- 4.3.1 The Concept of a Graph -- 4.3.2 Property Graph -- 4.3.3 Graph Database -- 4.3.4 Neo4j -- 5 Security Management on Big Data of Business -- 5.1 Traceability Technology of Business Big Data -- 5.1.1 The Definition of Data Traceability -- 5.1.2 The Definition of PROV -- 5.1.3 The Constraint of PROV Traceability Graph -- 5.2 Privacy Protection of Business Big Data -- 5.2.1 Data Desensitization Technology -- 5.2.2 Differential Privacy Protection -- 5.2.3 K-anonymity -- 5.3 The Data Sharing of Commercial Big Data -- 5.3.1 Access Control -- 5.3.2 Zero Trust Architecture -- 5.3.3 Attribute Based Encryption -- 5.3.4 Homomorphic Encryption -- 5.4 Blockchain Technology -- 5.4.1 Peer to Peer Network -- 5.4.2 Digital Signature -- 5.4.3 Hash Function -- 5.4.4 SPV Lightweight Verification and Melkel Hash Tree -- 5.4.5 Application of Blockchain in Business Big Data -- 5.5 Business Big Data Management Case -- 5.5.1 Demand Analysis -- 5.5.2 Network Architecture Design -- 5.5.3 Data Storage Design -- 6 Big Commerce Data Knowledge Representation -- 6.1 Multi-granularity E-Commerce Entity Construction Model -- 6.1.1 Multi-granularity E-Commerce Entity Category -- 6.1.2 E-Commerce Entity Recognition -- 6.2 Multi-category E-Commerce Entity Relationship Extraction -- 6.2.1 Multi-category E-Commerce Entity Relationship Categories -- 6.2.2 Multi-category E-Commerce Entity Relationship Extraction Methods -- 6.3 Multi-level Knowledge Representation Model -- 6.3.1 Knowledge Representation Model Based on Natural Language Processing -- 6.3.2 Knowledge Representation Model Based on Relational Network -- 6.4 Case Studies of Big Commerce Data Knowledge Representation -- References 7 Business Big Data Knowledge Fusion -- 7.1 Semantic Extraction and Semantic Association -- 7.1.1 Subgraph Matching Algorithm for RDF -- 7.1.2 Knowledge Graph Keyword Search Algorithm -- 7.1.3 Semantic Association Ranking Techniques -- 7.2 User Profile Construction -- 7.2.1 User Data Collection -- 7.2.2 Segmentation of User Groups -- 7.2.3 Building a User Profile -- 7.2.4 Application of User Profiling -- 7.3 Knowledge Graph Construction -- 7.3.1 Knowledge Extraction -- 7.3.2 Knowledge Integration -- 7.3.3 Knowledge Storage and Graph Database Neo4j -- 7.4 Knowledge Reasoning and Interpretability -- 7.4.1 Knowledge Discovery and Reasoning -- 7.4.2 Rule-Based Knowledge Reasoning -- 7.4.3 Graph-Based Knowledge Reasoning -- 7.4.4 Neural Network-Based Knowledge Inference -- 7.4.5 Interpretability Analysis of Knowledge Reasoning -- 7.5 Business Big Data Knowledge Fusion Case -- 7.5.1 Introduction to Knowledge Fusion Tools -- 7.5.2 Technical Challenges of Knowledge Fusion -- 7.5.3 A Classic Case of Business Big Data Knowledge Fusion -- 8 Common Business Big Data Management and Decision Model -- 8.1 Robust Multi-task Learning for Clustering -- 8.1.1 Background -- 8.1.2 Problem Formalization -- 8.1.3 Cluster Multitasking Learning Based on Representative Tasks -- 8.2 Recommendations that Integrate User Interests -- 8.2.1 Background -- 8.2.2 Related to the Definition -- 8.2.3 Modeling Endogenous and Exogenous Interests of Users -- 8.2.4 Modeling Missing Data -- 8.2.5 A Recommendation Model that Incorporates User Interests -- 8.3 A Multi-objective Reinforcement Learning Framework for Community Deception -- 8.3.1 Introduction to Community Hiding Algorithms -- 8.3.2 Community Hiding Based on Multi-objective Reinforcement Learning -- 8.4 Mining of Periodic Coactive Populations in Trajectory Data -- 8.4.1 Background -- 8.4.2 Problem Formalization 8.4.3 Mining Algorithm for Periodic Populations in Trajectory Data -- 8.5 A Purchase Prediction Method Based on Semi-supervised Multi-view Learning -- 8.5.1 Feature Construction of co-EM-LR Model -- 8.5.2 Online Travel Customer Segmentation -- 8.5.3 Analysis of Online Travel Purchasing Patterns -- 8.5.4 Structure of the co-EM-LR Model -- 8.6 Recommendation Based on Probabilistic Matrix Decomposition and Feature Fusion -- 8.6.1 Application Scenarios of the PMF-MAI Model -- 8.6.2 Feature Construction of PMF-MAI Model -- 8.6.3 Structure of the PMF-MAI Model -- 8.7 Indoor Positioning Technology Based on Asynchronous Sensor -- 8.7.1 Indoor Positioning Technology Background -- 8.7.2 Asynchronous Sensing Method -- 8.7.3 Indoor Area Location Method for Asynchronous Sensing Data -- 8.8 Graph K-means Algorithm Based on Leader Recognition, Dynamic Game and Viewpoint Evolution -- 8.8.1 Study Scenarios, Motivations, and Meanings -- 8.8.2 Basic Knowledge and Problem Definition -- 8.8.3 Specific Framework -- 8.8.4 Experiment -- 8.8.5 Conclusion -- 9 Application of Business Big Data Management and Decision Making -- 9.1 Malicious User Fraud Detection -- 9.1.1 Malicious User Comment Detection -- 9.1.2 Recommended System Support Attack Detection -- 9.1.3 Credit Card Fraud Detection -- 9.2 Online Purchase Decision Model -- 9.2.1 Purchase Prediction Model -- 9.2.2 Personalized Recommendation Model -- 9.2.3 Sales Forecasting Model -- 9.3 Related Applications of Tourism E-Commerce -- 9.3.1 Point of Interest POI and Travel Package Recommendation -- 9.3.2 Travel Itinerary Planning -- 9.4 Business Applications of Location-Based Services -- 9.4.1 APP Takeaway Food -- 9.4.2 Car-Hailing Route Planning -- 9.4.3 Restaurant, Hotel and Gas Station Recommendation Based on Location Service -- References Erscheint auch als Druck-Ausgabe Cao, Jie E-Commerce Big Data Mining and Analytics Singapore : Springer,c2023 9789819935871 |
| spellingShingle | Cao, Jie E-Commerce Big Data Mining and Analytics Intro -- Preface -- I. Why Write a Book -- II. Organization of the Book -- III. Target Readers -- IV. Study Suggestions -- Acknowledgements -- Contents -- About the Author -- 1 Introduction -- 1.1 Overview of Business Big Data Mining and Applications -- 1.2 Big Data Infrastructure -- 1.2.1 Infrastructure Layer -- 1.2.2 Big Data Layer -- 1.3 Overview of Big Data Research in Commerce -- 1.3.1 Big Data Fusion -- 1.3.2 Knowledge Fusion -- 1.3.3 Trajectory Big Data Mining -- 1.3.4 Knowledge Graphs -- 1.3.5 User Portraits -- 1.3.6 E-Commerce Recommendation System -- References -- 2 Data Collection in the Era of Big Data -- 2.1 Data Types of Business Big Data -- 2.1.1 Structured Data -- 2.1.2 Semi-structured Data -- 2.1.3 Unstructured Data -- 2.2 Online Business Big Data Collection Solution -- 2.2.1 Enterprise Data Collection -- 2.2.2 Web Crawler Data -- 2.2.3 Mobile Device Data -- 2.2.4 Database Data Collection -- 2.3 Offline Business Big Data Collection Solution -- 2.3.1 Physical Data Collection -- 2.3.2 Activity Data Collection -- 2.4 Cases of Business Big Data Collection -- 2.4.1 Precise User Portrait Description -- 2.4.2 Social Platform User Description -- 3 Pre-processing Big Data for Business -- 3.1 Business Big Data Pre-processing Techniques -- 3.1.1 Data Acquisition -- 3.1.2 Data Cleaning -- 3.1.3 Data Transformation -- 3.1.4 Data Integration -- 3.1.5 Data Imputation -- 3.2 Inconsistency Elimination Strategies for Multi-source Heterogeneous Commerce Big Data -- 3.3 Semantic Extraction and Analysis of Business Big Data -- 3.3.1 What Is Semantics -- 3.3.2 Semantic Analysis in Big Data -- 3.4 Business Big Data Pre-processing Case -- 4 Big Data Database for Business -- 4.1 Key-Value Store -- 4.1.1 Background to the Development of Key-Value Store -- 4.1.2 Key-Value Database Versus Relational Databases -- 4.1.3 Key Value Database Advantages 4.1.4 Redis -- 4.2 Column Family Store -- 4.2.1 Column Family Database Storage Structure -- 4.2.2 Column Family Database Features -- 4.2.3 HBase -- 4.3 Graph Store -- 4.3.1 The Concept of a Graph -- 4.3.2 Property Graph -- 4.3.3 Graph Database -- 4.3.4 Neo4j -- 5 Security Management on Big Data of Business -- 5.1 Traceability Technology of Business Big Data -- 5.1.1 The Definition of Data Traceability -- 5.1.2 The Definition of PROV -- 5.1.3 The Constraint of PROV Traceability Graph -- 5.2 Privacy Protection of Business Big Data -- 5.2.1 Data Desensitization Technology -- 5.2.2 Differential Privacy Protection -- 5.2.3 K-anonymity -- 5.3 The Data Sharing of Commercial Big Data -- 5.3.1 Access Control -- 5.3.2 Zero Trust Architecture -- 5.3.3 Attribute Based Encryption -- 5.3.4 Homomorphic Encryption -- 5.4 Blockchain Technology -- 5.4.1 Peer to Peer Network -- 5.4.2 Digital Signature -- 5.4.3 Hash Function -- 5.4.4 SPV Lightweight Verification and Melkel Hash Tree -- 5.4.5 Application of Blockchain in Business Big Data -- 5.5 Business Big Data Management Case -- 5.5.1 Demand Analysis -- 5.5.2 Network Architecture Design -- 5.5.3 Data Storage Design -- 6 Big Commerce Data Knowledge Representation -- 6.1 Multi-granularity E-Commerce Entity Construction Model -- 6.1.1 Multi-granularity E-Commerce Entity Category -- 6.1.2 E-Commerce Entity Recognition -- 6.2 Multi-category E-Commerce Entity Relationship Extraction -- 6.2.1 Multi-category E-Commerce Entity Relationship Categories -- 6.2.2 Multi-category E-Commerce Entity Relationship Extraction Methods -- 6.3 Multi-level Knowledge Representation Model -- 6.3.1 Knowledge Representation Model Based on Natural Language Processing -- 6.3.2 Knowledge Representation Model Based on Relational Network -- 6.4 Case Studies of Big Commerce Data Knowledge Representation -- References 7 Business Big Data Knowledge Fusion -- 7.1 Semantic Extraction and Semantic Association -- 7.1.1 Subgraph Matching Algorithm for RDF -- 7.1.2 Knowledge Graph Keyword Search Algorithm -- 7.1.3 Semantic Association Ranking Techniques -- 7.2 User Profile Construction -- 7.2.1 User Data Collection -- 7.2.2 Segmentation of User Groups -- 7.2.3 Building a User Profile -- 7.2.4 Application of User Profiling -- 7.3 Knowledge Graph Construction -- 7.3.1 Knowledge Extraction -- 7.3.2 Knowledge Integration -- 7.3.3 Knowledge Storage and Graph Database Neo4j -- 7.4 Knowledge Reasoning and Interpretability -- 7.4.1 Knowledge Discovery and Reasoning -- 7.4.2 Rule-Based Knowledge Reasoning -- 7.4.3 Graph-Based Knowledge Reasoning -- 7.4.4 Neural Network-Based Knowledge Inference -- 7.4.5 Interpretability Analysis of Knowledge Reasoning -- 7.5 Business Big Data Knowledge Fusion Case -- 7.5.1 Introduction to Knowledge Fusion Tools -- 7.5.2 Technical Challenges of Knowledge Fusion -- 7.5.3 A Classic Case of Business Big Data Knowledge Fusion -- 8 Common Business Big Data Management and Decision Model -- 8.1 Robust Multi-task Learning for Clustering -- 8.1.1 Background -- 8.1.2 Problem Formalization -- 8.1.3 Cluster Multitasking Learning Based on Representative Tasks -- 8.2 Recommendations that Integrate User Interests -- 8.2.1 Background -- 8.2.2 Related to the Definition -- 8.2.3 Modeling Endogenous and Exogenous Interests of Users -- 8.2.4 Modeling Missing Data -- 8.2.5 A Recommendation Model that Incorporates User Interests -- 8.3 A Multi-objective Reinforcement Learning Framework for Community Deception -- 8.3.1 Introduction to Community Hiding Algorithms -- 8.3.2 Community Hiding Based on Multi-objective Reinforcement Learning -- 8.4 Mining of Periodic Coactive Populations in Trajectory Data -- 8.4.1 Background -- 8.4.2 Problem Formalization 8.4.3 Mining Algorithm for Periodic Populations in Trajectory Data -- 8.5 A Purchase Prediction Method Based on Semi-supervised Multi-view Learning -- 8.5.1 Feature Construction of co-EM-LR Model -- 8.5.2 Online Travel Customer Segmentation -- 8.5.3 Analysis of Online Travel Purchasing Patterns -- 8.5.4 Structure of the co-EM-LR Model -- 8.6 Recommendation Based on Probabilistic Matrix Decomposition and Feature Fusion -- 8.6.1 Application Scenarios of the PMF-MAI Model -- 8.6.2 Feature Construction of PMF-MAI Model -- 8.6.3 Structure of the PMF-MAI Model -- 8.7 Indoor Positioning Technology Based on Asynchronous Sensor -- 8.7.1 Indoor Positioning Technology Background -- 8.7.2 Asynchronous Sensing Method -- 8.7.3 Indoor Area Location Method for Asynchronous Sensing Data -- 8.8 Graph K-means Algorithm Based on Leader Recognition, Dynamic Game and Viewpoint Evolution -- 8.8.1 Study Scenarios, Motivations, and Meanings -- 8.8.2 Basic Knowledge and Problem Definition -- 8.8.3 Specific Framework -- 8.8.4 Experiment -- 8.8.5 Conclusion -- 9 Application of Business Big Data Management and Decision Making -- 9.1 Malicious User Fraud Detection -- 9.1.1 Malicious User Comment Detection -- 9.1.2 Recommended System Support Attack Detection -- 9.1.3 Credit Card Fraud Detection -- 9.2 Online Purchase Decision Model -- 9.2.1 Purchase Prediction Model -- 9.2.2 Personalized Recommendation Model -- 9.2.3 Sales Forecasting Model -- 9.3 Related Applications of Tourism E-Commerce -- 9.3.1 Point of Interest POI and Travel Package Recommendation -- 9.3.2 Travel Itinerary Planning -- 9.4 Business Applications of Location-Based Services -- 9.4.1 APP Takeaway Food -- 9.4.2 Car-Hailing Route Planning -- 9.4.3 Restaurant, Hotel and Gas Station Recommendation Based on Location Service -- References |
| title | E-Commerce Big Data Mining and Analytics |
| title_auth | E-Commerce Big Data Mining and Analytics |
| title_exact_search | E-Commerce Big Data Mining and Analytics |
| title_full | E-Commerce Big Data Mining and Analytics |
| title_fullStr | E-Commerce Big Data Mining and Analytics |
| title_full_unstemmed | E-Commerce Big Data Mining and Analytics |
| title_short | E-Commerce Big Data Mining and Analytics |
| title_sort | e commerce big data mining and analytics |
| work_keys_str_mv | AT caojie ecommercebigdataminingandanalytics |