Big Data Management and Analytics : 9th European Summer School, EBISS 2019, Berlin, Germany, June 30 – July 5, 2019, Revised Selected Papers

This book constitutes 5 revised tutorial lectures of the 9th European Business Intelligence and Big Data Summer School, eBISS 2019, held in Berlin, Germany, during June 30 – July 5, 2019. The tutorials were given by renowned experts and covered advanced aspects of business intelligence and big data. This summer school, presented by leading researchers in the field, represented an opportunity for postgraduate students to equip themselves with the theoretical and practical skills necessary for developing challenging business intelligence applications. Preface Organization Contents Actionable Conformance Checking: From Intuitions to Code 1 Introduction 2 Related Work 3 Process Models and Event Logs 4 Conformance Checking 4.1 Quality Dimensions to Relate Process Models and Event Logs 4.2 Computing Conformance Checking Artefacts 5 Code Snippets for Conformance Checking 5.1 Event Log Exploration 5.2 The Computation of Conformance Checking Artefacts 6 Concluding Remarks References Introduction to Text Analytics 1 Introduction 2 Definition of Text Analytics 3 Sources of Textual Data 4 Processing of Texts: The Pipeline 4.1 Step 1. Data Parsing 4.2 Step 2. Text Segmentation 4.3 Step 3. Identification of Named Entities 4.4 Step 4. Disambiguation 4.5 Step 5. Describing the Text 4.6 Step 6: Analytics: Topic Tagging 5 Application Scenarios 5.1 Sentiment Analysis 5.2 Search and Retrieval 6 Case Study 7 Summary References Automated Machine Learning: Techniques and Frameworks 1 Introduction 2 Automated Machine Learning 2.1 Hyper-parameter Optimization 2.2 AutoML Tools and Frameworks 3 Automated Deep Learning 3.1 Neural Architecture Search for Deep Learning 3.2 AutoDL Frameworks 4 Open Challenges and Future Directions 5 Conclusion References Travel-Time Computation Based on GPS Data 1 Introduction 2 Data Foundation 2.1 Data Model 2.2 Data 3 Logical Model 3.1 Dimensions 3.2 Fact Table 4 Data Cleaning Method 4.1 Map-Matching 4.2 Weather Class 4.3 Speedmaps 5 Results 5.1 Weather Classes to Include 5.2 Wind Analysis 6 Related Work 7 Conclusion References Laplacian Matrix for Dimensionality Reduction and Clustering 1 Intuition 1.1 Heat Diffusion Analogy of Laplacian Eigenmaps 1.2 Heat Diffusion Analogy of Spectral Clustering 1.3 Heat Diffusion Equation for Connected Heat Reservoirs 1.4 Laplacian Matrix 1.5 Solution of the Heat Diffusion Equation 2 Formalism 2.1 Simple Graphs 2.2 Matrix Representation 2.3 Optimization Problem 2.4 Associated Eigenvalue Problem 2.5 The Role of the Weighted Normalization Constraint 2.6 Symmetric Normalized Laplacian Matrix 2.7 Random Walk Normalized Laplacian Matrix 2.8 Summary of Mathematical Properties 3 Algorithms 3.1 Similarity Graphs 3.2 Laplacian Eigenmaps (LEM) 3.3 Locality Preserving Projections (LPP) 3.4 Spectral Clustering References Author Index This book constitutes 5 revised tutorial lectures of the 9th European Business Intelligence and Big Data Summer School, eBISS 2019, held in Berlin, Germany, during June 30 - July 5, 2020. The tutorials were given by renowned experts and covered advanced aspects of business intelligence and big data. This summer school, presented by leading researchers in the field, represented an opportunity for postgraduate students to equip themselves with the theoretical and practical skills necessary for developing challenging business intelligence applications