Advances in databases and information systems : 26th European conference, ADBIS 2022, Turin, Italy, September 5-8, 2022, proceedings / Silvia Chiusano, Tania Cerquitelli, Robert Wrembel (editors).

Format
Book
Language
English
Published/​Created
  • Cham, Switzerland : Springer, [2022]
  • ©2022
Description
1 online resource (419 pages)

Details

Subject(s)
Editor
Series
Lecture notes in computer science ; Volume 13389. [More in this series]
Bibliographic references
Includes bibliographical references and index.
Source of description
Description based on print version record.
Contents
  • Intro
  • Preface
  • Organization
  • Abstracts of the Keynote Talks
  • Toward AI-Powered Data-Driven Education
  • Insider Stories: Analyzing Stress, Depression, and Staff Welfare at Major US Companies from Online Reviews
  • Tensor Query Processing: Neural Network to speed up Databases and Classical ML!
  • Understanding and Rewiring Cities and Societies: A Computational Social Science Perspective
  • Contents
  • Keynote Talk and Tutorials
  • Understanding and Rewiring Cities
  • 1 Introduction
  • 2 Neighborhoods' Characteristics and Urban Vitality
  • 3 How Safety Perception Influences Vitality
  • 4 The Interplay of Neighborhoods' Socio-Economic Conditions, Urban Environment, People Behaviors, and Crime
  • 5 The Impact of COVID-19 Pandemic on Human Behavior
  • 6 Looking Ahead
  • References
  • AI Approaches in Processing and Using Data in Personalized Medicine
  • 2 Different Sources of Patients' Medical Big Data - Collection and Processing
  • 3 Emergent Artificial Intelligence Approaches for Supporting Quality Medical Decisions
  • 4 Medical Decision Support Systems
  • 4.1 Smart Ambient Intelligent Living Environments
  • 4.2 Intelligent System for Supporting Cancer Patients
  • 5 Conclusion
  • Explainable, Interpretable, Trustworthy, Responsible, Ethical, Fair, Verifiable AI... What's Next?
  • 2 Explainability
  • 3 Interpretability
  • 4 The Problem of Bias
  • 5 Fairness
  • 6 Verification
  • 7 Accountability
  • 8 Conclusions
  • OLAP and NoSQL: Happily Ever After
  • 1 Introduction and Motivation
  • 2 Schema-on-Read Approaches
  • 2.1 Graph OLAP
  • 2.2 Approximate OLAP
  • 3 Schema-on-Write Approaches
  • 3.1 Mono-Model Approaches
  • 3.2 Multi-model Approaches
  • 4 Conclusion
  • What's New in Temporal Databases?
  • 2 Query Processing of Primitive Operators.
  • 2.1 Temporal Selection
  • 2.2 Temporal Joins
  • 3 New Directions in Models and Semantics
  • 4 Systems
  • 5 Conclusion and Future Directions
  • Graph Processing
  • An Algebra for Path Manipulation in Graph Databases
  • 2 Related Work
  • 3 Data Model
  • 4 Path Algebra
  • 4.1 Operations over Paths
  • 4.2 Operations over Sets of Paths
  • 4.3 Examples of Queries
  • 5 Properties
  • 6 Use Cases
  • 6.1 Transportation
  • 6.2 Proteins
  • 7 Discussion
  • 7.1 Labels and Repeated Results
  • 7.2 Notions of Compatibility
  • 7.3 Paths Queries Across Multiple Graphs
  • Road Network Graph Representation for Traffic Analysis and Routing
  • 3 Road Network Graph Modelling
  • 3.1 Primal Graph
  • 3.2 Dual Graph
  • 4 Generation of Traffic Data
  • 5 Traffic Data Integration
  • 6 Routing
  • 6.1 Managing Road Closures
  • 7 Road Network Analysis
  • 8 Conclusion
  • Parallel Discovery of Top-k Weighted Motifs in Large Graphs
  • 2 Related Research
  • 3 Background
  • 4 The Proposed Approach
  • 4.1 Correctness
  • 4.2 Extension to 4-Cliques
  • 5 Performance Evaluation
  • 6 Conclusions
  • A Data Quality Framework for Graph-Based Virtual Data Integration Systems
  • 3 Preliminaries
  • 3.1 Running Example
  • 3.2 Formal Background
  • 4 Managing Data Quality in Virtual Data Integration
  • 4.1 DC Generation and Graph-Based Representation
  • 4.2 Global DC Management
  • 5 Global Query Rewriting with Global DCs
  • 5.1 Query with DCs
  • 6 Validation
  • 7 Conclusions and Future Work
  • Time Series and Data Streams
  • Generating Comparative Explanations of Financial Time Series
  • 2 Literature Review
  • 3 Data Overview
  • 4 Financial Data Summarizer
  • 5 Experiments
  • 5.1 Intrinsic Evaluation.
  • 5.2 Extrinsic Summary Validation
  • 6 Conclusions and Future Works
  • Summarizing Edge-Device Data via Core Items
  • 3.1 Apache Storm
  • 3.2 Stream Summarization
  • 4 Algorithms for Core Item Sets Computation
  • 4.1 SoftSieving
  • 5 Experimental Evaluation
  • 6 Conclusion
  • Parallel Techniques for Variable Size Segmentation of Time Series Datasets
  • 2 Problem Definition and Background
  • 2.1 SAX Representation
  • 2.2 Similarity Queries
  • 2.3 Problem Statement
  • 3 Adaptive SAX Based on the Representation's Sum of Squared Errors (ASAX_SSE)
  • 3.1 Sum of Squared Errors (SSE)
  • 3.2 SSE of PAA Representation Considering One Segment (LSSE)
  • 3.3 SSE of PAA Representation Considering All Segments (GSSE)
  • 3.4 Variable-Size Segmentation Based on SSE Measurement
  • 3.5 Lower Bounding of the Similarity Measure
  • 4 Parallel Versions of ASAX_SSE
  • 4.1 Parallelization on Data
  • 4.2 Parallelization on Segments
  • 5.1 Setup
  • 5.2 Precision of k-Nearest Neighbor Search
  • 5.3 Scalability
  • 6 Related Work
  • 7 Conclusion
  • On Line Analytical Processing
  • ORTree: Tuning Diversified Similarity Queries by Means of Data Partitioning
  • 2 Background
  • 2.1 Range-Tree
  • 2.2 MAM - Omni-Technique
  • 2.3 The Diversity Problem
  • 2.4 Diversity Algorithms
  • 2.5 Candidate Selection
  • 3 Methodology
  • 4 Experiments
  • 4.1 Index Creation Time
  • 4.2 Quality Experiments
  • 4.3 Number of Elements Retrieved
  • 4.4 Query Time Evaluation
  • 5 Conclusions and Future Work
  • A Knowledge-Based Approach to Support Analytic Query Answering in Semantic Data Lakes*-12pt
  • 2 Case Study: Azure COVID-19 Data Lake
  • 3 Semantic Data Lake: Data Model
  • 3.1 Metadata Layer
  • 3.2 Knowledge Layer.
  • 4 Integration and Mapping Discovery
  • 5 Query Answering
  • 6 Evaluation
  • Insight-Based Vocalization of OLAP Sessions
  • 3 Overview
  • 4 Formal Background
  • 5 The Vocalization Process
  • 5.1 Insight Generation
  • 5.2 Insight Selection
  • 5.3 Vocalization
  • 6 A Closer Look at the VOOL Modules
  • 7 Evaluation and Conclusion
  • Advanced Querying
  • Querying Temporal Anomalies in Healthcare Information Systems and Beyond
  • 3 Temporal Anomalies
  • 3.1 Preliminaries
  • 3.2 Definition of Temporal Anomalies
  • 3.3 Anomaly Labelling and Retrieval
  • 4 SQL Implementations
  • 4.1 Unfold/Fold
  • 4.2 Unfold/Fold Join Filtered
  • 4.3 Window Function
  • 5.1 Setup and Dataset
  • 5.2 Results
  • 6 Conclusion and Future Work
  • Soft Spatial Querying on JSON Data Sets
  • 2.1 Brief Introduction to Fuzzy Sets
  • 2.2 Related Work
  • 2.3 The J-CO Framework
  • 3 Case Study and J-CO-QL+ Script
  • 3.1 Case Study
  • 3.2 Definition of Fuzzy Concepts
  • 3.3 Processing a GeoJSON Document
  • 3.4 Soft Spatial Querying
  • 4 Conclusions
  • Maximum Range-Sum for Dynamically Occurring Objects with Decaying Weights
  • 2 Preliminaries
  • 3 Approximate Solution to DDW-MaxRS
  • 3.1 Properties of the Approximate Solution
  • 3.2 Memory-Efficient Approximate Solution
  • 4 Generalization
  • 5 Experimental Study
  • Performance
  • Storage Management with Multi-Version Partitioned BTrees
  • 2 Architecture of Multi-Version Partitioned BTrees
  • 3 Cached Partition: Stop Re-writing Valid Data
  • 4 Garbage Collection and Space Reclamation
  • 6 Conclusion.
  • Generalization Aware Compression of Molecular Trajectories
  • Analysing Workload Trends for Boosting Triple Stores Performance
  • 2 Background and Related Work
  • 2.1 RDF Graph and Queries Processing
  • 2.2 Indexing and Cache
  • 2.3 Replication
  • 2.4 Workload Adaption
  • 2.5 Heat Query
  • 3 Heat Item Gets Colder
  • 3.1 Smoothing Methods
  • 3.2 Exponential Smoothing
  • 3.3 Holt-Winters' Additive Method
  • 4 Experimental Evaluation
  • 4.1 Exponential Smoothing
  • 4.2 Hot Data Parts
  • 4.3 Seasonality Factors and Capacity
  • 5 Conclusion and Future Work
  • Machine Learning
  • Comparison of Models Built Using AutoML and Data Fusion
  • 3 Problem Statement
  • 4 Proposed Approach
  • 4.1 Datasets Description
  • 4.2 Data Fusion Process
  • 4.3 Experimental Design
  • 4.4 Performance Evaluation
  • Dimensional Data KNN-Based Imputation
  • 3 DW Dimension
  • 4 Distance Between Dimension Instances
  • 4.1 Attribute Distance
  • 4.2 Hierarchy Level Instance Distance
  • 4.3 Hierarchy Instance Distance
  • 4.4 Dimension Instance Weight
  • 5 H-OLAPKNN Imputation
  • 5.1 H-OLAPKNN Overview
  • 5.2 Imputation for Parameters by OLAPKNN
  • 6 Experimental Assessment
  • 6.1 Technical Environment and Datasets
  • 6.2 Experimental Methodology
  • 6.3 Results and Analysis
  • 7 Conclusion and Future Work
  • Feature Ranking from Random Forest Through Complex Network's Centrality Measures*2mm
  • 2.1 Feature Selection and Ranking
  • 2.2 Random Forests
  • 2.3 Complex Networks
  • 3 From Trees in a Random Forest to a Complex Network
  • 4 Experimental Setup.
  • 5 Results and Discussion.
ISBN
3-031-15740-0
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