Database and Expert Systems Applications

Name: Database and Expert Systems Applications | 35th International Conference, DEXA 2024, Naples, Italy, August 26-28, 2024, Proceedings, Part I
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35th International Conference, DEXA 2024, Naples, Italy, August 26-28, 2024, Proceedings, Part I

Christine Strauss Toshiyuki Amagasa Giuseppe Manco Gabriele Kotsis A. Min Tjoa Ismail Khalil(Herausgeber*in)

Springer (Verlag)

Erschienen am 17. August 2024

XXVII, 277 Seiten

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Inhalt

Intro
Preface
Organization
Abstracts of Keynote Talks
Multimodal Deep Learning in Medical Imaging
Digital Humanism as an Enabler for a Holistic Socio-Technical Approach to the Latest Developments in Computer Science and Artificial Intelligence
Deep Entity Processing in the Era of Large Language Models: Challenges and Opportunities
Contents - Part I
Contents - Part II
Financial and Economic Data Analysis
CSPRD: A Financial Policy Retrieval Dataset for Chinese Stock Market
1 Introduction
2 Related Work
2.1 Retrieval Augmented Generation
2.2 Dense Retrievers
2.3 Specialised Financial Datasets
3 The Policy Retrieval Dataset for Stock Market in China
3.1 Data Collection
3.2 Data Processing
3.3 Unsupervised MoE Selection
3.4 Expert Annotation
3.5 Dataset Release
4 CSPR-MQA Pre-training
4.1 Data Preprocessing
4.2 Encoding
4.3 Decoding
5 Experiments
5.1 Models
5.2 Evaluation Metrics
5.3 Results and Analysis
6 Limitations
6.1 Compromise Between Labor Costs and Decision Comprehensiveness
6.2 Limited Experiments on Pre-Trained Language Models
7 Conclusion
References
Leveraging Heterogeneous Text Data for Reinforcement Learning-Based Stock Trading Strategies
1 Introduction
2 Related Work
3 Preliminaries
3.1 Terms
3.2 Problem Definition
3.3 Base Model
4 Proposed Method
4.1 Encording
4.2 Feature Generation
4.3 Action Determination
5 Experiment
5.1 Datasets
5.2 Experimental Setup
5.3 Experimental Results
5.4 Comparison with Index-Based Method
5.5 Effects of Periods and Initial Assets
6 Conclusion
References
TCMIDP: A Comprehensive Database of Traditional Chinese Medicine for Network Pharmacology Research
1 Introduction
2 Database Contents and Access
3 Data Mining
4 User Evaluation
5 Conclusion
References
Graph Theory and Network Analysis
Fast Subgraph Search with Graph Code Indices
1 Introduction
2 Preliminaries
3 Related Work
4 Basic Concept of the Proposed Method
5 Graph Representation and Indexing of Databases
6 Subgraph Search with the Code Tree
7 Experimental Evaluation
7.1 Experimental Settings
7.2 Experimental Results
8 Conclusion
References
Completing Predicates Based on Alignment Rules from Knowledge Graphs
1 Introduction
2 Motivating Example
3 The SYRUP Approach
4 Experimental Study
5 Related Work
6 Conclusions and Future Work
References
Enriching Hierarchical Navigable Small World Searches with Result Diversification
1 Introduction
2 Preliminaries and Related Work
3 Material and Methods
4 Empirical Evaluation
5 Conclusions
References
An Efficient Indexing Method for Dynamic Graph kNN
1 Introduction
1.1 Existing Approaches and Challenges
1.2 Our Approaches and Contributions
2 Preliminary
2.1 Problem Definition
2.2 Previous Method: CT Index
3 Proposed Method: Dynamic CT
3.1 Ideas
3.2 Adding Nodes and Edges
3.3 Removing Nodes and Edges
3.4 Complexity Analysis
4 Experimental Evaluation
4.1 Efficiency for Updating
4.2 Efficiency for Adding/Removing Edges
5 Conclusion
References
Database Management and Query Optimization
Improving the Accuracy of Text-to-SQL Tools Based on Large Language Models for Real-World Relational Databases
1 Introduction
2 Related Work
3 A Real-World Benchmark for the Text-to-SQL Task
3.1 The Real-World Relational Database
3.2 The Sets of Views
3.3 The Test Questions and Their Ground Truth SQL Translations
4 The Proposed RAG-Based Technique
4.1 Generation of the Synthetic Dataset
4.2 The Proposed RAG-Based Techniques
5 Experiments with an RW-RDB
5.1 Experimental Setup
5.2 Results
6 Experiments with Mondial
7 Conclusions
References
QPSEncoder: A Database Workload Encoder with Deep Learning
1 Introduction
2 Related Work
3 QPSEncoder Framework
3.1 Physical Plan Encoding
3.2 SQL Query Encoding
3.3 Database Schema Encoding
4 Experiments
4.1 Experimental Setup
4.2 Effectiveness of QPSEncoder on Numeric Predicates.
4.3 Effectiveness of QPSEncoder on Mixed Predicates.
4.4 Effectiveness of Model Components
5 Conclusion
References
Efficient Random Sampling from Very Large Databases
1 Introduction
2 Related Work
3 The Proposed Algorithms
3.1 Random Sampling in B+Tree of Height Three
3.2 Random Sampling in B+Tree of Height Four
3.3 Generalization to Any B+Tree Height
4 Analysis
5 Simulation Study
5.1 Experiments Framework
5.2 Experiments Setup
5.3 Implementation Details
5.4 Results
6 Summary and Future Work
References
SQL-to-Schema Enhances Schema Linking in Text-to-SQL
1 Introduction
2 Related Work
2.1 Customized Machine Learning Fine-Tuned Methods
2.2 Stimulating General LLM with Prompting
3 Methodology
3.1 Evaluation Metrics
3.2 Introduction to Each Module
4 Experiments and Analysis
4.1 Experiment One
4.2 Experiment Two
4.3 Experiment Three
5 Conclusion
References
Efficient Algorithms for Top-k Stabbing Queries on Weighted Interval Data
1 Introduction
2 Preliminary
3 Algorithm Based on Interval Forest
3.1 Data Structure and Construction
3.2 Query Processing Algorithm
4 Algorithm Based on a Variant of Segment Tree
4.1 Variant of Segment Tree and Its Construction
4.2 Query Processing Algorithm
5 Conclusion
References
A Hierarchical Storage Mechanism for Hot and Cold Data Based on Temperature Model
1 Introduction
2 Related Work
3 System Architecture
3.1 Data Temperature Model
3.2 Data Hierarchical Storage Mechanism
4 Experimentation and Analysis
4.1 Experimental Environment and Configuration
4.2 Experiment on Local Hot and Cold Data Migration Management
4.3 Experiment on Local and Remote Data Migration Management
5 Conclusion
References
Machine Learning and Large Language Models
A Pre-trained Knowledge Tracing Model with Limited Data
1 Introduction
2 Related Work
3 Method
3.1 Transformer-Based Knowledge Tracing Model
3.2 BERT-Based Knowledge Tracing Model
4 Experiments
4.1 Datasets
4.2 Experimental Setup
4.3 Experiment Results and Analysis
5 Conclusions and Future Work
References
Chorus: More Efficient Machine Learning on Serverless Platform
1 Introduction
2 Background and Motivation
3 Lambda Synchronous Parallel (LSP) Model
4 Buffering in Parameter Server Model
5 Design of Chorus
5.1 Architecture Overview
6 Evaluation
6.1 Methodology
6.2 Lambda Synchronous Parallel (LSP) Model
6.3 Buffering System
6.4 Comparison
7 Conclusion
References
Evaluating Performance of LLaMA2 Large Language Model Enhanced by QLoRA Fine-Tuning for English Grammatical Error Correction
1 Introduction
2 Related Work
2.1 Grammar Error Correction
2.2 LLaMA2 Large Language Model
3 Methodology
3.1 Data Preparation
3.2 Prompts and Learning Strategies
3.3 Parameter Efficient Fine-Tuning via QLoRA
4 Experiments
4.1 Text Generation Settings
4.2 Parameter Settings of Training
4.3 Experimental Results
4.4 Error Type Analysis
4.5 Performance Evaluation of Open Source Language Tools
5 Conclusion
References
A Label Embedding Algorithm Based on Maximizing Normalized Cross-Covariance Operator
1 Introduction
2 Preliminaries
3 The Proposed Method
4 Experiments
4.1 Benchmark Data Sets and Evaluation Metrics
4.2 Compared Methods and Experimental Settings
4.3 Performance Evaluation and Analysis
5 Conclusion
References
Analyzing the Efficacy of Large Language Models: A Comparative Study
1 Introduction
2 Literature Review
3 Datasets and Preprocessing
3.1 Dataset Construction by Question-Answer Pair Generation
3.2 Fine-Tuning the LLM
3.3 Document Parsing and Text Comprehension
4 Methodology
4.1 Integrated Evaluation: BLEU, ROUGE, and Cosine Similarity
4.2 Accuracy Assessment Through Z-Score Outlier Detection
5 Observations and Results
5.1 Classification of Errors
5.2 Comparison of Performance of Our Framework
6 Conclusion and Future Directions
References
Leveraging Large Language Models for Flexible and Robust Table-to-Text Generation
1 Introduction
2 Related Work
3 Methods and Experiment Settings
4 Experiments Results
5 Conclusion
References
Recommender Systems and Personalization
Collaborative Filtering for the Imputation of Patient Reported Outcomes
1 Introduction
2 Related Work
3 Methodology
3.1 MDASI-HN Data
3.2 Collaborative Filtering (CF) for MDASI-HN
4 Evaluation
4.1 Evaluation Metrics:
5 Experimental Results
5.1 Experimental Setup
5.2 Data Statistics
5.3 CF Techniques Comparison
5.4 Effect of k on CF-SYM-PCC Imputation
5.5 Comparing CF-SYM-PCC Against Other Methods
5.6 Comparing CF-SYM-PCC and LI Techniques Per Symptom
5.7 PCC Correlation Symptom Clusters
6 Conclusion
References
Category-Aware Sequential Recommendation with Time Intervals of Purchases
1 Introduction
2 Method
2.1 Problem Setup
2.2 Category and Time Interval Aware Sequence Recommendation
2.3 Framework of the Dual Model
3 Performance Evaluation
3.1 Dataset Preprocessing and Evaluation Matric
3.2 Results of Performance Evaluation
4 Conclusion
References
A Soft Actor-Critic Algorithm for Sequential Recommendation
1 Introduction
2 Related Work
2.1 Multi-task Learning
2.2 TD3 in RMTL
2.3 Soft Actor-Critic Algorithm
3 Methodology
3.1 Our Framework
3.2 Actor
3.3 Critic
4 Experiments and Discussions
5 Conclusion
References
An Approach for Social-Distance Preserving Location-Aware Recommender Systems: A Use Case in a Hospital Environment
1 Introduction
2 Related Work
3 Location-Aware Recommender Architecture
4 Experimental Evaluation
5 Conclusions and Future Work
References
Author Index

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