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kNN Join for Dynamic High-Dimensional Data: A Parallel Approach
The k nearest neighbor (kNN) join operation is a fundamental task that combines two high-dimensional databases, enabling data points in the User...
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Multi-level Storage Optimization for Intermediate Data in AI Model Training
As Transformer-based large models become the mainstream of AI training, the development of hardware devices (e.g., GPUs) cannot keep up with the...
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Take a Close Look at the Optimization of Deep Kernels for Non-parametric Two-Sample Tests
The maximum mean discrepancy (MMD) test with deep kernel is a powerful method to distinguish whether two samples are drawn from the same...
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Balanced Hop-Constrained Path Enumeration in Signed Directed Graphs
Hop-constrained path enumeration, which aims to output all the paths from two distinct vertices within the given hops, is one of the fundamental...
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Probabilistic Reverse Top-k Query on Probabilistic Data
Reverse top-k queries have received much attention from research communities. The result of reverse top-k queries is a set of objects, which had the...
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IFGNN: An Individual Fairness Awareness Model for Missing Sensitive Information Graphs
Graph neural networks (GNNs) provide an approach for analyzing complicated graph data for node, edge, and graph-level prediction tasks. However, due...
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Discovering Densest Subgraph over Heterogeneous Information Networks
Densest Subgraph Discovery (DSD) is a fundamental and challenging problem in the field of graph mining in recent years. The DSD aims to determine,...
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Influence Maximization Revisited
Influence Maximization (IM) has been extensively studied, which is to select a set of k seed users from a social network to maximize the expected...
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Maximum Fairness-Aware (k, r)-Core Identification in Large Graphs
Cohesive subgraph mining is a fundamental problem in attributed graph analysis. The k-core model has been widely used in many studies to measure the...
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Social Recommendation Using Deep Auto-encoder and Confidence Aware Sentiment Analysis
The development of online social networks has attracted increasing interest in social recommendation. On the other hand, recommender systems based on...
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Finding a Second Wind: Speeding Up Graph Traversal Queries in RDBMSs Using Column-Oriented Processing
Recursive queries and recursive derived tables constitute an important part of the SQL standard. Their efficient processing is important for many...
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Investigating the Perceived Usability of Entity-Relationship Quality Frameworks for NoSQL Databases
Quality assessment of data models can be a challenging task due to its subjective nature. For the schemaless, heterogeneous and diverse group of...
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Approach Based on Bayesian Network and Ontology for Identifying Factors Impacting the States of People with Psychological Problems from Data on Social Media
Nowadays, social networks provide relevant information that is used in many contexts for different objectives. However, the major challenges remain...
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Data-Driven and Model-Driven Approaches in Predictive Modelling for Operational Efficiency: Mining Industry Use Case
In this study, we explore the effectiveness of a hybrid modelling approach that seamlessly integrates data-driven techniques, specifically Machine...
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A Comparative Analysis of Time Series Prediction Techniques a Systematic Literature Review (SLR)
This paper highlights the significance of systematic literature reviews and explores the different techniques employed in these reviews, including...
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Deep Learning Based on TensorFlow and Keras for Predictive Monitoring of Business Process Execution Delays
In order to enhance their performance and responsiveness, organizations must identify, manage, and monitor all business processes that involve...
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Cardiovascular Anomaly Detection Using Deep Learning Techniques
Cardiovascular diseases (CVD) refer to a group of health conditions that affect the heart and blood vessels. This can also include arterial damage in...
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AI-LMS: AI-Based Long-Term Monitoring System for Patients in Pandemics: COVID-19 Case Study
In the context of the ongoing COVID-19 pandemic, the need for robust health monitoring systems has become increasingly evident, especially for...
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Towards an Effective Attribute-Based Access Control Model for Neo4j
The graph data model is increasingly used in practice due to its flexibility in modeling complex real-life data. However, some security features...