This paper explores the architecture and functions of Intelligent database Management systems (IDBMS), which integrate advanced artificial intelligence (AI) technologies to enhance traditional database management. By ...
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ISBN:
(纸本)9798331528911;9798331528928
This paper explores the architecture and functions of Intelligent database Management systems (IDBMS), which integrate advanced artificial intelligence (AI) technologies to enhance traditional database management. By addressing the limitations of conventional systems, IDBMS aim to improve query optimization, resource utilization, and user interaction through machine learning, predictive analytics, and natural language processing. The paper outlines the core components and architectural models of IDBMS, details their functionalities, presents case studies demonstrating their effectiveness, and discusses future trends and challenges in the field.
Renewable energy sources, especially solar PV, are emerging as one of the most promising solutions to combat the global warming phenomenon and climate change. However, solar PV systems often require proper supervision...
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ISBN:
(纸本)9798350349009;9798350349016
Renewable energy sources, especially solar PV, are emerging as one of the most promising solutions to combat the global warming phenomenon and climate change. However, solar PV systems often require proper supervision due to their dependence on various environmental factors. In practice, supervisory control and data acquisition (SCADA) systems have been widely adopted to capture PV system parameters and store them for long-term accessibility. This study proposes a comprehensive solution for monitoring rooftop PV systems, including data logging, storage, and backup. The authors also provide a cloud-based service to access the database and visualize measurements in realtime as our main contributions. The conclusions suggest future improvements and recommend several practical applications utilizing our resources.
In the rapidly evolving landscape of blockchain technology, security emerges as a paramount concern. This paper introduces an innovative blockchain security threat awareness platform, designed to comprehensively addre...
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ISBN:
(纸本)9798350386066;9798350386059
In the rapidly evolving landscape of blockchain technology, security emerges as a paramount concern. This paper introduces an innovative blockchain security threat awareness platform, designed to comprehensively address the multifaceted security challenges within blockchain networks, particularly focusing on Ethereum contracts. Central to the platform is a dual-database architecture, blending a NoSQL database with a graph database, enhancing data management, and enabling intricate transaction network visualizations. The platform's Threat Detection module, utilizing Large Language Models (LLMs) in conjunction with traditional methods, offers a novel approach to identifying and categorizing vulnerabilities in Ethereum smart contracts. Complementing this, the Threat Evidence Collection module provides detailed post-attack analysis, tracing transactions to their sources and evaluating address risks. This module's capabilities extend to producing statistical reports, including the transactional history and risk evaluation of individual addresses. Demonstrated on the Ethereum blockchain, the platform showcases its proficiency in handling complex data, rapid threat detection, and extensive forensic analysis, presenting a robust solution to fortifying blockchain security and offering a proactive defense mechanism for users and developers in the blockchain environment.
This paper presents the WDCLF (Web Declare, Conceptual, Logical, Physical) method for web database design (WDB), which has been adapted to the needs of web applications. The WDCLF extends traditional database design a...
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In distribution networks where it is challenging to directly measure load-side voltage, frequent voltage fluctuations often occur, leading to frequent transformer tap adjustments. These frequent adjustments can shorte...
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ISBN:
(纸本)9798350375596;9798350375589
In distribution networks where it is challenging to directly measure load-side voltage, frequent voltage fluctuations often occur, leading to frequent transformer tap adjustments. These frequent adjustments can shorten transformer lifespan, increase maintenance costs, and compromise the stability of the entire distribution system. This study proposes a machine learning-based method to accurately predict load-side voltage and derive optimal tap adjustments. By doing so, it aims to improve voltage stability and reduce unnecessary tap changes. The distribution network was modeled using the Real-Time Digital Simulator (RTDS) as a digital twin, and OpenDSS was used to simulate various load scenarios and generate data. Machine learning models, including Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Random Forest, and XGBoost, were trained using this data. Real-time data management was handled by Microsoft SQL Server, ensuring systematic and secure data handling. Among the evaluated models, XGBoost demonstrated the best predictive performance, excelling at handling large datasets and preventing overfitting through regularization. The proposed method is expected to minimize unnecessary tap changes and significantly enhance both voltage stability and the efficiency of power systems.
In recent years many applications have moved to blockchain-based services due to the increased popularity of blockchain technology. The implementation of this technology in numerous domain applications, such as financ...
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Minimizing database access latency is crucial in serverless edge computing for many applications, but databases are predominantly deployed in cloud environments, resulting in costly network round-trips. Embedding an i...
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ISBN:
(纸本)9798400705397
Minimizing database access latency is crucial in serverless edge computing for many applications, but databases are predominantly deployed in cloud environments, resulting in costly network round-trips. Embedding an in-process database library such as SQLite into the serverless runtime is the holy grail for low-latency database access. However, SQLite's architecture limits concurrency and multitenancy, which is essential for serverless providers, forcing us to rethink the architecture for integrating a database library. We propose rearchitecting SQLite to provide asynchronous byte-code instructions for I/O to avoid blocking in the library and decoupling the query and storage engines to facilitate database and serverless runtime co-design. Our preliminary evaluation shows up to a 100x reduction in tail latency, suggesting that our approach is conducive to runtime/database co-design for low latency.
The proceedings contain 66 papers. The special focus in this conference is on database and Expert systemsapplications. The topics include: Novel Node Importance Measures to Improve Keyword Search over RDF Graphs;Quer...
ISBN:
(纸本)9783030276171
The proceedings contain 66 papers. The special focus in this conference is on database and Expert systemsapplications. The topics include: Novel Node Importance Measures to Improve Keyword Search over RDF Graphs;Querying in a Workload-Aware Triplestore Based on NoSQL databases;Reverse Partitioning for SPARQL Queries: Principles and Performance Analysis;PFed: Recommending Plausible Federated SPARQL Queries;representing and Reasoning About Precise and Imprecise Time Points and Intervals in Semantic Web: Dealing with Dates and Time Clocks;context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning;silverChunk: An Efficient In-Memory Parallel Graph Processing System;a Modular Approach for Efficient Simple Question Answering Over Knowledge Base;scalable Machine Learning in the R Language Using a Summarization Matrix;energy Efficient Data Placement and Buffer Management for Multiple Replication;ML-PipeDebugger: A Debugging Tool for Data Processing Pipelines;correlation Set Discovery on Time-Series Data;anomaly Subsequence Detection with Dynamic Local Density for Time Series;trajectory Similarity Join for Spatial Temporal database;multiviewpoint-Based Agglomerative Hierarchical Clustering;Triplet-CSSVM: Integrating Triplet-Sampling CNN and Cost-Sensitive Classification for Imbalanced Image Detection;discovering Partial Periodic High Utility Itemsets in Temporal databases;using Mandatory Concepts for Knowledge Discovery and Data Structuring;topological Data Analysis with ϵ -net Induced Lazy Witness Complex;analyzing Sequence Pattern Variants in Sequential Pattern Mining and Its Application to Electronic Medical Record systems;querying Knowledge Graphs with Natural Languages;composing Distributed Data-Intensive Web Services Using Distance-Guided Memetic Algorithm;keyword Search Based Mashup Construction with Guaranteed Diversity;adaptive Caching for Data-Intensive Scientific Workflows in the Cloud;Succinct BWT-Based Sequence Prediction.
Data parallel primitives are highly optimized general-purpose algorithms designed only for GPUs and are used as building blocks to develop applications. However, existing data parallel primitives cannot handle data la...
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ISBN:
(纸本)9783031683114;9783031683121
Data parallel primitives are highly optimized general-purpose algorithms designed only for GPUs and are used as building blocks to develop applications. However, existing data parallel primitives cannot handle data larger than the GPU memory size. In this paper, we propose an extension to existing data parallel primitives to efficiently handle data larger than the GPU memory size by cooperatively using both GPUs and CPUs. Moreover, we evaluate the impact of these primitives when applying them to large data processing applications, with respect to both performance and software development cost.
High availability (HA) in MySQL databases remains a critical challenge, particularly due to risks such as single point of failure (SPOF) and limited scalability in existing setups. Previous studies have explored vario...
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