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Record Nr. |
UNINA9910814780503321 |
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Autore |
David Michael M. |
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Titolo |
Advanced standard SQL dynamic structured data modeling and hierarchical processing / / Michael M. David, Lee Fesperman |
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Pubbl/distr/stampa |
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Boston : , : Artech House, , ©2013 |
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[Piscataqay, New Jersey] : , : IEEE Xplore, , [2013] |
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ISBN |
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Descrizione fisica |
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1 online resource (406 p.) |
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Collana |
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Artech House computing library |
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Altri autori (Persone) |
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FespermanLee |
DavidMichael M |
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Disciplina |
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Soggetti |
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SQL (Computer program language) |
Data structures (Computer science) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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"This revised and updated edition of Advanced ANSI SQL data modeling and structure processing ..."--Pref. |
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Nota di bibliografia |
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Includes bibliographical references (pages 361-364) and index. |
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Nota di contenuto |
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Advanced Standard SQL Dynamic Structured Data Modeling and Hierarchical Processing; Contents; Preface; Introduction; Part I: The Basics of the RelationalJoin Operation; 1 Relational Join Introduction; 1.1 Standard Inner Join Review; 1.2 Problems with Relational Join Processing; 1.3 Outer Join Review; 1.4 Problems with Previous Outer Join Syntax; 1.5 Conclusion; 2 The Standard SQL Join Operation; 2.1 Standard SQL Join Syntax; 2.2 Standard SQL Join Operation; 2.3 Standard SQL Join Does Not Follow the Cartesian Product Model; 2.4 Determining Standard SQL Join Associativity and Commutativity. |
2.5 What Outer Join Commutativity Is2.6 What Outer Join Associativity Is; 2.7 Hierarchictivity in Addition to Associativity and Commutativity; 2.8 Conclusion; 3 Standard SQL Join Types and Their Operation; 3.1 FULL Outer Join; 3.2 One-Sided Outer Join; 3.3 INNER Join; 3.4 CROSS Join; 3.5 UNION Join; 3.6 Intermixing Join Types; 3.7 Conclusion; 64. |
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Sommario/riassunto |
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"Based on the Artech House classic, ANSI SQL Data Modeling and Structure Processing, this expanded and updated book offers you an essential tool for utilizing the ANSI SQL outer join operation to perform simple or complex hierarchical data modeling and structure processing. The book provides you with a comprehensive review of the outer join operation, its powerful syntax and semantics, and new features and |
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capabilities. This revised resource introduces several important new concepts such as relationship and hierarchical integration at the hierarchical processing level, multipath hierarchical automatic XML query processing, dynamic structured data processing using automatic metadata maintenance, and advanced data transformations. Featuring more than 230 illustrations, the book shows you how to tap the full power of data structure extraction technology that gathers data structure meta information naturally embedded in ANSI SQL specifications. You discover existing, but previously unknown, SQL capabilities for improving performance. The book explains how to perform multitable outer joins and combine relational structures with hierarchical structures. Moreover you learn how to establish a default database standard for hierarchical data modeling and structure processing." |
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2. |
Record Nr. |
UNISA996630870603316 |
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Autore |
Antonacopoulos Apostolos |
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Titolo |
Pattern Recognition : 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part V / / edited by Apostolos Antonacopoulos, Subhasis Chaudhuri, Rama Chellappa, Cheng-Lin Liu, Saumik Bhattacharya, Umapada Pal |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (510 pages) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 15305 |
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Altri autori (Persone) |
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ChaudhuriSubhasis |
ChellappaRama |
LiuCheng-Lin |
BhattacharyaSaumik |
PalUmapada |
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Disciplina |
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Soggetti |
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Computer vision |
Machine learning |
Computer Vision |
Machine Learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Multi-views Enhanced Spatio-Temporal Adaptive Transformer for Urban Traffic Prediction -- QPDet: Queuing People Detector for Aerial Images based on Adaptive Soft Label Assignment Strategy -- Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness -- IFFusion: Illumination-free Fusion Network for Infrared and Visible Images -- Infrared and visible image fusion method based on learnable joint sparse low-rank separation representation -- Glare-SNet：Unsupervised Glare Suppression Balance Network -- Learning to Detect Lithography Defects in SEM Images -- Time-aware Intent Contrastive Learning with Rare-class Sample Generator for Sequential Recommendation -- UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning -- Effects of Primary Capsule Shapes and Sizes in Capsule Networks -- ASwin-YOLO: Attention – Swin Transformers in YOLOv7 for Air-to-Air Unmanned Aerial Vehicle Detection -- Quaternion Squeeze and Excitation Networks: Mean , Variance , Skewness , Kurtosis As One Entity -- Dualswin-Ynet: A novel bimodal fusion network for ship detection in remote sensing images -- STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting -- Bi-UNet:Bi-level Routing Attention Unet-shaped Network based on Explicit Visual Prompt -- Learning Dynamic Representations in Large Language Models for Evolving Data Streams -- Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly Detection -- Pneumonia Classification in chest X-ray images using Explainable Slot-Attention Mechanism -- SegNet-ATT: Cross-Channel and Spatial Attention-Enhanced U-Net for Semantic Segmentation of Flood Affected Areas -- WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking -- Detection of Oral Potentially Malignant Lesions through Tranformer-based Segmentation Models -- ROI-Aware Dynamic Network Quantization for Neural Video Compression -- SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning -- TVT: Training-free Vision Transformer Search on Tiny Datasets -- One-Shot Classification is Enough for Automatic Label Mapping -- Sustainable and Explainable Neural Network for Real-Time Time Series Classification -- StressViT: Splitting and Compressing Vision Transformer through Edge-Cloud Collaboration -- Effective Layer Pruning Through Similarity Metric Perspective -- A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics -- Constant Time Decision Trees and Random Forest. |
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Sommario/riassunto |
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The multi-volume set of LNCS books with volume numbers 15301-15333 constitutes the refereed proceedings of the 27th International Conference on Pattern Recognition, ICPR 2024, held in Kolkata, India, during December 1–5, 2024. The 963 papers presented in these proceedings were carefully reviewed and selected from a total of 2106 submissions. They deal with topics such as Pattern Recognition; Artificial Intelligence; Machine Learning; Computer Vision; Robot Vision; Machine Vision; Image Processing; Speech Processing; Signal Processing; Video Processing; Biometrics; Human-Computer Interaction (HCI); Document Analysis; Document Recognition; Biomedical Imaging; Bioinformatics. |
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