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Big Data Analytics and Knowledge Discovery de Robert Wrembel

Big Data Analytics and Knowledge Discovery: Lecture Notes in Computer Science

Editat de Robert Wrembel, Johann Gamper, Gabriele Kotsis, A Min Tjoa, Ismail Khalil
en Limba Engleză Paperback – 10 aug 2023
This book constitutes the proceedings of the 25th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2023, which took place in Penang, Malaysia, during August 29-30, 2023.
The 18 full papers presented together with 19 short papers were carefully reviewed and selected from a total of 83 submissions.

They were organized in topical sections as follows: Data quality; advanced analytics and pattern discovery; machine learning; deep learning; and data management.
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Specificații

ISBN-13: 9783031398308
ISBN-10: 3031398300
Pagini: 416
Ilustrații: XVI, 400 p. 147 illus., 107 illus. in color.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.63 kg
Ediția:1st edition 2023
Editura: Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

An Integration of TextGCN and Autoencoder into Aspect-based Sentiment Analysis.- OpBerg: Discovering causal sentences using optimal alignments.- Text-based Causal Inference on Irony and Sarcasm Detection.- Sarcastic RoBERTa: a RoBERTa-based deep neural network detecting sarcasm on Twitter.- A Fast NDFA-Based Approach to Approximate Pattern-Matching for Plagiarism Detection in Blockchain-Driven NFTs.- On Decisive Skyline Queries.- Safeness: Suffix Arrays driven Materialized View Selection Framework for Large-Scale Workloads.- A Process Warehouse for Process Variants Analysis.- Feature Selection Algorithms.- Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data.- Multi-label Online Streaming Feature Selection Algorithms via Extending Alpha Investing Strategy.- Feature Selection Under Fairness and Performance Constraints.- Time Series Processing.- Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines.- Pathology Data Prioritisation: A Study Using Multi-Variate Time Series.- Outlier/Anomaly detection of univariate time series: A dataset collection and benchmark.- Automatic Machine Learning-based OLAP Measure Detection for Tabular Data.- Discovering Overlapping Communities based on Cohesive Subgraph Models over Graph Data.- Discovery of Keys for Graphs.- OPTIMA: Framework Selecting Optimal Virtual Model to Query Large Heterogeneous Data.- . Q-VIPER: Quantitative Vertical Bitwise Algorithm to Mine Frequent Patterns.- Enhanced Sliding Window-Based Periodic Pattern Mining from Dynamic Streams.- Explainable Recommendations for Wearable Sensor Data Machine Learning.- SLA-Aware Cloud Query Processing with Reinforcement Learning-based MultiObjective Re-Optimization.- Distance Based K-Means Clustering.- Grapevine Phenology Prediction: A Comparison of Physical and Machine Learning Models.