Big Data Analytics and Knowledge Discovery 24th International Conference, DaWaK 2022, Vienna, Austria, August 22–24, 2022, Proceedings

This volume LNCS 13428 constitutes the papers of the 24 th International Conference on Big Data Analytics and Knowledge Discovery, held in August 2022 in Vienna, Austria.

The 12 full papers presented together with 12 short papers in this volume were carefully reviewed and selected from a total of 57 submissions.
The papers reflect a wide range of topics in the field of data integration, data warehousing, data analytics, and recently big data analytics, in a broad sense. The main objectives of this event are to explore, disseminate, and exchange knowledge in these fields.

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.