Big Data Analytics and Knowledge Discovery

This book constitutes the refereed proceedings of the 21st International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2019, held in Linz, Austria, in September 2019.

The 12 full papers and 10 short papers presented were carefully reviewed and selected from 61 submissions. The papers are organized in the following topical sections: Applications; patterns; RDF and streams; big data systems; graphs and machine learning; databases.

Applications.- Detecting the Onset of Machine Failure Using Anomaly Detection Methods.- A Hybrid Architecture for Tactical and Strategic Precision Agriculture.- Urban analytics of big transportation data for supporting smart cities.- Patterns.- Frequent Item Mining When Obtaining Support is Costly.- Mining Sequential Pattern of Historical Purchases for E-Commerce Recommendation.- Discovering and Visualizing Efficient Patterns in Cost/Utility Sequences.- Efficient Row Pattern Matching using Pattern Hierarchies for Sequence OLAP.- Statistically Significant Discriminative Patterns Searching.- RDF and Streams.- Multidimensional Integration of RDF datasets.- RDFPartSuite: Bridging Physical and Logical RDF Partitioning.- Mining quantitative temporal dependencies between interval-based streams.- Democratization of OLAP DSMS.- Big Data Systems.- Leveraging the Data Lake - Current State and Challenges.- SDWP: A New Data Placement Strategy for Distributed Big DataWarehouses in Hadoop.- Improved Programming-Language Independent MapReduce on Shared-Memory Systems.- Evaluating Redundancy and Partitioning of Geospatial Data in Document-Oriented Data Warehouses.- Graphs and Machine Learning.- Scalable Least Square Twin Support Vector Machine Learning.- Finding Strongly Correlated Trends in Dynamic Attributed Graphs.- Text-based Event Detection: Deciphering Date Information Using Graph Embeddings.- Efficiently Computing Homomorphic Matches of Hybrid Pattern Queries on Large Graphs.- Databases.- From Conceptual to Logical ETL Design using BPMN and Relational Algebra.- Accurate Aggregation Query-Result Estimation and Its Efficient Processing on Distributed Key-Value Store.