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Big Data Analytics and Knowledge Discovery : 23rd International Conference, DaWaK 2021, Virtual Event, September 27-30, 2021, Proceedings / edited by Matteo Golfarelli, Robert Wrembel, Gabriele Kotsis, A Min Tjoa, Ismail Khalil.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI ; SL 3, 12925
- Information Systems and Applications, incl. Internet/Web, and HCI ; 12925
- Language:
- English
- Subjects (All):
- Database management.
- Artificial intelligence.
- Data mining.
- Application software.
- Data structures (Computer science).
- Information theory.
- Database Management.
- Artificial Intelligence.
- Data Mining and Knowledge Discovery.
- Computer and Information Systems Applications.
- Data Structures and Information Theory.
- Local Subjects:
- Database Management.
- Artificial Intelligence.
- Data Mining and Knowledge Discovery.
- Computer and Information Systems Applications.
- Data Structures and Information Theory.
- Physical Description:
- 1 online resource (XIII, 282 pages) : 87 illustrations, 73 illustrations in color.
- Edition:
- 1st ed. 2021.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2021.
- System Details:
- text file PDF
- Summary:
- This volume LNCS 12925 constitutes the papers of the 23rd International Conference on Big Data Analytics and Knowledge Discovery, held in September 2021. Due to COVID-19 pandemic it was held virtually. The 12 full papers presented together with 15 short papers in this volume were carefully reviewed and selected from a total of 71 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.
- Contents:
- Performance
- Bounding Box Representation of Co-Location Instances for L-infinity Induced Distance Measure
- Benchmarking Data Lakes Featuring Structured and Unstructured Data with DLBench
- Towards an Adaptive Multidimensional Partitioning for Accelerating Spark SQL
- Selecting Subexpressions to Materialize for Dynamic Large-scale Workloads
- Prediction Techniques
- A Chain Composite Item Recommender for Lifelong Pathways
- Health Analytics on COVID-19 Data with Few-Shot Learning
- Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory
- Knowledge Representation
- Universal Storage Adaption for Distributed RDF-triple Stores
- RDF Data Management is an Analytical Market, not a Transaction one
- Document Ranking for Curated Document Databases using BERT and Knowledge Graph Embeddings: Introducing GRAB-Rank
- Advanced Analytics
- Contextual and Behavior Factors Extraction from Pedestrian Encounter Scenes Using Deep Language Models
- Spark based Text Clustering Method using Hashing
- Impact of textual data augmentation on linguistic pattern extraction to improve the idiomaticity of extractive summaries
- Explainability in Irony Detection
- Efficient Graph Analytics in Python for Large-scale Data Science
- Machine Learning and Deep Learning
- A New Accurate Clustering Approach for Detecting Different Densities in High Dimensional Data
- ODCA: an Outlier Detection Approach to Deal with Correlated Attributes
- A Novel Neurofuzzy Approach for Semantic Similarity Measurement
- Data Warehouse Processes and Maintenance
- Integrated Process Data and Organizational Data Analysis for Business Process Improvement
- Smart-Views: Decentralized OLAP View Management using Blockchains
- A workload-aware change data capture framework for data warehousing
- Machine Learning and Analtyics
- Motif Based Feature Vectors: Towards a Homogeneous Data Representation for Cardiovascular Diseases Classification
- Filter-based Feature Selection Methods for Industrial Sensor Data: A Review
- A Declarative Framework for mining Top-k High Utility Itemsets
- Multi-label Feature Selection Algorithm via Maximizing Label Correlation-aware Relevance and Minimizing Redundance with Mutation Binary Particle Swarm Optimization
- Mining Partially-Ordered Episode Rules with the Head Support
- Boosting Latent Inference of Resident Preference from Electricity Usage - A Demonstration on Online Advertisement Strategies.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-86534-4
- 9783030865344
- Access Restriction:
- Restricted for use by site license.
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