Atjaunināt sīkdatņu piekrišanu

E-grāmata: Big Data Analytics and Knowledge Discovery: 23rd International Conference, DaWaK 2021, Virtual Event, September 27-30, 2021, Proceedings

Edited by , Edited by , Edited by , Edited by , Edited by
  • Formāts - EPUB+DRM
  • Cena: 65,42 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

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.
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.