Data Management Technologies and Applications
7th International Conference, DATA 2018, Porto, Portugal, July 26-28, 2018, Revised Selected Papers
Published on 20. July 2019
Book
Paperback/Softback
XI, 211 pages
978-3-030-26635-6 (ISBN)
Description
This book constitutes the thoroughly refereed proceedings of the 7th International Conference on Data Management Technologies and Applications, DATA 2018, held in Porto, Portugal, in July 2018. The 9 revised full papers were carefully reviewed and selected from 69 submissions. The papers deal with the following topics: databases, big data, data mining, data management, data security, and other aspects of information systems and technology involving advanced applications of data.
More details
Series
Edition
2019 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
66 s/w Abbildungen, 51 farbige Abbildungen
XI, 211 p. 117 illus., 51 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 13 mm
Weight
347 gr
ISBN-13
978-3-030-26635-6 (9783030266356)
DOI
10.1007/978-3-030-26636-3
Schweitzer Classification
Other editions
Additional editions
Christoph Quix | Jorge Bernardino
Data Management Technologies and Applications
7th International Conference, DATA 2018, Porto, Portugal, July 26-28, 2018, Revised Selected Papers
E-Book
07/2019
Springer
€53.49
Available for download
Content
Constructing a Data Visualization Recommender System.- A Comprehensive Prediction Approach for Hardware Asset Management.- Linear vs. Symbolic Regression for Adaptive Parameter Setting in Manufacturing Process.- Graph Pattern Index for Neo4j Graph Databases.- Architectural Considerations for a Data Access Marketplace based Upon API Management.- FPGA vs. SIMD: Comparison for Main Memory-based Fast Column Scan.- Infectious Disease Prediction Modelling using Synthetic Optimization Approaches.- Concept Recognition with Convolutional Neural Networks to Optimize Keyphraze Extraction.- Deep Neural Trading: Comparative Study with Feed Forward, Recurrent and Autoencoder Networks.