Hypothesis Generation and Interpretation: Design Principles and Patterns for Big Data Applications
Gespeichert in:
1. Verfasser: | |
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Cham
Springer International Publishing
2024
Cham Springer |
Ausgabe: | 1st ed. 2024 |
Schriftenreihe: | Studies in Big Data
139 |
Schlagworte: | |
Online-Zugang: | BFB01 BTU01 FHA01 FHD01 FHI01 FHM01 FHN01 FHR01 FKE01 FLA01 FRO01 FWS01 FWS02 HTW01 TUM01 UBA01 UBG01 UBM01 UBR01 UBT01 UBW01 UBY01 UEI01 UER01 UPA01 Volltext |
Beschreibung: | 1 Online-Ressource (XII, 372 p. 177 illus., 125 illus. in color) |
ISBN: | 9783031435409 |
ISSN: | 2197-6511 |
DOI: | 10.1007/978-3-031-43540-9 |
Internformat
MARC
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Datensatz im Suchindex
DE-BY-FWS_katkey | 1062644 |
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author | Ishikawa, Hiroshi |
author_facet | Ishikawa, Hiroshi |
author_role | aut |
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discipline | Informatik |
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doi_str_mv | 10.1007/978-3-031-43540-9 |
edition | 1st ed. 2024 |
format | Electronic eBook |
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owner_facet | DE-860 DE-91 DE-BY-TUM DE-19 DE-BY-UBM DE-Aug4 DE-898 DE-BY-UBR DE-861 DE-523 DE-859 DE-473 DE-BY-UBG DE-29 DE-863 DE-BY-FWS DE-1050 DE-20 DE-862 DE-BY-FWS DE-92 DE-355 DE-BY-UBR DE-824 DE-522 DE-384 DE-M347 DE-573 DE-703 DE-706 DE-739 DE-634 |
physical | 1 Online-Ressource (XII, 372 p. 177 illus., 125 illus. in color) |
psigel | ZDB-2-SCS ZDB-2-SCS_2024 |
publishDate | 2024 |
publishDateSearch | 2024 |
publishDateSort | 2024 |
publisher | Springer International Publishing Springer |
record_format | marc |
series2 | Studies in Big Data |
spellingShingle | Ishikawa, Hiroshi Hypothesis Generation and Interpretation Design Principles and Patterns for Big Data Applications Theory of Computation Database Management Data Mining and Knowledge Discovery Machine Learning Big Data Complex Systems Computer science Database management Data mining Machine learning Big data System theory |
title | Hypothesis Generation and Interpretation Design Principles and Patterns for Big Data Applications |
title_auth | Hypothesis Generation and Interpretation Design Principles and Patterns for Big Data Applications |
title_exact_search | Hypothesis Generation and Interpretation Design Principles and Patterns for Big Data Applications |
title_exact_search_txtP | Hypothesis Generation and Interpretation Design Principles and Patterns for Big Data Applications |
title_full | Hypothesis Generation and Interpretation Design Principles and Patterns for Big Data Applications by Hiroshi Ishikawa |
title_fullStr | Hypothesis Generation and Interpretation Design Principles and Patterns for Big Data Applications by Hiroshi Ishikawa |
title_full_unstemmed | Hypothesis Generation and Interpretation Design Principles and Patterns for Big Data Applications by Hiroshi Ishikawa |
title_short | Hypothesis Generation and Interpretation |
title_sort | hypothesis generation and interpretation design principles and patterns for big data applications |
title_sub | Design Principles and Patterns for Big Data Applications |
topic | Theory of Computation Database Management Data Mining and Knowledge Discovery Machine Learning Big Data Complex Systems Computer science Database management Data mining Machine learning Big data System theory |
topic_facet | Theory of Computation Database Management Data Mining and Knowledge Discovery Machine Learning Big Data Complex Systems Computer science Database management Data mining Machine learning Big data System theory |
url | https://doi.org/10.1007/978-3-031-43540-9 |
work_keys_str_mv | AT ishikawahiroshi hypothesisgenerationandinterpretationdesignprinciplesandpatternsforbigdataapplications |