Results 191 to 200 of about 21,629,133 (329)
Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data. [PDF]
J Digit Imaging, 2023 Kaspar M +6 more
europepmc +1 more source
Dynamic Query Scheduling in Parallel Data Warehouses [PDF]
, 2002 Holger Märtens +2 more
openalex +1 more source
The Challenge of Handling Structured Missingness in Integrated Data Sources
Advanced Intelligent Discovery, EarlyView.As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson +6 more
wiley +1 more source
Amoxicillin-Induced Neurotoxicity: Contribution of a Healthcare Data Warehouse to the Determination of a Toxic Concentration Threshold. [PDF]
Antibiotics (Basel), 2023 Lalanne S +7 more
europepmc +1 more source
A methodology for building a data warehouse in a scientific environment [PDF]
, 2002 Karl Aberer, Klemens Hemm
openalex +1 more source
Advancements in Graphdiyne‐Based Multiscale Catalysts for Green Hydrogen Energy Conversion
Advanced Intelligent Discovery, EarlyView.This review systematically explores the fundamental characteristics of graphdiyne (GDY), cutting‐edge field of GDY‐based multiscale catalysts within sustainable energy conversion systems.Special emphasis is placed on the structure‒property relationships in different reactions.
Qian Xiao, Lu Qi, Siao Chen, Yurui Xue
wiley +1 more source
Snowflake Data Warehouse for Large-Scale and Diverse Biological Data Management and Analysis. [PDF]
Genes (Basel)Koreeda T, Honda H, Onami JI.
europepmc +1 more source
Prolactin Level Changes according to Atypical Antipsychotics Use: A Study Based on Clinical Data Warehouse. [PDF]
Clin Psychopharmacol Neurosci, 2023 Kim S +5 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee +3 more
wiley +1 more source