Ontotext

AUGUST 31, 2023

The growth of large language models drives a need for trusted information and capturing machine-interpretable knowledge, requiring businesses to recognize the difference between a semantic knowledge graph and one that isn’t—if they want to leverage emerging AI technologies and maintain a competitive edge.

Modeling 64

Modeling Optimization Knowledge Discovery Enterprise 64

Ontotext

MARCH 8, 2023

Graphs boost knowledge discovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. With the size of data and dropping attention spans of online users, digital personalization has become one of the top priorities for companies’ business models.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Domino Data Lab

DECEMBER 4, 2020

NCA doesn’t require the assumption of a specific compartmental model for either drug or metabolite; it is instead assumption-free and therefore easily automated [1]. PharmaceUtical Modeling And Simulation (or PUMAS) is a suite of tools to perform quantitative analytics for pharmaceutical drug development [2]. Mean residence time.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

FineReport

JULY 16, 2021

You need the ability of data analysis to aid in enterprise modeling. It is a process of using knowledge discovery tools to mine previously unknown and potentially useful knowledge. It is an active method of automatic discovery. Data Visualization. Data Analysis.

Business Intelligence 98

Business Intelligence Informatics Data Warehouse Data mining 98

Ontotext

SEPTEMBER 15, 2020

There must be a representation of the low-level technical and operational metadata as well as the ‘real world’ metadata of the business model or ontologies. Create a human AND machine-meaningful data model. Formalize your data model using standards like RDF Schema and OWL. Integrate data with ETL or virtualization.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). The models created using these algorithms could be evaluated against appropriate metrics to verify the model’s credibility. Data Mining Models. Deployment.

Data mining 77

Data mining KDD Data Science Forecasting 77

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136