Ontotext

APRIL 6, 2023

Going back to our example of a smart vehicle, what we talked about is only a small part of what knowledge graphs can do in the automotive industry. More and more companies are using them to improve a variety of tasks from product range specification and risk analysis to supporting self-driving cars.

Manufacturing 83

Manufacturing Cost-Benefit Knowledge Discovery Optimization 83

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. Use Case #4: Financial Risk Detection and Prediction The financial industry is made up of a network of markets and transactions.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Smart Data Collective

MARCH 13, 2020

The better strategy is to demarcate each data science project into four distinct phases : Phase 1: Preliminary Analysis. Phase 4: Knowledge Discovery. When these two elements are in harmony, there are fewer delays and less risk of data corruption. It’s overwhelming to look at a data science project from the top down.

Data Science 100

Data Science Key Performance Indicator Knowledge Discovery Visualization 100

The Unofficial Google Data Science Blog

JULY 22, 2020

by ALEXANDER WAKIM Ramp-up and multi-armed bandits (MAB) are common strategies in online controlled experiments (OCE). These strategies involve changing assignment weights during an experiment. The first is a strategy called ramp-up and is advised by many experts in the field [1].

Experimentation 95

Experimentation Statistics Testing Strategy 95

FineReport

JULY 16, 2021

Through this way, it can support current corporate analysis and future decision or strategy making. 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. INTERFACE OF BI SYSTEM. Features of BI systems.

Business Intelligence 98

Business Intelligence Informatics Data Warehouse Data mining 98

Domino Data Lab

MAY 20, 2021

propose a different strategy where the minority class is over-sampled by generating synthetic examples. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. The class imbalance problem: Significance and strategies. In their 2002 paper Chawla et al. Chawla et al.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136