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

Phase 4: Knowledge Discovery. When these two elements are in harmony, there are fewer delays and less risk of data corruption. Phase 3: Data Visualization. With the data analyzed and stored in spreadsheets, it’s time to visualize the data so that it can be presented in an effective and persuasive manner.

Data Science 100

Data Science Key Performance Indicator Knowledge Discovery Visualization 100

FineReport

JULY 16, 2021

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. The company can lower the risk value of the red line and monitor the situation in real time. Data Visualization. How BI system solve the problem?

Business Intelligence 98

Business Intelligence Informatics Data Warehouse Data mining 98

Domino Data Lab

AUGUST 1, 2021

This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. This is to be expected, as there is no reason for a perfect 50:50 separation of the good vs. bad credit risk. Conference on Knowledge Discovery and Data Mining, pp. 1 570 0 570 Name: credit, dtype: int64.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Domino Data Lab

MAY 20, 2021

This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. Chawla et al. Indeed, in the original paper Chawla et al. 30(2–3), 195–215. link] Ling, C. X., & Li, C.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136