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). This data alone does not make any sense unless it’s identified to be related in some pattern.

Data mining 77

Data mining KDD Data Science Forecasting 77

Ontotext

AUGUST 19, 2020

Separating the definitions of the metadata from the data had the benefit of simplifying validation and introducing flexibility. Faster and easier knowledge discovery has obvious cost benefits and reduces duplication of effort. Metadata started including descriptions and source information.

Metadata 59

Metadata Knowledge Discovery Cost-Benefit Data Governance 59

Ontotext

AUGUST 22, 2023

Using machine readable definitions, it creates a highly interconnected data object that delivers high value and meaning as well. Semantically integrated data makes metadata meaningful, allowing for better interpretation, improved search, and enhanced knowledge-discovery processes. How to Get a Semantic Edge?

Metadata 52

Metadata Knowledge Discovery Big Data Enterprise 52

Ontotext

APRIL 4, 2019

We apply Artificial Intelligence techniques to understand the value locked in this data so we can extract knowledge that can benefit people. Milena Yankova : There are two definitions of Artificial Intelligence. One area where computers will definitely not replace us is our creativity. But that doesn’t worry me too much.

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

Ontotext

AUGUST 16, 2019

Let’s start with a quick definition of the basics. Semantic technology is a broad technological term that covers specific technological approaches, principles and methodologies for managing data and knowledge. Some of that journey has been recorded in a previous blog post. What This Training Is.

Knowledge Discovery 40

Knowledge Discovery Technology Metadata Interactive 40

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. Linked Data, subscriptions, purchased datasets, etc.).

Enterprise 52

Enterprise Knowledge Discovery Risk Machine Learning 52

Domino Data Lab

JUNE 15, 2021

This definition makes UDFs somewhat similar to stored procedures, but there are a number of key differences between the two. This facilitates knowledge discovery, handover, and regulatory compliance, and allows the individual data scientists to focus on work that accelerates research and speeds model deployment. About Domino.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

In practice, we have gotten good results by normalizing responses for the first-order effects of factors ignored by our unit definition. Brendan McMahan et al, "Ad Click Prediction: a View from the Trenches" , Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2013. [3]

KDD 40

KDD Testing Machine Learning Measurement 40

The Unofficial Google Data Science Blog

JANUARY 14, 2016

To understand this better we need a few definitions. The paper gained much attention because, having conducted the largest study of its kind, it was understood to debunk the idea definitively. The surprise is that the effect sizes of practical significance are often extremely small from a traditional statistical perspective.

Experimentation 42

Experimentation Statistics Metrics Measurement 42

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

Another way to build a classifier for variance reduction is to address the rare event problem directly — what if we could predict a subset of instances in which the event of interest will definitely not occur? This would make the event more likely in the complementary set and hence mitigate the variance problem.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Ontotext

NOVEMBER 11, 2024

It’s the connections and the graph that make the knowledge graph, not the language used to represent the data. A key feature of a knowledge graph is that entity descriptions should be interlinked to one another. The definition of one entity includes another entity. This linking is how the graph forms (e.g.,

Metadata 52

Metadata Knowledge Discovery Data Integration Management 52