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

Ontotext

OCTOBER 28, 2021

often want to find information about a particular medical product, for example, if any serious adverse reactions have been reported for it. FROCKG (Fact Checking for Large Enterprise Knowledge Graphs) is a Eurostars-2 project that aims to develop efficient approaches to ensure the veracity of facts contained in enterprise knowledge graphs.

Visualization 52

Visualization Interactive Knowledge Discovery Dashboards 52

Ontotext

APRIL 4, 2019

Milena Yankova : We help the BBC and the Financial Times to model the knowledge available in various documents so they can manage it. Milena Yankova : What we did for the BBC in the previous Olympics was that we helped journalists publish their reports faster. What exactly do you do for them? I think artists can relax.

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

Ontotext

MAY 18, 2020

These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. Again, the overall aim is to extract knowledge from data and, through algorithms based on artificial intelligence, to assist medical professionals in routine diagnostics processes.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

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. DASHBOARD REPORTING (by FineReport). REPORT FILLING. CALCULATING REPORT.

Business Intelligence 98

Business Intelligence Informatics Data Warehouse Data mining 98

The Unofficial Google Data Science Blog

JULY 22, 2020

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. In practice, one may want to use more complex models to make these estimates. For example, one may want to use a model that can pool the epoch estimates with each other via hierarchical modeling (a.k.a.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Ontotext

JULY 29, 2021

One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases. We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledge discovery. What Does The Semantic Web Already Do for Us?

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. More precisely, our model is that $theta$ is drawn from a prior that depends on $t$, then $y$ comes from some known parametric family $f_theta$. Here, our items are query-ad pairs. Calculate posterior quantities of interest.

KDD 40

KDD Testing Machine Learning Modeling 40