How artificial intelligence is used in data warehouse?

One of the many technologies covered under the shade of artificial intelligence and data warehouse cloud computing. One of them is machine learning which is described by Wikipedia as a field of computer science which is used to give instructions to the computers.

The technology, which is a main portion of the data analytics technologies which power the modern data warehouse, characteristics algorithms that can make predictions on their own about data and its insights without being hampered by specific guidelines and instructions.

For its part, artificial intelligence (AI) is the capability of machines to think like humans. It stems from the conception that “given enough data and compute power, machines will be enough to assume and learn using mathematical process of the human brain,” stated John Santaferraro, director at Enterprise Management Associates (EMA).

Here are some insights:

A data warehouse unifies and combines large amounts of data from different types of sources. Its analytical capabilities allow organizations to gain valuable business insights from their data to get better in decision making. Over time, it creates a historical record that can be invaluable to data scientists and business analysts. Because of these capabilities, a data warehouse can be considered the “sole source of truth” for an organization. Data warehouses are relational environments that are used for data analysis, particularly historical data.

Read also : What is the theory of Data Mining

The infrastructure important for machine learning

There is a lot of discussion about what’s possible with machine learning process, but not as much discuss about how to enable it. After all, machine learning is a process to use software to crunch through information, and crunch through numbers in a way that human beings could never do in a long years—but to make that occur, you want the analytics and you want the data infrastructure.

Being effective with machine learning needs having a healthy understanding of the information infrastructure to support it. … It’s very good if you have a data infrastructure that can manage transactions because once you’re maintaining transactions you can contact, then you are recording the situation of the business, which changes over time. To know machine learning well, and in a process that brings you to the present, you need those machine learning algorithms to be running on pure data that exactly reflects the business today, as opposed to making it some time ago. Another element of the information infrastructure that you want to be assure you have in place, might be the ability to rapidly ingest information—and that fits accurately in line with what’s happening with the Internet of Things, or what’s causing with people wanting to gather datq from mobile applications or web applications.

Improving image recognition with a real time information warehouse

We did some work in original-time image recognition, with an organization named Thorn. They are a non-profit dedicated to working to save children against sexual exploitation on the web. … The goal at Thorn is to monitor what ads are running up on the internet, to watch ads showing specific faces and choose those faces more quickly, to help law enforcement may be track down and protect these children. Thorn had been working on a more traditional information pipeline for real-time appearance recognition, but was having challenges keeping up with the volume of fresh imagery that was becoming in every day, and matching that against a large volume of images that they have in their information. What we were able to do with them is to implement a machine knowledge function called dot_product. … It essentially permits you to compare the accurately of two vectors, and you can compare in different methods, sometimes with cosine as similar, sometimes with euclidean distance.

In the facial remembering example,

you’re taking the appearence of the face and you’re taking the specifications on the face—the eyeballs, the positions of the mouth, the ear lobes—and making a numerical vector. By implementing that function in a real-time information warehouse like MemSQL, that is made with a SQL engine to crunch through this data totally , we were capable to help Thorn improve the performance of their image remembrance by up to 1,000 fold.

Eva Fernandez

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