Big data technologies are inextricably linked with
scientific, research, and commercial activities. They cover all areas in which more
and more efficient storage and classification systems are required. In
commerce, big data is most often cast off to foresee business development or
test marketing hypotheses. For future analysis, information should be
collected, cleaned, organized, and tagged. Learn more about working with big
data here and then we'll discuss some of the basic techniques.
Collect and integrate data
Working with big data often involves collecting a variety of
information from a variety of sources. To work with this data, you must put it
together. You cannot just load the data into the database, as different sources
can create them in different formats. This is where combining and integrating
data will help when irregular information is brought together in a single form.
To convert the data into a single set-up, they convert
similar forms of variable data into one, for example, converting the female and
female name fields to a consistent name, etc. Then the data is augmented. If
there are two foundations of data about one object, information from the first
source is supplemented with data from the second to get a comprehensive
picture.
They also filter out redundant data, for example when some data is
missing from the source. Or you collect unnecessary information that is not
required for analysis, and simply complicate and increase the computation time.
In cases where there are several different sources, data
inclusion applies. Let's say your store uses multiple sales channels, such as
offline, through your website, and through the marketplace. The numbers should
be consolidated and merged into a single database to obtain complete
information on sales and market demand. Traditional methods of combining data
are mostly based on extraction, transformation, and loading (ETL). After mixing,
big data is subjected to further analysis and other manipulations. For example:
data is retrieved, cleaned and processed, placed in an enterprise data
warehouse, and then retrieved for analysis.
Machine learning and neural networks
The method is based on experimental analysis of data and the
succeeding construction of algorithms for self-learning systems. Machine
learning solves the unruly of finding patterns in data. Based on this data, the
algorithm can make certain predictions. Appliance learning can be classified as
artificial intelligence methods because it does not directly solve a problem,
but teaches you to apply the solution to many similar problems. Some advanced
machine learning analysis techniques are implemented using neural networks. They
consist of many reproduction neurons, which, when trained, form connections,
and then can analyze information.
Neural networks apply a common algorithm, take data as
input, and execute it through their neurons. At the output, the neural network
produces the result. Very often, NN can find a pattern that is not obvious to
human analysis. For a neural system to work, it must first be trained.
Typically, neural networks are used for those tasks that contain large amounts
of data, since this process is one of the most expensive among many machine
learning models.
Machine learning technology frees the programmer from having
to explain in detail to the machine how to solve a problem. Instead, the
computer can find a solution on its own. The algorithm obtains a set of
required data and then uses it to process the data. For example, machines learn
to recognize images and classify them. They can recognize text, numbers, people
and landscapes. Computers identify the distinguishing features for
classification and take into account the context of their use.
It is quite natural that in all cases one should not expect
the absolutely correct choice, since mistakes do happen. Successful and
unsuccessful recognition results are entered into the database, which allows
the program to learn from its mistakes and better cope with the task at hand.
In theory, the process of improvement can develop indefinitely, since this is
the essence of the learning process. For example, when sorting data, say,
images of male and female faces, reference samples of faces are sent to a
neural network, where it is clearly distinguished which are male and which are
exclusively female. Then the neural network will understand by what criteria to
distinguish between faces, that is, it will learn how to do it.
Then test the neural network and send it a new clean sample,
but without specifying whether people are of a particular gender. This will
help you understand the error rate of the neural network and how acceptable it
is to you. If, after training and testing, the proportion of incorrect
decisions remains acceptable, then you can process big data using a neural
network.
