What is Data Stream Mining?

Storing and analyzing data has become increasingly important for the growth and success of companies. When used well, data turns into valuable information that allows forecasts to be made and, consequently, actions and strategies to be defined for businesses in different areas. 

Meanwhile, data generation is becoming faster and more intense and information is arriving in ever-increasing streams. In order to be able to analyze it in real time, or in the shortest time possible, specific techniques are required. 

For this reason, a set of specific techniques for analyzing these flows has been developed under the name of Data Stream Mining. These techniques consist of extracting knowledge from fast and continuous data records that arrive at the system in a flow. 

A data stream, in turn, is an ordered sequence of instances in time, digitally coded and used to represent the information collected. Data streams can be telephone conversations, bank transactions, internet search engine queries or even the data recorded by a sensor. 

Main challenges of Machine Learning algorithms

According to PUCPR professor and 4KST founder Fabrício Enembreck, data flows present several challenges for algorithms, including: changing concepts, temporal dependencies, large amounts of data, response time, restrictive model generation and memory limitations.

"Real-world problems in general tend to be very dynamic. For example, a consumer's behavior can change as they get older, a group of people can change their opinion about a product or a political party, the attacks a network receives can change as new barriers are created, and so on," he explains.

Learning from data from which the distribution can change over time is a challenging task, since conventional algorithms assume that the data distribution is static.

Use of Data Stream Mining in companies

Data Stream Mining is being used in a wide variety of sectors of commerce and industry to identify problems and predict results. Through this process, it is possible to increase your company's income, cut costs, improve customer relations, reduce financial risks, etc. 

In education, for example, it is possible to predict student performance and develop strategies to ensure that they maintain good results and identify those who need more guidance. Or even predict dropout and default rates with high assertiveness.

In the retail sector, on the other hand, data mining makes it possible to get to know your customers' tastes, improve relationship and marketing strategies, reduce the risk of default and even recommend products and predict sales. 

Our expertise

4KST specializes in technologies for mining data streams and has its own algorithm. It comes from PUCPR's Research area and has been tested in corporate environments.

Our algorithm can be used to generate predictive models in real time (anytime - on the fly) from continuous and infinite streams of data, surpassing conventional approaches in terms of scalability, adaptability and accuracy. 

The features of the technology developed by 4KST have numerous advantages over conventional statistical and machine learning models.

Advantages of 4KST Data Stream Mining technology

Cost: While traditional statistical and Machine Learning models have a short life cycle, the models generated by 4KST are perennial and do not require updating, reducing personnel and processing costs.

Assertiveness: Our approach does not use the traditional model of training, validating and testing predictive models. Our algorithms specialize in adapting the predictive model to each new instance. Increased assertiveness can mean millions for our clients.

Computational cost: while statistical and Machine Learning techniques require all data to be available in memory to be analyzed, the technology proposed by 4KST employs incremental approaches, drastically reducing the demand for memory and processing.

Simplicity of use: The algorithms are developed to work autonomously, with minimal human intervention. This means that data pre-processing processes, the selection of variables and instances, can be customized, but can also take place automatically.

Risk: The predictive models developed with 4KST's technology are designed to withstand and adapt to the changes that the input data undergoes over time. This means that once developed, if the application domain is dynamic, the model will continue to work because it detects and adapts to these changes in temporal patterns.

Cloud: Both 4KST and its competitors offer business models based on cloud services. However, because our technology uses data stream techniques, we can offer our clients a fixed budget for the cost of generating a model, regardless of the amount of data the client intends to use to feed the algorithm.

To find out more about our predictive models, please contact us using the form below.