What is Data Stream Mining?

Data storage and analysis have proven to be increasingly important to the growth and success of companies. When used effectively, data becomes valuable information that enables companies to make predictions and, consequently, to define actions and strategies for businesses in various sectors. 

Meanwhile, data is being generated at an ever-faster and more intense rate, and information is arriving in ever-larger streams. Specific techniques are needed to analyze this data in real time—or at least as quickly as possible. 

For this reason, a set of specific techniques for analyzing these streams has been developed, known as Data Stream Mining. These techniques involve extracting knowledge from fast, continuous data records that arrive at the system in a stream. 

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

Key Challenges in Machine Learning Algorithms

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

“Real-world problems, in general, tend to be very dynamic. For example, a consumer’s behavior may change as they age; a group of people may change their opinion about a product or a political party; the attacks a network faces may change as new defenses are put in place; and so on,” he explains.

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

The Use of Data Stream Mining in Businesses

Data Stream Mining is being used in a wide variety of commercial and industrial sectors to identify problems and predict outcomes. Through this process, it is possible to increase your company's revenue, cut costs, improve customer relationships, reduce financial risks, and more. 

In education, for example, it is possible to predict student performance, develop strategies to help them maintain good results, and identify those who need more guidance. It is also possible to predict dropout and default rates with a high degree of accuracy.

In the retail sector, data mining makes it possible to understand customer preferences, refine customer relationship and marketing strategies, reduce the risk of default, and even recommend products and forecast sales. 

Our expertise

4KST specializes in data stream mining technologies and uses its own proprietary algorithm. The technology originated in the research department at PUCPR and has been tested in corporate environments.

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

The features of the technology developed by 4KST offer 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 lifecycle, the models generated by 4KST are long-lasting and do not require updates, thereby reducing personnel and processing costs.

Accuracy: Our approach does not use the traditional model of training, validation, and testing predictive models. Our algorithms are designed to adapt the predictive model to each new instance. Increased accuracy can mean millions in savings for our clients.

Computational cost: While statistical and machine learning techniques require all data to be available in memory for analysis, the technology proposed by 4KST employs incremental approaches, drastically reducing memory and processing demands.

Ease of use: The algorithms are designed to operate autonomously, with minimal human intervention. This means that processes such as data preprocessing, variable selection, and instance selection can be customized, but they can also occur automatically.

Risk: Predictive models developed using 4KST technology are designed to withstand and adapt to changes in the input data over time. This means that once developed, if the application domain is dynamic, the model will continue to function 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 customers a fixed price for the cost of generating a model, regardless of the amount of data the customer intends to use to feed the algorithm.

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