Big Data, as its name suggests, refers to a lot of data. However, it is not just the amount of data mentioned here, and it does not only refer to the large size of the data. There is no clear boundary between big and small data. It also includes factors such as the speed of data production, structure, and accuracy. This data is usually collected from millions of users worldwide via cloud systems.
While traditional databases can process data up to a certain size, modern sources, such as social media, sensors, mobile applications, smart devices, and digital interactions, can generate petabytes of data in a short time. This data can be in many different forms, such as text, audio, video, and sensor data. Big data does not have to belong to a single project. It is not expected that the collected data will have a specific meaning or reach a conclusion on any subject. The complex and huge data sets obtained are referred to as Big Data.
Big Data systems are not only concerned with storing data. At the same time, the collected data must also be transformed into meaningful information. This process requires methods that go beyond classical database management systems. Distributed processing systems such as Hadoop and Apache Spark process data in parallel by dividing it into different pieces. In this way, millions of data lines can be analyzed in seconds. Big data analytics empowers decision-making processes.
At the point of processing all this data, data mining comes into play. Data mining is the process of extracting useful information from large-scale data and mining information. It includes stages such as discovering data sets, perceiving data sets, and detecting potential patterns from data sets. Data mining aims to discover structures, statistical relationships, or predictable behaviors that are not clearly visible in raw data but are repetitive. The purpose of data mining is not to extract or mine the data itself. A large amount of data already exists, and data mining extracts meaning or valuable information from this data.
Machine learning is a branch of artificial intelligence that allows computers to learn through data without being explicitly programmed. In other words, instead of giving commands to machines and asking for results, we provide them with many examples and let them learn how to behave through experience based on these examples. The concept of “learning” here means recognizing patterns in the collected data and making predictions based on these patterns.
Systems that work with machine learning work on mathematical structures that have certain models. These models analyze past data to create a function; this function produces predictions in response to new data. Machine learning algorithms focus on how computers can use data to learn strategies and behaviors in certain contexts.
One of the most common types of machine learning is supervised learning. In this method, the system is trained with past data and the labels of this data. During the training process, statistical patterns in the collected data are discovered, and mathematical functions corresponding to them are created. Then, when new data arrives, operations such as classification, regression, or prediction are performed based on this function.
If there is a functional value that needs to be changed in the schema, this value is edited, and the system is improved in this way. In another type, unsupervised learning, the data is not labeled, and the model tries to discover similarities between the data by matching it with previously presented data.
For example, let’s assume that the database has a dataset of apple images. Most of the apple images are analyzed by the system, which has the label “apple” in their description, image name, and image. If this label is the majority in the dataset it has collected, the system identifies the images it has and similar images it will encounter in the future as apples.
At this stage, artificial intelligence comes into play. Artificial Intelligence is a wide field that enables machines to perform human-like tasks. The general goal of artificial intelligence is to provide software with human-specific skills such as seeing, speaking, understanding, and decision-making. Artificial intelligence achieves this goal by using machine learning techniques. However, beyond machine learning, other techniques such as logical inference, rule-based systems, and optimization algorithms also fall within the scope of artificial intelligence. Artificial intelligence requires a large amount of data for its decision-making mechanism. Therefore, these three concepts are interconnected like the links of a chain.
Big data is the fuel of artificial intelligence and machine learning. There is no learning without data. Machine learning learns from this data and makes statistical inferences. Artificial intelligence uses this learned information for a purpose by interacting with its environment.
As a result, these three areas cannot be considered separately. Learning would be inadequate without big data, artificial intelligence would be blind without machine learning, and these systems would be mere tools that display data without artificial intelligence. Each one empowers the other, and when they work together, truly intelligent systems emerge. At today’s point in technology, we see these three working together in many areas, from healthcare to financial analysis, from city security to personal assistants. The deepening of this harmonious structure, the quality of artificial intelligence systems, and how well these layers are integrated are directly proportional to each other.
