Data Stream Mining is the process of extracting meaningful information from continuous, rapid data streams that have many attributes and terms. It refers to informational structure extraction as models and patterns from continuous data streams. The goal was how to extract knowledge from different subsets of a dataset and integrate these generated knowledge structures in order to gain a global model of the whole dataset. Data Streams can be conceived as a continuous and changing sequence of data that continuously arrive at a system to store or process so it has different challenges such as computing, storing, querying and mining data stream.
To acquire knowledge base from raw data, emphasis is placed on innovative data stream mining concepts and techniques. A data stream is massive, fast changing and infinite in nature. It is an Ordered Sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities.
In applications like Computer network traffic, web searches, video surveillance, weather forecasting, phone conversations, sensor data, satellites, vital signals monitoring data stream mining plays a key role to analyze the continuous data. Recently, the data generation rates in some data sources become faster than ever before. This rapid generation of continuous streams of information has challenged our storage, communication and computation capabilities in computing systems. Massive data stream can be accumulated beyond databases and data warehouses. The meaningful, effective and efficient analysis of this data in such different forms becomes tedious task and also the issue of memory constraints has to handle as enormous data is generated continuously. Nowadays, the quantity of data that is created every two days is estimated to be 5 exabytes.
• Google receives over 2 million search queries.
• YouTube users upload 48 hours of new video.
• Facebook users share 684,000 bits of content.
• Twitter users send more than 100,000 tweets.
• Email users send more than 204 million messages.
• Mobile Web receives 217 new users.
• Apple receives around 47,000 application downloads.
• Brands receive more than 34,000 Facebook 'likes'.
• Tumblr blog owners publish 27,000 new posts.
• WordPress users publish close to 350 new blog posts.
Storing continuous data stream is a great challenge for storing devices with this while processing the data errors, noise, missing values, unwanted data, have to be removed. There are many classification algorithms available for concept drifting data streams. These algorithms support multidimensional analysis and decision making. Additional data stream analysis techniques are required for depth analysis.
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