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Data stream mining is a strategy that involves identifying and extracting information from an active data stream. With this approach, the idea is to pull the data without creating any type of interruption in the stream itself, making it possible for others to also make use of the data even as the extraction is taking place. This type of data stream mining effort can involve all sorts of data, ranging from voice to video transmission over the Internet and even to day to day tasks like withdrawing money from a bank account using an automated teller machine or holding a telephone conversation.
One of the characteristics of data stream mining is the ability to accurately project or predict how to locate the information desired and what type of knowledge discovery tools will aid in locating and successfully extracting the desired information. For example, when a customer initiates a transaction using an automated teller machine, the programming for the machine initiates a search for relevant account information, locates the data and then determines if the amount of the transaction will reduce the account balance below an allowable amount, based on the way the account is structured. From there, the programming can return relevant data to the user, such as documenting the successful completion of the requested transaction and providing the account balance remaining after the credit or debit involved with the transaction is recorded.
Another common example of data stream mining is the basic web search using a browser. With this application, the end user enters search values into a field, and the software that drives the browser seeks to interpret those values and return data that has some relevance to the search criteria. Depending on how the browser is configured, this may also include a feature that seeks to anticipate the intent of the search being conducted and offer additional words or phrases that may help to refine the search more to the user’s liking. Once the user has settled on the search phrase, the browser returns results in order of ranking, using algorithms that are relevant to the configuration of the browser itself.
One of the chief benefits of data stream mining is the ability to access and search data without actually prohibiting others to make use of that same data. Since data streams are constantly updating, the results of the extraction may change from time to time. For example, conducting a web search using a specific search phrase may yield one set of results today, but provide a slightly different set of results tomorrow, based on what new information has entered the data stream and how the search engine ranks that data.
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