Final answer:
Stores of data too large for conventional database systems are known as Big Data, which require advanced processing solutions like supercomputers and AI. The Sloan Survey in astronomy and in silico research in biology are examples of Big Data applications.
Step-by-step explanation:
Stores of data so vast that conventional database management systems cannot handle them are known as Big Data. This term refers to large and complex data sets that require advanced data processing applications to handle them efficiently. This is because they are too large, too fast-changing, or too complex for traditional data processing tools.
For instance, astronomers dealing with Big Data from projects like the Sloan Survey need supercomputers and advanced algorithms to manage and analyze data flowing in at 8 megabytes per second, adding up to more than 15 terabytes over the project's lifetime. To harness the power of such vast information, fields like astronomy have adopted new methods like citizen science, leveraging crowdsourcing to classify immense datasets.
Moreover, growing data scales in various domains, such as biology and computer science, have led to an increased demand for specialists capable of interpreting Big Data through in-silico research. The rise of Big Data is inseparable from the development of technologies like artificial intelligence (AI) and the capabilities of modern supercomputers, which can process the equivalent of human lifetimes of data in a fraction of the time.