Scientific discovery within data streams

The term ‘data-stream' is an increasingly overloaded expression. It often means different things to different people, depending on domain, usage or operation. Harold (2003) draws the following analogy:

For our purposes we define a data-stream as a series of data (e.g. credit card transactions arriving at a clearing office, cellular phone traffic or environmental data from satellites) arriving in real time, that have an initiation, a continuous ingest of data, but with no expectations on the amount, length, or end of the data flow. The data stream does not have a database or repository as an intrinsic part of its definition–it is a ‘one-look' opportunity from the perspective of data stream analytics. We call each data element in the stream a token and the complexity of these tokens ranges from simple (e.g. characters in a sentence: “T H I S I S A S T R E A M...”) to extremely complex (e.g. a detailed transaction record). The volume of data-streams is usually massive, and while each individual token may be rather uninformative, taken as a whole they describe the nature of the changing phenomena over time.