Data fusion is the process of gathering data from a number of different sources. It can be used to create a more efficient reading of the subject being studied. Many experts believe that this process provides a more precise reading of a given subject, because it relies on diverse viewpoints and models for analysis.
There are three different levels of data fusion: low, intermediate, and high. In low fusion processes, raw data is collected from various sources. This allows users to gather new, potentially more accurate readings of the data being studied.
The process of intermediate data fusion is also known as feature fusion. In this process, the features of data from various sources are combined for a more accurate reading. This process may also include the gathering of relevant features from the same sets of data.
Finally, high data fusion is a process that is also referred to as decision processing. This kind of fusion requires the gathering of decisions from a number of different experts to create a single conclusion. This process might include methods such as statistical analysis and voting.
In the world of business, data fusion is often contrasted with data integration. Data integration differs from fusion in that integration is the process of gathering data from different sources when the data set remains unchanged in terms of size and function. Fusion, on the other hand, often leads to data reduction in which the size of each data set is reduced. In some cases, the identities of the sets are replaced.
One example of data integration in business takes place when two companies merge their software or computer programs. While each business retains its original size and function, their data sets are combined. This process may be performed for increased efficiency or productivity.
When the process of data fusion occurs between businesses, there is data reduction. In other words, neither company retains its original size or function. Instead, the identity and size of each business is altered as a result of the fusion. This may occur in cases when one business acquires another.
Data fusion and integration may also be used in various scientific studies. For example, these processes may be applied to geospatial studies in which specialists track complex movements and behaviors across a particular terrain. Marine biologists might use these processes in order to track the migrations of various species.
Data fusion is also used in various consumer industries, such as the credit card industry. A consumer's spending habits may be collected and fused to create a consumer profile. This process may be used to determine instances of credit card fraud by identifying peculiar credit card purchases and usage in unfamiliar locations.
