![]() It will apply a different set of logic to different categories of entities to give an almost human matching ability. We know that Chris could be spelt in several different ways and (as with any data) there may be spelling mistakes. As are surnames, although they may change when someone marries. We’d know that date of birth was important. As humans, we could look at a list of people and would instinctively apply matching logic. These entities could all be categorised as People. To explain it in a simple way: Smart matching in analyst’s notebook is achieved by categorising entities and applying almost human matching logic.įor example, a visualisation may contain a police officer, a victim, a suspect, a prisoner, a male and a female entity. For this reason, the i2 analysis suite does provide some matching functionality Smart Matching – i2 Analyst’s Notebook In reality, it’s not always feasible or realistic to get rid of all duplicates. The nature of tools like i2 is that they find connections between entities in order for that to work the data must be clean a free of duplicates. An issue which is magnified once you start trying to load said data into a visual analysis tool like i2. ![]()
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