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The data management problem, whether solved by a machine or a personal secretary, can be divided into the following stages:
Finding new information.These stages can be couched in terms of the actions of a number of simpletons comprising a primitive mind:
Collectors
find, retrieve, or accept new pages likely to interest the user
(from the web and ftp sites, mail and news, and the desktop).
They answer the question: "What's out there?"
They are like effectors.
Examiners
parse collected pages to determine their detectable characteristics.
They answer the question: "What properties does it have?"
They are like sensors.
Clusterers
group entities into clusters of related entities.
They answer the question: "What's it like?"
(or perhaps "Where do I put it?").
They are like memory.
Cluster mergers
decide whether two clusters should be merged into one.
Cluster splitters
decide whether a cluster should be split into two new clusters.
Cluster creators
decide whether a new cluster should be created.
Cluster killers
decide whether a cluster should die.
(Note that a cluster can die
yet the entities it contains could survive the death.)
Comparators
determine the characteristics of entities
that distinguish them from other entities.
They answer the question: "What makes it special?"
They are like perception.
Evaluators
test entities for desirable characteristics.
They answer the question: "Will my user like it?"
They are like emotions.
Mappers
map entities into a virtual space.
They answer the question: "How do I display it?"
Pollsters
determine what entities most interest the user.
They answer the question: "What does my user like?"
Purgers
mark old, unused entities.
They answer the question: "What does my user dislike?"
Modelers
order entities based on which ones seem to be most
useful to the user now.
They answer the question:
"What is my user likely to be searching for now? (or soon?)"
Orderers
order entity attributes based on which ones seem to be most
predictive of strong user interest.
Advisors I:
figure out how to better determine which entities the user is likely to
be interested in.
Advisors II:
figure out which entities are likely to be representative of the
current most interesting entities.
Cluster Advisors
figure out how to better cluster related entities.
Filter Advisors
figure out tests to tell whether a new page might be interesting.
They answer the question: "What is likely to interest my user in
future?"
Analyst Advisors
figure out what new entity characteristics to look for.
They answer the question: "What shall I look for next?"
They are like curiosity (?)
Collector Advisors
figure out where to look for new interesting entities.
They answer the question: "Where shall I look next?"
(or perhaps "What should I focus on next?")
They are like attention.