8. The event-collection-and-event-processing system of claim 2 , wherein identifying the topic-assignment output for the initially processed event comprises: for each event-topic signature computed for each event topic, identifying one or more weights, each weight of the one or more weights corresponding to a word of the plurality of words in the initially processed event and being identified using the event-topic signature; and computing a score based on the one or more weights; assigning the processed event to an event topic corresponding to the event-topic signature corresponding to a largest computed score.
2. An event-collection-and-event-processing system comprising: one or more computer systems, each having one or more processors, one or more memories, and one or more mass-storage devices; an event-collection subsystem, operating within one or more of the one or more computer systems, which: receives encoded data from one or more browser applications, executing on one or more remote user computers, the encoded data event generated by instrumentation within one or more instrumented web pages processed and rendered for display by the one or more browser applications; processes the received encoded data to produce a set of initially processed events, each of the set of initially processed events having an initial number of data entities; and stores the set of initially processed events in one or more of the one or more memories; an abstraction layer, operating within one or more of the one or more computer systems, that: receives the set of initially processed events from the event-collection subsystem; further processes the set of initially processed events to generate a corresponding set of processed events, each processed event in the set of processed events having a data entity that represents a topic assignment output assigned to the processed event, the further processing including: accessing a set of current distributions, the set of current distributions including: a regular-word distribution associated with a global topic; a seed-word distribution associated with the global topic; for each of a set of topics, a regular-word distribution associated with the topic; and for each of the set of topics, a seed-word distribution associated with the topic, the seed-word distribution associated with the topic including, for each seed word a plurality of seed words, a quantity indicating a number of observations where the seed word was included in an event and was associated with the topic; performing a set of iteration operations that include: for each word of a plurality of words in the set of initially processed events: determining, based on the set of current distributions, whether the word corresponds to a regular word or a seed word; determining, based on the set of current distributions, whether the word corresponds to a global topic or a discovered topic; and updating the set of current distributions based on, for each word of the plurality of words, the determination as to whether the word corresponds to a regular word or a seed word and the determination as to whether the word corresponds to a global topic or a discovered topic; and based on one or more iterations of the set of iteration operations, identifying, for each initially processed event in the set of initially processed events, the topic-assignment output to be represented in a processed event in the set of processed events corresponding to the initially processed event, and an event-consuming application, operating within one or more of the one or more computer systems, that receives the topic-assignment outputs of the set of initially processed events from the abstraction layer and uses the topic-assignment outputs processed events to produce one or more results.