All service packs for Exchange Server are supported.
Natural Language Processing
Many words express the same concept in several forms, such as:
Content perspective
This report is confidential.
This report is for internal use only.
This document is classified.
Spam perspective
Make lots of money from home!
Make lots of cash from your house!
Earn big $$$ at home!
To find synonyms and patterns with parallel content, information, content, spam and pornography concepts, Concept Manager uses several key tools, including:
Thresholding - searching for similar misspelled words - e.g. a search for "confidential" also produces a match for "confidental"
Thesaurus matching - searching for words with similar meaning - e.g. a search for "money" produces a match for the words "cash," "currency," "dollars" and "$$$", while "confidential" locates "internal use only", "classified", and "secret"
Stemming - searching for additional suffixes and prefixes to words or root words themselves as well as plurals, tenses, adjectives, adverbs, etc - e.g. a search for "confidential" produces a match for "confidential" and "confidentiality". A
search for "triggers" yields matches for "trigger", "triggered", and "triggering"
Numeric Equivalence - searching for numeric valuas or ranges whether they are represented as numeric characters, words, or combinations of words and numerics - e.g. a search for ">1000" produces a match for "1234", "1,234", "million", "58 hundred", "3K", and "one and half million"
Regular Expression Matching - searching for paticular patterns of characters, numerics, punction, or combinations
of each
Dictionary matching - searching for words that don't exist in the English language - e.g. spam emails containing strings of randomly generated characters towards the end of the email subject or body
Protects corporate liability and data leakage, eliminates email harassment and manages email content consistently, accurately and reliably
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A highly intelligent and intuitive new tool in the fight against spam and pornographic content
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Uses "fuzzy logic" and natural language processing to determine the concept of an email
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Dramatically higher spam detection rates
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Fewer "false positives" = savings in costs, time and productivity for administrators and users
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Easy to administer and manage with self-updating policies
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