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Consider the problem of locating documents about motorcycle maintenance for the keywords motorcycle and maintenance Suppose that the keywords for each document are the words in the title and the names of the authors The document titled Motorcycle Repair would not be retrieved, since the word maintenance does not occur in its title We can solve that problem by making use of synonyms Each word can have a set of synonyms de ned, and the occurrence of a word can be replaced by the or of all its synonyms (including the word itself) Thus, the query motorcycle and repair can be replaced by motorcycle and (repair or maintenance) This query would nd the desired document Keyword-based queries also suffer from the opposite problem, of homonyms, that is single words with multiple meanings For instance, the word object has different meanings as a noun and as a verb The word table may refer to a dinner table, or to a relational table Some keyword query systems attempt to disambiguate the meaning of words in documents, and when a user poses a query, they nd out the intended meaning by asking the user The returned documents are those that use the term in the intended meaning of the user However, disambiguating meanings of words in documents is not an easy task, so not many systems implement this idea In fact, a danger even with using synonyms to extend queries is that the synonyms may themselves have different meanings Documents that use the synonyms with an alternative intended meaning would be retrieved The user is then left wondering why the system thought that a particular retrieved document is relevant, if it contains neither the keywords the user speci ed, nor words whose intended meaning in the document is synonymous with speci ed keywords! It is therefore advisable to verify synonyms with the user, before using them to extend a query submitted by the user.

you could combine the Enrollment and Offering tables As you will see later in this chapter, the operation of combining tables on matching values is known as a join Understanding the connections between tables (or ways that tables can be combined) is crucial for extracting useful data

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An effective index structure is important for ef cient processing of queries in an information-retrieval system Documents that contain a speci ed keyword can be ef ciently located by using an inverted index, which maps each keyword Ki to the set Si of (identi ers of) the documents that contain Ki To support relevance ranking based on proximity of keywords, such an index may provide not just identi ers of documents, but also a list of locations in the document where the keyword appears Since such indices must be stored on disk, the index organization also attempts to minimize the number of I/O operations to retrieve the set of (identi ers of) documents that contain a keyword Thus, the system may attempt to keep the set of documents for a keyword in consecutive disk pages The and operation nds documents that contain all of a speci ed set of keywords K1 , K2 , , Kn We implement the and operation by rst retrieving the sets of document identi ers S1 , S2 , , Sn of all documents that contain the respective keywords

SNMP TRAP CATEGORIES The various Severity levels for traps are categorized as shown in the following Status Legend illustration Each category has a corresponding color

The McGraw Hill Companies, 2001

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You should be aware that other terminology is used besides table, row, and column Table 35 shows three roughly equivalent terminologies The divergence in terminology is due to the different groups that use databases The table-oriented terminology appeals to end users; the set-oriented terminology appeals to academic researchers; and the record-oriented terminology appeals to information systems professionals In practice, these terms may be mixed For example, in the same sentence you might hear both "tables" and "fields" You should expect to see a mix of terminology in your career

When the actions of a trap from a network device are defined to change the severity category and update the network map, this change of color will be represented on the map This feature makes it easy for a network manager to determine which devices need attention just by viewing the map

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