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The intersection, S1 S2 Sn , of the sets gives the document identi ers of the desired set of documents The or operation gives the set of all documents that contain at least one of the keywords K1 , K2 , , Kn We implement the or operation by computing the union, S1 S2 Sn , of the sets The not operation nds documents that do not contain a speci ed keyword Ki Given a set of document identi ers S, we can eliminate documents that contain the speci ed keyword Ki by taking the difference S Si , where Si is the set of identi ers of documents that contain the keyword Ki Given a set of keywords in a query, many information retrieval systems do not insist that the retrieved documents contain all the keywords (unless an and operation is explicitly used) In this case, all documents containing at least one of the words are retrieved (as in the or operation), but are ranked by their relevance measure To use term frequency for ranking, the index structure should additionally maintain the number of times terms occur in each document To reduce this effort, they may use a compressed representation with only a few bits, which approximates the term frequency The index should also store the document frequency of each term (that is, the number of documents in which the term appears)

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Each keyword may be contained in a large number of documents; hence, a compact representation is critical to keep space usage of the index low Thus, the sets of documents for a keyword are maintained in a compressed form So that storage space is saved, the index is sometimes stored such that the retrieval is approximate; a few relevant documents may not be retrieved (called a false drop or false negative), or a few irrelevant documents may be retrieved (called a false positive) A good index structure will not have any false drops, but may permit a few false positives; the system can lter them away later by looking at the keywords that they actually contain In Web indexing, false positives are not desirable either, since the actual document may not be quickly accessible for ltering Two metrics are used to measure how well an information-retrieval system is able to answer queries The rst, precision, measures what percentage of the retrieved documents are actually relevant to the query The second, recall, measures what percentage of the documents relevant to the query were retrieved Ideally both should be 100 percent Precision and recall are also important measures for understanding how well a particular document ranking strategy performs Ranking strategies can result in false negatives and false positives, but in a more subtle sense False negatives may occur when documents are ranked, because relevant documents get low rankings; if we fetched all documents down to documents with very low ranking there would be very few false negatives However, humans would rarely look beyond the rst few tens of returned documents, and may thus miss relevant documents because they are not ranked among the top few Exactly what is a false negative depends on how many documents are examined Therefore instead of having a single number as the measure of recall, we can measure the recall as a function of the number of documents fetched.

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Before you start to plan a test, you'll have an idea about what kind of test you want to run One of the biggest threats to a successful test is a poor plan a plan with no clear directives Without a plan, you cannot efficiently test everything there is to test about any given application Testing directives are particular criteria that you need to test against This will focus on the items most likely to affect rollout success or failure It is imperative that you start by clearly defining your test objectives: What is it specifically that you want to know What questions do you want answered Here are some possible questions: Is a 256 Kbps line going to be enough for my network devices If this application fails, does it cause others to fail How many users can run this application concurrently Is this new version of software stable Is this application time dependent Before setting out to test an application, you'll want to plan your testing scenario Following are descriptions of five major types of tests that you can conduct You will prob- ably find that your testing directives entail a combination of these approaches This is expected and will be useful provided that you clearly define exactly what it is you are trying to test, and test only one aspect of an application at a time Trying to answer too many questions simultaneously will confound your results and invalidate your work

The McGraw Hill Companies, 2001

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