A proximity operator is a way of specifying that two terms in a query must occur close to each other in a document, where closeness may be measured by limiting the allowed number of intervening words or by reference to a structural unit such as a sentence or paragraph. (查看原文)
INTERSECTWITHSKIPS(p1, p2)
1 answer ← h i
2 while p1 = NIL and p2 = NIL
3 do if docID(p1) = docID(p2)
4 then ADD(answer, docID(p1))
5 p1 ← next(p1)
6 p2 ← next(p2)
7 else if docID(p1) < docID(p2)
8 then if hasSkip(p1) and (docID(skip(p1)) ≤ docID(p2))
9 then while hasSkip(p1) and (docID(skip(p1)) ≤ docID(p2))
10 do p1 ← skip(p1)
11 else p1 ← next(p1)
12 else if hasSkip(p2) and (docID(skip(p2)) ≤ docID(p1))
13 then while hasSkip(p2) and (docID(skip(p2)) ≤ docID(p1))
14 do p2 ← skip(p2)
15 else p2 ← next(p2)
16 return answer (查看原文)
(1) How many keys are we likely to have?
(2) Is the number likely to remain static, or change a lot, and in the case of changes, are we likely to only have new keys inserted, or to also have some keys in the dictionary be deleted?
(3) What are the relative frequencies with which various keys will be accessed? (查看原文)
1. Retain the first letter of the term.
2. Change all occurrences of the following letters to ’0’ (zero): ’A’, E’, ’I’, ’O’,
’U’, ’H’, ’W’, ’Y’.
3. Change letters to digits as follows:
B, F, P, V to 1.
C, G, J, K, Q, S, X, Z to 2.
D,T to 3.
L to 4.
M, N to 5.
R to 6.
4. Repeatedly remove one out of each pair of consecutive identical digits.
5. Remove all zeros from the resulting string. Pad the resulting string with
trailing zeros and return the first four positions, which will consist of a
letter followed by three digits. (查看原文)
BSBINDEXCONSTRUCTION()
1 n ← 0
2 while (all documents have not been processed)
3 do n ← n +1
4 block ← PARSENEXTBLOCK()
5 BSBI-INVERT(block)
6 WRITEBLOCKTODISK(block, fn)
7 MERGEBLOCKS( f1, . . . , fn; fmerged) (查看原文)
VBENCODENUMBER(n)
1 bytes ← hi
2 while true
3 do PREPEND(bytes, n mod 128)
4 if n < 128
5 then BREAK
6 n ← n div 128
7 bytes[LENGTH(bytes)] += 128
8 return bytes
VBENCODE(numbers)
1 bytestream ← hi
2 for each n ∈ numbers
3 do bytes ← VBENCODENUMBER(n)
4 bytestream ← EXTEND(bytestream, bytes)
5 return bytestream
VBDECODE(bytestream)
1 numbers ← hi
2 n ← 0
3 for i ← 1 to LENGTH(bytestream)
4 do if bytestream[i] < 128
5 then n ← 128× n + bytestream[i]
6 else n ← 128× n + (bytestream[i] − 128)
7 APPEND(numbers, n)
8 n ← 0
9 return numbers (查看原文)
The representation of a set of documents as vectors in a common vector space is known as the vector space model ...the view of queries as vectors in the same vector space as the document collection. (查看原文)
The outermost loop beginning Step 3 repeats the updating of Scores, iterating over each query term t in turn. In Step 5 we calculate the weight in the query vector for term t. Steps 6-8 update the score of each document by adding in the contribution from term t. This process of adding in contributions one query term at a time is sometimes known as term-at-a-time scoring or accumulation, and the N elements of the array Scores are therefore known as accumulators. For this purpose, it would appear necessary to store, with each postings entry, the weight wf(t,d) of term t in document d (we have thus far used either tf or tf-idf for this weight, but leave open the possibility of other functions to be developed in Section 6.4). (查看原文)
