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CS 430: Information Discovery

Lecture 4

Files Structures for Inverted Files

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Course Administration

• Assignment 1 has been posted on the web site.

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Right Threaded Binary Tree

Threaded tree:

A binary search tree in which each node uses an otherwise-empty left child link to refer to the node's in-order predecessor and an empty right child link to refer to its in-order successor.

Right-threaded tree:

A variant of a threaded tree in which only the right thread, i.e. link to the successor, of each node is maintained.

Knuth vol 1, 2.3.1, page 325.

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Right Threaded Binary Tree

From: Robert F. Rossa

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Definitions

Keyword:

A term that is used to describe the subject matter in a document. It is sometimes called an index term. In full text indexing, every word in the text is treated as a keyword (with the exception of stopwords).

Keywords can be extracted automatically from a document or assigned by a human cataloguer or indexer.

Controlled vocabulary:

A list of words that can be used as keywords. For example, in a retrieval system used for research papers in medicine, the controlled vocabulary might be a list of medical terms.

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Restrictions in Building Inverted Files

• Underlying character set, e.g., printable ASCII, Unicode, UTF8.

• Whether to use a controlled vocabulary. If so, what words to include.

• List of stopwords.

• Rules to decide the beginning and end of words, e.g., spaces or punctuation.

• Character sequences not to be indexed, e.g., sequences of numbers.

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Representation of Inverted Files

Index file: Stores list of terms (keywords). Designed for rapid searching and processing range queries. May be held in memory.

Postings file: Stores list of postings for each term. Designed for rapid evaluation of Boolean operators. May be stored sequentially.

Document file: [Repositories for the storage of document collections are covered in CS 502.]

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Set Records UniqueTerms

A 2,653 5,123

B 38,304 c.25,000

Sizes of Inverted Files

Set A has an average of 14 postings per term and a maximum of over 2,000 postings per term.

Set B has an average of 88 postings per record.

Examples from Harman and Candela, 1990

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B-trees

B-tree of order m:

A balanced, multiway search tree:

• Each node stores many keys

• Root has between 2 and 2m keys. All other internal nodes have between m and 2m keys.

• If ki is the ith key in a given internal node

-> all keys in the (i-1)th child are smaller than ki

-> all keys in the ith child are bigger than ki

• All leaves are at the same depth

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B+-tree

B+-tree:

• A B-tree is used as an index

• Data is stored in the leaves of the tree, known as buckets

50 65

10 25 55 59 70 81 90

... D9 D51 ... D54 D66... D81 ...

Example: B+-tree of order 2, bucket size 4

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B-tree Discussion

For a discussion of B-trees, see Frake, Section 2.3.1, pages 18-20.

• B-trees combine fast retrieval with moderately efficient updating.

• Bottom-up updating is usual fast, but may require recursive tree climbing to the root.

• The main weakness is poor storage utilization; typically buckets are only 0.69 full.

• Various algorithmic improvements increase storage utilization at the expense of updating performance.

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Signature Files

Inexact filter: A quick test which discards many of the non-qualifying items.

Advantages

• Much faster than full text scanning -- 1 or 2 orders of magnitude• Modest space overhead -- 10% to 15% of file• Insertion is straightforward

Disadvantages

• Sequential searching no good for very large files• Some hits are false hits

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Signature Files

Signature size. Number of bits in a signature, F.

Word signature. A bit pattern of size F with m bits set to 1 and the others 0.

The word signature is calculated by a hash function.

Block. A sequence of text that contains D distinct words.

Block signature. The logical OR of all the word signatures in a block of text.

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Signature Files

Example

Word Signature

free 001 000 110 010text 000 010 101 001

block signature 001 010 111 011

F = 12 bits in a signature

m = 4 bits per word

D = 2 words per block

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Signature Files

A query term is processed by matching its signature against the block signature.

(a) If the term is in the block, its word signature will always match the block signature.

(b) A word signature may match the block signature, but the word is not in the block. This is a false hit.

The design challenge is to minimize the false drop probability, Fd .

Frake, Section 4.2, page 47 discussed how to minimize Fd. The rest of this chapter discusses enhancements to the basic algorithm.

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Tries

Basic concept

The text is divided into unique semi-infinite strings, or sistrings. Each sistring has a starting position in the text, and continues to the right until it is unique.

The sistrings are stored in (the leaves of) a tree, the suffix tree. Common parts are stored only once.

Each sistring can be associated with a location within a document where the sistring occurs. Subtrees below a certain node represent all occurrences of the substring represented by that node.

Suffix trees (and similar suffix arrays) have a size of the same order of magnitude as the input documents.

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Tries: Suffix Tree

Example: suffix tree for the following words:

begin beginning between bread break

b

e rea

gin tween d k

_ ning

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Tries: Sistrings

A binary example

String: 01 100 100 010 111

Sistrings: 1 01 100 100 010 1112 11 001 000 101 113 10 010 001 011 14 00 100 010 1115 01 000 101 11

6 10 001 011 17 00 010 1118 00 101 11

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Tries: Lexical Ordering

7 00 010 1114 00 100 010 1118 00 101 115 01 000 101 111 01 100 100 010 111

6 10 001 011 13 10 010 001 011 12 11 001 000 101 11

Unique remaining subtrie indicated in red

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Trie: Basic Concept

7

4 8

5 1

2

6 3

0

0

0

0

0

0

0

0

0

1

1

1

11

1

1

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Patricia Tree

7

4 8

5 1

2

6 3

0

0

0

00

0

0

0

1

1

1

110 1

1

1

2 2

3 3 4

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Single-descendant nodes are eliminated.

Nodes have bit number.

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Oxford English Dictionary