1. 1 Indexing and Searching (File Structures) Modern Information Retrieval (C hapter 8) With G. Navarro

2. 2 File Struces Inverted Files Signatures PAT Trees Sequential Searching Compression

3. 3 Inverted Files Information Retrieval: Data Structures and Algorithms (Chapters 3) W.B. Frakes and R. Baeza-Yates (Eds.) 1992.

4. 4 Inverted Files Characteristics  A word-oriented mechanism based on sorted list of keywords, with each keyword having links to the documents containing that keyword. Preprocessing  Each document is assigned a list of keywords or attributes.  Each keyword (attribute) is associated with relevance weights.

5. 5 1. The input text is parsed into a list of words along with their location in the text. (time and storage consuming operation) 2. This list is inverted from a list of terms in location order to a list of terms in alphabetical order. 3. Add term weights, or reorganize or compress the files. Inversion of Word List

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7. 7 Structure and Construction Structure (split the index into two files)  Vocabulary: O(n  ) according to Heaps ’ Law  Occurrences : depends on the addressing granularity Construction  The vocabulary is stored in lexicographical order and points to posting list.  Posting file ： the lists of occurrences are stored contiguously

8. 8 (document #, frequency) Dictionary and Postings File

9. 9 Vocabulary and Posting File

10. 10 Structures used in Inverted Files Vocabulary  Sorted Arrays  Hashing Structures  Keyword Trees: Tries (digital search trees) The Search Procedure  Vocabulary search  Retrieval of occurrences  Manipulation of occurrences

11. 11 Size of an Inverted File Block addressing  The text is divided in blocks, and the occurrences point to the blocks instead of full inverted indices where exact occurrences are recorded

12. 12 Cost Advantage  easy to implement Disadvantage  updating the index is expensive

13. 13 Signature Files Information Retrieval: Data Structures and Algorithms (Chapters 4) W.B. Frakes and R. Baeza-Yates (Eds.) Englewood Cliffs, NJ: Prentice Hall, 1992.

14. 14 Signature Files Characteristics  Word-oriented index structures based on hashing  Low overhead (10%~20% over the text size) at the cost of forcing a sequential search over the index  Suitable for not very large texts  Inverted files outperform signature files for most applications

15. 15 Construction and Search Word-oriented index structures base on hashing  Maps words to bit masks of B bits  Divides the text in blocks of b words each  The mask is obtained by bitwise ORing the signatures of all the words in the text block. Search  Hash the query to a bit mask W  If W & Bi = W, the text block may contain the word

16. 16 Example Four blocks:  This is a text. A text has many words. Words are made from letters. 000101 110101 100100 101101  Hash(text) = 000101  Hash(many)= 110000  Hash(words)= 100100  Hash(made)= 001100  Hash(letters)= 100001 Block 4: 001100 OR100001 101101

17. 17 False Drop Assumes that m bits are randomly set in the mask Let =m/B For b words, the probability that a given bit of the mask is set is 1-(1-1/B) bm 1-e -b Hence, the probability that the l random bits are also set is F d =(1-e -b )  False alarm F d is minimized for =ln(2)/b F d = 2 -m m = B ln2/b

18. 18 Assume documents span exactly one logical block the size of document signature F = the size of block signature B Sequential Signature File (SSF)

19. 19 Classification of Signature-Based Methods Horizontal partitioning Grouping similar signatures together and/or providing an index on the signature matrix may result in better-than- linear search. Vertical partitioning Storing the signature matrix column-wise improves the response time on the expense of insertion time.

20. 20 Classification of Signature-Based Methods Vertical partitioning  without compression bit-sliced signature files (BSSF, B ’ SSF) frame sliced (FSSF) generalized frame-sliced (GFSSF)  with compression compressed bit slices (CBS) doubly compressed bit slices (DCBS) no-false-drop method (NFD)

21. 21 Classification of Signature-Based Methods Sequential storage of the signature matrix  without compression sequential signature files (SSF)  with compression bit-block compression (BC) variable bit-block compression (VBC) Horizontal partitioning  data independent partitioning Gustafson ’ s method partitioned signature files  data dependent partitioning 2-level signature files 5-trees

22. 22 Criteria The storage overhead The response time on single word queries The performance on insertion, as well as whether the insertion maintains the “ append- only ” property

23. 23 Vertical Partitioning Idea avoid bringing useless portions of the document signature in main memory Methods  store the signature file in a bit-sliced form or in a frame-sliced form  store the signature matrix column-wise to improve the response time on the expense of insertion time

24. Bit-Sliced Signature Files (BSSF) Transposed bit matrix transpose represent documents (document signature)

25. F bit-files search:(1) retrieve m bit-files. e.g., the word signature of free is 001 000 110 010 the document contains “free”: 3rd, 7th, 8th, 11th bit are set i.e., only 3rd, 7th, 8th, 11th files are examined. (2) “and” these vectors. The 1s in the result N-bit vector denote the qualifying logical blocks (documents). (3) retrieve text file through pointer file. insertion: require F disk accesses for a new logical block (document), one for each bit-file, but no rewriting documents

26. 26 Frame-Sliced Signature File (FSSF) Ideas  Random disk accesses are more expensive than sequential ones  Force each word to hash into bit positions that are closer to each other in the document signature  these bit files are stored together and can be retrieved with a few random accesses Procedures  The document signature (F bits long) is divided into k frames of s consecutive bits each.  For each word in the document, one of the k frames will be chosen by a hash function.  Using another hash function, the word sets m bits in that frame.

27. 27 documents frames Each frame will be kept in consecutive disk blocks. Frame-Sliced Signature File (Cont.)

28. 28 FSSF (Continued) Example (n=2, B=12, s=6, f=2, m=3) WordSignature free000000 110010 text010110 000000 doc. signature010110 110010 Search  Only one frame has to be retrieved for a single word query. I.E., only one random disk access is required. e.g., search documents that contain the word “ free ” ->because the word signature of “ free ” is placed in 2nd frame, only the 2nd frame has to be examined.  At most k frames have to be scanned for an k word query. Insertion  Only f frames have to be accessed instead of F bit-slices.

29. Horizontal Partitioning documents 1. Goal: group the signatures into sets, partitioning the signature matrix horizontally. 2. Grouping criterion

30. 30 Partitioned Signature Files Using a portion of a document signature as a signature key to partition the signature file. All signatures with the same key will be grouped into a so-called “ module ”. When a query signature arrives,  examine its signature key and look for the corresponding modules  scan all the signatures within those modules that have been selected

31. 31 Suffix Trees

32. 32 Suffix Trees and Suffix Arrays Each position in the text is considered as a text suffix Index points are selected form the text, which point to the beginning of the text positions which will be retrievable

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34. 34 Suffix arrays The main drawbacks of Suffix Array are its costly construction process. Allow binary searches done by comparing the contents of each pointer. Supra-indices (for large suffix array)

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37. 37 Construction of Suffix Arrays for Large Texts

38. 38 Sequential Searching

39. 39 Algorithms Brute Force Knuth-Morris-Pratt Boyer-Moore Family Shift-Or Suffix Automaton

40. 40 Knuth-Morris-Pratt

41. 41 Boyer-Moore Family

42. 42 Shift-Or

43. 43 Suffix Automaton

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45. 45 Pattern Matching

46. 46 Algorithms Searching allowing errors  Dynamic Programming  Automaton Regular Expressions and Extended patterns Pattern Matching Using Indices  Inverted files  Suffix Trees and Suffix Arrays

47. 47 Dynamic Programming

48. 48 Automaton

49. 49 Regular Expressions

50. 50 Pattern Matching Using Indices Inverted Files  The types of queries such as suffix or substring queries, searching allowing errors and regular expressions, are solved by a sequential search  The restriction is to find approximate matches or regular expressions that span many word.

51. 51 Pattern Matching Using Indices Suffix Trees  Suffix trees are able to perform complex searches Word, prefix, suffix, substring, and Range queries Regular expressions Unrestricted approximate string matching  Useful in specific areas Find the longest substring Find the most common substring of a fixed size

52. 52 Pattern Matching Using Indices Suffix Arrays  Some patterns can be searched directly in the suffix array without simulation the suffix tree  Word, prefix, suffix, subword search and range search

53. 53 Compression Compressed text--Huffman coding  Taking words as symbols  Use an alphabet of bytes instead of bits Compressed indices  Inverted Files  Suffix Trees and Suffix Arrays  Signature Files