1. MG4J: Managing Gigabytes for Java Exercise Ida Mele

2. Indexing in MG4J is centered around documents Package: it.unimi.di.big.mg4j.document The object document, which is the instance of the class Document, represents a single document that can be indexed Different documents have different number and type of fields. For example, E-mail: from, to, date, subject, body HTML page: title, url, body Ida Mele MG4J - exercise1 Document

3. Ida Mele MG4J - exercise2 Document Summary of methods:

4. Package: it.unimi.di.big.mg4j.document DocumentCollection is a randomly addressable lists of documents Ida Mele MG4J - exercise3 DocumentCollection

5. FileSetDocumentCollection Package: it.unimi.di.big.mg4j.document The main method of FileSetDocumentCollection allows to build and serialize a set of documents specified by their filenames Ida Mele MG4J - exercise4

6. Document Factory Package: it.unimi.di.big.mg4j.document The factory turns a pure stream of bytes (file) into a document made by several fields (title and text) Ida Mele MG4J - exercise5

7. Standard MG4J Document Factories CompositeDocumentFactory HtmlDocumentFactory IdentityDocumentFactory MailDocumentFactory PdfDocumentFactory ReplicatedDocumentFactory PropertyBasedDocumentFactory TRECHeaderDocumentFactory ZipDocumentCollection.ZipFactory Ida Mele MG4J - exercise6

8. Query Package: it.unimi.di.big.mg4j.query To query the index we can use the main method of the class Query We can submit queries by using: command line web browser QueryEngine: The query engine receives the query and returns the ranked list of results HttpQueryServer: A simple web server for query processing Ida Mele MG4J - exercise7

9. Indexing and querying: exercise TECHNICAL REQUIREMENTS: UNIX Operating System Java (>=6) Document collection and the libraries are available at: http://www.dis.uniroma1.it/~mele/WebIR.html Ida Mele MG4J - exercise8

10. Set the classpath Download and extract htmlDIS.tar.gz Download and extract lib.zip Download the file set-classpath.sh Edit the first line of the file set-classpath.sh: replace your_directory with the path of the folder containing all the.jar files (lib folder) Set the CLASSPATH: source set-classpath.sh Ida Mele MG4J - exercise9

11. Building the collection of documents (1) Help: java it.unimi.di.big.mg4j.document.FileSetDocumentCollection -- help Create the collection: find htmlDIS -iname \*.html | java it.unimi.di.big.mg4j.document.FileSetDocumentCollection -f HtmlDocumentFactory -p encoding=UTF-8 dis.collection find returns the list of files, one per line. This list is provided as input to the main method of the FileSetDocumentCollection Ida Mele MG4J - exercise10

12. We need also to specify a factory (the -f option) and the encoding as a property The name of the collection is dis.collection The collection does not contain the files, but only their names Deleting or modifying files of htmlDIS directory may cause inconsistence in the collection Building the collection of documents (2) Ida Mele MG4J - exercise11

13. Building the index Help: java it.unimi.di.big.mg4j.tool.IndexBuilder --help Create the index: java it.unimi.di.big.mg4j.tool.IndexBuilder --downcase -S dis.collection dis --downcase: this option forces all the terms to be downcased -S : specifies that we are producing an index for the specified collection. If the option is omitted, Index expects to index a document sequence read from standard input dis: basename of the index If you have memory problem, you can use -Xmx for allocating more memory to Java: java -Xmx512M it.unimi.di.big.mg4j.tool.IndexBuilder --downcase -S dis.collection dis Ida Mele MG4J - exercise12

14. dis-{text,title}.terms: contain the terms of the dictionary. One term per line more dis-text.terms dis-{text,title}.stats: contain statistics more dis-text.stats dis-{text,title}.properties: contain global information more dis-text.properties Index files (1) Ida Mele MG4J - exercise13

15. dis- { text,title}.frequencies: for each term, there is the number of documents with the term (  -code) dis-{text,title}.globcounts: for each term, there is the number of occurrence of the term (  -code) dis-{text,title}.offset: for each term, there is the offset (  - code) Index files (2) Ida Mele MG4J - exercise14

16. dis-{title,text}.sizes: contain the list of the document sizes. The document size is the number of words contained in each document (  - code) dis-{text,title}.batch : temporary files with sub-indices (  -code). Use the option --keep-batches to not delete temporary files dis-{text,title}.index: contain the index (  -code) Index files (3) Ida Mele MG4J - exercise15

17. Web server Help: java it.unimi.di.big.mg4j.query.Query --help Querying the index: java it.unimi.di.big.mg4j.query.Query -h -i FileSystemItem -c dis.collection dis-text dis-title Command line: {text, title} > computer Web browser: http://localhost:4242/Queryhttp://localhost:4242/Query Ida Mele MG4J - exercise16

18. Search one word: The result is the set of documents that contain the specified word Example: computer AND: more than one term separated by whitespace or by AND or &. The result is the set of documents that contain all the specified words Example: computer science Example: computer AND science Example: computer & science Query (1) Ida Mele MG4J - exercise17

19. OR: more than one term separated by OR or |. The result is the set of documents that contain any of the given words Example: conference | workshop NOT: the operator NOT or ! is used for negation Example: conference & ! workshop Parentheses: the parentheses are used to enforce priority in complex queries Example: university & (rome | california) Query (2) Ida Mele MG4J - exercise18

20. Proximity restriction: the words must appear within a limited portion of the document Example: (university rome)~6 Phrase: using “ ” we can look for documents that contain the exact phrase Example: “university of rome la sapienza” Ordered AND: more than one term separated by < Example: computer < science < department Query (3) Ida Mele MG4J - exercise19

21. Wildcard (*): wildcard queries can be submitted appending * at the end of a term Example: infor* Index specifiers: prefixing a query with the name of an index followed by : you can restrict the search to that index Example: title:computer Example: text:computer science AND title:FOCS Query (4) Ida Mele MG4J - exercise20

22. MG4J provides sophisticated query tuning To use this features, we must use the command line interface $ --- to get some help on the available options Some examples: $mode --- to choose the kind of results Example: > $mode short $selector --- to choose the way the snippet or intervals are shown Example: > $selector 3 40 Sophisticated queries (1) Ida Mele MG4J - exercise21

23. Other examples: $mplex --- when multiplexing is on, each query is multiplexed to all indices. When a scorer is used, it is a good idea to use multiplexing Example: > $mplex on $score --- to choose the scorer Example: > $score VignaScorer $weight --- to change the weight of the indices. This is useful when multiplexing is on Example: >$weight text:1 title:3 Sophisticated queries (2) Ida Mele MG4J - exercise22

24. Scorer are important for ranking the documents result of a query. Default: BM25Scorer and VignaScorer ConstantScorer. Each document has a constant score (default is 0) >$score ConstantScorer CountScorer. It is the product between the number of occurrences of the term in the document and the weight assigned to the index >$score CountScorer Scorer (1) Ida Mele MG4J - exercise23

25. TfIdfScorer. It implements TF/IDF TF is the term frequency of the term t for the document d: c/l; where c is the number of occurrences of t in d and l is the length of d IDF is the inverse document frequency of the term t in the collection: log(N/f); where N is the number of documents in the collection and f is the number of documents where t appears >$score TfIdfScorer Scorer (2) Ida Mele MG4J - exercise24

26. DocumentRankScorer. The scores of documents are stored in a text file >$score DocumentRankScorer nameFile Scorer (3) Ida Mele MG4J - exercise25

27. A virtual field produces pieces of text that refer to other documents (possibly belonging to the collection) Referrer: the document that is referring to another document Referee: the document to which a piece of text of the Referrer is referring to Intuitively, the Referrer gives us information about the Referee The Referrer produces in a virtual field a number of fragments of text, each referring to a Referee The content of a virtual field is a list of pairs made by the piece of text (called virtual fragment) and by some string that is aimed at representing the Referee (called the document spec) Virtual fields (1) Ida Mele MG4J - exercise26

28. Virtual fields (2) Ida Mele MG4J - exercise27 In the case of the HTML document: the document spec is a URL (as specified in the href attribute) the virtual fragment is the content of the anchor element and some surrounding text (anchor context) The HTMLDocumentFactory produces the pairs (document spec, virtual fragment)

29. Create the list of URL of the documents in the collection: java it.unimi.di.big.mg4j.tool.ScanMetadata -S dis.collection -u dis.urls Create the document resolver. It is able to map the document spec produced by some document factory into actual references to documents in the collection Given a document spec, the resolver will decide whether the spec really refers to a document in the collection or not, and in the first case it will find out to which document the spec refers to: java it.unimi.di.big.mg4j.tool.URLMPHVirtualDocumentResolver -o dis.urls dis-anchor.resolver Virtual fields (3) Ida Mele MG4J - exercise28

30. Building the index: java it.unimi.di.big.mg4j.tool.IndexBuilder -a -v anchor:dis- anchor.resolver --downcase -S dis.collection dis Querying the index: java it.unimi.di.big.mg4j.query.Query -h -i FileSystemItem -c dis.collection dis-text dis-title dis-anchor {text, title, anchor} > anchor:conference {text, title, anchor} > title:combinatorial algorithms AND anchor:conference {text, title, anchor} > text:RoboCup AND anchor:info Virtual fields (4) Ida Mele MG4J - exercise29

31. All the virtual fragments that refer to a given document of the collection are like a single text, called virtual text Virtual fragments coming from different anchors are concatenated, and they are in a text file This may produce false positive results For example, the query anchor:(computer AND science) produces as result a list of documents that contain both the words in some of their anchors, but not necessarily in the same anchor Virtual gap (1) Ida Mele MG4J - exercise30

32. To avoid such kinds of false positives, we can use virtual gaps The virtual gap is a positive integer, representing the virtual space left between different virtual fragments For example, if the virtual gap is 64 (the default), anchors are concatenated by leaving 64 “empty words” between subsequent fragments We can submit the query: >anchor:(computer AND science)~64 and we will be sure that only documents containing both the term in the same anchor are retrieved Virtual gap (2) Ida Mele MG4J - exercise31

33. If the anchor is longer than 64 characters, we can still have false positives In the indexing phase, it is possible to specify a different virtual gap For example, we can use: java it.unimi.di.big.mg4j.tool.IndexBuilder -a -g anchor:100 -v anchor:dis-anchor.resolver --downcase -S dis.collection dis It uses 100 characters for the virtual gap Virtual gap (3) Ida Mele MG4J - exercise32

34. Term map (1) A simple representation of a dictionary is the term list (the file.terms): a text file containing the whole dictionary, one term per line, in index order (the first line contains the term with index 0, the second line the term with index 1, etc.) A more efficient representation is based on a monotone minimal perfect hash function: it is a very compact data structure that is able to answer to the question "What is the index of the term XXX?” You can build such a function from a sorted term list using: java it.unimi.dsi.sux4j.mph.MinimalPerfectHashFunction titles.mph dis-title.terms Ida Mele MG4J - exercise33

35. Term map (2) Monotone minimal perfect functions have a serious limit: they can answer correctly to the question "What is the index of the term XXX?” but only if the term appears in the dictionary To solve this problem, we can use a signed function For terms not in the dictionary, the function will answer with a special value (-1) that means "the word is not in the dictionary” java it.unimi.dsi.util.ShiftAddXorSignedStringMap titles.mph titles.map mycollection-title.terms Ida Mele MG4J - exercise34

36. Term map (3) Wildcard searches require the use of a prefix map A prefix map is able to answer correctly to the question "What are the indices of terms starting with the characters YYY?” If terms are lexicographically sorted, the answer is a pair of integers, representing the first and the last index of terms satisfying the property We can build a prefix map by using: java it.unimi.dsi.util.ImmutableExternalPrefixMap -b4Ki -o dis-title.terms dis-title.dict Ida Mele MG4J - exercise35

37. Homework 1.Read the MG4J (big) manual: http://www.dis.uniroma1.it/~mele/teaching/WebIR/manual -mg4j.pdf http://www.dis.uniroma1.it/~mele/teaching/WebIR/manual -mg4j.pdf 2.Repeat the exercise 3.Create your own document collection, build the inverted index (with or without virtual fields), then submit some queries and try the different scorers Ida Mele MG4J - exercise36