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Data Indexing

This guide explores the use of Lucene, through Eclipse, for data indexing, focusing on effective storage and recall of information. It demonstrates a two-pass algorithm for indexing various city-related data, optimizing searches, and organizing categorization for files like "city.txt." The process includes creating preliminary indexes with hash maps to eliminate duplicates and querying for easy recall, while addressing resource consumption and syntax challenges inherent in large datasets. This approach significantly reduces search times, enhancing data management and retrieval.

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Data Indexing

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  1. RohanSah Data Indexing

  2. Focus • Used Lucene (Indexing Lib.) via Eclipse • Store data • Categories • Types • File names • Easy recall ==city.txt== Chicago city Illinois New York city popular hot dogs Index Chicago city Illinois New York city popular Hot dogs

  3. Why? • Mentor: Data mining & context guesses • Over text, guess nouns and descriptions related to them • (Astronaut – space, moon, NASA, etc…) • Indexing allows easier recall • Running searches over extremely large files takes up too much time • Simpler format

  4. Algorithm • 2-pass algorithm ==city.txt== (Multiple list/text files) Chicago city Illinois New York city popular hot dogs Make preliminary index Store category names to Hash Map -Remove dups. Use terms in hash map and query prelim index Create final index

  5. Shortcomings • List format requires large storage • Hash map and preliminary index consume resources • Specific syntax for lists: • Index can be specific [category] \t [description] \t [description]…

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