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Data File Structures

10/18/2000. CSI660. 2. Accessing data during retrieval. Scan the entire collection SLOW!Typical in early (batch) retrieval systems Still used today, in hardware form (e.g., Fast Data Finder) Computational and I/O costs are O(characters in collection) Practical for only small" collections Use

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Data File Structures

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    1. 10/18/2000 CSI660 1 Data & File Structures Indexing data structures Efficient search techniques

    2. 10/18/2000 CSI660 2 Accessing data during retrieval Scan the entire collection – SLOW! Typical in early (batch) retrieval systems Still used today, in hardware form (e.g., Fast Data Finder) Computational and I/O costs are O(characters in collection) Practical for only “small” collections Use indexes for direct access – FAST! Evaluation time O(query term occurrences in collection) Practical for “large” collections Many opportunities for optimization Hybrids: Use small index, then scan a subset of the collection

    3. 10/18/2000 CSI660 3 What should an Index Contain? Database systems index primary and secondary keys This is the hybrid approach Index provides fast access to a subset of database records Scan subset to find solution set IR Problem: Cannot predict keys that people will use in queries Every word in a document is a potential search term IR Solution: Index by all keys (words)

    4. 10/18/2000 CSI660 4 How is the index accessed? Access by the atoms of a query language “features” or “keys” or “terms” Most common feature types: Words in text, punctuation Manually assigned terms (controlled and uncontrolled vocabulary) Document structure (sentence and paragraph boundaries) Inter- or intra-document links (e.g., citations) Composed features Feature sequences (phrases, names, dates, monetary amounts) Feature sets (e.g., synonym classes)

    5. 10/18/2000 CSI660 5 Indexing Words What is a word? Embedded punctuation (e.g., DC-10, long-term, AT&T) Case folding (e.g., New vs new, Apple vs apple) Stopwords (e.g., the, a, its) Morphology (e.g., computer, computers, computing, computed) Index granularity has a large impact on speed and effectiveness Index stems only? Index surface forms only? Index both?

    6. 10/18/2000 CSI660 6 What else to store in the index? Features depend upon the retrieval model Boolean Statistical (tf, df, ctf, doclen, maxtf) Often about 10% the size of the raw data, compressed Positional information Feature location within document Granularities include word, sentence, paragraph, etc Coarse granularities are less precise, but take less space Word-level granularity about 20-30% the size of the raw data, compressed

    7. 10/18/2000 CSI660 7 Common Index Implementations Bitmaps Signature files Inverted files Hashing n-grams

    8. 10/18/2000 CSI660 8 Common Index Components Dictionary (lexicon) Postings document ids, word positions

    9. 10/18/2000 CSI660 9 Bitmaps Bag-of-words index only For each term, allocate vector with one bit per document if term present in document n, set nth bit to 1, otherwise 0 Retrieval by Boolean operations very fast space efficient for common terms (why?) space inefficient for rare terms (why?) Good compression with run-length encoding Difficult to add/delete documents (why?) Not widely used

    10. 10/18/2000 CSI660 10 Signature Files

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