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"Managing Gigabytes" and the use of compression in search

"Managing Gigabytes: Compressing and Indexing Documents and Images" is a book by Ian H. Witten, Alistair Moffat, and Timothy C. Bell. It delves into the techniques for compressing and indexing large amounts of text and images, making it a seminal work in the field of information retrieval and data compression.

The central idea supporting the claim that better search can be achieved through data compression is rooted in the following concepts:

1. Inverted Indexes and Compression: The book introduces the concept of an inverted index, which is a fundamental data structure used in search engines. An inverted index lists, for every word, the documents in which it appears. Given the vast amount of data, these indexes can be enormous. By compressing the index, not only is storage saved, but search operations can also be sped up, as reading compressed data from disk can be faster than reading uncompressed data.

2. Redundancy and Information Content: Compression algorithms work by identifying and eliminating redundancy in data. In the context of text and document retrieval, redundancy can be seen as words or phrases that don't contribute much to the overall meaning or content of the document. By focusing on compressing this redundancy, the essential information content of the document is preserved, making searches more efficient and relevant.

3. Compression as a Filtering Mechanism: When data is compressed, irrelevant or less important information is often filtered out. This can be leveraged to improve search by ensuring that the search algorithm focuses on the most relevant parts of the data, thereby improving the quality of search results.

4. Efficiency in Retrieval: Compressed data structures, like compressed inverted indexes, can be processed more quickly than their uncompressed counterparts. This is because they often require fewer disk accesses and can be loaded into memory faster. The efficiency gained in retrieval time can be significant, especially when dealing with large datasets.

5. Phrase Searching and Compression: The book discusses techniques for efficient phrase searching within compressed text. By using compression techniques, it's possible to search for phrases without decompressing the entire text, making the search process faster.

6. Bitmap Indexing and Compression: For certain types of queries, bitmap indexes are used. These can be large, but they are highly compressible. Compressed bitmap indexes can be operated on directly (without decompression) to answer queries, leading to faster query processing.

In essence, the book "Managing Gigabytes" presents the intertwined relationship between compression and search. It posits that effective compression techniques not only save storage space but also enhance the efficiency and quality of search operations. The principles laid out in the book have been foundational in the development of modern search engines and large-scale information retrieval systems.