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| Content Provider | ACM Digital Library |
|---|---|
| Author | Trotman, Andrew O'Keefe, Richard Crane, Matt |
| Abstract | The time cost of searching with an inverted index is directly proportional to the number of postings processed and the cost of processing each posting. Dynamic pruning reduces the number of postings examined. Pre-calculation then quantization of term / document weights reduces the cost of evaluating each posting. The effect of quantization on precision, latency, and index size is examined herein. We show empirically that there is an ideal size (in bits) for storing the quantized scores. Increasing this adversely affects index size and search latency; decreasing it adversely affects precision. We observe a relationship between the collection size and ideal quantization size, and provide a way to determine the number of bits to use from the collection size. |
| Starting Page | 1221 |
| Ending Page | 1224 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450322638 |
| DOI | 10.1145/2505515.2507860 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-10-27 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Impact ordered indexes Quantization |
| Content Type | Text |
| Resource Type | Article |
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