Name
Abstract | ||
|---|---|---|
We introduce a scheme to address the trade-off between the identification rate, search and memory complexities in large-scale identification systems. We use a special database organization by assigning database entries to a set of possibly overlapping clusters. The clusters are generated based on statistics of both database entries and queries. The decoding procedure is accomplished in two stages. First, a list of clusters related to the query is detected. Then, refinement checks are performed on members of the detected clusters to produce a unique index. We investigate the minimum achievable search complexity for binary symmetric sources. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6854315 | ICASSP |
Keywords | Field | DocType |
pattern clustering,refinement check,query statistics,content identification,unique index,database entry statistics,data compression,identification capacity,two stage decoding scheme,computational complexity,identification rate,database organization,overlapping cluster set,content identification capacity,binary symmetric sources,memory complexity,search complexity,decoding,query processing,clustering | Data mining,Cluster (physics),Computer science,Theoretical computer science,Decoding methods,List decoding,Binary number | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Farzad Farhadzadeh | 1 | 63 | 10.01 |
| Ke Sun | 2 | 163 | 21.00 |
| Sohrab Fredowsi | 3 | 0 | 0.34 |