Real-life performance of metric searching
| Authors | |
|---|---|
| Year of publication | 2010 |
| Type | Article in Periodical |
| Magazine / Source | SIGSPATIAL Special |
| MU Faculty or unit | |
| Citation | |
| Web | http://portal.acm.org/citation.cfm?id=1862421&jmp=references&coll=portal&dl=GUIDE&CFID=104917642&CFTOKEN=26274662# |
| Field | Informatics |
| Keywords | similarity searching; real-life performance; metric space |
| Description | Similarity is a central notion throughout human lives and it will soon become the prevalent strategy for dealing with digital content also in computer systems. But the exponential growth of data makes the scalability and performance issues serious matters of concern. Contemporary decentralized media of mass communication allowing cooperative and collaborative practices enable users autonomously contribute to production of global media, whose elements are in fact related by numerous multi-facet links of similarity. As an example, consider the sites like Flickr, YouTube, or Facebook that host user-contributed heterogeneous content for a variety of events. Accordingly, the core ability of future data processing systems is the similarity management of large and ever growing volumes of data. In a simplified way, the real-life performance can be constrained from two points of view: (1) the query response time, and (2) the query execution throughput, i.e. the number of queries processed per a unit of time. |
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