Towards High Similarity Search Throughput by Dynamic Query Reordering and Parallel Processing
| Authors | |
|---|---|
| Year of publication | 2017 |
| Type | Article in Proceedings |
| Conference | Advances in Databases and Information Systems : 21st European Conference, ADBIS 2017, Nicosia, Cyprus, September 24-27, 2017, Proceedings |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1007/978-3-319-66917-5_18 |
| Field | Informatics |
| Keywords | stream processing; similarity search; parallel processing |
| Description | Current era of digital data explosion calls for employment of content-based similarity search techniques since traditional searchable metadata like annotations are not always available. In our work, we focus on a scenario where the similarity search is used in the context of stream processing, which is one of the suitable approaches to deal with huge amounts of data. Our goal is to maximize the throughput of processed queries while a slight delay is acceptable. We extend our previously published technique that dynamically reorders the incoming queries in order to use our caching mechanism more effectively. The extension lies in adoption of a parallel computing environment which allows us to process multiple queries simultaneously. |
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