Publication details
Continuous Time-Dependent kNN Join by Binary Sketches
| Authors | |
|---|---|
| Year of publication | 2018 |
| Type | Article in Proceedings |
| Conference | IDEAS 2018 : 22nd International Database Engineering & Applications Symposium, June 18-20, 2018, Villa San Giovanni, Italy |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1145/3216122.3216159 |
| Keywords | continuous kNN similarity join; time-dependent similarity; binary sketches |
| Description | An important functionality of current social applications is real-time recommendation, which is responsible for suggesting relevant published data to the users based on their preferences. By representing the users and the published data in a metric space, each user can be recommended with their k nearest neighbors among the published data. We consider the scenario when the relevance of a published data item to a user decreases as the data gets older, i.e., a time-dependent distance function is applied. We define the problem as the continuous time-dependent kNN join and provide a solution to a broad range of time-dependent functions. In addition, we propose a binary sketch-based approximation technique used to speed up the join evaluation by replacing expensive metric distance computations with cheap Hamming distances. |
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