Name
Abstract | ||
|---|---|---|
In this paper, we address the problem of processing reverse top-k queries in a parallel and distributed setting. Given a database of objects, a set of user preferences, and a query object q, the reverse top-k query returns the subset of user preferences for which the query object belongs to the top-k results. Although recently, the reverse top-k query operator has been studied extensively, its CPU-intensive nature results in prohibitively expensive processing cost, when applied on vast-sized data sets. This limitation motivates us to explore a parallel processing solution, to enable reverse top-k query evaluation over GBs of data in reasonable execution time. To the best of our knowledge, this is the first work that addresses the problem of parallel reverse top-k query processing. We propose a solution to this problem, called DiPaRT, which is based on MapReduce and is provably correct. DiPaRT is empirically evaluated using GB-sized data sets. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDE.2019.00148 | 2019 IEEE 35th International Conference on Data Engineering (ICDE) |
Keywords | Field | DocType |
Silicon,Task analysis,Parallel processing,Query processing,Servers,Distributed databases,Partitioning algorithms | Data mining,Data set,Task analysis,Computer science,Parallel processing,Server,Execution time,Operator (computer programming),Distributed database | Conference |
ISSN | ISBN | Citations |
1084-4627 | 978-1-5386-7474-1 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Panagiotis Nikitopoulos | 1 | 4 | 3.49 |
| Georgios A. Sfyris | 2 | 3 | 0.72 |
| Akrivi Vlachou | 3 | 751 | 39.95 |
| Christos Doulkeridis | 4 | 899 | 55.91 |
| Orestis Telelis | 5 | 1 | 0.69 |