Top-K Queries Over Digital Traces
| dc.contributor.advisor | Yu, Xiaohui | |
| dc.creator | Li, Yifan | |
| dc.date.accessioned | 2019-03-05T15:01:25Z | |
| dc.date.available | 2019-03-05T15:01:25Z | |
| dc.date.copyright | 2018-12-07 | |
| dc.date.issued | 2019-03-05 | |
| dc.date.updated | 2019-03-05T15:01:25Z | |
| dc.degree.discipline | Computer Science | |
| dc.degree.level | Master's | |
| dc.degree.name | MSc - Master of Science | |
| dc.description.abstract | Recent advances in social and mobile technology have enabled an abundance of digital traces (in the form of mobile check-ins, WiFi hotspots handshaking, etc.) revealing the physical presence history of diverse sets of entities. One challenging, yet important, task is to identify k entities that are most closely associated with a given query entity based on their digital traces. We propose a suite of hierarchical indexing techniques and algorithms to enable fast query processing for this problem at scale. We theoretically analyze the pruning effectiveness of the proposed methods based on a human mobility model which we propose and validate in real life situations. Finally, we conduct extensive experiments on both synthetic and real datasets at scale, evaluating the performance of our techniques, confirming the effectiveness and superiority of our approach over other applicable approaches across a variety of parameter settings and datasets. | |
| dc.identifier.uri | http://hdl.handle.net/10315/35922 | |
| dc.language.iso | en | |
| dc.rights | Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests. | |
| dc.subject | Computer science | |
| dc.subject.keywords | Spatio-temporal data | |
| dc.subject.keywords | Top-k query | |
| dc.subject.keywords | Hashing | |
| dc.subject.keywords | Human mobility | |
| dc.title | Top-K Queries Over Digital Traces | |
| dc.type | Electronic Thesis or Dissertation |
Files
Original bundle
1 - 1 of 1