TileHeat

Abstract

Public geospatial services are now commonly available on the Web. These services often render maps to users by dividing the maps into tiles. Given that geospatial services experience significant user load, it is desirable to pre-compute tiles at a time of low load in order to increase overall performance. Based on our analysis of the request log of a public geospatial service provider, we observe that times of low load occur with a periodic pattern. In addition, our analysis shows that tile access patterns exhibit strong spatial skew.Based on these observations, we propose an adaptive strategy restricting the set of tiles that are pre-computed to fit the low load time window. Ideally, the restricted tile set should deliver performance comparable to the full tile set. To achieve this result, tiles should be selected based on their expected popularity. Our key observation is that the popularity of a tile can be estimated by analyzing the tiles that users have previously requested. Our adaptive strategy constructs heatmaps of previous requests and uses this information to decide which tiles to pre-compute. We examine two alternative heuristics, one of which exploits that nearby tiles have a high likelihood of having similar popularity. We evaluate our methods against a real production workload, and observe that the latter heuristic achieves a 25% increase in the hit ratio compared to current methods, without pre-computing a larger set of tiles.

The Marker Cluster

“…As briefly mentioned in the introduction, the existing research regarding marker clusters is mostly looking at applications (Komninos, Besharat, Ferreira, & Garofalakis, 2013) or at the algorithmic backbone of the clusters; that means the algorithms that decide how the clustering is computed (Bär & Hurni, 2011;Delort, 2010a;Kefaloukos et al, 2012;Stefanakis, 2005). In contrast to this existing computer-centered research, the human-centered hci research on marker clusters is still not very profound.…”

“…From a scientific point of view, most of the research on marker clusters focuses on the more technical elements, like e.g. the algorithms generating the clusters (Bär & Hurni, 2011;Delort, 2010a;Kefaloukos, Vaz Salles, & Zachariasen, 2012;Stefanakis, 2005). In contrast to this related research, we investigate the functionality of the marker cluster method and take the human perception into account.…”