Adaptation of a Neighbor Selection Markov Chain for Prefetching Tiled Web GIS Data

Lee, Dong Hoon; Kim, Jung Sup; Kim, Soo Duk; Kim, Ki-Chang; Kim, Yoo-Sung; Park, Jaehyun

doi:10.1007/3-540-36077-8_21

Cited by 17 publications

(23 citation statements)

References 6 publications

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“…There are several works in the literature that address object prefetching in Web GIS: [9,10] approximate which tiles will be used in advance based on the global tile access pattern of all users and the semantics of query; [11,12] use an heuristic method that considers the former actions of a given user. We propose another pre-fetching strategy, known as metatiling, that works as follows [13]: when the proxy receives a tile request from a client and a cache miss is produced, it requests a larger image tile (called metatile) to the remote backend.…”

Section: Tile Pre-fetchingmentioning

confidence: 99%

Web Map Tile Services for Spatial Data Infrastructures: Management and Optimization

García¹,

Castro²,

Verdú³

et al. 2012

Cartography - A Tool for Spatial Analysis

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Section: Tile Pre-fetchingmentioning

confidence: 99%

Web Map Tile Services for Spatial Data Infrastructures: Management and Optimization

García¹,

Castro²,

Verdú³

et al. 2012

Cartography - A Tool for Spatial Analysis

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“…We track zoom-in's using the inFlag variable (line 6). In contrast, an observed zoom-out tells the prediction engine to stop adding tiles to temp ROI (lines [8][9][10][11][12]. If the inFlag was set while the zoom-out occurred, we replace the user's old ROI with temp ROI (lines 9-10).…”

Section: General Recommendation Model Designmentioning

confidence: 99%

“…Therefore, we used prediction accuracy as one of our primary metrics for comparison, similar to Lee et al [12]. To compute this, we ran our models in parallel while stepping through tile request logs, one request at a time.…”

Section: Measuring Accuracymentioning

confidence: 99%

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Dynamic Prefetching of Data Tiles for Interactive Visualization

Battle

Chang

Stonebraker

2016

Proceedings of the 2016 International Conference on Management of Data

148

123

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In this paper, we present ForeCache, a general-purpose tool for exploratory browsing of large datasets. ForeCache utilizes a clientserver architecture, where the user interacts with a lightweight clientside interface to browse datasets, and the data to be browsed is retrieved from a DBMS running on a back-end server. We assume a detail-on-demand browsing paradigm, and optimize the back-end support for this paradigm by inserting a separate middleware layer in front of the DBMS. To improve response times, the middleware layer fetches data ahead of the user as she explores a dataset.We consider two different mechanisms for prefetching: (a) learning what to fetch from the user's recent movements, and (b) using data characteristics (e.g., histograms) to find data similar to what the user has viewed in the past. We incorporate these mechanisms into a single prediction engine that adjusts its prediction strategies over time, based on changes in the user's behavior. We evaluated our prediction engine with a user study, and found that our dynamic prefetching strategy provides: (1) significant improvements in overall latency when compared with non-prefetching systems (430% improvement); and (2) substantial improvements in both prediction accuracy (25% improvement) and latency (88% improvement) relative to existing prefetching techniques.

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“…Another class of approaches [21] attempts to learn from past user behavior by keeping track of all paths visited in the past. Prefetching, i.e., predicting the next query location, is based on the history.…”

Section: Motivationmentioning

confidence: 99%

Computational Neuroscience Breakthroughs through Innovative Data Management

Tauheed

Nobari

Biveinis

et al. 2013

Advances in Databases and Information Systems

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Abstract. Simulations have become key in many scientific disciplines to better understand natural phenomena. Neuroscientists, for example, build and simulate increasingly fine-grained models (including subcellular details, e.g., neurotransmitter) of the neocortex to understand the mechanisms causing brain diseases and to test new treatments in-silico. The sheer size and, more importantly, the level of detail of their models challenges today's spatial data management techniques. In collaboration with the Blue Brain project (BBP) we develop new approaches that efficiently enable analysis, navigation and discovery in spatial models of the brain. More precisely, we develop an index for the scalable and efficient execution of spatial range queries supporting model building and analysis. Furthermore, we enable navigational access to the brain models, i.e., the execution of of series of range queries where he location of each query depends on the previous ones. To efficiently support navigational access, we develop a method that uses previous query results to prefetch spatial data with high accuracy and therefore speeds up navigation. Finally, to enable discovery based on the range queries, we conceive a novel in-memory spatial join. The methods we develop considerably outperform the state of the art, but more importantly, they enable the neuroscientists to scale to building, simulating and analyzing massively bigger and more detailed brain models.

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Adaptation of a Neighbor Selection Markov Chain for Prefetching Tiled Web GIS Data

Cited by 17 publications

References 6 publications

Web Map Tile Services for Spatial Data Infrastructures: Management and Optimization

Web Map Tile Services for Spatial Data Infrastructures: Management and Optimization

Dynamic Prefetching of Data Tiles for Interactive Visualization

Computational Neuroscience Breakthroughs through Innovative Data Management

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