Characterizing User Behavior on Web Mapping Systems Using Real-World Data

Braga, Vinrcius G.; Corr, Sand L.; Rodrigues, Vagner J. do Sacramento; Cardoso, Kleber Vieira

doi:10.1109/iscc.2018.8538635

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…When the user ends a browsing session, our extension groups all the collected data and sends it to a server. A detailed description of our data collection methodology is presented in [6], [22].…”

Section: Data Collection Methodologymentioning

confidence: 99%

“…In [22], we introduced the methodology we use to anonymously collect data from users of a client application of Google Maps. In [6], we used the collected data and introduced a preliminary model of how WMS users browse a Web map. Here, we present the complete description of the model and build on top of it to develop a new workload generator for WMSs, namely MUSeGen.…”

Section: B Related Workmentioning

confidence: 99%

“…The testbed consists of two computers connected through a Gigabit connection. The server runs one instance of the Nginx Web server, 5 which operates as a load balancer for eight instances of the TileServer GL, 6 an open-source and widely used map server for vector and raster maps. The tiles are effectively served by these instances.…”

Section: Case Study: Performance Evaluation Of a Real Wms Under mentioning

confidence: 99%

“…In this article, we present a characterization of how users of desktop terminals browse a WMS in a client application of Google Maps. This characterization is conducted based on data gathered anonymously from user sessions of Google Maps, using an extension that we developed for the Google Chrome browser [6]. Based on this characterization, we propose a model that statistically describes user actions in a WMS session.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Characterization and Modeling of Web Map System Workload

et al. 2021

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Every month, billions of users access Web Map Systems (WMSs), such as Google Maps, to visualize geospatial data. A large number of users and the huge amount of data demanded by these applications make the design and development of WMSs a challenging task, especially in terms of performance and scalability. In this context, workload generators become crucial tools, as they help system administrators to plan the capacity of WMSs and design provisioning strategies for peak load scenarios. However, little is known about the workload patterns generated by WMS users. In this work, we use data anonymously collected from sessions of a client application of Google Maps to devise a model that describes how users of desktop terminals navigate in a Web map. Based on this model, we implement a workload generator called MUSeGen. We compare the workload patterns generated by MUSeGen against the workload patterns found in real data. Results show that MUSeGen generates synthetic traces whose navigation patterns closely match those found in real data. We also compare MUSeGen against HELP, a workload generator built upon previous findings on empirical knowledge on the usage of WMSs. Results show that the number of issued operations per session in HELP is, on average, four times lower than that in MUSeGen and the number of tiles requested is, on average, twice lower than that in our tool. In addition, navigation patterns in HELP are much simpler than in MUSeGen. These findings support the conclusion that MUSeGen produces more realistic workloads than HELP. To illustrate how such differences affect performance evaluation in practice, we carry out a performance evaluation of a real WMS under workloads generated by HELP and MUSeGen. Our evaluation shows that the system capacity under HELP is three times less than that obtained under MUSeGen, highlighting the value of MUSeGen.INDEX TERMS Web map user, tile-based systems, workload model, workload generation, performance evaluation.

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Section: Data Collection Methodologymentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: Case Study: Performance Evaluation Of a Real Wms Under mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Characterization and Modeling of Web Map System Workload

et al. 2021

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“…Ingensand et al [45] described a case study on implanting vector tiles and discussed the influence of standardization processes. Braga et al [46] studied user behavior with web maps by applying vector tiles. Mapbox and Maptiler, as companies pioneering map tile implementation, have also conducted performance testing.…”

Section: Performance Testingmentioning

confidence: 99%

Performance Testing on Vector vs. Raster Map Tiles—Comparative Study on Load Metrics

Nétek

Masopust

Pavlicek

et al. 2020

IJGI

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Recent developments in web map applications have widely affected how background maps are rendered. Raster tiles are currently considered as a regular solution, while the use of vector tiles is becoming more widespread. This article describes an experiment to test both raster and vector tile methods. The concept behind raster tiles is based on pre-generating an original dataset including a customized symbology and style. All tiles are generated according to a standardized scheme. This method has a few disadvantages: if any change in the dataset is required, the entire tile-generating process must be redone. Vector tiles manipulate vector objects. Only vector geometry is stored on the server, while symbology, rendering, and defining zoom levels run on the client-side. This method simplifies changing symbology or topology. Based on eight pilot studies, performance testing on loading time, data size, and the number of requests were performed. The observed results provide a comprehensive comparison according to specific interactions. More data, but only one or two tiles, were downloaded for vector tiles in zoom and move interactions, while 40 tiles were downloaded for raster tiles for the same interactions. Generally, the WebP format downloaded about three times fewer data than Portable Network Graphics (PNG).

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User Behavior Identification and Personalized Recommendation Based on Web Data Mining

2021

International Journal of Circuits, Systems and Signal Processing

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A good understanding of user behavior and consumption preferences can provide support for website operators to improve their service quality. However, the existing personalized recommendation systems generally have problems such as low Web data mining efficiency, low degree of automated recommendation, and low durability. Targeting at these unsolved issues, this paper mainly carries out the following works: Firstly, the authors established a user behavior identification and personalized recommendation model based on Web data mining, it gave the user behavior analysis process based on Web data mining, improved the traditional k-means algorithm, and gave the detailed execution steps of the improved algorithm; moreover, it also elaborated on the K nearest neighbor model based on user scoring information, the score matrix decomposition method, and the personalized recommendation method for network users. At last, experimental results verified the effectiveness of the constructed model.

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Characterizing User Behavior on Web Mapping Systems Using Real-World Data

Cited by 3 publications

References 14 publications

Data-Driven Characterization and Modeling of Web Map System Workload

Data-Driven Characterization and Modeling of Web Map System Workload

Performance Testing on Vector vs. Raster Map Tiles—Comparative Study on Load Metrics

User Behavior Identification and Personalized Recommendation Based on Web Data Mining

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