Using real-time online preprocessed mouse tracking for lower storage and transmission costs (ending)

in #technology2 months ago

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Result and discussion

Table 3 Example default mouse tracking data that can be seen in the database table. Column 1–3 are labels added using JavaScript, column 4 is the duration calculated from the difference between dates, and the rest of the columns are data retrieved from the DOM API

ID Name Date 2019/3/01 Duration Left click Right click Middle click Mouse X Mouse Y Scroll X Scroll Y
1 Student 1 11:06:39 13.674 False False False 0 0 0 0
2 Student 1 11:06:39 0.002 False False False 1197 317 0 0
... ... ... ... ... ... ... ... ... ... ...
286511 Student 23 14:44:09 0.002 False False False 1009 179 0 0

Table 4 Transmission server resource cost of three mouse tracking technique simulations. The first row are the mouse tracking method labels, the second row are the resource metric, and the first column are the statistical metrics

Statistics Default Mouse Tracking Simulation ROI Tracking Simulation Whole Page Tracking Simulation
CPU (%) RAM (MB) Data Rate (kB) CPU (%) RAM (MB) Data Rate (kB) CPU (%) RAM (MB) Data Rate (kB)
Minimum 0 2.88 0 0 1.75 0 0 1.58 0
Maximum 86 3.66 228.45 12 1.92 46.8 26 2.12 2.07
Median 3 3.29 5.62 0 1.86 0 0 2.07 0
Average 21.34 3.25 28.23 0.87 1.85 2.28 0.05 2.06 0.01
Standard deviation 29.09 0.25 36.8 1.24 0.06 5.05 0.46 0.04 0.08

Table 5 Example page tracking data that can be seen in the database table. Column 1, 2 are labels added using JavaScript, column 3 is the duration calculated from the difference between dates, and the rest of the columns are total value of data retrieved from the DOM API

Name Date 2019/3/01 Duration (seconds) Left clicks Right clicks Middle clicks Mouse moves Scrolls
Student 1 14:12:29 41 3 0 0 629 114
Student 2 14:44:09 90 7 0 0 1176 137
... ... ... ... ... ... ... ...
Student 22 11:55:14 2188 157 5 0 20912 6626
Student 23 11:57:37 2236 323 0 0 17982 6930

Table 6 Example ROI tracking data that can be seen in the database table. Column 1 - 3 are labels added using JavaScript, column 4 is the duration calculated from the difference between dates, column 5 is the area manually labelled by the administrator and the rest of the columns are total value of data retrieved from the DOM API

ID Name Date 2019/3/01 Duration (second) Area (x1,x2,y1,y2) Left clicks Right clicks Middle clicks Mouse moves Scrolls
1 Student 1 11:06:39 14.148 {“header”:[0,1920,0,64]} 0 0 0 1 1
2 Student 1 11:06:40 1.179 {“quiz1”:[529,1900,291,570]} 0 0 0 86 0
... ... ... ... ... ... ... ... ... ...
19062 Student 23 14:44:09 0.002 {“title”:[16,1904,150,270]} 0 0 0 1 0

trajectory-flow-heatmap-large.png

Fig. 7 Visualization of mouse tracking data. Default mouse tracking data can visualize exact points of location, the left image is click visualization and the middle image is a heatmap based on the duration the mouse cursor stays on each point, while ROI tracking can only visualize defined areas and show flows between areas shown on the right image

cpumstr.png

Fig. 8 CPU usage comparison between default mouse tracking, whole page tracking, and ROI tracking. The horizontal axis is the time interval. The vertical axis is the CPU usage in percentage

rammstr.png

Fig. 9 RAM usage comparison between default mouse tracking, whole page tracking, and ROI tracking. The horizontal axis is the time interval. The vertical axis is the RAM usage in megabytes

dtrmstr.png

Fig. 10 Data rate comparison between default mouse tracking, whole page tracking, and ROI tracking.The horizontal axis is the time interval. The vertical axis is the data rate in kilobytes per second

brwsperf.png

Fig. 11 The total script running time of three mouse tracking demo session by the author. The horizontal axis is the mouse tracking method. The data in order are from Mozila Firefox, Microsoft Edge, and Google Chrome. The vertical axis is the total running time in milliseconds. Among the three browsers Mozilla Firefox performs faster than Microsoft Edge and Internet Explore performs faster than Google Chrome for this work [60]

Default mouse tracking

It is well known that the advantage of default mouse tracking is the detailed and precise data it generates. An example is shown in Table 3. The exact x and y points of the locations of event occurrences, such as left clicks, right clicks, middle clicks, mouse movements, scrolls, zooms, and keyboard types, are recorded, including when and for how long each event occurs. Those geometrical data (x,y) make it possible to reproduce the mouse trajectory shown in Fig. 7, and adding the time information enables the trajectory’s replay.

The rumored disadvantage is the huge transmission and storage cost, and this seems to be true judging from Figs. 8, 9 and 10. For the 22 students in each session, the transmission resource cost statistics are shown in Table 4. The average data rate was 28 kilobytes per second (kBps) and was able to peak to 228 kBps. For the two sessions totalling 44 students, the data size was approximately 100 MB and Table 3 has 286511 rows. The CPU usage was hightly consumptive as well, while the RAM usage was not as consumptive. Even worse, mouse tracking is not a replacement for the existing logging method but rather an addition; in other words, it is expected to add an additional burden to the existing system if mouse tracking is implemented. These data were generated from a 2 1/2 h mouse tracking session; thus, imagine how much resource mouse tracking would consume if it were run on a university scale with thousands of students for 24 h daily. On the client the side, this method also shows the highest total JavaScript running in Fig. 11 among the other methods. It is suspected due to the large amount of HTTP Post to the server.

Webpage summarized mouse tracking

By omitting the geometrical data (x,y) and summarizing the numbers of events that occurred, the data became as small as possible, as shown in Figs. 8, 9, 10 (although they can be further reduced slightly by compression and removal of unnecessary characters and variables). The table was reduced to one row per webpage visit; in this case, Table 3 with 286511 rows was reduced to 26 rows, as shown in Table 5. As shown in Table 4, the data size was reduced from 100 MB to 16 kB. The average data rate was reduced from 28 kBps to 10 Bps. Although there is still RAM usage, CPU usage is slightly visible. Among the three mouse tracking methods mentioned in this work, this technique is the most advantageous in terms of resource cost. On the client the side, this method also shows the lowest total JavaScript running in Fig. 11 among the other methods. It is suspected due to the few amount of HTTP Post to the server.

However, the disadvantage compared to the three mouse tracking techniques is that it provides the poorest information that makes it impossible to create any visualizations, as shown in Fig. 7. The information tells only how many events (such as left clicks, right clicks, middle clicks, mouse movements, scrolls, zooms, and keyboard types) occurred and the length of time that the user spends on the webpage. Nevertheless, the information is richer than traditional logs, as shown in Table 5.

Region of interest mouse tracking

This technique is the best of the three, as the desired information is based on the analyst’s preferences, and there are lower resource costs than in the default mouse tracking shown in Figs. 8, 11. Analysts chooses the areas to be analyzed. In this case, the authors defined the following areas for the quiz session: header, title, menu, footer, and each question section. It can be seen in Fig. 7 that it is possible to create heatmaps of high activity areas, although it is not possible to create precise mouse trajectories as default mouse tracking, but it is possible to capture amounts of movement between areas. The duration is also based on each area. The data size is 5.4 MB with 19062 rows shown in Table 6. As shown in Table 4, the average data rate is 2.28 kBps and the average CPU and RAM usage are 0.87% and 1.85 MB which are lower than in default mouse tracking. Based on the algorithm of this method, the resource cost should be based on the number of defined areas, where the more areas, the larger the resource cost (note that default mouse tracking cost the largest because the webpage has been divided into the smallest possible areas, which are the x and y points of a webpage).

This technique is the best of the three, as the desired information is based on the analyst’s preferences, and there are lower resource costs than in the default mouse tracking shown in Figs. 8, 11. Analysts chooses the areas to be analyzed. In this case, the authors defined the following areas for the quiz session: header, title, menu, footer, and each question section. It can be seen in Fig. 7 that it is possible to create heatmaps of high activity areas, although it is not possible to create precise mouse trajectories as default mouse tracking, but it is possible to capture amounts of movement between areas. The duration is also based on each area. The data size is 5.4 MB with 19062 rows shown in Table 6. As shown in Table 4, the average data rate is 2.28 kBps and the average CPU and RAM usage are 0.87% and 1.85 MB which are lower than in default mouse tracking. Based on the algorithm of this method, the resource cost should be based on the number of defined areas, where the more areas, the larger the resource cost (note that default mouse tracking cost the largest because the webpage has been divided into the smallest possible areas, which are the x and y points of a webpage).

Conclusion and future work

Preprocessing mouse tracking data during real-time and online sessions helps reduce the storage and transmission costs and unexpectedly the JavaScript total running time on the client’s browser as well. The techniques presented in this work are whole page tracking and ROI tracking. Although the amount of reduced data is very dependent, there are fixed theories. The fixed theories are as follows: whole page tracking reduces the mouse tracking data into one row of tables per webpage visit, and ROI tracking reduces the data into one row of tables per area visit. Selecting the right technique can help reduce the storage and transmission costs while still obtaining the necessary data.

Although this concept works perfectly, but there are still problems with execution. Whole page tracking transmits the data only when the user leaves the page, and if the problems lie with the browser, there is currently no way to tell the user to wait before the transmission process finishes. There will definitely be cases where data are not fully transmitted. The problem for ROI tracking are that it cannot perform smart area definition and labelling. Normally, they are performed by humans. Therefore, one solution is to develop an artificial intelligence for this matter in the future.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the Mendeley repository titled ’Data for: Implementation of Real-Time Online Mouse Tracking Case Study in a Small Online Quiz’ in ’Pre-processed Mouse Tracking Data’ directory [61]: Tables 3, 5, 6 data are available in file mouse_tracking_mongolia_anonymous.ods [62]. Figures 8, 9, 10 and Table 4 are based on files: cpurammousetracking.txt [63], cpurampagetracking.txt [64], cpuramroitracking.txt [65], mousetrackingsimulationpcapng [66], pagetrackingsimulationpcapng [67], roitrackingsimulationpcapng [68].

Abbreviations

  • AdELEAdaptive: E-Learning via the eye tracking.AeLSEye tracking based adaptive and personalized e-learning systems.
  • API: Application programming interface
  • CPU: Central processing unit
  • CSS: Cascading style sheets
  • DOM: Document object model
  • e5Learning: Enhanced exploitation of eyes for effective e-learning
  • ESA: Eye tracking based emphatic software agen
  • GB: Gigabyte
  • GHz: Gigahertz
  • HTML: Hyper Text Markup Language
  • HTTP: Hyper text transfer protocol
  • kBps: Kilobytes per second
  • LMS: Learning management system
  • MB: Megabyte
  • MBps: Megabyte per second
  • MVC: Model-view controller
  • MVT: Model-view template
  • NPM: Node package manager
  • OGAMA: Open gaze and mouse analyzer
  • POI: Point of interest.
  • Protus: Programming tutoring system
  • RAM: Random access memory
  • ROI: Region of interest
  • UI: User interface
  • XML: Extensible Markup Language

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Acknowledgements

The authors would like to express their deepest gratitude to Otgontsetseg Sukhbaatar, Lodoiravsal Choimaa, and their students for participating in the mouse tracking quiz session. This manuscript has been proofread by Springer Nature Author Services.

Funding

Part of this work was supported by JSPS KAKENHI Grant-in-Aid for Scientific Research 19K1225100 and 15H02795.

Author information

Fajar Purnama and Tsuyoshi Usagawa are contributed equally to this work

Affiliations

Graduate School of Science and Technology, Kumamoto University, 2 Chome-39-1, Kurokami, Chuo Ward, Kumamoto, 860-8555, Japan Fajar Purnama & Tsuyoshi Usagawa

Contributions

Both the authors have participated since the beginning of the work. The main author wrote the manuscript, and the co-author revised the manuscript. Both authors read and approved the final manuscript.

Authors’ information

FP is currently a doctoral student at Graduate School of Science and Technology, Kumamoto University. His research area is in the field of educational technology. TU joined Kumamoto University in 1983 right after he received an M.E. degree from Tohoku University. In 1988, he received a Dr. Eng. from Tohoku University. Since 2004 he has been a professor, and he is a member of the IEEE, ASA, ASJ, INCE/J, JSET, and JSAI. He is interested in e-learning contents and systems, and acoustic signal processing.

Corresponding author

Correspondence to Fajar Purnama.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Additional information

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