- Open Access
Using real-time online preprocessed mouse tracking for lower storage and transmission costs
Journal of Big Data volume 7, Article number: 27 (2020)
Pageview is the most popular webpage analytic metric in all sectors including blogs, business, e-commerce, education, entertainment, research, social media, and technology. To perform deeper analysis, additional methods are required such as mouse tracking, which can help researchers understand online user behavior on a single webpage. However, the geometrical data generated by mouse tracking are extremely large, and qualify as big data. A single swipe on a webpage from left to right can generate a megabyte (MB) of data. Fortunately, the geometrical data of each x and y point of the mouse trail are not always needed. Sometimes, analysts only need the heat map of a certain area or perhaps just a summary of the number of activities that occurred on a webpage. Therefore, recording all geometrical data is sometimes unnecessary. This work introduces preprocessing during real-time and online mouse tracking sessions. The preprocessing that is introduced converts the geometrical data from each x and y point to a region-of-interest concentration, in other words only heat map areas that the analyzer is interested in. Ultimately, the approach used here is able to greatly reduce the storage and transmission cost of real-time online mouse tracking.
We are living in the age of information where information and communication technologies are becoming mainstream . Computers and the Internet have become inseparable from our daily lives. It is now possible to do many things without the need to travel and wait; examples include, long-distance communication through online text, voice and video calls, social interaction via social media, online learning, ecommerce or online shopping, and many forms of entertainments such as online music, online videos, and online games. Most importantly, the Internet has become the primary source of information around the world.
Online analytics has become a major area of interest because it enables the study of users’ online behaviors. These include, users’ attentions and, students’ performance in online learning. Businesses also use online analytics to capture consumers’ interests in ecommerce, web design and evaluation, etc. Most websites today, use traditional web analytics such as page views, hits, and top exit pages . However, for interactional analysis, further metrics are required. For example, traditional web metrics cannot tell where a user is directing his or her attention or how much interaction has occurred on a web page. In other words, pageview can determine what, when, who, and (to a limited extent) where a user is viewing, but it cannot determine which part of the webpage (the more detailed “where”) and how a user is viewing it .
The best method available today for measuring user attention is eye tracking . This method tracks eye ball movements to determine where the user is gazing. The most fundamental aspects of eye movements are fixation and saccade, where fixation is the process of fixing the gaze to a certain point of interest (POI), and saccade is the process of moving the gaze to another POI . This method has been implemented in many fields, mainly in computer science, engineering, education, medicine, and psychology. However today, eye tracking requires expensive and specialized hardware that is not suitable for wide implementation .
Although eye tracking remains a tool for the laboratory, an alternative method has been invented, mouse tracking . Instead of eye movements, mouse tracking tracks mouse movements and other helpful events. The fundamental strategy of mouse tracking is the recording of mouse clicks, mouse movements, and scrolls. Eye and mouse tracking have been implemented in the fields of education [8, 9], reading patterns , search engines , visual navigation , web evaluation and usability [13, 14]. Mouse tracking can be treated either independently  or as a correlation to eye tracking  in other words, as replacement.
The biggest problem with default mouse tracking (as well as eye tracking) is the huge volume of data generated, which can be categorized as big data [17, 18]. This high volume is due to the use of geometrical data where each event that occurs on each point of the webpage is recorded. If the distance between left and right is 1000 pixels, then a swipe from left to right will generate 1000 rows of tables. However, analyst may not need all of the mouse tracking data that is generated. Therefore, this paper proposes preprocessing the data based on the analyst’s needs. The preprocessing in this case determines the region of interest in other words; which area the tracking should capture rather than capturing each point of interest. Furthermore, the preprocessing is conducted not only online, but also in real-time mouse tracking session.
Implementations of eye and mouse tracking
The use of eye  and mouse  tracking began in the early 20th century, and since then, there have already been many laboratory experiments conducted using these technologies. Today, there are many attempted implementations of eye and mouse tracking, but it is unclear how widespread and long-running they are. For eye tracking, there is no chance of implementation outside laboratory unless one of two requirements is met: (1) affordable and mainstream hardware  or (2) optimal usage of web cameras [21, 22] on laptops and/or cameras on smartphones. By contrast, widespread implementation of mouse tracking is already possible because the required hardware is available by default in all computers and smartphones, but the problem is the generation of big data (the same is true of eye tracking as well).
The following are selected attempts at implementation eye tracking:
Eye tracking based emphatic software agent (ESA), an eye tracking software that captures the state of awareness of the learners and responds accordingly .
Enhanced exploitation of eyes for effective e-learning (e5Learning) .
Eye tracking based adaptive and personalized e-Learning Systems (AeLS) .
Eye tracking based Eye tracking based programming tutoring system (Protus) .
The following are selected attempts at implementation mouse tracking:
A mouse tracking web application developed by Zushi et al.  for their own specific learning management system (LMS).
Some commercial and open source software programs are as follows:
Open Gaze and Mouse Analyzer (OGAMA), an open-source software designed to analyze eye and mouse movements in slideshow study designs .
Mousetrap, an integrated, open-source mouse-tracking package on OpenSesame for laboratory experiments .
Known commercial applications: Lucky Orange, Hotjar, Crazy Egg, Fullstory, Ptengine, Heatmap.com, Smartlook, ContentSquare, SessionCam, Seevolution, Capturly, Inspectlet, MouseFlow, Clicktale, and Tamboo .
Mouse tracking in web development
The implementation of mouse tracking is based on DOM events, specifically mouse, touch, and user interface (UI) events which are actions that occurs as a result of the user’s mouse actions or as result of state change of the user interface or elements of a DOM tree . Our previous work  uses jQuery to access the DOM API and receives information that are related to mouse, touch, and UI events. They can be stored into default dynamic variables or in an ArrayBuffer for enhanced performance. The list of events are as following:
Mousedown: when either one of the mouse buttons are pressed (usually left, middle, or right button)
Mouseup: when either pressed mouse buttons are released
Mousemove: when the mouse cursor moves
Mouseleave: when the mouse leaves an element (we only indicate when temporary leaving a webpage)
Mouseenter: when the mouse enters an element (we only indicate when temporary entering a webpage)
Beforeunload: when the webpage almost closes
Scroll: when the webpage scrolls
Touchstart: when a computer device screen is touching
Touchend: when a touch from touchstart is removed
Touchmove: when a touch is moving
Touchcancel: when a touch is interrupted
Resize: when the webpage is zoomed in or out
The information is then processed by adding important labels such as the date of the received information and duration by calculating the difference between the current and previous received events. Finally the information is either stored locally or sent to a server using hyper text transfer protocol (HTTP) post method. Traditionally, the information is transmitted all at once at the end of the session, but in our study , we found that it is better to transmit them in real-time without delay. The difference between offline, regular online, and real-time online mouse tracking is shown in Fig. 2.
Default eye and mouse tracking generates big data
Although eye and mouse tracking are not yet mainstream, rumors spread that due to large amounts of data generated, they could not be widely implemented other than at big corporations such as Google and Microsoft, which have gigantic data centers. University network and server administrators are hesitant to implement tracking technologies because they not only generate massive amounts of data, but also eye and mouse tracking are not replacements for existing systems but rather additions. Huang et al.  performed a mouse tracking experiment on Bing’s search engine and immediately reduced the sampling rate because the data were too large.
Leiva and Huang  believed that a swipe could generate a megabyte of data and the authors further investigated and proved that rumor to be true. While a half-year of Moodle log data with approximately 40 students is only approximately 300 kilobytes (kB) , mouse tracking data and other event data generated by approximately 22 students reaches approximately 100 megabytes (MB) in only 2 h , and that figure will double if eye tracking is included. Imagine how much data would be produced by a university with a large number of students if mouse tracking were running on its website for years.
According to an article by Adekitan et al. , Nigerian University Internet traffic can reach terabytes (TB) in a week and is regarded as big data. The authors’ previous mouse tracking session  also reaches the same level of Internet traffic if over 100 students are present. Other than volume, mouse tracking met the other 5Vs criteria of big data : velocity, the amount of data generated especially in real-time which is explained in further sections, veracity; meaning that data loss may often occur due to limited connectivity, which can lead to inconsistent data; variety, which is discussed in further sections and previous work ; and value, which is discussed in the next paragraph.
It would be wise to start investing in eye and mouse tracking just as big companies today are investing in big data , as the data generated by eye and mouse tracking are valuable. By analyzing big data, interesting information can be derived that gives us the knowledge needed to make optimal decisions . Just as companies study customers’ data to find opportunities to increase their revenues , traders analyze historical trading data and current sentiments to find optimal positions , researchers study optimal prevention, diagnosis, and treatments in Medicare , and planners monitor smart cities , researchers can use eye and mouse tracking to identify online viewers’ attention, behavior, their evaluation of web contents, etc.
Reducing eye and mouse tracking data
During high-intensity activities, a user may generate an average of 70 or 70 events per second , meaning that 70 rows per second will be generated on a table. The traditional way of reducing the size of these tracking data is by reducing the sampling rate . Furthermore, the sampling rate should be adaptive and not static. In other words, snapshots should only be taken when an event occurs such as when the mouse cursor moves or a click occurs, and snapshots should not be taken during idle sessions. Performing transmission in real-time helps distribute the transmission burden across time, avoiding bottlenecks. In other words, the tracking data are immediately transmitted to the server at each event occurrence rather than transmitting the mouse tracking data all at once at the end of the session. Compression methods can also be utilized as demonstrated in Leiva and Huang’s work , but their transmission method is still likely not real-time and is suspected to transmit the compressed tracking data all at once in the end of each session.
On the other hand, the preprocessing technique presented in this paper is designed to work in real-time. Not only does it reduce the data cost but also distributes the transmission burden across the time domain. The cause of the enormous data generation is the geometrical data or tracking of each location where the events occur, in other words the x and y coordinates. Tracking these coordinates provides rich data but sometimes all of that data is not needed. For example, Rodrigues et al.  only analyzed the amount of key up, key down, mouse down, mouse up, mouse wheel, and mouse movement to measure students’ stress, and Li et al.  only needed the time spent on each page. In such cases, the geometrical data can be omitted.
At other times, geometrical data are needed; however, it is not each precise x and y point that is needed but rather each area of the page (multiple points) . Preprocessing is common in any data analysis to derive useful data prior to transmission and storage of the collected data. However, the preprocessing presented in this work is performed on the client before transmission and storage to reduce the server’s burden. Unlike typical preprocessing which is performed to filter redundant data, the preprocessing in this work is specifically based on the demands of the administrators or analyzers; in this case, preprocessing omits the geometrical data or groups them to represent certain areas. This study is a complete work of one the author’s previous works .
Method and simulation
Each web framework may developed their own bindings to access the DOM API. However, the most fundamental implementation is still injecting the mouse tracking code into the script section no matter which web framework is used, which is the default option if the web framework did not developed their own bindings. Below is a list of few web frameworks:
Spring is an application framework and inversion of control container for the Java platform. The framework’s core features can be used by any Java application, but there are extensions for building web applications on top of the Java Enterprise Edition platform. Java is one of the earliest programming language used to make an application and it’s still popular today. Java has its own bindings to connect to the DOM API.
Angular is a complete rewrite to TypeScript based from the same team that built AngularJS. It is a web framework mainly maintained by Google and by a community of individuals and corporations to address many of the challenges encountered in developing single-page applications. It is one of the most popular framework to build web applications on mobile. The mouse events can be added on the components or templates .
Three techniques of mouse tracking
For convenience, the techniques of mouse tracking are divided into three types, as shown in Table 2. They are called default mouse tracking, whole page tracking, and ROI tracking. The default mouse tracking precisely records the geometrical data of the event occurrence such as the horizontal x and vertical y of left clicks, right clicks, middle clicks, mouse movements, scrolls, zooms, and if desired keyboard types. The duration between each event is also measured. Whole page tracking omits the geometrical data and summarizes the number of left clicks, right clicks, middle clicks, mouse movements, scrolls, zooms, and if desired keyboard types that occurred on the webpage. In other words, the amount of activity is measured but not where or when it occurs, and only the total amount of time that the user spends on a webpage is recorded. The most complicated task is ROI tracking, which is a gray area between default mouse tracking and whole page tracking. ROI tracking defines the areas of a webpage to be tracked, for example how many left clicks, right clicks, middle clicks, mouse movements, scrolls, zooms, and keyboard types occurred and how long they occurred on a header, menu, content, footer, etc. This method is ideal because it meets the analyst’s requirements and reduces unnecessary resource costs, but the drawback is the heavy labor required to manually define the areas of each webpage. Automatic area definition is possible to certain degree. One way is by attaching “mouseenter” DOM event listener to every element and using “offset” DOM HTML to return the position of the element. Offset DOM HTML returns the left and top element distance from the outermost of the webpage, and by using “width” and “height” DOM HTML to calculate the element’s size, it is possible to find the bottom and right as well. However, the limitation is that it cannot perform smart labelling where it can only extract attributes, texts, and values of the element. An illustration comparing the three types of mouse tracking is shown in Fig. 4.
The flowchart for each implementation of real-time and online mouse tracking is shown in Fig. 5. For default mouse tracking, the information on the event is transmitted to the database each time an event occurrs. For example, when a click occurs, the client immediately transmits the information on where and when it occurs. For whole page tracking, the information is summarized as the number of events that occurred, and they are transmitted to the server when the client closes the webpage. The size of the transmitted data is only slightly larger than that of transmitting single click data when using default mouse tracking. Last, for ROI tracking, the information on the webpage area is summarized and transmitted after the mouse cursor leaves the area, and the process repeats on each movement between areas until the webpage is closed.
The three mouse tracking method are tested on the client and server. Since the author lacks subjects to perform an implementation, a simulation based on previous mouse tracking data was conducted on the server. The mouse tracking data contain mouse tracking records from two quiz sessions in Moodle. They were conducted on the 3rd of January 2019 between approximately 12:00 and 14:30 Japan standard time. There are 2 sessions, with each session lasting approximately an hour and including 22 students (44 total students participating) from the School of Engineering and Applied Sciences, National University of Mongolia accessing the Moodle server at the Human Interface and Cyber Communication Laboratory, Kumamoto University. The data were preprocessed to exclude nonstudents and webpages other than the quiz page data. In other words, the simulation is purely a mouse tracking data transmission, which excludes most of the process, such as accessing the server and navigating the whole Moodle page. This approach shows lower resource consumption than the previous work .
The setup can be seen in Fig. 6 where a laptop functioning as a client is peer to peer connected to a personal computer functioning as a server. The mouse tracking data are converted into page tracking and ROI tracking data based on Table 2. Three sessions were conducted: the first session was the sending of mouse tracking data to the server, the second session was the sending of page tracking data to the server, and the third session was the sending of ROI tracking data to the server. Since the mouse tracking data contain time interval information between the sending of each event, it is possible to capture the scenario almost exactly.
During these sessions, the data rate is observed, and the central processing unit (CPU) and random access memory (RAM) usages are measured on the server. Figure 6 shows that one laptop serves as a client to send the data to the server which is a personal computer. The client is an MSI Laptop with i7-7820 HK 2.9 gigahertz (GHz) x8 32 gigabyte (GB) RAM while the server is an i7-6850 HK 3.6 GHz x12 32 GB RAM personal computer and the peer to peer connection is a 10 megabyte per second (MBps) network.
Chrome version 80.0.3987.132 that uses V8 Engine.
Firefox version 74.069 that uses SpiderMonkey Engine.
Microsoft Edge version 80.0.361.62 that uses Chakra.
Result and discussion
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.
Webpage summarized mouse tracking
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).
The disadvantage currently is the lack of smart area definition and labelling. The possibility of area definition is restricted to the parent elements. The possibility of labelling is only information available on elements’ attribute, text, and value. To perform custom area definition and labelling, tha analysts must define them manually that requires considerable time and labor.
Conclusion and future work
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 :
Figures 8, 9, 10 and Table 4 are based on files: cpurammousetracking.txt , cpurampagetracking.txt , cpuramroitracking.txt , mousetrackingsimulationpcapng , pagetrackingsimulationpcapng , roitrackingsimulationpcapng .
E-Learning via the eye tracking.AeLSEye tracking based adaptive and personalized e-learning systems.
Application programming interface
Central processing unit
Cascading style sheets
Document object model
Enhanced exploitation of eyes for effective e-learning
Eye tracking based emphatic software agen
Hyper Text Markup Language
Hyper text transfer protocol
Kilobytes per second
Learning management system
Megabyte per second
Node package manager
Open gaze and mouse analyzer
Point of interest.
Programming tutoring system
Random access memory
Region of interest
Extensible Markup Language
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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.
Part of this work was supported by JSPS KAKENHI Grant-in-Aid for Scientific Research 19K1225100 and 15H02795.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Purnama, F., Usagawa, T. Using real-time online preprocessed mouse tracking for lower storage and transmission costs. J Big Data 7, 27 (2020). https://doi.org/10.1186/s40537-020-00304-x
- Mouse tracking
- Storage cost
- Transmission cost
- Geometrical data
- Region of interest
- Big data