Development of Input Libraries With Intel XLSDK to Capture Data for App Start Prediction

Using Intel's System Usage Reporting library and the accompanying XLSDK, we can create "monitors" of user and computer activity, known as collectors or Input Libraries, and create activity logs which we can then analyze to provide preloading solutions for the user. We have focused on the creation of these input libraries to have data to use in future collaboration with Intel, in which we would use the data we collected for analysis. We developed four different Input Libraries, each using a different template and measuring different categories of inputs from the user and computer. The first, the mouse input Input Library, keeps a log of the cursor coordinates as the user moves the mouse around. Second, the user waiting Input Library keeps a timer based log of the cursor icon as the user uses the computer. The third, a foreground window Input Library, creates a log entry whenever the foreground window (the window in front of all other windows) changes whether it be automatically (such as a notification pop up) or by user input (clicking the taskbar). Finally, the fourth Input Library is the desktop mapper, which, when triggered by a change in the foreground window, maps all the windows on the desktop in z-order and stores pertinent information about each window e.g. position and size. Each of these Input Libraries are coded differently in fundamental ways, and measure changes in different ways as well. By using the data provided by Libraries like these, we can determine preloading schedules for the individual user

INTELlinext: A Fully Integrated LSTM and HMM-Based Solution for Next-App Prediction With Intel SUR SDK Data Collection

International audienceAs the power of modern computing devices increases, so too do user expectations for them. Despite advancements in technology, computer users are often faced with the dreaded spinning icon waiting for an application to load. Building upon our previous work developing data collectors with the Intel System Usage Reporting (SUR) SDK, we introduce INTELlinext, a comprehensive solution for next-app prediction for application preload to improve perceived system fluidity. We develop a Hidden Markov Model (HMM) for prediction of the k most likely next apps, achieving an accuracy of 70% when k = 3. We then implement a long short-term memory (LSTM) model to predict the total duration that applications will be used. After hyperparameter optimization leading to an optimal lookback value of 5 previous applications, we are able to predict the usage time of a given application with a mean absolute error of ~45 seconds. Our work constitutes a promising comprehensive application preload solution with data collection based on the Intel SUR SDK and prediction with machine learning

INTELlinext: A Fully Integrated LSTM and HMM-Based Solution for Next-App Prediction With Intel SUR SDK Data Collection

International audienceAs the power of modern computing devices increases, so too do user expectations for them. Despite advancements in technology, computer users are often faced with the dreaded spinning icon waiting for an application to load. Building upon our previous work developing data collectors with the Intel System Usage Reporting (SUR) SDK, we introduce INTELlinext, a comprehensive solution for next-app prediction for application preload to improve perceived system fluidity. We develop a Hidden Markov Model (HMM) for prediction of the k most likely next apps, achieving an accuracy of 70% when k = 3. We then implement a long short-term memory (LSTM) model to predict the total duration that applications will be used. After hyperparameter optimization leading to an optimal lookback value of 5 previous applications, we are able to predict the usage time of a given application with a mean absolute error of ~45 seconds. Our work constitutes a promising comprehensive application preload solution with data collection based on the Intel SUR SDK and prediction with machine learning

Development of Input Libraries With Intel XLSDK to Capture Data for App Start Prediction

Using Intel's System Usage Reporting library and the accompanying XLSDK, we can create "monitors" of user and computer activity, known as collectors or Input Libraries, and create activity logs which we can then analyze to provide preloading solutions for the user. We have focused on the creation of these input libraries to have data to use in future collaboration with Intel, in which we would use the data we collected for analysis. We developed four different Input Libraries, each using a different template and measuring different categories of inputs from the user and computer. The first, the mouse input Input Library, keeps a log of the cursor coordinates as the user moves the mouse around. Second, the user waiting Input Library keeps a timer based log of the cursor icon as the user uses the computer. The third, a foreground window Input Library, creates a log entry whenever the foreground window (the window in front of all other windows) changes whether it be automatically (such as a notification pop up) or by user input (clicking the taskbar). Finally, the fourth Input Library is the desktop mapper, which, when triggered by a change in the foreground window, maps all the windows on the desktop in z-order and stores pertinent information about each window e.g. position and size. Each of these Input Libraries are coded differently in fundamental ways, and measure changes in different ways as well. By using the data provided by Libraries like these, we can determine preloading schedules for the individual user