Splitting the Web Analytics Atom

Kirsh, Ilan; Joy, Mike

doi:10.1145/3405962.3405984

Cited by 19 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Heat maps of the copied data identified some complex words [8]. These copy to clipboard operations can help identifying the sub-page metrics and acts as an important component for key performance indicators [24]. Complex words can be simplified or a metadata attached could be the action for better understanding of readers [25].…”

Section: A Smartphone As a Pedagogical Toolmentioning

confidence: 99%

“…On the contrary frequently copied sentences can be a positive key performance indicator of a valid content and can be further utilized for functionality like -automatic text summarization [2], recommendations, etc. This data can be used to understand the users interest and also for Search engine optimization [3,24].…”

Section: A Smartphone As a Pedagogical Toolmentioning

confidence: 99%

“…Most researchers have captured feedback using surveys, as an explicit method to understand the quality of consumed content. Implicit methods of determining the quality of the content still remains unexplored [19,20,21,22,23,24,25].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Based Smartphone Screen Gesture Recognition Using Smartphone Embedded Accelerometer and Gyroscope

Bhatele,

Bedekar

2024

Preprint

View full text Add to dashboard Cite

In the recent times, smartphone usage has become increasingly popular for learning. User’s exhibit multiple gesture interactions with smartphones, while reading, which can provide valuable implicit feedback about the content consumed. Smartphones have many embedded sensors which capture plethora of user interaction data. The on-device Gyroscope and Accelerometer can be enabled to capture the variations done due to gesture interactions like scrolling, pinch to zoom, tap, orientation change and screen capture. This research work is based on training machine learning classifier models with smartphone sensors’ readings to identify the users screen gesture interactions. Data for the classifier is collected by from 47 users in total using an android application. Aggregated time domain feature extraction has been computed on the preprocessed data. Four groups of data have been used to train the models. Extensive experiments are done to test the success of proposed system using Random Forests, Support Vector Machine (SVM), Extreme Gradient Boost (XGB), ADA boost, Naïve Bayes (NB) and K-Nearest Neighbour (KNN). Detailed analysis of the success rate and accuracy calculation have been performed. Best identification accuracy of 97.58% is achieved by Random Forest Classifier followed by Extreme Gradient Boost and K-Nearest Neighbour with accuracy 95.97% and 93.55% respectively.

show abstract

Section: A Smartphone As a Pedagogical Toolmentioning

confidence: 99%

Section: A Smartphone As a Pedagogical Toolmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Based Smartphone Screen Gesture Recognition Using Smartphone Embedded Accelerometer and Gyroscope

Bhatele,

Bedekar

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…However, a significant barrier to using web analytics is that it has raised concerns about user privacy on the internet, and this has resulted in anti-tracking tools, which degrades the quality of the data. [17][18][19][20][21][22][23] 1.…”

Section: Web Analytics Web Analytics and Big Datamentioning

confidence: 99%

Exploring the Role of Online Courses in COVID-19 Crisis Management in the Supply Chain Sector—Forecasting Using Fuzzy Cognitive Map (FCM) Models

Nasiopoulos,

Arvanitidis,

Mastrakoulis

et al. 2023

Forecasting

View full text Add to dashboard Cite

Globalization has gotten increasingly intense in recent years, necessitating accurate forecasting. Traditional supply chains have evolved into transnational networks that grow with time, becoming more vulnerable. These dangers have the potential to disrupt the flow of goods or several planned actions. For this reason, increased resilience against various types of risks that threaten the viability of an organization is of major importance. One of the ways to determine the magnitude of the risk an organization runs is to measure how popular it is with the buying public. Although risk is impossible to eliminate, effective forecasting and supply chain risk management can help businesses identify, assess, and reduce it. As a result, good supply chain risk management, including forecasting, is critical for every company. To measure the popularity of an organization, there are some discrete values (bounce rate, global ranking, organic traffic, non-branded traffic, branded traffic), known as KPIs. Below are some hypotheses that affect these values and a model for the way in which these values interact with each other. As a result of the research, it is clear how important it is for an organization to increase its popularity, to increase promotion in the shareholder community, and to be in a position to be able to predict its future requirements.

show abstract

“…Key performance indicators are web analytics that have been extracted from the examined websites and have been utilized quantitatively [50]. The WA KPIs are divided into two categories; on the one hand, there are the technical factors which include "Fully Loaded Time", Total Page Size", and website's "Requests", and on the other hand, the behavioral KPIs, such as "Organic Traffic", "Average Duration", and "Pages per Visits" [4,51,52]. The authors extracted the behavioral KPIs from the SEMrush and Alexa platforms, and the technical factors were extracted from the GTmetrix platform.…”

Section: Web Analytics Key Performance Indicators (Kpis) and Corporat...mentioning

confidence: 99%

The Effects of Logistics Websites’ Technical Factors on the Optimization of Digital Marketing Strategies and Corporate Brand Name

et al. 2022

View full text Add to dashboard Cite

In a world overwhelmed with unstructured information, logistics companies increasingly depend on their websites to acquire new customers and maintain existing ones. Following this rationale, a series of technical elements may set the ground for differentiating one logistics website from another. Nevertheless, a suitable digital marketing strategy should be adopted in order to build competitive advantage. In this paper, the authors attempt to respond by implementing an innovative methodology building on web analytics and big data. The first phase of the research collects data for 180 days from 7 world-leading logistics companies. The second phase presents the statistical analysis of the gathered data, including regression, correlations, and descriptive statistics. Subsequently, Fuzzy Cognitive Mapping (FCM) was employed to illustrate the cause-and-effect links among the metrics in question. Finally, a predictive simulation model is developed to show the intercorrelation among the metrics studied as well as various optimization strategies. Research findings reveal a significant correlation between the logistics websites’ technical factors and the growth of the corporate brand name.

show abstract

Splitting the Web Analytics Atom

Cited by 19 publications

References 22 publications

Machine Learning Based Smartphone Screen Gesture Recognition Using Smartphone Embedded Accelerometer and Gyroscope

Machine Learning Based Smartphone Screen Gesture Recognition Using Smartphone Embedded Accelerometer and Gyroscope

Exploring the Role of Online Courses in COVID-19 Crisis Management in the Supply Chain Sector—Forecasting Using Fuzzy Cognitive Map (FCM) Models

The Effects of Logistics Websites’ Technical Factors on the Optimization of Digital Marketing Strategies and Corporate Brand Name

Contact Info

Product

Resources

About