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About 10,000 papers for “User analysis”

User Needs Analysis

Tom Brinck, Darren Gergle, Scott D. Wood · Elsevier eBooks · 2002

Cited by 11

User Needs Analysis

Mike Hobday, Howard Rush · Industry and Higher Education · 1997

This paper presents the ‘user needs analysis' – a simple tool designed to build trust and confidence between industrial managers and academic researchers involved in long-term collaborative science research. The authors provide a step-by-step explanation of how the tool has been implemented in three large organizations currently participating in a three-year project concerned with innovation management. The paper shows how multiple needs can be identified and the benefits which can acrue to both sides in the collaboration.

Cited by 2

User Interface Analysis

Roman Říha · Digital Repository (National Repository of Grey Literature) · 2013

Graphical user interfaces (GUIs) are ubiquitous and many programs use them as the only way to communicate with users. GUIs are often quite different in various applications, especially in the way they behave and how they are created. The goal of this work is to create a program for the Windows platform which will be able to get a description of various GUIs using the standard WinAPI using some heuristic approaches or via a human intervention. The program will be written with an emphasis on generality, extensibility and speed. Powered by TCPDF (www.tcpdf.org)

Cited by 0

User Activity Analysis

Eve Astrid Andersson, Philip Greenspun, Andrew Grumet · 2006

This chapter contains sections titled: Step 1: Ask the Right Questions, Step 2: Look at What's Easily Available, Step 3: Figure Out What Extra Information You Need to Record, Exercise 1: See How the Other Half Lives, Security Risks of Running Programs in Response to a Web Request, Exercise 2: Comedy of Errors, Real-Time Error Notifications, Exercise 3: Talk to Your Client, Exercise 4: Design a Data Warehouse, More, Time and Motion

Cited by 0

User journey games: automating user-centric analysis

Paul Kobialka, Silvia Lizeth Tapia Tarifa, Gunnar R. Bergersen et al. · Software & Systems Modeling · 2024

Abstract The servitization of business is moving industry to business models driven by customer demand. Customer satisfaction is connected with financial rewards, forcing companies to invest in their users’ experience. User journeys describe how users maneuver through a service. Today, user journeys are typically modeled graphically, and lack formalization and analysis support. This paper proposes a formalization of user journeys as weighted games between the user and the service provider and a systematic data-driven method to derive these user journey games from system logs, using process mining techniques. As the derived games may contain cycles, we define an algorithm to transform user journeys games with cycles into acyclic weighted games, which can be model checked using "Image missing" to uncover potential challenges in a company’s interactions with its users and derive company strategies to guide users through their journeys. Finally, we propose a user journey sliding-window analysis to detect changes in the user journey over time by model checking a sequence of generated games. Our analysis pipeline has been evaluated on an industrial case study; it revealed design challenges within the studied service and could be used to derive actionable recommendations for improvement.

Cited by 7Open access

User Journey Games: Automating User-Centric Analysis

Kobialka, Paul, Tapia Tarifa, S. Lizeth, Bergersen, Gunnar R. et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

Dataset of the industry case studies as described in the README, this version is the journal extension of the previous conference publication.Case_study.ipynb shows how to use the data, the outputs are included in the github.Detailed descriptions of files and the data can be found in the README.

Cited by 0Open access

User Behavior Analysis for User Profile Prediction

Al Qaisi, Islam · RIT Scholar Works (Rochester Institute of Technology) · 2022

The importance of user behavior analysis is becoming increasingly valuable to Software as a Service (SaaS) businesses such as Kpler. Users of big data applications require attention in order not to lose customer loyalty in a competitive business environment. With data analytics techniques, knowledge can be extracted about the user behaviors, and that would be a beneficial exercise to the business when it comes to improving services to the customers whether it’s a tailored customer support, targeted marketing campaigns or enhanced product features. This capstone project is focused on predicting user profiles through their web usage behavior. A data mining approach was adopted to reach the objective of this project, and interesting patterns were derived from the analysis of the usage data. The main profiles of Kpler users are as follows: trader, operator and analyst. There are two different angles to the usage on the platform, Map and Analytics. It was found that the map feature on the platform was the most frequently used by all user profiles due to the richness of the information presented on the map. The analytics feature of the platform is frequently being used by trader and analyst profiles since they are user-friendly and can analyze trends fairly quickly. Because these patterns can change over time, predicting user profiles can become interesting in evaluating their usage. We’ve selected Random Forest as the classification model, which resulted with the following metrics: train accuracy 96.9%, test accuracy 49.5%, precision: 97%, recall 96% and f-1 score 97%. These scores are considered to be a good starting point for a promising future work on this topic for the company.

Cited by 0

User Journey Games: Automating User-Centric Analysis

Kobialka, Paul, Tapia Tarifa, S. Lizeth, Bergersen, Gunnar R. et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

Dataset of the industry case studies as described in the README, this version is the journal extension of the previous conference publication.Case_study.ipynb shows how to use the data, the outputs are included in the github.Detailed descriptions of files and the data can be found in the README.

Cited by 0Open access

User Behavior Analysis For Detecting Compromised User Accounts

Marko Jurisic · Zenodo (CERN European Organization for Nuclear Research) · 2025

This repository contains the experiment and evaluation source code for the PhD thesis User Behavior Analysis For Detecting Compromised User Accounts by Marko Jurisic

Cited by 0Open access

IPTV: User Behavior Analysis

Bo Xiao, Jinyao Yan, Xiao Guo et al. · 2009

Through anatomization on Internet Protocol Television (IPTV) users' behaviors, the paper targets at finding existing problems in the service of IPTV, giving suggestions to current IPTV operators to help them find out the correct direction for future marketing strategies. Refer to some theory of communication and based on the analysis of information collected from data compilation and our interviews with experts, the results reveal the reasons that why more and more people subscribe IPTV and what the existing problems are in the current development of IPTV. In addition, we give advises on how to solve these problems. As for the future of IPTV, from the above-mentioned analysis, we conclude the IPTV development strategies and development emphasis.

Cited by 6

Interactive User Group Analysis

Behrooz Omidvar-Tehrani, Sihem Amer-Yahia, Alexandre Termier · 2015

User data is becoming increasingly available in multiple domains ranging from phone usage traces to data on the social Web. The analysis of user data is appealing to scientists who work on population studies, recommendations, and large-scale data analytics. We argue for the need for an interactive analysis to understand the multiple facets of user data and address different analytics scenarios. Since user data is often sparse and noisy, we propose to produce labeled groups that describe users with common properties and develop IUGA, an interactive framework based on group discovery primitives to explore the user space. At each step of IUGA, an analyst visualizes group members and may take an action on the group (add/remove members) and choose an operation (exploit/explore) to discover more groups and hence more users. Each discovery operation results in k most relevant and diverse groups. We formulate group exploitation and exploration as optimization problems and devise greedy algorithms to enable efficient group discovery. Finally, we design a principled validation methodology and run extensive experiments that validate the effectiveness of IUGA on large datasets for different user space analysis scenarios.

Cited by 31

MATHCAD analysis User manual

Egler, J. (PC Software, Hilden (Germany, F.R.)) · OpenGrey (Institut de l'Information Scientifique et Technique) · 1990

Cited by 0

Neuroimaging Analysis Kit - user

Bellec Pierre · Figshare · 2013

The user's guide of the release 0.6.4.1 of the Neuroimaging Analysis Kit (NIAK). This software includes multiple processing pipelines, mainly targeted at functional magnetic resonance imaging.

Cited by 0Open access

Netflix User Engagement Analysis

Margaret Mary T, G Prathap, Archita Tanu et al. · 2025

Netflix has transformed viewing by pioneering streaming technology that deeply engages users, driven in part by its recommendation system aligning content with user interests. This research explores engagement drivers such as personalized recommendations, content variety, viewing behavior, and economic models. Netflix enhances user experience, retention, and reduces churn through machine learning. The article examines binge-watching culture, multi-device access, A/B testing for content optimization, and addresses ethical concerns like data privacy and algorithmic fairness, while highlighting emerging trends in content personalization shaping the future of streaming.

Cited by 0Open access

Conducting User Needs Analysis

GR De La Flor · Bristol Research (University of Bristol) · 2005

This session presents an overview of techniques used to build an understanding what user want from the systems you are building. Users can give you insight into the types of functionality, content, site structure and interface design. An overview of requirements gathering techniques will be presented. Including tips on one-to-one interviewing, observation of current practices, facilitating focus groups and developing questionaires.

Cited by 0