Use of Self-Service Query Tools Varies by Experience and Research Knowledge

Psychometric modeling of the pervasive use of Facebook through psychophysiological measures: Stress or optimal experience?

The vision of user experience is making life of users of products as convenient as possible, especially during the interaction with a productor a service. An important aspect of perceived convenience is the user experience of a product. The visual design and especially the interaction design has a major influence on this perception. In order to achieve the vision, user experience experts apply different types of tasks. One type of task is to analyze how users carry out tasks and what user's needs or problems are. Another type of task is to design user experience solutions and other typical task type deals with carrying out usability evaluations in order to find problems in using software application. In the course of user experience activities, many data are being collected. Many of the collected data relates to certain activities of users. For the user experience area there exist just a few tools, which support typical tasks in different ways. None of the tools supports linking results of user experience work to user experience meta-data. Why is it a problem? The current tools do not support an access to user experience project data with generic search and filter criteria like case etc. This makes the access to user experience research data difficult and the comparison of user experience project data between different projects inefficient. In general, results of different user experience projects are difficult to reuse. The core idea of data pattern for allocating user experience Meta-Data to User Experience research data is to associate user experience project data with user experience meta-data. The data pattern considers associating user experience project data with user experience meta-data partially automatically and partially manually by the user. The key idea is that we want to reuse project data like the project sponsor, the application area, the industry, use cases etc. as user experience meta-data and assign them to user experience research data. The benefits of the data pattern are: Reusing results of user experience research projects. Making access to available results more efficient. Direct comparison of available results is supported and more efficient. UX Office is the typical instance for application.

Advances in technology allow for two-way communication in the form of online discussions. One of the online discussion platforms is Quora, which provides a space for users to discuss and exchange information. Analysis of Quora website visitor traffic data shows a decrease of 15.66% in the last quarter of 2024, indicating challenges in maintaining user activity. This study aims to determine the level of usability of the Quora website and the cause of the decline in Quora user activity in terms of user experience using System Usability Scale (SUS) analysis with moderated usability testing and User Experience Questionnaire (UEQ). Sentiment analysis is used to enrich knowledge of the causes of decreased user activity. The data of this research was obtained by distributing questionnaires with 32 samples which are the representation of undergraduate students from Malang City. The results showed that the user experience of the Quora website still needs to be improved. This can be seen in the results of the System Usability Scale (SUS) analysis which gets a value of 54.06 which is below the average of 68. User Experience Questionnaire (UEQ) analysis shows that the perspicuity aspect gets the lowest score of -0.938, indicating the lack of clarity of navigation on the Quora website. Sentiment analysis with machine learning shows 53% of user reviews have negative sentiments towards the user experience of the Quora website. Quora is expected to improve the quality of the system interface to improve the overall user experience.

Cloud logs can be categorized into on-line, off-line, and near-line logs based on the access frequency. Among them, near-line logs are mainly used for debugging, which means they prefer a low query latency for better user experience. Besides, the storage system for near-line logs prefers a low overall cost including the storage cost to store compressed logs, and the computation cost to compress logs and execute queries. These requirements pose challenges to achieving fast and cheap cloud log storage. This article proposes LogGrep, the first log compression and query tool that exploits both static and runtime patterns to properly structurize and organize log data in fine-grained units. The key idea of LogGrep is “vertical partitioning”: it stores each log entry into multiple partitions by first parsing logs into variable vectors according to static patterns and then extracting runtime pattern(s) automatically within each variable vector. Based on such runtime patterns, LogGrep further decomposes the variable vectors into fine-grained units called “Capsules” and stamps each Capsule with a summary of its values. During the query process, LogGrep can avoid decompressing and scanning Capsules that cannot match the keywords, with the help of the extracted runtime patterns and the Capsule stamps. We further show that the interactive debugging can well utilize the advantages of the vertical-partitioning-based method and mitigate its weaknesses as well. To this end, LogGrep integrates incremental locating and partial reconstruction to mitigate the read amplification incurred by vertical-partitioning-based method. We evaluate LogGrep on 37 cloud logs from the production environment of Alibaba Cloud and the public datasets. The results show that LogGrep can reduce the query latency and the overall cost by an order of magnitude compared with state-of-the-art works. Such results have confirmed that it is worthwhile applying a more sophisticated vertical-partitioning-based method to accelerate queries on compressed cloud logs.

The purpose of this research is to offer a technique for assessing user experience in mobile applications utilizing AIAM technology. Due to ineffective and time-consuming nature of conventional data gathering techniques (such as user interviews and user inference), AIAM concentrates on using Artificial Intelligence (AI) to assess and enhance user experience. Logs from a mobile application may be used to gather information about user activity. Only a few parameters of data are utilized in the process of surfing and running mobile applications to ensure the privacy of users. The method's objective is to create the deep neural network prototype as close as feasible to a user's experience when using a mobile app. For particular objectives, we create and employ application interfaces to train computational models. The click data from all users participating in a certain task is shown on these projected pages. User activity may therefore be mapped in connected and hidden layers of the system. Finally, the social communications application is used to test the efficacy of the suggested method by implementing the improved design.

Background and Purpose: In recent years, people pay more and more attention to physical health. As the epidemic continues to affect people’s life and bring certain restrictions to people’s travel, home fitness has become a new trend. Since there is no trainer to participate in home fitness in real time, the standardization, consistency and standard of movements need to be corrected and adjusted for bodybuilders. How to enhance the service experience of bodybuilders in the exercise process of mobile apps? After analyzing the advantages and disadvantages of intelligent fitness products at the present stage, and finding the development direction of intelligent fitness products in the future is the part of this paper. By improving the intelligent fitness product service system to improve users’ fitness experience, aiming at the comparative experience of several fitness apps and discovering problems, the problems and innovation points to be explored at the current stage of fitness APP are explored through user research, and the problems are put into the user journey map to compare the improved APP. To ensure the standardization, consistency and standardization of users’ movements in the process of fitness, and to provide users with various and more perfect home fitness experience and improve the service system of fitness APP.Methods: Users’ behavioral process and experience changes were studied by using the user journey map method, and users’ evaluation in the experience of fitness was analyzed by Using KANO model. The user journey map can accurately locate the state changes of users in the service system, and find out the positioning of intelligent fitness products through psychological state, behavior, contact points and other parts. KANO model can more carefully analyze the functional attributes and user satisfaction of each intelligent fitness product. Through user demand classification and importance analysis, it is found that by comparing the similarities and differences between the two attributes of intelligent fitness products, it can find the attributes that need to be improved and innovative at the present stage. Through two methods to find out the intelligent fitness products service system needs to be improved and the needs of users.Results: According to the user evaluation of a series of intelligent fitness products, users pay special attention to the effectiveness, accuracy, standard movement and the effect of their own fitness. Fitness effectiveness has focused on fitness in the process of interactive, accuracy is mainly aimed at the user action and power whether standard, consistent, to ensure the safety of users fitness effect and fitness, how to through the establishment of intelligent fitness service system is to establish the accuracy of the connection between the product and user interaction process and an important point of perfect service system. Establishing a more perfect user feedback system can improve the quality of service and enhance user fitness experience. Through the investigation of users with different fitness needs, the main problem reflected in the user experience flow chart is how to find the most accurate fitness mode for users, and how to reduce the application time of users to fitness APP to reduce the time cost of fitness standards. Finally, user demand analysis of KANO model and user service process analysis of user journey map are combined to find user demand points and innovation points in the process of fitness service. Not only should we consider the user’s physical strength and attention consumption during the exercise process, but also help users relieve fatigue and establish good fitness habits after the exercise.Conclusion: At present, online fitness has been able to meet the basic needs of users in the fitness process. How to improve the quality of users’ online fitness, increase user engagement, ensure the safety, quality and user experience of the fitness process, and establish a more perfect intelligent fitness service system are the innovation points to be explored in the future intelligent fitness. Through the analysis of user fitness process by KANO model and user journey map, it is found that intelligent fitness products lack interaction with users at the present stage, the problem of mismatching with users’ fitness environment in the process of use, and the lack of consistency of users’ actions and forces. In the future, intelligent fitness products should focus on the establishment of a perfect fitness service system, through virtual reality technology, big data and other new technologies to accurately explore user touch points, improve user fitness experience. It provides some reference for the development of intelligent fitness field.KeywordsUser journey mapThe KANO modelCustomer satisfactionInteraction processIntelligent fitness service system

Live workflow monitoring and the resulting user interaction in industrial settings faces a number of challenges. A formal workflow may be unknown or implicit, data may be sparse and certain isolated actions may be undetectable given current visual feature extraction technology. This paper attempts to address these problems by inducing a structural workflow model from multiple expert demonstrations. When interacting with a naive user, this workflow is combined with spatial and temporal information, under a Bayesian framework, to give appropriate feedback and instruction. Structural information is captured by translating a Markov chain of actions into a simple place/transition petri-net. This novel petri-net structure maintains a continuous record of the current workbench configuration and allows multiple sub-sequences to be monitored without resorting to second order processes. This allows the user to switch between multiple sub-tasks, while still receiving informative feedback from the system. As this model captures the complete workflow, human inspection of safety critical processes and expert annotation of user instructions can be made. Activity classification and user instruction results show a significant on-line performance improvement when compared to the existing Hidden Markov Model or pLSA based state of the art. Further analysis reveals that the majority of our model's classification errors are caused by small de-synchronisation events rather than significant workflow deviations. We conclude with a discussion of the generalisability of the induced place/transition petri-net to other activity recognition tasks and summarise the developments of this model.

BackgroundUser experience and engagement are critical elements of mental health apps’ abilities to support users. However, work examining the relationships among user experience, engagement, and popularity has been limited. Understanding how user experience relates to engagement with and popularity of mental health apps can demonstrate the relationship between subjective and objective measures of app use. In turn, this may inform efforts to develop more effective and appealing mental health apps and ensure that they reach wide audiences.ObjectiveWe aimed to examine the relationship among subjective measures of user experience, objective measures of popularity, and engagement in mental health apps.MethodsWe conducted a preregistered secondary data analysis in a sample of 56 mental health apps. To measure user experience, we used expert ratings on the Mobile App Rating Scale (MARS) and consumer ratings from the Apple App Store and Google Play. To measure engagement, we acquired estimates of monthly active users (MAU) and user retention. To measure app popularity, we used download count, total app revenue, and MAU again.ResultsMARS total score was moderately positively correlated with app-level revenue (Kendall rank [T]=0.30, P=.002), MAU (T=0.39, P<.001), and downloads (T=0.41, P<.001). However, the MARS total score and each of its subscales (Engagement, Functionality, Aesthetics, and Information) showed extremely small correlations with user retention 1, 7, and 30 days after downloading. Furthermore, the total MARS score only correlated with app store rating at T=0.12, which, at P=.20, did not meet our threshold for significance.ConclusionsMore popular mental health apps receive better ratings of user experience than less popular ones. However, user experience does not predict sustained engagement with mental health apps. Thus, mental health app developers and evaluators need to better understand user experience and engagement, as well as to define sustained engagement, what leads to it, and how to create products that achieve it. This understanding might be supported by better collaboration between industry and academic teams to advance a science of engagement.

Modelling user experience with web sites: Usability, hedonic value, beauty and goodness

Non-formal learning serves as an alternative education that plays a role in assisting schools and communities in addressing issues in the field of education. Kuncie's website, which focuses on non-formal education, specializes in online business learning and mentoring. Several factors that can influence user experience include internet connection quality, user interface, technical support availability, interaction and engagement, and learning personalization. This study aims to assess the user experience of the Kuncie website before and after design improvements, with the goal of enhancing the overall user experience. The research employs a qualitative method, specifically a case study, with in-depth qualitative data collection using heuristic methods by experts. The evaluation of user experience identified 101 issues with 29 priority improvements grouped into 10 heuristic principles. The priority for improvements is based on severity levels 4 and 3. A new prototype mockup design was created based on the expert recommendations, summarized into 29 solutions. Subsequently, a validation stage (iterative prototyping) was conducted with experts and active users ofKuncie.The validation results showed 23 solutions with an "agree" status and 6 solutions with an "agree with notes" status.Through user interview testing, all Kuncie users expressed that the new design has a more elegant appearance and a login flow that is easier to understand.

Editor's SummaryIn his 15 years of teaching information architecture classes, Thom Haller's aim for students was to identify their goals and help them work toward them. His 10‐week courses focused on useful knowledge about how humans think and use information and how good information architecture can facilitate the process. The courses covered visual thinking, user actions, interconnections between information items and navigating mental models. From the theories and techniques introduced in the course, the most important points would continually surface when the class considered the question, “What sticks?” Weekly and at the end of a course, the students reflected on lessons with lasting value about the field, user experience and the practice of information architecture. In recent discussions with Haller, former students reported on enduring principles that stuck, resonating through their daily work. They agreed that the core tenet of information architecture — that making things simple and understandable creates the best user experience — is equally true in other domains. The concept is graphically represented by a triangle of connected fingers, symbolizing audience, goals and success metrics around a communication product.

In this research we present a non-invasive Brain-Machine Interface (BMI) system that allows patients with motor paralysis conditions to control electronic appliances in a hospital room. The novelty of our system compared to other BMI applications is that our system gradually becomes autonomous by learning user actions (i.e. turning on/off window, lights, etc.) under certain environment conditions (temperature, illumination, etc.) and brain states (i.e. awake, sleepy, etc.). By providing learning capabilities to the system, patients are relieved from mental fatigue or stress caused by continuously controlling appliances using a BMI. We present an interface that allows the user to select and control appliances using electromyogram signals (EMG) generated by muscle contractions such as eyebrow movement. Our learning approach consists in two steps: 1) monitoring user actions, input data from sensors distributed around the room, and Electroencephalogram (EEG) data from the user, and 2) using an extended version of the Bayes Point Machine approach trained with Expectation Propagation to approximate a posterior probability from previously observed user actions under a similar combination of brain states and environmental conditions. Experimental results with volunteers demonstrate that our system provides satisfactory user experience and achieves over 85% overall learning performance after only a few trials.

You make what you measure is a familiar mantra at data-driven companies. Accordingly, companies must be careful to choose North Star metrics that create a better product. Metrics fall into two general categories: direct count metrics such as total revenue and monthly active users, and nuanced quality metrics regarding value or other aspects of the user experience. Count metrics, when used exclusively as the North Star, might inform product decisions that harm user experience. Therefore, quality metrics play an important role in product development. We present a five-step framework for developing quality metrics using a combination of machine learning and product intuition. Machine learning ensures that the metric accurately captures user experience. Product intuition makes the metric interpretable and actionable. Through a case study of the Endorsements product at LinkedIn, we illustrate the danger of optimizing exclusively for count metrics, and showcase the successful application of our framework toward developing a quality metric. We show how the new quality metric has driven significant improvements toward creating a valuable, user-first product.

We purpose a system that can semi-automatically generate ontology-based annotations for a social network by leveraging the annotations provided by the most active user (also known as the central actor). Social context of an image is of central importance in our approach towards semantic semi-automatic annotation. For the context of an image, we consider several factors like geo-reference, time and relationship among actors in social networks. Our system leverages the context, which is automatically available along with the image. We have also extended Social Network Analysis techniques by considering the granularity of relationships among actors under consideration. In this paper a semantic web framework is used to represent the social network and to deal with the diversity of relationships. OntoCAIM ontology is developed which not only encompasses Social Network Analysis functionality but also defines mechanism to annotate images with an underlying ontology.

Actitracker is a smartphone-based activity-monitoring service to help people ensure they receive sufficient activity to maintain proper health. This free service allowed people to set personal activity goals and monitor their progress toward these goals. Actitracker uses machine learning methods to recognize a user's activities. It initially employs a universal model generated from labeled activity data from a panel of users, but will automatically shift to a much more accurate personalized model once a user completes a simple training phase. Detailed activity reports and statistics are maintained and provided to the user. Actitracker is a research-based system that began in 2011, before fitness trackers like Fitbit were popular, and was deployed for public use from 2012 until 2015, during which period it had 1,000 registered users. This paper describes the Actitracker system, its use of machine learning, and user experiences. While activity recognition has now entered the mainstream, this paper provides insights into applied activity recognition, something that commercial companies rarely share.