The Analysis of Portals Considering Mobile Clients

2014 ◽  
Vol 18 (3) ◽  
pp. 306-310
Author(s):  
Gergely Kocsis ◽  
◽  
Péter Ekler ◽  
István Albert
Keyword(s):  
System Design ◽  
Web Sites ◽  
User Behavior ◽  
Web Analytics ◽  
Design Decisions ◽  
Performance Bottlenecks ◽  
System Failures ◽  
Business Analysis ◽  
Mobile Clients

Web analytics are used to retrieve anonymous information about users. We focus here on websites that support mobile clients. This information is important from the perspective of business analysis as web analytics help in making appropriate design decisions. Popular web sites may handle several million page views a day, so poor system design – even that related only to collecting statistics on user behavior – may produce performance bottlenecks or even system failures. This paper presents measurements based on a userdata database for a large mobile supported website and a model used when designing such sites.

Personalization Systems and Their Deployment as Web Site Interface Design

2011 ◽  
pp. 147-155
Author(s):  
Nanda Kumar
Keyword(s):  
Human Computer Interaction ◽  
Web Sites ◽  
Interface Design ◽  
Web Site ◽  
Practical Relevance ◽  
Direct Impact ◽  
Design Decisions ◽  
Communication Capability ◽  
The Impact ◽  
The Web

This chapter reviews the different types of personalization systems commonly employed by Web sites and argues that their deployment as Web site interface design decisions may have as big an impact as the personalization systems themselves. To accomplish this, this chapter makes a case for treating Human-Computer Interaction (HCI) issues seriously. It also argues that Web site interface design decisions made by organizations, such as the type and level of personalization employed by a Web site, have a direct impact on the communication capability of that Web site. This chapter also explores the impact of the deployment of personalization systems on users’ loyalty towards the Web site, thus underscoring the practical relevance of these design decisions.

Personalization Systems and Their Deployment as Web Site Interface Design Decisions

2009 ◽  
pp. 212-219
Author(s):  
Nanda Kumar
Keyword(s):  
Human Computer Interaction ◽  
Web Sites ◽  
Interface Design ◽  
Web Site ◽  
Practical Relevance ◽  
Direct Impact ◽  
Design Decisions ◽  
Communication Capability ◽  
The Impact ◽  
The Web

This chapter reviews the different types of personalization systems commonly employed by Web sites and argues that their deployment as Web site interface design decisions may have as big an impact as the personalization systems themselves. To accomplish this, this chapter makes a case for treating Human-Computer Interaction (HCI) issues seriously. It also argues that Web site interface design decisions made by organizations, such as the type and level of personalization employed by a Web site, have a direct impact on the communication capability of that Web site. This chapter also explores the impact of the deployment of personalization systems on users’ loyalty towards the Web site, thus underscoring the practical relevance of these design decisions.

Sporting Facebook: A Content Analysis of NCAA Organizational Sport Pages and Big 12 Conference Athletic Department Pages

2011 ◽  
Vol 4 (4) ◽  
pp. 422-444 ◽  
Author(s):  
Laci Wallace ◽  
Jacquelyn Wilson ◽  
Kimberly Miloch
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Web Sites ◽  
Brand Management ◽  
College Athletics ◽  
User Behavior ◽  
Management Tool ◽  
Communication Tools ◽  
College And University ◽  
Use Of Social Media

Social-media Web sites provide a strategic means for college and university athletic departments to build and maintain a strong brand presence when cultivating relationships with Facebook users. The purpose of this study was to examine the use of social media as a brand-management tool in college athletics. Specifically, this study examined the use of Facebook in the NCAA (N = 10) and in the Big 12 Athletic Conference (N = 12) by content posted throughout the 2010–11 season. These Facebook pages were examined to determine how major college sport organizations were using communication tools, types of brand-management factors, and marketing coverage. The data revealed statistically significant differences in content posted by season, type of communication tools, and fan interaction. The results from this content analysis were used to conceptualize branding, marketing, and Facebook user behavior.

Knowing Behavior Helps Insure Models Against Breakage: A Commentary on Kaber’s “Issues in Human–Automation Interaction Modeling”

2017 ◽  
Vol 12 (1) ◽  
pp. 67-69 ◽  
Author(s):  
Evan Byrne
Keyword(s):  
System Design ◽  
System Performance ◽  
User Behavior ◽  
Automated Systems ◽  
Interaction Modeling ◽  
Levels Of Automation

This commentary on Kaber’s review of human–automation interaction (HAI) modeling and levels of automation (LOA) highlights some of the challenges designers of automated systems face as a result of a heterogeneous user base. It advocates for understanding the variability in the intended user base to facilitate decisions on whether to constrain user behavior or the system design to optimize overall system performance and the need to anticipate adaptive user strategies before system deployment. It argues that the predictive efficacy of LOA models depends on the heterogeneity of the user base and an increased understanding of behavior through evaluation of breakdowns in HAI.

scholarly journals An Evaluation Framework for Reputation Management Systems*

2010 ◽  
pp. 282-308 ◽  
Author(s):  
Andrew G. West ◽  
Sampath Kannan ◽  
Insup Lee ◽  
Oleg Sokolsky
Keyword(s):  
User Behavior ◽  
General Purpose ◽  
Evaluation Framework ◽  
Reputation Management ◽  
Design Decisions ◽  
Behavior Simulation ◽  
Initial Belief ◽  
Peer To Peer Networks ◽  
Management Techniques ◽  
Trust Calculation

Reputation management (RM) is employed in distributed and peer-to-peer networks to help users compute a measure of trust in other users based on initial belief, observed behavior, and run-time feedback. These trust values influence how, or with whom, a user will interact. Existing literature on RM focuses primarily on algorithm development, not comparative analysis. To remedy this, the authors propose an evaluation framework based on the trace-simulator paradigm. Trace file generation emulates a variety of network configurations, and particular attention is given to modeling malicious user behavior. Simulation is trace-based and incremental trust calculation techniques are developed to allow experimentation with networks of substantial size. The described framework is available as open source so that researchers can evaluate the effectiveness of other reputation management techniques and/or extend functionality. This chapter reports on the authors’ framework’s design decisions. Their goal being to build a general-purpose simulator, the authors have the opportunity to characterize the breadth of existing RM systems. Further, they demonstrate their tool using two reputation algorithms (EigenTrust and a modified TNA-SL) under varied network conditions. The authors’ analysis permits them to make claims about the algorithms’ comparative merits. They conclude that such systems, assuming their distribution is secure, are highly effective at managing trust, even against adversarial collectives.

A Deep Learning Based Approach to Predict Sequential Design Decisions

2019 ◽  
Author(s):  
Molla Hafizur Rahman ◽  
Charles Xie ◽  
Zhenghui Sha
Keyword(s):  
Markov Chain ◽  
System Design ◽  
Design Process ◽  
Design Space ◽  
Markov Chain Model ◽  
Sequential Design ◽  
Chain Model ◽  
Short Term ◽  
Design Decisions ◽  
Term Memory

Abstract During a design process, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are important to the development of new algorithms embedded with human intelligence to augment computational design. In this paper, we develop a deep learning based approach to model and predict designers’ sequential decisions in a system design context. The core of this approach is an integration of the function-behavior-structure model for design process characterization and the long short term memory unit model for deep leaning. This approach is demonstrated in a solar energy system design case study, and its prediction accuracy is evaluated benchmarked on several commonly used models for sequential design decisions, such as Markov Chain model, Hidden Markov Chain model, and random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to reply on both short-term and long-term memory of past design decisions in guiding their decision making in future design process. Our approach is general to be applied in many other design contexts as long as the sequential design action data is available.

Emergency management system design for accurate data: a cognitive analytics management approach

2021 ◽  
Vol 34 (2) ◽  
pp. 697-717
Author(s):  
Abbas Tarhini ◽  
Puzant Balozain ◽  
F.Jordan Srour
Keyword(s):  
Software Engineering ◽  
Emergency Management ◽  
System Design ◽  
Management Approach ◽  
Content Type ◽  
System Failures ◽  
Emergency Management System ◽  
The Right ◽  
Critical Process

PurposeThis paper uses a cognitive analytics management approach to analyze, understand and solve the problems facing the implementation of information systems and help management do the needed changes to enhance such a critical process; the emergency management system in the health industry is analyzed as a case study.Design/methodology/approachCognitive analytics management (CAM) framework (Osman and Anouz, 2014) is used. Cognitive process: The right questions are asked to understand the behavior of every process and the flow of its corresponding data; critical data variables were identified, guidelines for identifying data sources were set. Analytics process: Techniques of data analytics were applied to the selected data sets, problems were identified in user–system interaction and in the system design. The analysis process helped the management in the management process to make right decisions for the right change.FindingsUsing the CAM framework, the analysis to the Lebanese Red Cross case study identified system user-behavior problems and also system design problems. It identified cases where distributed subsystems are vulnerable to time keeping errors and helped the management make knowledgeable decisions to overcome major obstacles by implementing several changes related to hardware design, software implementation, human resource training, operational and human-technology changes. CAM is a novel and feasible software engineering approach for handling system failures.Originality/valueThe paper uses CAM framework as an approach to overcome system failures and help management do the needed changes to enhance such a critical process. This work contributes to the software engineering literature by introducing CAM as a new agile methodology to be used when dealing with system failures. Furthermore, this study is an action research that validated the CAM theoretical framework in a health emergency context in Lebanon.

Active Constraint Deduction: A Framework for Expert Systems in Mechanical System Design

1987 ◽  
Author(s):  
K. Ishii ◽  
P. Barkan
Keyword(s):  
System Design ◽  
Optimal Solution ◽  
Limiting Factors ◽  
System Specification ◽  
Activity Data ◽  
Active Constraint ◽  
Design Decisions ◽  
Active Constraints ◽  
Monotonicity Analysis

Abstract This paper presents a framework for applying AI techniques to mechanical engineering design. In particular, we focus on the determination of the limiting factors, i.e., the bottlenecks, of system design. We refer to the bottlenecks as active constraints and propose a method called Active Constraint Deduction (ACD.) A Knowledge-base is constructed from the activity-data, each of which includes a constraint predicted to be active, the validity intex that indicates the confidence of the claim, and the justification for the claim. Given a set of design constraints, ACD deduces the candidate active constraints from the system specification. The deduced information is then used in conjunction with monotonicity analysis to determine the actual active constraints on which the design decisions should be based. The use of ACD allows designers to rapidly obtain the optimal solution. We illustrate the proposed method by an example: the system design of coal-fired power generation plants.

Optimization of automated flight data preparation system design process

2021 ◽  
Author(s):  
A.G. Andreev ◽  
S.A. Zhurbin ◽  
G.V. Kazakov ◽  
V.V. Koryanov
Keyword(s):  
System Design ◽  
Design Process ◽  
Methodological Approach ◽  
Data Preparation ◽  
Practical Application ◽  
Design Decisions ◽  
Flight Data ◽  
Upper Level ◽  
Technical Measures

The paper introduces a methodological approach to optimizing the automated flight data preparation system design process by using organizational and technical measures to minimize the number of design errors and miscalculations of an accidental and deliberate nature. The study proposes to build the structure of the system design process according to the principle “from the general to the particular”, based on which the connections between different stages of design are determined in the form of surjective and bijective correspondences. In the diagram of design stages of the automated data preparation system, each lower level of the hierarchy is a decomposition of the elements of the adjacent upper level into more specific elements. The main methods of improving the quality of system design, methods of semantic control of the correctness of the design decisions, and syntactic control of the correctness of the developed documentation for the design of the automated data preparation system are proposed. The main thirteen problems of system design are considered, which, in practical application, will acquire a more specific form and significantly improve the designed system’s quality.

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