A Reinforcement Learning Based Model for Adaptive ServiceQuality Management in E-Commerce Websites

Providing high-quality service to all users is adifficult and inefficient strategy for e-commerce providers,especially when Web servers experience overload condi-tions that cause increased response time and requestrejections, leading to user frustration and reduced revenue.In an e-commerce system, customer Web sessions havediffering values for service providers. These tend to: givepreference to customer Web sessions that are likely tobring more profit by providing better service quality. Thispaper proposes a reinforcement-learning based adaptivee-commerce system model that adapts the service qualitylevel for different Web sessions within the customer’snavigation in order to maximize total profit. The e-com-merce system is considered as an electronic supply chainwhich includes a network of basic e- providers used tosupply e-commerce services for end customers. The learneragent noted as e-commerce supply chain manager(ECSCM) agent allocates a service quality level to thecustomer’s request based on his/her navigation pattern inthe e-commerce Website and selects an optimized combi-nation of service providers to respond to the customer’srequest. To evaluate the proposed model, a multi agentframework composed of three agent types, the ECSCMagent, customer agent (buyer/browser) and service provideragent, is employed. Experimental results show that theproposed model improves total profits through costreduction and revenue enhancement simultaneously andencourages customers to purchase from the Websitethrough service quality adaptation

Autonomic management of virtualized resources in cloud computing

The last five years have witnessed a rapid growth of cloud computing in business, governmental and educational IT deployment. The success of cloud services depends critically on the effective management of virtualized resources. A key requirement of cloud management is the ability to dynamically match resource allocations to actual demands, To this end, we aim to design and implement a cloud resource management mechanism that manages underlying complexity, automates resource provisioning and controls client-perceived quality of service (QoS) while still achieving resource efficiency. The design of an automatic resource management centers on two questions: when to adjust resource allocations and how much to adjust. In a cloud, applications have different definitions on capacity and cloud dynamics makes it difficult to determine a static resource to performance relationship. In this dissertation, we have proposed a generic metric that measures application capacity, designed model-independent and adaptive approaches to manage resources and built a cloud management system scalable to a cluster of machines. To understand web system capacity, we propose to use a metric of productivity index (PI), which is defined as the ratio of yield to cost, to measure the system processing capability online. PI is a generic concept that can be applied to different levels to monitor system progress in order to identify if more capacity is needed. We applied the concept of PI to the problem of overload prevention in multi-tier websites. The overload predictor built on the PI metric shows more accurate and responsive overload prevention compared to conventional approaches. To address the issue of the lack of accurate server model, we propose a model-independent fuzzy control based approach for CPU allocation. For adaptive and stable control performance, we embed the controller with self-tuning output amplification and flexible rule selection. Finally, we build a QoS provisioning framework that supports multi-objective QoS control and service differentiation. Experiments on a virtual cluster with two service classes show the effectiveness of our approach in both performance and power control. To address the problems of complex interplay between resources and process delays in fine-grained multi-resource allocation, we consider capacity management as a decision-making problem and employ reinforcement learning (RL) to optimize the process. The optimization depends on the trial-and-error interactions with the cloud system. In order to improve the initial management performance, we propose a model-based RL algorithm. The neural network based environment model, which is learned from previous management history, generates simulated resource allocations for the RL agent. Experiment results on heterogeneous applications show that our approach makes efficient use of limited interactions and find near optimal resource configurations within 7 steps. Finally, we present a distributed reinforcement learning approach to the cluster-wide cloud resource management. We decompose the cluster-wide resource allocation problem into sub-problems concerning individual VM resource configurations. The cluster-wide allocation is optimized if individual VMs meet their SLA with a high resource utilization. For scalability, we develop an efficient reinforcement learning approach with continuous state space. For adaptability, we use VM low-level runtime statistics to accommodate workload dynamics. Prototyped in a iBalloon system, the distributed learning approach successfully manages 128 VMs on a 16-node close correlated cluster

Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given

Reimagining the Journal Editorial Process: An AI-Augmented Versus an AI-Driven Future

The editorial process at our leading information systems journals has been pivotal in shaping and growing our field. But this process has grown long in the tooth and is increasingly frustrating and challenging its various stakeholders: editors, reviewers, and authors. The sudden and explosive spread of AI tools, including advances in language models, make them a tempting fit in our efforts to ease and advance the editorial process. But we must carefully consider how the goals and methods of AI tools fit with the core purpose of the editorial process. We present a thought experiment exploring the implications of two distinct futures for the information systems powering today’s journal editorial process: an AI-augmented and an AI-driven one. The AI-augmented scenario envisions systems providing algorithmic predictions and recommendations to enhance human decision-making, offering enhanced efficiency while maintaining human judgment and accountability. However, it also requires debate over algorithm transparency, appropriate machine learning methods, and data privacy and security. The AI-driven scenario, meanwhile, imagines a fully autonomous and iterative AI. While potentially even more efficient, this future risks failing to align with academic values and norms, perpetuating data biases, and neglecting the important social bonds and community practices embedded in and strengthened by the human-led editorial process. We consider and contrast the two scenarios in terms of their usefulness and dangers to authors, reviewers, editors, and publishers. We conclude by cautioning against the lure of an AI-driven, metric-focused approach, advocating instead for a future where AI serves as a tool to augment human capacity and strengthen the quality of academic discourse. But more broadly, this thought experiment allows us to distill what the editorial process is about: the building of a premier research community instead of chasing metrics and efficiency. It is up to us to guard these values

Business to business online revenue management.

With the emergence of the Internet, electronic commerce (e-commerce), revenue management and especially applications that combine both are becoming increasingly an area of innovation for service industries. E-commerce has introduced efficiencies across the service chain and it has allowed improvements to take place within and across organizations. Revenue management when combined with ecommerce and done online not only improves resource management but it can be used as a strategic tool to gain competitive advantage. This chapter examines the current approaches and future trends in these very exciting and promising areas

WEB recommendations for E-commerce websites

In this part of the thesis we have investigated how the navigation utilizing web recommendations can be implemented on the e-commerce websites based on integrated data sources. The integrated e-commerce websites are an interesting use case for web recommendations. One of the reasons for this interest is that many modern, large and economically successful e-commerce websites follow the integrated approach. Another reason is that especially in the integrated environment, due to the lack of the pre-defined semantic connections between the data, the web recommendations step forward as means of enabling user navigation. In this chapter we have presented the architecture for the websites based on integrated data sources named EC-Fuice. We have also presented the prototypical implementation of our architecture which serves as a proof-of-concept and investigated the challenges of creating navigation on an integrated website. The following issues were addressed in this part of the thesis: Combination of several state-of-the-art tools and techniques in the fields of databases, data integration, ontology matching and web engineering into one generic architecture for creating integrated websites. Comparative experiments with several techniques for instance matching (also known as record linkage or duplicate detection). Investigation on using the ontology matching to facilitate the instance matching. Comparative experiments with several techniques for ontology matching. Investigations on the instance-based ontology matching and the possibilities for combining instance-based ontology matching with other techniques for ontology matching. Investigation of the possibilities to improve user navigation in the integrated data environment with different types of web recommendations. Review of the related work in the fields of data integration and ontology matching and discussion of the contact points between the research described here and other related projects. The main contributions of the research described in this part of the thesis are the EC-Fuice architecture, the novel method for matching e-commerce ontologies based on combination of instance information and metadata information, the experimental results of ontology and instance matching performed by different matching algorithms and the classification of the types of recommendations which can be used on an integrated e-commerce website

A consumer perspective e-commerce websites evaluation model

Existing website evaluation methods have some weaknesses such as neglecting consumer criteria in their evaluation, being unable to deal with qualitative criteria, and involving complex weight and score calculations. This research aims to develop a hybrid consumer-oriented e-commerce website evaluation model based on the Fuzzy Analytical Hierarchy Process (FAHP) and the Hardmard Method (HM). Four phases were involved in developing the model: requirements identification, empirical study, model construction, and model confirmation. Requirements identification and empirical study were to identify critical web-design criteria and gather online consumers' preferences. Data, collected from 152 Malaysian consumers using online questionnaires, were used to identify critical e-commerce website features and scale of importance. The new evaluation model comprised of three components. First, the consumer evaluation criteria that consist of the important principles considered by consumers; second, the evaluation mechanisms that integrate FAHP and HM consisting of mathematical expressions that handle subjective judgments, new formulas to calculate the weight and score for each criterion; and third, the evaluation procedures consisting of activities that comprise of goal establishment, document preparation, and identification of website performance. The model was examined by six experts and applied to four case studies. The results show that the new model is practical, and appropriate to evaluate e-commerce websites from consumers' perspectives, and is able to calculate weights and scores for qualitative criteria in a simple way. In addition, it is able to assist decision-makers to make decisions in a measured objective way. The model also contributes new knowledge to the software evaluation fiel