Data Mining - Concepts and Techniques.

Being able to identify the groups of clients that are responsible for a significant portion of a Web site's requests can be helpful to both the Web site and the clients. In a Web application, it is beneficial to move content closer to groups of clients that are responsible for large subsets of requests to an origin server. We introduce clusters---a grouping of clients that are close together topologically and likely to be under common administrative control. We identify clusters using a ``network-aware" method, based on information available from BGP routing table snapshots.

On Network-Aware Clustering of Web Clients

https://www2.econ.iastate.edu/tesfatsi/ACEHistoricalSurvey.SHCheng2011.pdf

Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective

The Mizar system is one of the pioneering systems aimed at supporting mathematical proof development on a computer that have laid the groundwork for and eventually have evolved into modern interactive proof assistants. We claim that an important milestone in the development of these systems was the creation of organized libraries accumulating all previously available formalized knowledge in such a way that new works could effectively re-use all previously collected notions. In the case of Mizar, the turning point of its development was the decision to start building the Mizar Mathematical Library as a centrally-managed knowledge base maintained together with the formalization language and the verification system. In this paper we show the process of forming this library, the evolution of its design principles, and also present some data showing its current use with the modern version of the Mizar proof checker, but also as a rich corpus of semantically linked mathematical data in various areas including web-based and natural language proof presentation, maths education, and machine learning based automated theorem proving.

/pdf/the-role-of-the-mizar-mathematical-library-for-interactive-3ko53mzzjl.pdf

The Role of the Mizar Mathematical Library for Interactive Proof Development in Mizar

Inductive transfer learning has attracted increasing attention for the training of effective model in the target domain by leveraging the information in the source domain. However, most transfer learning methods are developed for a specific model, such as the commonly used support vector machine, which makes the methods applicable only to the adopted models. In this regard, the generalized hidden-mapping ridge regression (GHRR) method is introduced in order to train various types of classical intelligence models, including neural networks, fuzzy logical systems and kernel methods. Furthermore, the knowledge-leverage based transfer learning mechanism is integrated with GHRR to realize the inductive transfer learning method called transfer GHRR (TGHRR). Since the information from the induced knowledge is much clearer and more concise than that from the data in the source domain, it is more convenient to control and balance the similarity and difference of data distributions between the source and target domains. The proposed GHRR and TGHRR algorithms have been evaluated experimentally by performing regression and classification on synthetic and real world datasets. The results demonstrate that the performance of TGHRR is competitive with or even superior to existing state-of-the-art inductive transfer learning algorithms.

Generalized Hidden-Mapping Ridge Regression, Knowledge-Leveraged Inductive Transfer Learning for Neural Networks, Fuzzy Systems and Kernel Methods

New Frontiers in Applied Artificial Intelligence

This paper presents a novel algorithm for distribution of user requests sent to a Web-server cluster driven by a Web switch. Our algorithm called FARD (fuzzy adaptive request distribution) is a client-and-server-aware, dynamic and adaptive dispatching policy. It assigns each incoming request to the server with the least expected response time, estimated for that individual request. To estimate the expected response times FARD uses the fuzzy estimation mechanism. With respect to the requirement of modifiability of the model, FARD uses a neural network provided with innate abilities for learning and adaptation. We implemented a prototype FARD-based Web switch that was used in experiments carried out to compare its performance to well known representative request distribution algorithms. The measurements show that FARD benefits can be significant, especially for heterogeneous Web clusters.

A fuzzy adaptive request distribution algorithm for cluster-based Web systems

When browsing the Web, users demand the low latency, high throughput and high availability of Web pages. Therefore each Web site should be measured, tested and evaluated from the user's perspective. In the paper, we present the architecture of the system MWING that is able to test, measure and diagnose Web sites from the user point of view. It is a new generation system that adds new functions and extends the functionality and performance of the previously developed WING tool. MWING is now the multiagent measurement platform able to cooperate with independently developed measurement agents. Here we present how the MWING works and how the new architecture meets the ultimate user needs.

MWING: A Multiagent System for Web Site Measurements

In this article we present the application of data mining (DM) to web performance forecasting. The main emphasis is to present the experimental design and data preparation steps of web performance forecasting based on web performance mining (WPM) method and developed in a wide area network using a multi-agent monitoring system MWING.

Leszek Borzemski

Papers

New Frontiers in Applied Artificial Intelligence

A fuzzy adaptive request distribution algorithm for cluster-based Web systems

MWING: A Multiagent System for Web Site Measurements

The experimental design for data mining to discover web performance issues in a wide area network

Information systems architecture and technology