Evaluation of linkage-based web discovery systems

In recent years, the widespread use of the WWW has brought information retrieval systems into the homes o f many millions people. Today, we have access to many billions o f documents (web pages) and have (free-of-charge) access to powerful, fast and highly efficient search facilities over these documents provided by search engines such as Google. The "first generation" of web search engines addressed the engineering problems o f web spidering and efficient searching for large numbers o f both users and documents, but they did not innovate much in the approaches taken to searching. Recently, however, linkage analysis has been incorporated into search engine ranking strategies. Anecdotally, linkage analysis appears to have improved retrieval effectiveness o f web search, yet there is little scientific evidence in support o f the claims for better quality retrieval, which is surprising. Participants in the three most recent TREC conferences (1999, 2000 and 2001) have been invited to perform benchmarking o f information retrieval systems on web data and have had the option o f using linkage information as part of their retrieval strategies. The general consensus from the experiments of these participants is that linkage information has not yet been successfully incorporated into conventional retrieval strategies. In this thesis, we present our research into the field o f linkage-based retrieval of web documents. We illustrate that (moderate) improvements in retrieval performance is possible if the undedying test collection contains a higher link density than the test collections used in the three most recent TREC conferences. We examine the linkage structure o f live data from the WWW and coupled with our findings from crawling sections o f the WWW we present a list o f five requirements for a test collection which is to faithfully support experiments into linkage-based retrieval o f documents from the WWW. We also present some o f our own, new, vanants on linkage-based web retrieval and evaluate their performance in comparison to the approaches o f others

3rd EGEE User Forum

We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation research topics and seven performance management methods. Task allocation research areas are resource allocation, load-Balancing, and scheduling. Performance management includes monitoring and control, power and energy management, resource utilization optimization, quality of service management, fault management, virtual machine management, and network management. The study proposes new techniques to enhance cloud computing work allocation and performance management. Short-comings in each approach can guide future research. The research's findings on cloud data center task allocation and performance management can assist academics, practitioners, and cloud service providers in optimizing their systems for dependability, cost-effectiveness, and scalability. Innovative methodologies can steer future research to fill gaps in the literature

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Study on the Performance of TCP over 10Gbps High Speed Networks

Internet traffic is expected to grow phenomenally over the next five to ten years. To cope with such large traffic volumes, high-speed networks are expected to scale to capacities of terabits-per-second and beyond. Increasing the role of optics for packet forwarding and transmission inside the high-speed networks seems to be the most promising way to accomplish this capacity scaling. Unfortunately, unlike electronic memory, it remains a formidable challenge to build even a few dozen packets of integrated all-optical buffers. On the other hand, many high-speed networks depend on the TCP/IP protocol for reliability which is typically implemented in software and is sensitive to buffer size. For example, TCP requires a buffer size of bandwidth delay product in switches/routers to maintain nearly 100\% link utilization. Otherwise, the performance will be much downgraded. But such large buffer will challenge hardware design and power consumption, and will generate queuing delay and jitter which again cause problems. Therefore, improve TCP performance over tiny buffered high-speed networks is a top priority. This dissertation studies the TCP performance in 10Gbps high-speed networks. First, a 10Gbps reconfigurable optical networking testbed is developed as a research environment. Second, a 10Gbps traffic sniffing tool is developed for measuring and analyzing TCP performance. New expressions for evaluating TCP loss synchronization are presented by carefully examining the congestion events of TCP. Based on observation, two basic reasons that cause performance problems are studied. We find that minimize TCP loss synchronization and reduce flow burstiness impact are critical keys to improve TCP performance in tiny buffered networks. Finally, we present a new TCP protocol called Multi-Channel TCP and a new congestion control algorithm called Desynchronized Multi-Channel TCP (DMCTCP). Our algorithm implementation takes advantage of a potential parallelism from the Multi-Path TCP in Linux. Over an emulated 10Gbps network ruled by routers with only a few dozen packets of buffers, our experimental results confirm that bottleneck link utilization can be much better improved by DMCTCP than by many other TCP variants. Our study is a new step towards the deployment of optical packet switching/routing networks

Elicitation and management of user requirements in market-driven software development

Market-driven software development companies experience challenges in requirements management that many traditional requirements engineering methods and techniques do not acknowledge. Large markets, limited contact with end users, and strong competition forces the market-driven software development company to constantly invent new, selling requirements, frequently release new versions with an accompanying pressure of short time-to-market, and take both the technical and financial risks of development. This thesis presents empirical results from case studies in requirements elicitation and management at a software development company. The results include techniques to explore, understand, and handle bottlenecks in the requirements process where requirements continuously arrive at a high rate from many different stakeholders. Through simulation of the requirements process, potential bottlenecks are identified at an early stage, and fruitless improvement attempts may be avoided. Several techniques are evaluated and recommended to support the market-driven organisation in order to increase software quality and avoid process overload situations. It is shown that a quick and uncomplicated in-house usability evaluation technique, an improved heuristic evaluation, may be adequate to get closer to customer satisfaction. Since needs and opportunities differ between markets, a distributed prioritisation technique is suggested that will help the organisation to pick the most cost-beneficial and customer satisfying requirements for development. Finally, a technique based on automated natural language analysis is investigated with the aim to help resolve congestion in the requirements engineering process, yet retaining ideas that may bring a competitive advantage