Context-aware meta search engine for distributed web service

The ubiquity of the Internet and Web has led to the emergency of several Web search engines with varying capabilities. A weakness of existing search engines is the very extensive amount of hits that they can produce. Moreover, only a small number of web users actually know how to utilize the true power of Web search engines. Therefore, there is a need for searching infrastructure to help ease and guide the searching efforts of web users toward their desired objectives. In this paper, we propose a context-based meta-search engine and discuss its implementation on top of the actual Google.com search engine. The proposed meta-search engine benefits the user the most when the user does not know what exact document he or she is looking for. Comparison of the context-based meta-search engine with both Google and Guided Google shows that the results returned by context-based meta-search engine is much more intuitive and accurate than the results returned by both Google and Guided Google.

Direct versus search engine traffic : an innovative approach to demand analysis in the property market

Purpose – The application of “Google” econometrics (Geco) has evolved rapidly in recent years and can be applied in various fields of research. Based on accepted theories in existing economic literature, this paper seeks to contribute to the innovative use of research on Google search query data to provide a new innovative to property research.

Design/methodology/approach – In this study, existing data from Google Insights for Search (GI4S) is extended into a new potential source of consumer sentiment data based on visits to a commonly-used UK online real-estate agent platform (Rightmove.co.uk). In order to contribute to knowledge about the use of Geco's black box, namely the unknown sampling population and the specific search queries influencing the variables, the GI4S series are compared to direct web navigation.

Findings – The main finding from this study is that GI4S data produce immediate real-time results with a high level of reliability in explaining the future volume of transactions and house prices in comparison to the direct website data. Furthermore, the results reveal that the number of visits to Rightmove.co.uk is driven by GI4S data and vice versa, and indeed without a contemporaneous relationship.

Originality/value – This study contributes to the new emerging and innovative field of research involving search engine data. It also contributes to the knowledge base about the increasing use of online consumer data in economic research in property markets.

A co-recommendation algorithm for web searching

This paper presents an approach called the Co-Recommendation Algorithm, which consists of the features of the recommendation rule and the co-citation algorithm. The algorithm addresses some challenges that are essential for further searching and recommendation algorithms. It does not require users to provide a lot of interactive communication. Furthermore, it supports other queries, such as keyword, URL and document investigations. When the structure is compared to other algorithms, the scalability is noticeably easier. The high online performance can be obtained as well as the repository computation, which can achieve a high group-forming accuracy using only a fraction of Web pages from a cluster.

Utilizing hyperlink transitivity to improve web page clustering

The rapid increase of web complexity and size makes web searched results far from satisfaction in many cases due to a huge amount of information returned by search engines. How to find intrinsic relationships among the web pages at a higher level to implement efficient web searched information management and retrieval is becoming a challenge problem. In this paper, we propose an approach to measure web page similarity. This approach takes hyperlink transitivity and page importance into consideration. From this new similarity measurement, an effective hierarchical web page clustering algorithm is proposed. The primary evaluations show the effectiveness of the new similarity measurement and the improvement of web page clustering. The proposed page similarity, as well as the matrix-based hyperlink analysis methods, could be applied to other web-based research areas..

Optimal multicast tree routing for cluster computing in hypercube interconnection networks

Cluster computation has been used in the applications that demand performance, reliability, and availability, such as cluster server groups, large-scale scientific computations, distributed databases, distributed media-on-demand servers and search engines etc. In those applications, multicast can play the vital roles for the information dissemination among groups of servers and users. This paper proposes a set of novel efficient fault-tolerant multicast routing algorithms on hypercube interconnection of cluster computers using multicast shared tree approach. We present some new algorithms for selecting an optimal core (root) and constructing the shared tree so as to minimize the average delay for multicast messages. Simulation results indicate that our algorithms are efficient in the senses of short end-to-end average delay, load balance and less resource utilizations over hypercube cluster interconnection networks.

Spectral kernels for classification

Spectral methods, as an unsupervised technique, have been used with success in data mining such as LSI in information retrieval, HITS and PageRank in Web search engines, and spectral clustering in machine learning. The essence of success in these applications is the spectral information that captures the semantics inherent in the large amount of data required during unsupervised learning. In this paper, we ask if spectral methods can also be used in supervised learning, e.g., classification. In an attempt to answer this question, our research reveals a novel kernel in which spectral clustering information can be easily exploited and extended to new incoming data during classification tasks. From our experimental results, the proposed Spectral Kernel has proved to speedup classification tasks without compromising accuracy.

Image featuring for retrieval of multimedia documents

The utilization of massive multimedia documents collections, such as multimedia documents in the global Internet, needs search engines which can rank using both text and image evidence. Massive size and (dynamic) nature of collection can make manual indexing prohibitively expensive in such situations. Traditional search engines utilize only text components of multimedia documents. But there are information needs, which require the utilization of image evidence. In this paper, we investigate image-feature for large and heterogeneous collections. Both the nature and complexities of information needs are key elements for an effective retrieval. Retrieval needs that depend on perceptual similarities (as found in art galleries, building architecture) require the utilization of visual cues. In such situations, the retrieval of multimedia document based on image ranking can provide higher effectiveness. Experimental results show that effectiveness of ranking based on image feature can be higher where perceptual similarities are key elements for retrieval than the retrieval effectiveness of algorithms based on text ranking algorithms

Online newsgathering : research and reporting for journalism

If you are a journalist of any kind, you now realize that you need to know how to find the information you need online. This book shows you how to find declassified governmental files and statistics of all kinds, outlines the use of simple and complex search engines for small and large data gathering, and provides directories of subject experts. This book is for the many journalists around the world who didn't attend a formal journalism school before going to work, those who were educated before online research became mainstream, and for any student studying journalism today. It will teach you how to use the Internet wisely, efficiently, and comprehensively so that you will always have your facts straight and fast.

Algorithms and applications of preference based ranking for information retrieval

In this thesis, the author designed three sets of preference based ranking algorithms for information retrieval and provided the corresponsive applications for the algorithms. The main goal is to retrieve recommended, high similar and valuable ranking results to users.

Dopamine dysregulation syndrome : implications for a dopamine hypothesis of bipolar disorder

Objective: Rational therapeutic development in bipolar is hampered by a lack of pathophysiological model. However, there is a wealth of converging data on the role of dopamine in bipolar disorder. This paper therefore examines the possibility of a dopamine hypothesis for bipolar disorder.

Method: A literature search was conducted using standard search engines Embase, PyschLIT, PubMed and MEDLINE. In addition, papers and book chapters known to the authors were retrieved and examined for further relevant articles.

Results: Collectively, in excess of 100 articles were reviewed from which approximately 75% were relevant to the focus of this paper.

Conclusion: Pharmacological models suggest a role of increased dopaminergic drive in mania and the converse in depression. In Parkinson’s disease, administration of high-dose dopamine precursors can produce a ‘maniform’ picture, which switches into a depressive analogue on withdrawal. It is possible that in bipolar disorder there is a cyclical process, where increased dopaminergic transmission in mania leads to a secondary down regulation of dopaminergic receptor sensitivity over time. This may lead to a period of decreased dopaminergic transmission, corresponding with the depressive phase, and the repetition of the cycle. This model, if verified, may have implications for rational drug development.

Balanced efficacy, safety, and tolerability recommendations for the clinical management of bipolar disorder

Objective:  To provide practical and clinically meaningful treatment recommendations that amalgamate clinical experience and research findings for each phase of bipolar disorder.

Methods:  A comprehensive search of the literature was undertaken using electronic database search engines (Medline, PubMed, Cochrane reviews) using key words (e.g., bipolar depression, mania, treatment). All relevant randomised controlled trials were examined, along with review papers, meta-analyses, and book chapters known to the authors. In addition, the recommendations from accompanying papers in this supplement have been distilled and captured in the form of summary boxes. The findings, in conjunction with the clinical experience of international researchers and clinicians who are practiced in treating mood disorders, formed the basis of the treatment recommendations within this paper.

Results:  Balancing clinical experience with evidence informed and lead to the development of practical clinical recommendations that emphasise the importance of safety and tolerability alongside efficacy in the clinical management of bipolar disorder.

Conclusions:  The current paper summarises the treatment recommendations relating to each phase of bipolar disorder while providing additional, evidence-based, practical insights. Medication-related side effects and monitoring strategies highlight the importance of safety and tolerability considerations, which, along with efficacy information, should be given equal merit.

Medication adherence self-report instruments : implications for practice and research

Background: After an acute cardiac event, adhering to recommendations for pharmacologic therapy is important in achieving optimal health outcomes. Considering the impressive evidence base for cardiovascular pharmacotherapy, strategies for promoting adherence are less well developed. Furthermore, accessing reliable, valid, and cost-effective mechanisms of monitoring adherence in the research and clinical settings is challenging. Aim: The aim of this article was to review published self-report measures assessing and monitoring medication adherence in cardiovascular disease and provide recommendations for research into medication adherence. Methods: The electronic databases CINAHL, Medline, and Science Direct were searched using the key search terms medication adherence and/or compliance, cardiovascular, self-report measures, and questionnaires. The World Wide Web was searched using the Google and Google Scholar search engines, and reference lists of retrieved documents were reviewed. The search strategy was verified by a health librarian. Instruments were included if they specifically addressed medication adherence as a discrete construct rather than a disease-specific or a generic health status measurement. Findings: Despite of the problems with medication adherence identified in the literature, only 7 instruments met the search criteria. There was limited use of instruments across studies and settings to enable comparison across populations and extensive psychometric evaluation. Conclusions: Medication adherence is a complex, multifaceted construct dependent on a range of physical, social, economic, and psychological considerations. In spite of the importance of adherence in ensuring optimal cardiovascular outcomes, conceptual underpinnings and methods of assessing medication adherence require further discussion and debate.

Racism as a determinant of health: a protocol for conducting a systematic review and meta-analysis

Background: 

Google hacking defence based on honey pages

 Many web servers contain some dangerous pages (we name them eigenpages) that can indicate their vulnerabilities. Therefore, some worms such as Santy locate their targets by searching for these eigenpages in search engines with well-crafted queries. In this paper, we focus on the modeling and containment of these special worms targeting web applications. We propose a containment system based on honey pots. We make search engines randomly insert a few honey pages that will induce visitors to the pre-established honey pots among the search results for the arriving queries. And then infectious can be detected and reported to the search engines when their malicious scans hit the honey pots. We find that the Santy worm can be well stopped by inserting no more than two honey pages in every one hundred search results. We also solve the challenging issue to dynamically generate matching honey pages for those dynamically arriving queries. Finally, a prototype is implemented to prove the technical feasibility of this system. © 2013 by CESER Publications.