Web Directories Research Articles

Attention for Web Directory Advertisements: A Top-Down or Bottom-Up Process?

ABSTRACTWeb directories have attracted many advertisers with their special advantages in their large user base. Until recently, attention mechanism of advertisements (ad) on web directories is not well understood. To investigate how the ad location and color of web directories influence users’ attention, this study uses eye tracking to measure the participants’ search time, total fixation duration and the location of the first fixation. Results reveal that visual attention on the ad area of a web directory is user-driven and follows a top-down process. The location of users’ first-fixations is the center of the screen. Ad links that place in the center area and on the top-left corner would increase users’ notice. Ad links that change color in the center area have the advantages of attracting user attention. Our findings suggest that ad links should be placed in the center area or on the top-left corner to increase users’ notice. Ad links placed in the center area should be designed using salient color to catch users’ visual attention.

The Quality of the Websites of Agritourism Farms in Visegrad Group Countries in the Light of Selected Synthetic Measures

Abstract Subject and purpose of work: The aim of the work was to evaluate the quality of the websites of agritourism farms in the Visegrad Group countries. Materials and methods: Evaluation of website quality was based on selected “site authority” measures, from the so-called SEO metrics: Trust Rank, Moz Rank and Page Authority, as well as measurement of the selected performance parameters of websites. The research was conducted on a group of 400 websites of agritourism farms, whose addresses were obtained from selected web directories. Results: In the case of as many as 331 sites (approx. 83%) Trust Rank lower than 1 point was recorded. The majority of tested websites are characterised by an average value of the Moz Rank index and a low value of the Page Authority index. The average time taken to load the website in the browser window for the first time was about 2.7 seconds, with as many as 68 (17%) websites being loaded in over 5 seconds. Conclusions: The tested websites are characterised by low values of the authority index, which may indicate poor quality, poor visibility in search results and a lack of recommendations from other valuable sites. Low index values may also mean that, despite the global potential, the tested websites have a merely local impact. Their existence is known to a small number of people and their Internet visibility is negligible or nonexistent. Websites published in such a manner do not fulfil their function.

SMART CRAWLER: A TWO-STAGE CRAWLER FOR EFFICIENTLY HARVESTING DEEP-WEB INTERFACES

As wide area of web grows at a very fast pace, there has been increased interest in techniques that help efficiently locate wide web interfaces. However, due to the large volume of web resources and the dynamic nature of deep web, achieving large coverage and high efficiency is a challenging issue. We propose a two-stage framework, namely Smart Crawler, for efficient harvesting wide web interfaces. In the first stage, It is site based searching for center pages with the help of search engines, it avoid to visit large number of pages. To achieve more accurate results for a focused crawl, It is ranking the websites to prioritize highly relevant ones for a given topic. In the second step, It searches fast in-site searching by extracting most relevant links with an adaptive link-ranking. To eliminate bias on visiting some it also contain highly relevant links in hidden web directories, we design a link tree data structure to achieve wider coverage for a website.

The Present State of Interspecific Hydralization of the Genus Larix

Species of the genus Larix P. Mill. easily hybridize with each other and the main barrier to the emergence of natural spontaneous hybrids is their geographic isolation. Interspecific hybridization and selection of the genus Larix , in comparison with the other representatives of coniferous species, is important and hence needs further investigation. The study reveales that due to the relatively young age of the genus and great propensity for hybridization to date among botanists is no consensus on the number of species in the genus Larix P. Mill. Heterosis in L. × marschlinsii is reported to have become the main cause of interest hybridization of botanists and foresters in Europe, Asia and North America. They have been studied in detail the ways the appearance and characteristics of many spontaneous natural larch hybrids. To achieve the objective of the study we have traced and analyzed the most recent literature on the issues studied and uses of our proven method of finding at global web directories taxonomic names of plants. This, in combination, allowed highlighting achievements of a number of researchers and interspecific hybridization to form a hybrid larch list. To conclude, the list of hybrids exceed the total number of existing species of larch, but is currently about 10 % of the total number of possible combinations of interspecies crossed. In the list of hybrids 6 species are represented, indicating incomplete representation of the different species in the hybridization process. As a result of interspecific hybridization we have obtained some valuable cultivars. We have also manifested heterosis in some combinations, which caused the intensification of growth and resistance to negative factors in the hybrid offspring. New useful features that appeared in natural and artificial hybrids larches show promise continued research in the field of hybridization of species of genus Larix P. Mill.

Optimizing Hierarchical Visualizations with the Minimum Description Length Principle.

In this paper we examine how the Minimum Description Length (MDL) principle can be used to efficiently select aggregated views of hierarchical datasets that feature a good balance between clutter and information. We present MDL formulae for generating uneven tree cuts tailored to treemap and sunburst diagrams, taking into account the available display space and information content of the data. We present the results of a proof-of-concept implementation. In addition, we demonstrate how such tree cuts can be used to enhance drill-down interaction in hierarchical visualizations by implementing our approach in an existing visualization tool. Validation is done with the feature congestion measure of clutter in views of a subset of the current DMOZ web directory, which contains nearly half million categories. The results show that MDL views achieve near constant clutter level across display resolutions. We also present the results of a crowdsourced user study where participants were asked to find targets in views of DMOZ generated by our approach and a set of baseline aggregation methods. The results suggest that, in some conditions, participants are able to locate targets (in particular, outliers) faster using the proposed approach.

Development of Web Directory of Aerospace Testing Facilities in India (ATFI)

The paper is an outcome of a major research project funded by the Aeronautics Research and Development Board (AR&DB), DRDO. The primary intent of this project is to create a web-based database of testing facilities in aerospace and to facilitate sophisticated searchable database on WWW platform. The output of the project report can be treated as a working catalogue to provide narrative information describing each facility, its testing capabilities, unique characteristics, location, organisation, key persons, etc. Usage of the database is divided into passive and active. It enables to identify specific data requirements needed to quantify the status of the country's technological maturation from which an assessment can be made for research, development and testing of future aerospace vehicles. It also helps in designing standard operating procedure for providing Aerospace Test facilities existing in DRDO labs to the private sector (including under ‘Make in India’ initiative) on payment basis.

An Improved Optimized Web Page Classification using Firefly Algorithm with NB Classifier (WPCNB)

web is a huge repository of information which needs for accurate automated classifiers for Web pages to maintain Web directories and to increase search engines performance. In web page classification problem each term in each HTML/XML tag of each Web page can be taken as a feature, an efficient methods to select best features to reduce feature space of the Web page classification problem derived here. Classification of Web page content is essential to many tasks in Web information retrieval such as maintaining, web directories and focused crawling. The uncontrolled nature of Web content presents additional challenges to Web page classification as compared to traditional text classification, but the interconnected nature of hypertext also provides features that can assist the process. As in derived work reviewed in Web page classification, the importance of these Web-specific features and algorithms, describe state-of-the-art practices, and track the underlying assumptions behind the use of information from neighboring pages. This work, our aimed to optimize best features selection for Web page classification problem. Since Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, that simulates the flash pattern and characteristics of fireflies. Clustering is a popular data analysis technique to identify homogeneous groups of objects based on the values of their attributes. Here FA is used for clustering on benchmark problems which is being found more suitable than Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and other nine methods used. The web page optimization using Naive Bayes classifier (WPCNB) is an improved optimized web page classification using firefly algorithm with NB classifier. this work is tested on research banking data set where firefly algorithm used for web optimization and Naive Bayes (NB) classifier used for classification of pages in contrast to selected pages with reference to different fireflies. The entitled work is being found better in terms of feature measure(FM),accuracy, precision etc. parameters with respect to existing key concepts.it is also an search optimization approach and can be enhanced by different genetic algorithm(GA)based classifiers use in future.

Web directories: selected features and their impact on directory quality

Purpose – The purpose of this paper is statistical examination of nearly 7,000 web directories and an analysis of factors which affect their quality measured by PageRank. Design/methodology/approach – The authors analysed 6,821 directories registered at www.katalogiseo.info/. The following information about the directories was examined: the year of registration on the website, the directory’s PageRank value, the existence of an active IP address, backlink requests, a fee charged for submission to the directory, as well as directory moderation and subject. Statistical analyses were performed with the use of Microsoft Excel, version 2010, and R software, version 3.0.0. The PageRank values were collected with a software written in Python. Findings – The study has shown a gradual increase in popularity of directories as one of the basic tools in search engine optimisation. The analysis has indicated a relatively high percentage of spam web directories. The evidence of this is the number of directories with undetermined PageRank values. The study revealed that careful management of a directory and its subject have key impact on directory quality measured by PageRank. Originality/value – Relatively few publications focus on the problem of web directories which represent a very large group of websites created solely to manipulate web search engine rankings. This paper discusses the phenomenon of web directories, reveals the percentage of spam directories, and factors which affect their quality measured by PageRank.

SmartCrawler: A Two-Stage Crawler for Efficiently Harvesting Deep-Web Interfaces

As deep web grows at a very fast pace, there has been increased interest in techniques that help efficiently locate deep-web interfaces. However, due to the large volume of web resources and the dynamic nature of deep web, achieving wide coverage and high efficiency is a challenging issue. We propose a two-stage framework, namely SmartCrawler, for efficient harvesting deep web interfaces. In the first stage, SmartCrawler performs site-based searching for center pages with the help of search engines, avoiding visiting a large number of pages. To achieve more accurate results for a focused crawl, SmartCrawler ranks websites to prioritize highly relevant ones for a given topic. In the second stage, SmartCrawler achieves fast in-site searching by excavating most relevant links with an adaptive link-ranking. To eliminate bias on visiting some highly relevant links in hidden web directories, we design a link tree data structure to achieve wider coverage for a website. Our experimental results on a set of representative domains show the agility and accuracy of our proposed crawler framework, which efficiently retrieves deep-web interfaces from large-scale sites and achieves higher harvest rates than other crawlers.

Reading assessments for students with ASD: a survey of summative reading assessments used in special educational schools in the UK

Schools have an obligation to assess the literacy skills of their students, and the provision of reading instruction to students includes the ability to measure progress in this area. However, the design of reading tests includes the ability not only to read words, but also the ability to verbalise them. This presents a particular challenge for practitioners working with students with autism spectrum disorders (ASD) who can be nonverbal in many cases. How this issue is generally overcome is currently unknown. A survey was developed, in the form of an online multiple‐choice questionnaire, in order to determine which tests are currently being used in the UK to assess the reading abilities of nonverbal students, and to examine the opinions of the education practitioners who use them, in relation to their suitability. Using the schools web directory, e‐mail invitations were sent to 1,050 special educational needs schools across the UK, and 70 schools responded to the invitation. Respondents suggested that the majority of practitioners hold little faith in the ability of current reading assessments to provide an accurate picture of reading ability for students with ASD, and this holds particularly true for nonverbal pupils with ASD. One purpose of education assessment is to establish a baseline of students’ ability in order to plan for lifelong learning and achievement. If there is an inability on the part of schools accurately to assess the reading abilities of nonverbal students with ASD, then it would be fair to assume that this could have a negative impact on the provision of learning opportunities for this population.

This is the future: A reconstruction of the UK business web space (1996–2001)

The Internet and the World Wide Web in particular have dramatically changed the way in which many companies operate. On the Web, even the smallest and most localised business has a potential global reach, and the development of online payment has redefined the selling market in most sectors. Boundaries and borders are being radically rediscussed. This article reconstructs the early approach of UK businesses to the World Wide Web between 1996 and 2001, a period in which the Web started to spread but it was not as engrained in everyday life as it would be in the following decade. While the fast and dispersed nature of the Web makes it almost impossible to accurately reconstruct the Web sphere in its historical dimension, this article proposes a methodology based on the usage of historical Web directories to access and map past Web spheres.

Improved Term Weighting Technique for Automatic Web Page Classification

Automatic web page classification has become inevitable for web directories due to the multitude of web pages in the World Wide Web. In this paper an improved Term Weighting technique is proposed for automatic and effective classification of web pages. The web documents are represented as set of features. The proposed method selects and extracts the most prominent features reducing the high dimensionality problem of classifier. The proper selection of features among the large set improves the performance of the classifier. The proposed algorithm is implemented and tested on a benchmarked dataset. The results show the better performance than most of the existing term weighting techniques.

Smart Detection and Classification of Application-Layer Intrusions in Web Directories

The Republic of Croatia homepage and directory of Croatian web servers (www.hr) attracts several thousand visitors daily, which makes it the target of various attacks. In order to lower the risk from such attacks, we propose a concept for an intrusion detection system and a classifier of detected intrusions. We first examined the concepts of existing intrusion detection systems and combined their individual benefits into a concept best suited for protecting web services on the application layer. The proposed concept uses machine learning techniques for both intrusion detection and classification. Intrusion detection, observed through analysis of requests, is implemented by a feed-forward neural network, while intrusion classification is done using self-organizing maps. The case study and preliminary evaluation is presented on the Republic of Croatia homepage (www.hr), followed by guidelines for further research.

Enhancing the Performance of Feature Selection using a Hybrid Genetic Algorithm

Information Retrieval (IR) issues have attracted increasing attention due to the growing availability of the documents. The retrieval of web pages is more challenging due to the ambiguous nature of the unstructured information found in these pages. Ontologies help to overcome the disambiguate nature of the natural language by the use of standard terms that relate to specific concepts. Ontology is a hierarchy of concepts with attributes and relations that defines an agreed terminology to describe semantic networks of interrelated information units. Ontology provides a vocabulary of classes and properties to describe a domain, emphasizing the sharing of knowledge and the consensus about its representation. This research focuses on IR systems moving from a lexical to semantic interpretation to match object and queries on a semantic basis. In natural language, many words are ambiguous giving different meanings based on the context and situation. Therefore, development of web directories, classification of web pages and analysis of topic-specific search are useful. Classification of contents makes an important part of most of the content management and retrieval activities. The underlying objective of this research work is to develop an effective and efficient feature selection and classification algorithm that can achieve good accuracy in classifying web pages.

Open Directory Project based universal taxonomy for Personalization of Online (Re)sources

Content personalization reflects the ability of content classification into (predefined) thematic units or information domains. Content nodes in a single thematic unit are related to a greater or lesser extent. An existing connection between two available content nodes assumes that the user will be interested in both resources (but not necessarily to the same extent). Such a connection (and its value) can be established through the process of automatic content classification and labeling. One approach for the classification of content nodes is the use of a predefined classification taxonomy. With the help of such classification taxonomy it is possible to automatically classify and label existing content nodes as well as create additional descriptors for future use in content personalization and recommendation systems. For these purposes existing web directories can be used in creating a universal, purely content based, classification taxonomy. This work analyzes Open Directory Project (ODP) web directory and proposes a novel use of its structure and content as the basis for such a classification taxonomy. The goal of a unified classification taxonomy is to allow for content personalization from heterogeneous sources. In this work we focus on the overall quality of ODP as the basis for such a classification taxonomy and the use of its hierarchical structure for automatic labeling. Due to the structure of data in ODP different grouping schemes are devised and tested to find the optimal content and structure combination for a proposed classification taxonomy as well as automatic labeling processes. The results provide an in-depth analysis of ODP and ODP based content classification and automatic labeling models. Although the use of ODP is well documented, this question has not been answered to date.

English

As the information contained within the web is increasing day by day, organizing this information could be a necessary requirement.The data mining process is to extract information from a data set and transform it into an understandable structure for further use. Classification of web page content is essential to many tasks in web information retrieval such as maintaining web directories and focused crawling.The uncontrolled type of nature of web content presents additional challenges to web page classification as compared to the traditional text classification, but the interconnected nature of hypertext also provides features that can assist the process. In this paper the web classification is discussed in detail and its importance in field of data mining is explored.

WEB APPLICATION BASED FILE TRANSFER IN CUSTOMIZED CLOUD

Web Application based File Transfer app in a Custom ized Cloud is an open source and cloud web app base d on the File Transfer system application which should be organized, desig ned by the app designer and developed by the app de veloper, the webapp based file transfer system which it runs on the open clou d web server with the graphical user based interfac e system. The user interface turns automatically the customized web application environment into file transfer system management sy stem. It uses the cloud web directory automated services with the powerful mana gement tools as the web file transfer with the mult iple options in the admin dashboard interface. It allows to configure the cus tom designs. Web application users or the web app scan be modified or updated according to the developer needs, so the web file t ransfer application visitors or the users can open the webapp accounts in the file transfer web application for creating the file tran sfer based user accounts. It can store or save the unlimited data without any bandwidth permissions. As the webapp administrator allows the web moderators to setup the individual u ser quotas and the customized quota for the portal users known as the quota system for webapp. The application administra tors and the application moderators will get the full access to the webapp p ortal administration managing area and to the permi ssions to block the individual and the unknown users, webapp site ads can be embed ded into the webapp site portal or the cloud file twebsite. This application can also be converted into the cus tomized individual mobile application such as for t he various os- android, the windows phone etc. It allows the individual users o r individual administrators to share their experien ce in the form of comments and the customized user article modules. Webapp portal enables to allow the various file formats where it can store the data files and other data on the file transfer oriented web-cloud. It’s as fully loaded features with the directory service mode in order to optimize the custom data and to optimize the file transfer orien ted cloud server optimization. Web application can also be deployed on any file transfer system. The web app can be installed on th e various operating platform which it allows the ad ministrators to embed the search engine into the Cloud File Transfer Web site .

A study of relevance propagation in large topic ontologies

Topic ontologies or web directories consist of large collections of links to websites, arranged by topic in different categories. The structure of these ontologies is typically not flat because there are hierarchical and nonhierarchical relationships among topics. As a consequence, websites classified under a certain topic may be relevant to other topics. Although some of these relevance relations are explicit, most of them must be discovered by an analysis of the structure of the ontologies. This article proposes a family of models of relevance propagation in topic ontologies. An efficient computational framework is described and used to compute nine different models for a portion of the Open Directory Project graph consisting of more than half a million nodes and approximately 1.5 million edges of different types. After performing a quantitative analysis, a user study was carried out to compare the most promising models. It was found that some general difficulties rule out the possibility of defining flawless models of relevance propagation that only take into account structural aspects of an ontology. However, there is a clear indication that including transitive relations induced by the nonhierarchical components of the ontology results in relevance propagation models that are superior to more basic approaches.

Investigate the Performance of Document Clustering Approach Based on Association Rules Mining

The challenges of the standard clustering methods and the weaknesses of Apriori algorithm in frequent termset clustering formulate the goal of our research. Based on Association Rules mining, an efficient approach for Web Document Clustering (ARWDC) has been devised. An efficient Multi-Tire Hashing Frequent Termsets algorithm (MTHFT) has been used to improve the efficiency of mining association rules by targeting improvement in mining of frequent termset. Then, the documents are initially partitioned based on association rules. Since a document usually contains more than one frequent termset, the same document may appear in multiple initial partitions, i.e., initial partitions are overlapping. After making partitions disjoint, the documents are grouped within the partition using descriptive keywords, the resultant clusters are obtained effectively. In this paper, we have presented an extensive analysis of the ARWDC approach for different sizes of Reuter's datasets. Furthermore the performance of our approach is evaluated with the help of evaluation measures such as, Precision, Recall and F-measure compared to the existing clustering algorithms like Bisecting K-means and FIHC. The experimental results show that the efficiency, scalability and accuracy of the ARWDC approach has been improved significantly for Reuters datasets. The internet has become the largest data repository, facing the problem of information overload. The existence of an abundance of information, in combination with the dynamic and heterogeneous nature of the Web, makes information retrieval a tedious process for the average user. Search engines, Meta-Search engines and Web Directories have been developed in order to help the users quickly and easily satisfy their information need. The Search engine performs exact matching between the query terms and the keywords that characterize each web page and presents the results to the user. These results are long lists of URLs, which are very hard to search. Furthermore, users without domain expertise are not familiar with the appropriate terminology thus not submitting the right query terms, leading to the retrieval of more irrelevant pages. This has led to the need for the development of new techniques to assist users effectively navigate, trace and organize the available web documents, with the ultimate goal of finding those best matching their needs. Document Clustering is one of the techniques that can play an important role towards the achievement of this objective. Document clustering has become an increasingly important task in analyzing huge numbers of documents distributed among various sites. Furthermore organizing them into different groups called as clusters, where the documents in each cluster share some common properties according to defined similarity measure. The fast and high-quality document clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Document clustering has been studied intensively because of its wide applicability in areas such as Web Mining, Search Engines, Information Retrieval, and Topological Analysis. Document Clustering is different than document classification. In document classification, the classes (and their properties) are known a priori, and documents are assigned to these classes; whereas, in document clustering, the number, properties, or membership (composition) of classes is not known in advance. Thus, classification is an example of supervised machine learning and clustering that of

Personalizing Web Directories by Constructing User Community Models

Personalizing Web Directories by Constructing User Community Models