Web crawler research methodology

In economic and social sciences it is crucial to test theoretical models against reliable and big enough databases. The general research challenge is to build up a well-structured database that suits well to the given research question and that is cost efficient at the same time. In this paper we focus on crawler programs that proved to be an effective tool of data base building in very different problem settings. First we explain how crawler programs work and illustrate a complex research process mapping business relationships using social media information sources. In this case we illustrate how search robots can be used to collect data for mapping complex network relationship to characterize business relationships in a well defined environment. After that extend the case and present a framework of three structurally different research models where crawler programs can be applied successfully: exploration, classification and time series analysis. In the case of exploration we present findings about the Hungarian web agency industry when no previous statistical data was available about their operations. For classification we show how the top visited Hungarian web domains can be divided into predefined categories of e-business models. In the third research we used a crawler to gather the values of concrete pre-defined records containing ticket prices of low cost airlines from one single site. Based on the experiences we highlight some conceptual conclusions and opportunities of crawler based research in e-business. --e-business research,web search,web crawler,Hungarian web,social network analyis

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Doing blog research: the computational turn

Blogs and other online platforms for personal writing such as LiveJournal have been of interest to researchers across the social sciences and humanities for a decade now. Although growth in the uptake of blogging has stalled somewhat since the heyday of blogs in the early 2000s, blogging continues to be a major genre of Internet-based communication. Indeed, at the same time that mass participation has moved on to Facebook, Twitter, and other more recent communication phenomena, what has been left behind by the wave of mass adoption is a slightly smaller but all the more solidly established blogosphere of engaged and committed participants. Blogs are now an accepted part of institutional, group, and personal communications strategies (Bruns and Jacobs, 2006); in style and substance, they are situated between the more static information provided by conventional Websites and Webpages and the continuous newsfeeds provided through Facebook and Twitter updates. Blogs provide a vehicle for authors (and their commenters) to think through given topics in the space of a few hundred to a few thousand words – expanding, perhaps, on shorter tweets, and possibly leading to the publication of more fully formed texts elsewhere. Additionally, they are also a very flexible medium: they readily provide the functionality to include images, audio, video, and other additional materials – as well as the fundamental tool of blogging, the hyperlink itself. This chapter appeared in the Sage collection Research Methods & Methodologies in Education edited by James Arthur, Michael Waring, Robert Coe, and Larry V. Hedges. This version is a pre-print edition of the chapter

Ontology Based Approach for Services Information Discovery using Hybrid Self Adaptive Semantic Focused Crawler

Focused crawling is aimed at specifically searching out pages that are relevant to a predefined set of topics. Since ontology is an all around framed information representation, ontology based focused crawling methodologies have come into exploration. Crawling is one of the essential systems for building information stockpiles. The reason for semantic focused crawler is naturally finding, commenting and ordering the administration data with the Semantic Web advances. Here, a framework of a hybrid self-adaptive semantic focused crawler – HSASF crawler, with the inspiration driving viably discovering, and sorting out administration organization information over the Internet, by considering the three essential issues has been displayed. A semi-supervised system has been planned with the inspiration driving subsequently selecting the ideal limit values for each idea, while considering the optimal performance without considering the constraint of the preparation of data set. DOI: 10.17762/ijritcc2321-8169.15072

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Information retrieval in the Web: beyond current search engines

AbstractIn this paper we briefly explore the challenges to expand information retrieval (IR) on the Web, in particular other types of data, Web mining and issues related to crawling. We also mention the main relations of IR and soft computing and how these techniques address these challenges

Investigating the Impact of the Blogsphere: Using PageRank to Determine the Distribution of Attention

Much has been written in recent years about the blogosphere and its impact on political, educational and scientific debates. Lately the issue has received significant attention from the industry. As the blogosphere continues to grow, even doubling its size every six months, this paper investigates its apparent impact on the overall Web itself. We use the popular Google PageRank algorithm which employs a model of Web used to measure the distribution of user attention across sites in the blogosphere. The paper is based on an analysis of the PageRank distribution for 8.8 million blogs in 2005 and 2006. This paper addresses the following key questions: How is PageRank distributed across the blogosphere? Does it indicate the existence of measurable, visible effects of blogs on the overall mediasphere? Can we compare the distribution of attention to blogs as characterised by the PageRank with the situation for other forms of Web content? Has there been a growth in the impact of the blogosphere on the Web over the two years analysed here? Finally, it will also be necessary to examine the limitations of a PageRank-centred approach