78,490 research outputs found

    HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web

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    When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.Comment: Published in the proceedings of WWW'1

    An average linear time algorithm for web data mining

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    In this paper, we study the complexity of a data mining algorithm for extracting patterns from user web navigation data that was proposed in previous work.3 The user web navigation sessions are inferred from log data and modeled as a Markov chain. The chain's higher probability trails correspond to the preferred trails on the web site. The algorithm implements a depth-first search that scans the Markov chain for the high probability trails. We show that the average behaviour of the algorithm is linear time in the number of web pages accessed

    Computing the entropy of user navigation in the web

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    Navigation through the web, colloquially known as "surfing", is one of the main activities of users during web interaction. When users follow a navigation trail they often tend to get disoriented in terms of the goals of their original query and thus the discovery of typical user trails could be useful in providing navigation assistance. Herein, we give a theoretical underpinning of user navigation in terms of the entropy of an underlying Markov chain modelling the web topology. We present a novel method for online incremental computation of the entropy and a large deviation result regarding the length of a trail to realize the said entropy. We provide an error analysis for our estimation of the entropy in terms of the divergence between the empirical and actual probabilities. We then indicate applications of our algorithm in the area of web data mining. Finally, we present an extension of our technique to higher-order Markov chains by a suitable reduction of a higher-order Markov chain model to a first-order one

    Time Trails: ‘presencing’ digital heritage within our everyday lives

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    The Time Trails project is a collaboration between the Centre for Intermedia at the University of Exeter, Royal Albert Memorial Museum and Art Gallery, 1010 Media, and Exeter City Football Club Supporters Trust (2013). It is a mobile web app to allow users to follow, annotate and create trails using text, images and videos, and to respond to them via social media. Two trails narrating the history of Exeter City Football Club and its Supporters Trust, used for mobile learning and as part of sport and cultural tourism experiences are presented. We show how Time Trails can be used as a presencing tool to establish new ways of encountering and learning on digital heritage within our daily lives

    Processing and Analysis of Condensation Trails Data Using Database Systems

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    This article describes the causes and behavior of aircraft condensation trails, aircraft radar technology, communication between aircraft and ground control stations, description of the function, analysis and processing of the ADS-B receivers and registers data. It also deals with the design and creation of a suitable web application with a simple user interface and a database for storing and processing this data. The work was based mainly on academical materials and sources, focusing on the issue of condensation trails, ADS-B functionality and programming of web applications

    Lesson plan: Who am I? My digital footprint

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    The lesson plan describes the content and pedagogical patterns used to design and deliver a digital literacy session as part of the Birkbeck Library's provision on Information and Digital literacies program. This session primarily aims to give to the learners a space for reflection around their digital identity and the trails they leave in the cyberspace. It also aims to introduce the idea of the level of control learners can have on what is on the web about them and how to deal with the uncertainty

    'Where do heritage trails go to die?' stepping out at the British seaside

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    Heritage trails are an important route into accessing the histories of seaside resorts and communities. They are a multifaceted tourist product/experience, combining a series of complex tourism policy objectives around economic and cultural development. Trails are considered not just as a mechanism to historical learning about place but area located within a cultural politics of destination marketing and geographical imaginaries that spatialise the cultural heritage of place in different ways, emphasising local, transnational and global ‘claims to fame’. This chapter offers a critical overview of UK seaside heritage trails, based upon a mixed method qualitative study which included participatory research, site visits and a content analysis of trail brochures, leaflets, signage and web materials. A typology of seaside heritage trails is provided, to account for different social, political and economic objectives inherent in the proliferation of this significant coastal leisure product

    A fine grained heuristic to capture web navigation patterns

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    In previous work we have proposed a statistical model to capture the user behaviour when browsing the web. The user navigation information obtained from web logs is modelled as a hypertext probabilistic grammar (HPG) which is within the class of regular probabilistic grammars. The set of highest probability strings generated by the grammar corresponds to the user preferred navigation trails. We have previously conducted experiments with a Breadth-First Search algorithm (BFS) to perform the exhaustive computation of all the strings with probability above a specified cut-point, which we call the rules. Although the algorithm’s running time varies linearly with the number of grammar states, it has the drawbacks of returning a large number of rules when the cut-point is small and a small set of very short rules when the cut-point is high. In this work, we present a new heuristic that implements an iterative deepening search wherein the set of rules is incrementally augmented by first exploring trails with high probability. A stopping parameter is provided which measures the distance between the current rule-set and its corresponding maximal set obtained by the BFS algorithm. When the stopping parameter takes the value zero the heuristic corresponds to the BFS algorithm and as the parameter takes values closer to one the number of rules obtained decreases accordingly. Experiments were conducted with both real and synthetic data and the results show that for a given cut-point the number of rules induced increases smoothly with the decrease of the stopping criterion. Therefore, by setting the value of the stopping criterion the analyst can determine the number and quality of rules to be induced; the quality of a rule is measured by both its length and probability

    Inventorying Amenities and Usage of Lewiston-Auburn Trails

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    Trails provide numerous benefits to the community and the environment, and give a space for people to engage with the outdoors. Safe and well maintained trails promote healthy lifestyles by increasing the physical activity of the public, mitigate climate change by encouraging clean means of travel and promote community building by creating a space for people to interact with each other. While trails provide overarching benefits to the community at large, each trail presents unique benefits, challenges and usage patterns, and every trail user has their own relationship with the trails that they use. The L/A Trails organization found a need to collect more data on the usage and opinions of the trails in the Lewsiton/ Auburn area and to find out more information about the various features and amenities that each trail provides to the community. In this report, we detail an investigation of four pillars of study that come together to provide a comprehensive look at the trails, the usage and the public opinions of the trails in the Lewiston Auburn area. Our first pillar of study details a methodology for collecting usage information from trails in Lewiston and Auburn, and provides proof-of-concept data from two trails in the area, Whitman Spring Road in Auburn, ME and the Riverside Greenway Trail in Lewiston, ME. Our second pillar of study gives survey feedback from trail users with graphs that display trends in public understanding and use of the trails. Our third pillar of study provides information about the various amenities that each trail offers and compares this data to the information listed online in order to assess the accuracy and availability of public information about the trails in the L/A area. Our fourth pillar of study expands upon this amenities data and provides a sample section of a possible public facing web map that would inform users about the location, the available amenities and the directions to all of the L/A trails. Each of these pillars come together to provide preliminary data about the physical trails in the area, the people who use the trails and the connection between the trails and the public. We encourage L/A Trails to continue to expand on the data gathered in this study to find new ways to improve the trails in response to usage and feedback patterns, and to make the information about the trails more accessibility to the public in order to increase community engagement with the trails

    To set up pedagogical experiments in a virtual lab: methodology and first results

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    This paper concerns a methodology for setting up web based experiments by distinguishing two perspectives: the learning perspective (which tasks will be scheduled for pupils and tutors, how they are planned and what learning objectives they may attempt), and separately, the experiment perspective (how teachers or researchers may use trails of this experiment, the first to improve their teaching, the last to various research objectives). The both are described by computable “scenarios” expressed with an educational modeling language, Learning Design Language (LDL). Scenarios are then implemented on a platform (LearningLab platform) to be played by pupils and tutors and further analyzed by exploiting trails of each run
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