22,486 research outputs found
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
With the availability of vast amounts of user visitation history on
location-based social networks (LBSN), the problem of Point-of-Interest (POI)
prediction has been extensively studied. However, much of the research has been
conducted solely on voluntary checkin datasets collected from social apps such
as Foursquare or Yelp. While these data contain rich information about
recreational activities (e.g., restaurants, nightlife, and entertainment),
information about more prosaic aspects of people's lives is sparse. This not
only limits our understanding of users' daily routines, but more importantly
the modeling assumptions developed based on characteristics of recreation-based
data may not be suitable for richer check-in data. In this work, we present an
analysis of education "check-in" data using WiFi access logs collected at
Purdue University. We propose a heterogeneous graph-based method to encode the
correlations between users, POIs, and activities, and then jointly learn
embeddings for the vertices. We evaluate our method compared to previous
state-of-the-art POI prediction methods, and show that the assumptions made by
previous methods significantly degrade performance on our data with dense(r)
activity signals. We also show how our learned embeddings could be used to
identify similar students (e.g., for friend suggestions).Comment: published in KDD'1
Mediated data integration and transformation for web service-based software architectures
Service-oriented architecture using XML-based web services has been widely accepted by many organisations as the standard infrastructure to integrate heterogeneous and autonomous data sources. As a result, many Web service providers are built up on top of the data sources to share the data by supporting provided and required interfaces and methods of data access in a unified manner. In the context of data integration, problems arise when Web services are assembled to deliver an integrated view of data, adaptable to the specific needs of individual clients and providers. Traditional approaches of data integration and transformation are not suitable to automate the construction of connectors dedicated to connect selected Web services to render integrated and tailored views of data. We propose a declarative approach that addresses the oftenneglected data integration and adaptivity aspects of serviceoriented
architecture
Bidirectional Growth based Mining and Cyclic Behaviour Analysis of Web Sequential Patterns
Web sequential patterns are important for analyzing and understanding users
behaviour to improve the quality of service offered by the World Wide Web. Web
Prefetching is one such technique that utilizes prefetching rules derived
through Cyclic Model Analysis of the mined Web sequential patterns. The more
accurate the prediction and more satisfying the results of prefetching if we
use a highly efficient and scalable mining technique such as the Bidirectional
Growth based Directed Acyclic Graph. In this paper, we propose a novel
algorithm called Bidirectional Growth based mining Cyclic behavior Analysis of
web sequential Patterns (BGCAP) that effectively combines these strategies to
generate prefetching rules in the form of 2-sequence patterns with Periodicity
and threshold of Cyclic Behaviour that can be utilized to effectively prefetch
Web pages, thus reducing the users perceived latency. As BGCAP is based on
Bidirectional pattern growth, it performs only (log n+1) levels of recursion
for mining n Web sequential patterns. Our experimental results show that
prefetching rules generated using BGCAP is 5-10 percent faster for different
data sizes and 10-15% faster for a fixed data size than TD-Mine. In addition,
BGCAP generates about 5-15 percent more prefetching rules than TD-Mine.Comment: 19 page
Data integration through service-based mediation for web-enabled information systems
The Web and its underlying platform technologies have often been used to integrate existing software and information systems. Traditional techniques for data representation and transformations between documents are not sufficient to support a flexible and maintainable data integration solution that meets the requirements of modern complex Web-enabled software and information systems. The difficulty
arises from the high degree of complexity of data structures, for example in business and technology applications, and from the constant change of data and its
representation. In the Web context, where the Web platform is used to integrate different organisations or software systems, additionally the problem of heterogeneity
arises. We introduce a specific data integration solution for Web applications such as Web-enabled information systems. Our contribution is an integration technology
framework for Web-enabled information systems comprising, firstly, a data integration technique based on the declarative specification of transformation rules and the construction of connectors that handle the integration and, secondly, a mediator architecture based on information services and the constructed connectors to handle the integration process
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This is what I'm doing and why: reflections on a think-aloud study of digital library users' information behaviour
Many user-centred studies of digital libraries include a think-aloud element – where users are asked to verbalise their thoughts, interface actions and sometimes their feelings whilst using digital libraries to help them complete one or more information tasks. These studies are usually conducted with the purpose of identifying usability issues related to the system(s) used or understanding aspects of users‟ information behaviour. However, few of these studies present detailed accounts of how their think-aloud data was collected and analysed or provide detailed reflection on their methodologies. In this paper, we discuss and reflect on the decisions made when planning and conducting a think-aloud study of lawyers‟ interactive information behaviour. Our discussion is framed by Blandford et al.‟s PRET A Rapporter („ready to report‟) framework – a framework that can be used to plan, conduct and describe user-centred studies of digital library use from an information work perspective
Web Caching and Prefetching with Cyclic Model Analysis of Web Object Sequences
Web caching is the process in which web objects are temporarily stored to reduce bandwidth consumption, server load and latency. Web prefetching is the process of fetching web objects from the server before they are actually requested by the client. Integration of caching and prefetching can be very beneficial as the two techniques can support each other. By implementing this integrated scheme in a client-side proxy, the perceived latency can be reduced for not one but many users. In this paper, we propose a new integrated caching and prefetching policy called the WCP-CMA which makes use of a profit-driven caching policy that takes into account the periodicity and cyclic behaviour of the web access sequences for deriving prefetching rules. Our experimental results have shown a 10%-15% increase in the hit ratios of the cached objects and 5%-10% decrease in delay compared to the existing schem
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The role of human factors in stereotyping behavior and perception of digital library users: A robust clustering approach
To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception
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