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Top-k User-Defined Vertex Scoring Queries in Edge-Labeled Graph Databases

We consider identifying highly ranked vertices in large graph databases such as social networks or the Semantic Web where there are edge labels. There... (more)

Exploring and Analysing the African Web Ecosystem

It is well known that internet infrastructure deployment is progressing at a rapid pace in the African continent. A flurry of recent research has... (more)

A Rule-Based Transducer for Querying Incompletely Aligned Datasets

A growing number of Linked Open Data sources (from diverse provenances and about different domains) that can be freely browsed and searched to find... (more)

You, the Web, and Your Device: Longitudinal Characterization of Browsing Habits

Understanding how people interact with the web is key for a variety of applications, e.g., from the design of effective web pages to the definition of successful online marketing campaigns. Browsing behavior has been traditionally represented and studied by means of clickstreams, i.e., graphs whose vertices are web pages, and edges are the paths... (more)

Understanding Cross-Site Linking in Online Social Networks

As a result of the blooming of online social networks (OSNs), a user often holds accounts on multiple sites. In this article, we study the emerging... (more)

Unsupervised Domain Ranking in Large-Scale Web Crawls

With the proliferation of web spam and infinite autogenerated web content, large-scale web crawlers require low-complexity ranking methods to... (more)

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About TWEB

The journal Transactions on the Web (TWEB) publishes refereed articles reporting the results of research on Web content, applications, use, and related enabling technologies.

The scope of TWEB is described on the Call for Papers page. Authors are invited to submit original research papers for consideration by following the directions on the Author Guidelines page.

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Forthcoming Articles
Analyzing Privacy Policies at Scale: From Crowdsourcing to Automated Annotations

Website privacy policies are often long and difficult to understand. While research shows that Internet users care about their privacy, they do not have the time to understand the policies of every website they visit, and most users hardly ever read privacy policies. Some recent efforts have aimed to use a combination of crowdsourcing, machine learning, and natural language processing to interpret privacy policies at scale, thus producing annotations for use in interfaces that inform Internet users of salient policy details. However, little attention has been devoted to studying the accuracy of crowdsourced privacy policy annotations, how crowdworker productivity can be enhanced for such a task, and the levels of granularity that are feasible for automatic analysis of privacy policies. In this paper we present a trajectory of work addressing each of these topics. We include analyses of crowdworker performance, evaluation of a method to make a privacy-policy oriented task easier for crowdworkers, a coarse-grained approach to labeling segments of policy text with descriptive themes, and a fine-grained approach to identifying user choices described in policy text. Together, results from these efforts show the effectiveness of using automated and semi-automated methods for extracting from privacy policies the details that are salient to Internet users' interests.

new phone, who dis? Modeling Millennials' Backup Behavior

Given the ever-rising frequency of malware attacks and other problems leading people to lose their files, backups are an important proactive protective behavior in which users can engage. Backing up files can prevent emotional and financial losses and improve overall user experience. Yet, we find that less than half of young adults perform mobile or computer backups at least every few months. To understand why, we model the factors that drive mobile and computer backup behavior, and changes in that behavior over time, using data from a panel survey of 384 diverse young adults. We develop a set of models that explain 37% and 38% of the variance in reported mobile and computer backup behaviors, respectively. These models show consistent relationships between Internet skills and backup frequency on both mobile and computer devices. We find that this relationship holds longitudinally: increases in Internet skills lead to increased frequency of computer backups. This paper provides a foundation for understanding what drives young adult's backup behavior. It concludes with recommendations for motivating people to back up and for future work modeling similar user behaviors.

Mining Abstract XML Data-Types

Schema matching is an indistinguishable part of various data engineering domains. The currently dominant standard of specifying schemas on the Web is the XML language. Since XML offers advanced modeling capabilities, schema grammars usually represent complex data models. Thus, the usage of schema matching approaches, which identify semantically similar complex XML data models, is vital in data engineering domains. However, the top-rated state-of-the-art matching approaches do not focus on the identification of semantically similar complex XML data models, but they simply match schema elements or combinations of schema elements. To fill in this gap in the literature, we represent schemas in a complete way (i.e. without discarding schema elements and relations) in order to be able to capture complex data models and we propose an automated approach that realizes the matching of complex data models by performing the following steps: (i) mining structural design patterns, usually encountered inside such data models, and (ii) matching the semantically similar ones. Since the traditional tree pattern mining and matching technique is computationally demanding and in some cases inefficient, our approach extends this technique with a pruning, an indexing, and a greedy technique for mining and matching patterns efficiently. The usage of the proposed efficiency techniques reduces the numbers of the mined patterns and the produced matchings. In particular, based on these techniques, our approach does not enumerate all possible patterns and matchings, but it enumerates only the best possible ones. To decide whether a pattern or a matching is better than another, these techniques calculate the confidences of patterns and matchings, respectively. The pattern and the matching confidences are calculated by using two suites of newly proposed metrics. We evaluate our approach in terms of its effectiveness (i.e. its capability in identifying semantically similar structural design patterns between different schemas) and its (time and space) efficiency. In particular, we evaluate the impact of the proposed pruning, indexing, and greedy techniques and of the suites of the (pattern and matching) confidence metrics on the efficiency and the effectiveness of our approach. We also evaluate the effectiveness variability of our approach (i.e. whether its effectiveness remains high in different schemas pairs). We use in our evaluation the schemas of the matching benchmark~\emph{XBenchMatch}, which has already been used for the joint evaluation of top-rated state-of-the-art matching approaches. Overall, the results of our evaluation show that the usage of structural design patterns helps our approach in matching complex XML data models effectively. Additionally, the proposed pruning, indexing, and greedy techniques and the suites of the (pattern and matching) confidence metrics succeed in keeping the effectiveness and the efficiency of our approach steadily high in various cases of schemas.

Imaginary People Representing Real Numbers: Generating Personas from Online Social Media Data

We develop a methodology to automate creating imaginary people, referred to as personas, by processing complex behavioral and demographic data of social media audiences. From a popular social media account containing more than 30 million interactions by viewers from 198 countries engaging with more than 4,200 online videos produced by a global media corporation, we demonstrate that our methodology has several novel accomplishments, including: (a) identifying distinct user behavioral segments based on the user content consumption patterns; (b) identifying impactful demographics groupings; and (c) creating rich persona descriptions by automatically adding pertinent attributes, such as names, photos, and personal characteristics. We validate our approach by implementing the methodology into an actual working system; we then evaluate it via quantitative methods by examining the accuracy of predicting content preference of personas, the stability of the personas over time, and the generalizability of the method via applying to two other datasets. Research findings show the approach can develop rich personas representing the behavior and demographics of real audiences using privacy preserving aggregated online social media data from major online platforms. Results have implications for media companies and other organizations distributing content via online platforms.

Test-Based Security Certification of Composite Services

The diffusion of service-based and cloud-based systems has brought to a scenario where software is often made available as services, offered as commodities over corporate networks or the global net. This scenario supports the definition of business processes as composite services, which are implemented via runtime composition of offerings provided by different suppliers. Fast and accurate evaluation of services' security properties becomes then a fundamental requirement. In this paper, we show how the verification of security properties of composite services can be handled by test-based security certification. Our approach builds on existing security certification schemes for monolithic services and extends them towards service compositions. It virtually certifies composite services, starting from certificates awarded to the component services. We describe three heuristic algorithms for generating runtime test-based evidence of the composite service holding the properties. These algorithms are compared with the corresponding exhaustive algorithm to evaluate their quality and performance. We also evaluate the proposed approach in a real-world industrial scenario, which considers ENGpay online payment system of Engineering Ingegneria Informatica S.p.A. The proposed industrial evaluation presents the utility and generality of the proposed approach by showing how certification results can be used as a basis to establish compliance to Payment Card Industry Data Security Standard (PCI DSS).

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