OpenRaiser/Intern-Atlas huggingface dataset:
The 4th temporal web analytics workshop (TempWeb'14)
Authors: Marc Spaniol, Julien Masanès, Ricardo Baeza-Yates
Venue: WWW
Year: 2014

In this paper we give an overview on the 4th Temporal Web Analytics Workshop (TempWeb). The goal of TempWeb is to provide a venue for researchers of all domains (IE/IR, Web mining, etc.) where the temporal dimension opens up an entirely new range of challenges and possibilities. The workshop's ambition is to help shaping a community of interest on the research challenges and possibilities resulting from the introduction of the time dimension in web analysis. Having a dedicated workshop will help, we believe, to take a rich and cross-domain approach to this new research challenge with a strong focus on the temporal dimension. For the fourth time, TempWeb has been organized in conjunction with the International World Wide Web (WWW) conference, being held on April 8, 2014 in Seoul, Korea.
Searching for design examples with crowdsourcing
Authors: Nikita Spirin, Motahhare Eslami, Jie Ding, Pooja Jain, Brian P. Bailey, Karrie Karahalios
Venue: WWW
Year: 2014

Examples are very important in design, but existing tools for design example search still do not cover many cases. For instance, long tail queries containing subtle and subjective design concepts, like "calm and quiet", "elegant", "dark background with a hint of color to make it less boring", are poorly supported. This is mainly due to the inherent complexity of the task, which so far has been tackled only algorithmically using general image search techniques. We propose a powerful new approach based on crowdsourcing, which complements existing algorithmic approaches and addresses their shortcomings. Out of many explored crowdsourcing configurations we found that (1) a design need should be represented via several query images and (2) AMT crowd workers should assess a query-specific relevance of a candidate example from a pre-built design collection. To test the utility of our approach, we compared it with Google Images in a query-by-example mode. Based on feedback from expert designers, the crowd selects more relevant design examples.
Online abusive users analytics through visualization
Authors: Anna Cinzia Squicciarini, Jules Dupont, Ruyan Chen
Venue: WWW
Year: 2014

In this demo, we present Abuse User Analytics (AuA), an analytical framework aiming to provide key information about the behavior of online social network users. AuA efficiently processes data from users' discussions, and renders information about users' activities in a easy to-understand graphical fashion with the goal of identifying deviant or abusive activities. Using animated graphics, AuA visualizes users' degree of abusiveness, measured by several key metrics, over user selected time intervals. It is therefore possible to visualize how users' activities lead to complex interaction networks, and highlight the degenerative connections among users and within certain threads.
Exploring the web of spatial data with facete
Authors: Claus Stadler, Michael Martin, Sören Auer
Venue: WWW
Year: 2014

The majority of data (including data published on the Web as Linked Open Data) has a spatial dimension. However, the efficient, user friendly exploration of spatial data remains a major challenge. We present Facete, a web-based exploration and visualization application enabling the spatial-faceted browsing of data with a spatial dimension. Facete implements a novel spatial data exploration paradigm based on the following three key components: First, a domain independent faceted filtering module, which operates directly on SPARQL and supports nested facets. Second, an algorithm that efficiently detects spatial information related to those resources that satisfy the facet selection. The detected relations are used for automatically presenting data on a map. And third, a workflow for making the map display interact with data sources that contain large amounts of geometric information. We demonstrate Facete in large-scale, real world application scenarios.
Systematic SLA data management
Authors: Katerina Stamou
Venue: WWW
Year: 2014

The cloud computing paradigm emerged with service oriented principles. In the cloud setting, organizations outsource their IT equipment and manage their business processes through virtual services that are typically exchanged over HTTP. Service Level Agreements (SLAs) depict the status of running services. SLAs represent operational contracts that allow providers to estimate their service avail- ability according to their resource capacity. The SLA data schema and content are operationally defined by the type, volume and relations of service elements that organizations operate on their physical resources. Current lack of a uniform SLA standardization leads to semantic and operational differences between SLAs, that are produced and consumed by different organizations. Such differences prohibit common business SLA practices in the cloud computing domain. Our research introduces systematic SLA data management to describe the formalization, storage and processing of SLAs over distributed computing environments. Services in scope are framed within the cloud computing context.
Semantic search engine with an intuitive user interface
Authors: Adam Styperek, Michal Ciesielczyk, Andrzej Szwabe
Venue: WWW
Year: 2014

It is crucial to enable regular users to explore RDF-compliant data bases in an effective way, regardless their knowledge about the SPARQL or the underlying ontology. Natural language querying have been proposed to address this issue. However it has unavoidably lower accuracy, as compared to systems with graph-based querying interfaces, which, in turn, are usually still too difficult for regular users. This paper presents a search system of user interface that is more user-friendly than widely known graph-based solutions.
Query interfaces understanding by statistical parsing
Authors: Weifeng Su, Yafei Li, Frederick H. Lochovsky
Venue: WWW
Year: 2014

Users submit queries to an online database via its query interface. Query interface parsing, which is important for many applications, understands the query capabilities of a query interface. Since most query interfaces are organized hierarchically, we present a novel query interface parsing method, StatParser (Statistical Parser), to automatically extract the hierarchical query capabilities of query interfaces. StatParser automatically learns from a set of parsed query interfaces and parses new query interfaces. StatParser starts from a small grammar and enhances the grammar with a set of probabilities learned from parsed query interfaces under the maximum-entropy principle. Given a new query interface, the probability-enhanced-grammar identifies the parse tree with the largest global probability to be the query capabilities of the query interface. Experimental results show that StatParser very accurately extracts the query capabilities and can effectively overcome the problems of existing query interface parsers.
Participatory disease detection through digital volunteerism: how the doctorme application aims to capture data for faster disease detection in thailand
Authors: Patipat Susumpow, Patcharaporn Pansuwan, Nathalie Sajda, Adam W. Crawley
Venue: WWW
Year: 2014

This paper reports the work in progress of incorporating a participatory disease detection mechanism into the existing web- and mobile device application DoctorMe in Thailand. As Southeast Asia has a high likelihood of hosting potential outbreaks of epidemics it is crucial to enable citizens to collectively contribute to improved public health through crowdsourced data, which is currently lacking. This paper focuses foremost on the localised approach, utilizing elements such as gamification, digital volunteerism and personalised health recommendations for participating users. DoctorMe's participatory disease detection approach aims to tap into the accelerating technological landscape in Thailand and to improve personal health and provide valuable data for institutional analysis that may prevent or decrease the impact of infectious disease outbreaks.
Towards large-scale graph stream processing platform
Authors: Toyotaro Suzumura, Shunsuke Nishii, Masaru Ganse
Venue: WWW
Year: 2014

In recent years, real-time data mining for large-scale time-evolving graphs is becoming a hot research topic. Most of the prior arts target relatively static graphs and also process them in store-and-process batch processing model. In this paper we propose a method of applying on-the-fly and incremental graph stream computing model to such dynamic graph analysis. To process large-scale graph streams on a cluster of nodes dynamically in a scalable fashion, we propose an incremental large-scale graph processing model called "Incremental GIM-V (Generalized Iterative Matrix-Vector Multiplication)". We also design and implement UNICORN, a system that adopts the proposed incremental processing model on top of IBM InfoSphere Streams. Our performance evaluation demonstrates that our method achieves up to 48% speedup on PageRank with Scale 16 Log-normal Graph (vertexes=65,536, edges=8,364,525) with 4 nodes, 3023% speedup on Random walk with Restart with Kronecker Graph with Scale 18 (vertexes=262,144, edges=8,388,608) with 4 nodes against original GIM-V.
Translation method of contextual information into textual space of advertisements
Authors: Yukihiro Tagami, Toru Hotta, Yusuke Tanaka, Shingo Ono, Koji Tsukamoto, Akira Tajima
Venue: WWW
Year: 2014

Contextual advertising has a key problem to determine how to select the ads that are relevant to the page content and/or the user information. We introduce a translation method that learns a mapping of contextual information to the textual features of ads by using past click data. This method is easy to implement and there is no need to modify an ordinary ad retrieval system because the contextual feature vector is simply transformed into a term vector with the learned matrix. We applied our approach with a real ad serving system and compared the online performance in A/B testing.
Automated runtime recovery for QoS-based service composition
Authors: Tian Huat Tan, Manman Chen, Étienne André, Jun Sun, Yang Liu, Jin Song Dong
Venue: WWW
Year: 2014

Service composition uses existing service-based applications as components to achieve a business goal. The composite service operates in a highly dynamic environment; hence, it can fail at any time due to the failure of component services. Service composition languages such as BPEL provide a compensation mechanism to rollback the error. But such a compensation mechanism has several issues. For instance, it cannot guarantee the functional properties of the composite service after compensation. In this work, we propose an automated approach based on a genetic algorithm to calculate the recovery plan that could guarantee the satisfaction of functional properties of the composite service after recovery. Given a composite service with large state space, the proposed method does not require exploring the full state space of the composite service; therefore, it allows efficient selection of recovery plan. In addition, the selection of recovery plans is based on their quality of service (QoS). A QoS-optimal recovery plan allows effective recovery from the state of failure. Our approach has been evaluated on real-world case studies, and has shown promising results.
Computational models for social influence analysis: [extended abstract]
Authors: Jie Tang, Jimeng Sun
Venue: WWW
Year: 2014

Social influence occurs when one's opinions, emotions, or behaviors are affected by others, intentionally or unintentionally. In this article, we survey recent research progress on social influence analysis. In particular, we first give a brief overview of related background knowledge, and then discuss what is social influence. We try to answer this question in terms of homophily and the process of influence and selection. After that, we focus on describing computational models for social influence including models for influence probability learning and influence diffusion. Finally, we discuss potential applications of social influence.
Working out the plot: the role of stories in social machines
Authors: Ségolène M. Tarte, David De Roure, Pip Willcox
Venue: WWW
Year: 2014

Although Social Machines do not have yet a formalized definition, some efforts have been made to characterize them from a ``machinery'' point of view. In this paper, we present a methodology by which we attempt to reveal the sociality of Social Machines; to do so, we adopt the analogy of stories. By assimilating a Social Machine to a story, we can identify the stories within and about that machine and how this storytelling perspective might reveal the sociality of Social Machines. After illustrating this storytelling approach with a few examples, we then propose three axes of inquiry to evaluate the health of a social machine: (1) assessment of the sociality of a Social Machine through evaluation of its storytelling potential and realization; (2) assessment of the sustainability of a Social Machine through evaluation of its reactivity and interactivity; and (3) assessment of emergence through evaluation of the collaboration between authors and of the distributed/mixed nature of authority.
Online analysis of information diffusion in twitter
Authors: Io Taxidou, Peter M. Fischer
Venue: WWW
Year: 2014

The advent of social media has facilitated the study of information diffusion, user interaction and user influence over social networks. The research on analyzing information spreading focuses mostly on modeling, while analyses of real-life data have been limited to small, carefully cleaned datasets that are analyzed in an offline fashion. In this paper, we present an approach for online analysis of information diffusion in Twitter. We reconstruct so-called information cascades that model how information is being propagated from user to user from the stream of messages and the social graph. The results show that such an inference is feasible even on noisy, large-scale, rapidly produced data. We provide insights into the impact of incomplete data and the effect of different influence models on the cascades. The observed cascades show a significant amount of variety in scale and structure.
Kindred domains: detecting and clustering botnet domains using DNS traffic
Authors: Matthew Thomas, Aziz Mohaisen
Venue: WWW
Year: 2014

In this paper we focus on detecting and clustering distinct groupings of domain names that are queried by numerous sets of infected machines. We propose to analyze domain name system (DNS) traffic, such as Non-Existent Domain (NXDomain) queries, at several premier Top Level Domain (TLD) authoritative name servers to identify strongly connected cliques of malware related domains. We illustrate typical malware DNS lookup patterns when observed on a global scale and utilize this insight to engineer a system capable of detecting and accurately clustering malware domains to a particular variant or malware family without the need for obtaining a malware sample. Finally, the experimental results of our system will provide a unique perspective on the current state of globally distributed malware, particularly the ones that use DNS.
CrossLanguageSpotter: a library for detecting relations in polyglot frameworks
Authors: Federico Tomassetti, Giuseppe Rizzo, Raphaël Troncy
Venue: WWW
Year: 2014

Nowadays, most of the web frameworks are developed using different programming languages, both for server and client side programmes. The typical scenario includes a general purpose language (e.g. Ruby, Python, Java) used together with different specialized languages: HTML, CSS, Javascript and SQL. All the artifacts are connected via different types of relations, most of which depend on the adopted framework. These cross-language relations are normally not captured by tools which require the developer to learn and to remember those associations in order to understand and maintain the application. This paper describes a library for detecting cross-language relations in polyglot frameworks. The library has been developed to be modular and to be easily integrated in existing IDEs. The library is publicly available at http://github.com/CrossLanguageProject/crosslanguagespotter
Query complex graph patterns: tools and applications
Authors: Hanghang Tong
Venue: WWW
Year: 2014

In his world-widely renowned book, Nobel laureate Herbert Simon pointed out that it is more the complexity of the environment, than the complexity of the individual persons, that determines the complex behavior of humans. The emergence of online social network sites and web 2.0 applications provides a new connected environment/context, where people generate, share and search massive human knowledge; and interact and collaborate with each other to collectively perform some complex tasks. In this talk, we focus on how to make sense of the collaboration data in the context of graphs/networks. To be specific, we will introduce a suite of tools for querying complex patterns from such graphs. Exemplar questions we aim to answer include (a) what makes a team more successful than others, and how to find the best replacement if one of its team members becomes unavailable? (b) how to find a group of authors from databases, data mining and bioinformatics and they collaborate with each other in a star-shape? (c) given a set of querying authors of interest, how to find somebody who initiates the research field these querying authors belong to, and how to summarize and visualize the querying authors' (d) how to incorporate users' preference into these complex queries' We will also introduce the computational challenges behind these querying tools and how to remedy them.
Multiple media analysis and visualization for understanding social activities
Authors: Masashi Toyoda
Venue: WWW
Year: 2014

The Web has involved diverse media services, such as blogs, photo/video/link sharing, social networks, and microblogs. These Web media react to and affect realworld events, while the mass media still has big influence on social activities.
Time-aware topic-based contextualization
Authors: Nam Khanh Tran
Venue: WWW
Year: 2014

In the past, various studies have been proposed to acquire the capacity to perceive and comprehend language in articles or human communications. Recently, researchers focus on higher semantic levels to what human would need to understand the contents of articles. While human can smoothly interpret documents when they have knowledge of the context of documents, they have difficulty with those as their context is lost or changes. In this PhD proposal, we address three novel research questions: detecting uninterpretable pieces in documents, retrieving contextual information and constructing compact context for the documents, then propose approaches to these tasks, and discuss related issues.
Indexing and analyzing wikipedia's current events portal, the daily news summaries by the crowd
Authors: Giang Binh Tran, Mohammad Alrifai
Venue: WWW
Year: 2014

Wikipedia's Current Events Portal (WCEP) is a special part of Wikipedia that focuses on daily summaries of news events. The WikiTimes project provides structured access to WCEP by extracting and indexing all its daily news events. In this paper we study this part of Wikipedia and take a closer look into its content and the community behind it. First, we provide descriptive analysis of the collected news events. Second, we compare between the news summaries created by the WCEP crowd and the ones created by professional journalists on the same topics. Finally, we analyze the revision logs of news events over the past 7 years in order to characterize the WCEP crowd and their activities. The results show that WCEP has reached a stable state in terms of the volume of contributions as well as the size of its crowd, which makes it an important source of news summaries for the public and the research community.
Who will trade with whom?: predicting buyer-seller interactions in online trading platforms through social networks
Authors: Christoph Trattner, Denis Parra, Lukas Eberhard, Xidao Wen
Venue: WWW
Year: 2014

In this paper we present the latest results of a recently started project that aims at studying the extent to which links between buyers and sellers, i.e. trading interactions in online trading platforms, can be predicted from external knowledge sources such as online social networks. To that end, we conducted a large-scale experiment on data obtained from the virtual world Second Life. As our results reveal, online social network data bears a significant potential (28% over the baseline) to predict links between buyers and sellers in online trading platforms.
Towards awareness and control in choreographed user interface mashups
Authors: Alexey Tschudnowsky, Stefan Pietschmann, Matthias Niederhausen, Martin Gaedke
Venue: WWW
Year: 2014

Recent research in the field of user interface (UI) mashups has focused on so-called choreographed compositions, where communication between components is not pre-defined by a mashup designer, but rather emerges from the components' messaging capabilities. Though the mashup development process gets simplified, such solutions bear several problems related to awareness and control of the emerging message flow. This paper presents an approach to systematically extend choreographed mashups with visualization and tailoring facilities. A first user study demonstrates that usability of the resulting solutions increases if proposed awareness and control facilities are integrated.
Ontology population from web product information
Authors: Damir Vandic, Lennart J. Nederstigt, Steven S. Aanen, Flavius Frasincar, Frederik Hogenboom
Venue: WWW
Year: 2014

With the vast amount of information available on the Web, there is an increasing need to structure Web data in order to make it accessible to both users and machines. E-commerce is one of the areas in which growing data congestion on the Web has serious consequences. This paper proposes a framework that is capable of populating a product ontology using tabular product information from Web shops. By formalizing product information in this way, better product comparison or recommendation applications could be built. Our approach employs both lexical and syntactic matching for mapping properties and instantiating values. The performed evaluation shows that instantiating consumer electronics from Best Buy and Newegg.com results in an F1 score of approximately 77%.
Mining user trails in critiquing based recommenders
Authors: Skanda Raj Vasudevan, Sutanu Chakraborti
Venue: WWW
Year: 2014

Critiquing based recommenders are very commonly used to help users navigate through the product space to find the required product by tweaking/critiquing one or more features. By critiquing a product, the user gives an informative feedback(i.e, which feature needs to be modified) about why they rejected a product and preferred the other one. As a user interacts with such a system, trails are left behind. We propose ways of leveraging these trails to induce preference models of items which can be used to estimate the relative utilities of products which can be used in ranking the recommendations presented to the user. The idea is to effectively complement knowledge of explicit user interactions in traditional social recommenders with knowledge implicitly obtained from trails.