OpenRaiser/Intern-Atlas huggingface dataset:
RESTful web services: principles, patterns, emerging technologies
Authors: Cesare Pautasso, Erik Wilde
Venue: WWW
Year: 2010

Recent technology trends in Web services indicate that a solution eliminating the perceived complexity of the WS-* standard technology stack may be in sight: advocates of Representational State Transfer (REST) have come to believe that their ideas explaining why the World Wide Web works are just as applicable to solve enterprise application integration problems and to radically simplify the plumbing required to implement a Service-Oriented Architecture (SOA). In this tutorial we give an introduction to the REST architectural style as the foundation for RESTful Web services. The tutorial starts from the basic design principles of REST and how they are applied to service oriented computing. Service-orientation concentrates on identifying self-contained units of functionality, which should then be exposed as easily reusable and repurposable services. This tutorial focuses not on the identification of those units, but on how to design the services representing them. We explain how decisions on the SOA level already shape the architectural style that will be used for the eventual IT architecture, and how the SOA process itself has to be controlled to yield services which can then be implemented RESTfully. We do not claim that REST is the only architectural style that can be used for SOA design, but we do argue that it does have distinct advantages for loosely coupled services and massive scale, and that any SOA approach already has to be specifically RESTful on the business level to yield meaningful input for IT architecture design.
INEX+DBPEDIA: a corpus for semantic search evaluation
Authors: José R. Pérez-Agüera, Javier Arroyo, Jane Greenberg, Joaquín Pérez-Iglesias, Víctor Fresno
Venue: WWW
Year: 2010

This paper presents a new collection based on DBpedia and INEX for evaluating semantic search performance. The proposed corpus is used to calculate the impact of considering document's structure on the retrieval performance of the Lucene and BM25 ranking functions. Results show that BM25 outperforms Lucene in all the considered metrics and that there is room for future improvements, which may be obtained using a hybrid approach combining both semantic technology and information retrieval ranking functions.
Protecting data in multi-stakeholder web service system
Authors: Tan Phan, Jun Han 0004, Garth Heward, Steven Versteeg
Venue: WWW
Year: 2010

Current Web Service security standards have inadequate support for end-to-end protection of data when some receivers of the data are unknown to the sender. This paper presents an approach to aid collaborative partner services in properly protecting each other's data. Our approach allows each partner to derive an adequate protection mechanism with minimum performance overhead for each message it sends based on those of the corresponding messages it receives. We modify the message handling mechanisms of Web Service engines to dynamically gather protection requirements for a given outgoing message by aggregating requirements from original owners of message data.
Constructing folksonomies by integrating structured metadata
Authors: Anon Plangprasopchok, Kristina Lerman, Lise Getoor
Venue: WWW
Year: 2010

Aggregating many personal hierarchies into a common taxonomy, also known as a folksonomy, presents several challenges due to its sparseness, ambiguity, noise, and inconsistency. We describe an approach to folksonomy learning based on relational clustering that addresses these challenges by exploiting structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr. We evaluate the learned folksonomy quantitatively by automatically comparing it to a reference taxonomy created by the Open Directory Project. Our empirical results suggest that the proposed approach improves upon the state-of-the-art folksonomy learning method.
DSNotify: handling broken links in the web of data
Authors: Niko Popitsch, Bernhard Haslhofer
Venue: WWW
Year: 2010

The Web of Data has emerged as a way of exposing structured linked data on the Web. It builds on the central building blocks of the Web (URIs, HTTP) and benefits from its simplicity and wide-spread adoption. It does, however, also inherit the unresolved issues such as the broken link problem. Broken links constitute a major challenge for actors consuming Linked Data as they require them to deal with reduced accessibility of data. We believe that the broken link problem is a major threat to the whole Web of Data idea and that both Linked Data consumers and providers will require solutions that deal with this problem. Since no general solutions for fixing such links in the Web of Data have emerged, we make three contributions into this direction: first, we provide a concise definition of the broken link problem and a comprehensive analysis of existing approaches. Second, we present DSNotify, a generic framework able to assist human and machine actors in fixing broken links. It uses heuristic feature comparison and employs a time-interval-based blocking technique for the underlying instance matching problem. Third, we derived benchmark datasets from knowledge bases such as DBpedia and evaluated the effectiveness of our approach with respect to the broken link problem. Our results show the feasibility of a time-interval-based blocking approach for systems that aim at detecting and fixing broken links in the Web of Data.
Towards comment-based cross-media retrieval
Authors: Martin Potthast, Benno Stein 0001, Steffen Becker 0001
Venue: WWW
Year: 2010

This paper investigates whether Web comments can be exploited for cross-media retrieval. Comparing Web items such as texts, images, videos, music, products, or personal profiles can be done at various levels of detail; our focus is on topic similarity. We propose to compare user-supplied comments on Web items in lieu of the commented items themselves. If this approach is feasible, the task of extracting and mapping features between arbitrary pairs of item types can be circumvented, and well-known text retrieval models can be applied instead - given that comments are available. We report on results of a preliminary, but nonetheless large-scale experiment which shows that, if comments on textual items are compared with comments on video items, topically similar pairs achieve a sufficiently high cross-media similarity.
A large-scale active learning system for topical categorization on the web
Authors: Suju Rajan, Dragomir Yankov, Scott Gaffney, Adwait Ratnaparkhi
Venue: WWW
Year: 2010

Many web applications such as ad matching systems, vertical search engines, and page categorization systems require the identification of a particular type or class of pages on the Web. The sheer number and diversity of the pages on the Web, however, makes the problem of obtaining a good sample of the class of interest hard. In this paper, we describe a successfully deployed end-to-end system that starts from a biased training sample and makes use of several state-of-the-art machine learning algorithms working in tandem, including a powerful active learning component, in order to achieve a good classification system. The system is evaluated on traffic from a real-world ad-matching platform and is shown to achieve high categorization effectiveness with a significant reduction in editorial effort and labeling time.
Distributing private data in challenged network environments
Authors: Azarias Reda, Brian D. Noble, Yidnekachew Haile
Venue: WWW
Year: 2010

Developing countries face significant challenges in network access, making even simple network tasks unpleasant. Many standard techniques - caching and predictive prefetching - help somewhat, but provide little or no assistance for personal data that is needed only by a single user. Sulula addresses this problem by leveraging the near-ubiquity of cellular phones able to send and receive simple SMS messages. Rather than visit a kiosk and fetch data on demand - a tiresome process at best - users request a future visit. If capacity exists, the kiosk can schedule secure retrieval of that user's data, saving time and more efficiently utilizing the kiosk's limited connectivity. When the user arrives at a provisioned kiosk, she need only obtain the session key on-demand, and thereafter has instant access. In addition, Sulula allows users to schedule data uploads. Experimental results show significant gains for the end user, saving tens of minutes of time for a typical email/news reading session. We also describe a small, ongoing deployment in-country for proof-of-concept, lessons learned from that experience, and provide a discussion on pricing and marketplace issues that remain to be addressed to make the system viable for developing-world access.
ourSpaces: linking provenance and social data in a virtual research environment
Authors: Richard Reid, Edoardo Pignotti, Peter Edwards, Adele Laing
Venue: WWW
Year: 2010

Web-based Virtual Research Environments (VREs) have been proposed as one way in which e-Science tools can be deployed to support and enhance the research process. We are exploring the use of Linked Data in combination with the Open Provenance Model (OPM) and Social Web concepts to facilitate interactions between people and data in the context of a VRE. In this demo we present the ourSpaces VRE and outline the technologies used to link together provenance, research artefacts, projects, geographical locations and social data in the context of interdisciplinary research.
Diversifying landmark image search results by learning interested views from community photos
Authors: Yuheng Ren, Mo Yu, Xin-Jing Wang, Lei Zhang 0001, Wei-Ying Ma
Venue: WWW
Year: 2010

In this paper, we demonstrate a novel landmark photo search and browsing system: Agate, which ranks landmark image search results considering their relevance, diversity and quality. Agate learns from community photos the most interested aspects and related activities of a landmark, and generates adaptively a Table of Content (TOC) as a summary of the attractions to facilitate the user browsing. Image search results are thus re-ranked with the TOC so as to ensure a quick overview of the attractions of the landmarks. A novel non-parametric TOC generation and set-based ranking algorithm, MoM-DPM Sets, is proposed as the key technology of Agate. Experimental results based on human evaluation show the effectiveness of our model and users' preference for Agate.
Conversion rate based bid adjustment for sponsored search
Authors: Benjamin Rey, Ashvin Kannan
Venue: WWW
Year: 2010

Advertisers use Sponsored Search to drive traffic to their site at a conversion rate and cost per conversion that provides value to them. However, very often advertisers bid at a constant price on a bundle of keywords, either for lack of enough data to fully optimize their bids at a keyword level, or indirectly by opting into Advanced Matching (AM) that allows an advertiser to reach a large number of queries while explicitly bidding only on a limited number. Then this single bid price reflects the return the advertiser gets from the full bundle. Under these conditions, the advertiser is competing too aggressively for some keyword auctions and with too low bids for others. In this paper, we propose a solution to improve the fairness of each keyword's bid prices within an AM bundle: adjusting the AM keyword bid by the ratio of its conversion rate to the conversion rate it would have reached had it been an Exact Match (EM). First we describe how we measure advertisers' conversion rates despite the opt-in nature of conversion tracking, and illustrate the need for bid adjustment in the context of AM. Then we present our approach to predict conversion rates in a robust manner. Our model uses a number of features capturing the quality of the match between the ad and the query. Then we describe how we adjust keyword bid prices to reflect their value to the advertiser thereby improving (1) the auction through fewer incorrectly high bids in the auction, (2) advertiser return through more auctions won by high value keywords and less by low value keywords, and (3) user satisfaction through higher conversion rate. Finally, we present experimental results from our live system.
IBM's jazz integration architecture: building a tools integration architecture and community inspired by the web
Authors: Scott Rich
Venue: WWW
Year: 2010

In this paper, we describe our experience in the Jazz project, beginning from a Classical repository- and Java-centric design and evolving towards an architecture which borrows heavily from the architecture of the Web. Along the way, we formed an open community to collaborate on adapting this architecture to various tool domains. Finally, we discuss our experience delivering the first generation of tools built and integrated using these techniques.
useKit: a step towards the executable web 3.0
Authors: Sven Rizzotti, Helmar Burkhart
Venue: WWW
Year: 2010

A lot of situations from daily life have found their counterpart in the internet. Buying or selling goods, discovering information or making social connections have been very successful in the virtual world and make the Web the central working place.
Sketcha: a captcha based on line drawings of 3D models
Authors: Steven A. Ross, J. Alex Halderman, Adam Finkelstein
Venue: WWW
Year: 2010

This paper introduces a captcha based on upright orientation of line drawings rendered from 3D models. The models are selected from a large database, and images are rendered from random viewpoints, affording many different drawings from a single 3D model. The captcha presents the user with a set of images, and the user must choose an upright orientation for each image. This task generally requires understanding of the semantic content of the image, which is believed to be difficult for automatic algorithms. We describe a process called covert filtering whereby the image database can be continually refreshed with drawings that are known to have a high success rate for humans, by inserting randomly into the captcha new images to be evaluated. Our analysis shows that covert filtering can ensure that captchas are likely to be solvable by humans while deterring attackers who wish to learn a portion of the database. We performed several user studies that evaluate how effectively people can solve the captcha. Comparing these results to an attack based on machine learning, we find that humans possess a substantial performance advantage over computers.
Unlocking the semantics of multimedia presentations in the web with the multimedia metadata ontology
Authors: Carsten Saathoff, Ansgar Scherp
Venue: WWW
Year: 2010

The semantics of rich multimedia presentations in the web such as SMIL, SVG, and Flash cannot or only to a very limited extend be understood by search engines today. This hampers the retrieval of such presentations and makes their archival and management a difficult task. Existing metadata models and metadata standards are either conceptually too narrow, focus on a specific media type only, cannot be used and combined together, or are not practically applicable for the semantic description of rich multimedia presentations.
Monitoring algorithms for negative feedback systems
Authors: Mark Sandler 0002, S. Muthukrishnan 0001
Venue: WWW
Year: 2010

There are many online systems where millions of users post original content such as videos, reviews of items such as products, services and businesses, etc. While there are general rules for good behavior or even formal Terms of Service, there are still users who post content that is not suitable. Increasingly, online systems rely on other users who view the posted content to provide feedback.
Web-scale k-means clustering
Authors: D. Sculley
Venue: WWW
Year: 2010

We present two modifications to the popular k-means clustering algorithm to address the extreme requirements for latency, scalability, and sparsity encountered in user-facing web applications. First, we propose the use of mini-batch optimization for k-means clustering. This reduces computation cost by orders of magnitude compared to the classic batch algorithm while yielding significantly better solutions than online stochastic gradient descent. Second, we achieve sparsity with projected gradient descent, and give a fast ε-accurate projection onto the L1-ball. Source code is freely available: http://code.google.com/p/sofia-ml
What the web can't do
Authors: David A. Shamma, Seth Fitzsimmonds, Joe Gregorio, Adam Hupp, Ramesh C. Jain, Kevin Marks
Venue: WWW
Year: 2010

This panel discusses how polling in the HTTPd protocol affects how we are building the next generation of the web and its applications. As other technologies (HTML, Javascript, etc.) move forward, we ask should the web's protocol also evolve or is it sufficient for the web to continue through just GET and POST?
Learning based access control in online social networks
Authors: Mohamed Shehab, Gorrell P. Cheek, Hakim Touati, Anna Cinzia Squicciarini, Pau-Chen Cheng
Venue: WWW
Year: 2010

Online social networking sites are experiencing tremendous user growth with hundreds of millions of active users. As a result, there is a tremendous amount of user profile data online, e.g., name, birthdate, etc. Protecting this data is a challenge. The task of access policy composition is a tedious and confusing effort for the average user having hundreds of friends. We propose an approach that assists users in composing and managing their access control policies. Our approach is based on a supervised learning mechanism that leverages user provided example policy settings as training sets to build classifiers that are the basis for auto-generated policies. Furthermore, we provide mechanisms to enable users to fuse policy decisions that are provided by their friends or others in the social network. These policies then regulate access to user profile objects. We implemented our approach and, through extensive experimentation, prove the accuracy of our proposed mechanisms.
Situation detection and control using spatio-temporal analysis of microblogs
Authors: Vivek K. Singh 0001, Mingyan Gao, Ramesh C. Jain
Venue: WWW
Year: 2010

Large volumes of spatio-temporal-thematic data being created using sites like Twitter and Jaiku, can potentially be combined to detect events, and understand various 'situations' as they are evolving at different spatio-temporal granularity across the world. Taking inspiration from traditional image pixels which represent aggregation of photon energies at a location, we consider aggregation of user interest levels at different geo-locations as social pixels. Combining such pixels spatio-temporally allows for creation of social images and video. Here, we describe how the use of relevant (media processing inspired) situation detection operators upon such 'images', and domain based rules can be used to decide relevant control actions. The ideas are showcased using a Swine flu monitoring application which uses Twitter data.
Structural analysis of the emerging event-web
Authors: Vivek K. Singh 0001, Ramesh C. Jain
Venue: WWW
Year: 2010

Events are the fundamental abstractions to study the dynamic world. We believe that the next generation of web (i.e. event-web), will focus on interconnections between events as they occur across space and time [3]. In fact we argue that the real value of large volumes of microblog data being created daily lies in its inherent spatio-temporality, and its correlation with the real-world events. In this context, we studied the structural properties of a corpus of 5,835,237 Twitter microblogs, and found it to exhibit Power laws across space and time, much like those exhibited by events in multiple domains. The properties studied over microblogs on different topics can be applied to study relationships between related events, as well as data organization for event-based, real-time, and location-aware applications.
Tagging and navigability
Authors: Adish Singla, Ingmar Weber
Venue: WWW
Year: 2010

We consider the problem of optimal tagging for navigational purposes in one's own collection. What is the best that a forgetful user can hope for in terms of ease of retrieving a labeled object? We prove that the number of tags has to increase logarithmically in the collection size to maintain a manageable result set. Using Flickr data we then show that users do indeed apply more and more tags as their collection grows and that this is not due to a global increase in tagging activity. However, as the additional terms applied are not statistically independent, users of large collections still have to deal with larger and larger result sets, even when more tags are used as search terms. We pose optimal tag suggestion for navigational purposes as an open problem.
Sampling high-quality clicks from noisy click data
Authors: Adish Singla, Ryen W. White
Venue: WWW
Year: 2010

Click data captures many users' document preferences for a query and has been shown to help significantly improve search engine ranking. However, most click data is noisy and of low frequency, with queries associated to documents via only one or a few clicks. This severely limits the usefulness of click data as a ranking signal. Given potentially noisy clicks comprising results with at most one click for a query, how do we extract high-quality clicks that may be useful for ranking? In this poster, we introduce a technique based on query entropy for noise reduction in click data. We study the effect of query entropy and as well as features such as user engagement and the match between the query and the document. Based on query entropy plus other features, we can sample noisy data to 15% of its overall size with 43% query recall and an average increase of 20% in precision for recalled queries.
What are the most eye-catching and ear-catching features in the video?: implications for video summarization
Authors: Yaxiao Song, Gary Marchionini, Chi Young Oh
Venue: WWW
Year: 2010

Video summarization is a mechanism for generating short summaries of the video to help people quickly make sense of the content of the video before downloading or seeking more detailed information. To produce reliable automatic video summarization algorithms, it is essential to first understand how human beings create video summaries with manual efforts. This paper focuses on a corpus of instructional documentary video, and seeks to improve automatic video summaries by understanding what features in the video catch the eyes and ears of human assessors, and using these findings to inform automatic summarization algorithms. The paper contributes a thorough and valuable methodology for performing automatic video summarization, and the methodology can be extended to inform summarization of other video corpuses.
Reining in the web with content security policy
Authors: Sid Stamm, Brandon Sterne, Gervase Markham
Venue: WWW
Year: 2010

The last three years have seen a dramatic increase in both awareness and exploitation of Web Application Vulnerabilities. 2008 and 2009 saw dozens of high-profile attacks against websites using Cross Site Scripting (XSS) and Cross Site Request Forgery (CSRF) for the purposes of information stealing, website defacement, malware planting, clickjacking, etc. While an ideal solution may be to develop web applications free from any exploitable vulnerabilities, real world security is usually provided in layers.