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Exploring the Emerging Type of Comment for Online Videos: DanMu

DanMu, an emerging type of user-generated comment, has become increasingly popular in recent years. Many online video platforms such as Tudou.com have... (more)

Adaptive Knowledge Propagation in Web Ontologies

We focus on the problem of predicting missing assertions in Web ontologies. We start from the assumption that individual resources that are similar in some aspects are more likely to be linked by specific relations: this phenomenon is also referred to as homophily and emerges in a variety of relational domains. In this article, we propose a method... (more)

Recommendation in a Changing World: Exploiting Temporal Dynamics in Ratings and Reviews

Users’ preferences, and consequently their ratings and reviews to items, change over time. Likewise, characteristics of items are also time-varying. By dividing data into time periods, temporal Recommender Systems (RSs) improve recommendation accuracy by exploring the temporal dynamics in user rating data. However, temporal RSs have to cope... (more)

Activity Recommendation with Partners

Recommending social activities, such as watching movies or having dinner, is a common function found in social networks or e-commerce sites. Besides certain websites which manage activity-related locations (e.g., foursquare.com), many items on product sale platforms (e.g., groupon.com) can naturally be mapped to social activities. For example,... (more)

Caching to Reduce Mobile App Energy Consumption

Mobile applications consume device energy for their operations, and the fast rate of battery depletion on mobile devices poses a major usability hurdle. After the display, data communication is the second-biggest consumer of mobile device energy. At the same time, software applications that run on mobile devices represent a fast-growing product... (more)

Modeling and Simulating the Web of Things from an Information Retrieval Perspective

Internet and Web technologies have changed our lives in ways we are not yet fully aware of. In the near future, Internet will interconnect more than... (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
Understanding Cross-site Linking in Online Social Networks

Online social networks (OSNs) have become a commodity in people's daily life. Given the diverse focuses of different OSN services, a user often has accounts on multiple sites. In this paper, we study the emerging "cross-site linking" function, which is supported by a number of mainstream OSN services. To gain a deep and systematic understanding of this function, we first conduct a data-driven analysis by using crawled profiles and social connections of all 60+ million Foursquare users. Our analysis has shown that the cross-site linking function is adopted by 57.10% of all Foursquare users, and the users who have enabled this function are more active than other users. We have also found that users who are more concerned with online privacy have a lower probability to enable the cross-site linking function. By further exploring the cross-site links between Foursquare and leading OSN sites, we formalize the cross-site information aggregation problem. Using the massive data collected from Foursquare, Facebook and Twitter, we demonstrate the usefulness and challenges of cross-site information aggregation. In addition to measurements, we also carry out a survey to let users provide their detailed opinions about cross-site linking. The survey reveals why people choose to or not to enable cross-site linking, and investigates the motivation and concerns of enabling this function.

Exploiting usage to predict instantaneous app popularity: Trend filters and retention rates

The popularity of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these measures is that they reflect usage only indirectly. We propose to exploit actual app usage statistics. Indeed, retention rates, i.e., the number of days users continue to interact with an installed app have been suggested to predict successful app lifecycles. We conduct the first independent and large-scale study of retention rates and usage trends on a database of app-usage data from a community of 339,842 users and more than 213,667 apps. Our analysis shows that, on average, applications lose 65% of their users in the first week, while very popular applications (top 100) lose only 35%. It also reveals, however, that many applications have more complex usage behavior patterns due to seasonality, marketing, or other factors. To capture such effects, we develop a novel app-usage trend measure which provides instantaneous information about the popularity of an application. Our analysis shows that roughly 40% of all apps never gain more than a handful of users (Marginal apps). Less than 0.4% of the remaining 60% are constantly popular (Dominant apps), 1% have a quick drain of usage after an initial steep rise (Expired apps), and 7% continuously rise in popularity (Hot apps). From these, we can distinguish, for instance, trendsetters from copycat apps. We conclude by demonstrating that usage behavior trend information can be used to develop better mobile app recommendations.

Unsupervised Domain Ranking in Large-Scale Web Crawls

With the proliferation of web spam and infinite auto-generated web content, large-scale web crawlers require low-complexity ranking methods to effectively budget their limited resources and allocate bandwidth to reputable sites. To shed light on Internet-wide spam avoidance, we study topology-based ranking algorithms on domain-level graphs from the two largest academic crawls -- a 6.3B-page IRLbot dataset and a 1B-page ClueWeb09 exploration. We first propose a new methodology for comparing the various rankings and then show that in-degree BFS-based techniques decisively outperform classic PageRank-style methods, including TrustRank. However, since BFS requires several orders of magnitude higher overhead and is generally infeasible for real-time use, we propose a fast, accurate, and scalable estimation method called TSE that can achieve much better crawl prioritization in practice. It is especially beneficial in applications with limited hardware resources.

Evaluating the Quality in Use of Corporate Web Sites: An Empirical Investigation

In a previous paper we presented a novel approach to the evaluation of quality in use of corporate web sites based on an original quality model (QM-U) and a related methodology to put it into practice (EQ-EVAL). This paper focuses on two research questions. The first one aims to investigate whether expected quality obtained through the application of EQ-EVAL methodology by employing a small panel of evaluators is a good approximation of actual quality obtained through experimentation with real users. In order to answer this research question, a comparative study has been carried out involving five evaluators and fifty real users. The second research question aims to demonstrate that the adoption of the EQ-EVAL methodology can provide useful information for web site improvement. Three original indicators, namely coherence, coverage and ranking have been defined in order to answer this second question, and an additional study comparing the assessments of two panels of five and ten evaluators respectively has been carried out. The results obtained in both comparative studies are largely positive and provide a rational support for the adoption of the EQ-EVAL methodology.

Faster Base64 Encoding and Decoding using AVX2 Instructions

Web developers use base64 formats to include images, fonts, sounds and other resources directly inside HTML, JavaScript, JSON and XML files. We estimate that billions of base64 messages are decoded every day. We are motivated to improve the efficiency of base64 encoding and decoding. Compared to state-of-the-art implementations, we multiply the speeds of both the encoding (H10×) and the decoding (H7×). We achieve these good results by using the single-instruction-multiple-data (SIMD) instructions available on recent Intel processors (AVX2). Our accelerated software abides by the specification and reports errors when encountering characters outside of the base64 set. It is available online as free software under a liberal license.

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.

Optimizing Whole-Page Presentation for Web Search

Modern search engines aggregate results from different verticals: webpages, news, images, video, shopping, knowledge cards, local maps, etc. Unlike ``ten blue links'', these search results are heterogeneous in nature and not even arranged in a list on the page. This revolution directly challenges the conventional ``ranked list'' formulation in ad hoc search. Therefore, finding proper presentation for a gallery of heterogeneous results is critical for modern search engines. We propose a novel framework that learns the optimal page presentation to render heterogeneous results onto search result page (SERP). Page presentation is broadly defined as the strategy to present a set of items on SERP, much more expressive than a ranked list. It can specify item positions, image sizes, text fonts, and any other styles as long as variations are within business and design constraints. The learned presentation is content-aware, i.e. tailored to specific queries and returned results. Simulation experiments show that the framework automatically learns eye-catchy presentations for relevant results. Experiments on real data show that simple instantiations of the framework already outperform leading algorithm in federated search result presentation. It means the framework can learn its own result presentation strategy purely from data, without even knowing the ``probability ranking principle''.

Localness of location-based knowledge sharing: A Study of Naver KiN "Here"

In location-based social Q&A, the questions related to a local community (e.g., local services and places) are typically answered by local residents (i.e., people who have the local knowledge). This study aims to deepen our understanding of location-based knowledge sharing through investigating general users behavioral characteristics, the topical and typological patterns related to the geographic characteristics, geographic locality of user activities, and motivations of local knowledge sharing. To this end, we analyzed a 12-month period Q&A dataset from Naver KiN Here and a supplementary survey dataset from 285 mobile users. Our results revealed several unique characteristics of location-based social Q&A. When compared with conventional social Q&A sites, Naver KiN Here had distinctive users behavior patterns and different topical/typological patterns. In addition, Naver KiN Here exhibited a strong spatial locality where the answers mostly had 1-3 spatial clusters of contributions, and a typical cluster spanned a few neighboring districts. We also uncovered unique motivators, e.g., ownership of local knowledge and a sense of local community. The findings reported in the paper have significant implications for the design of Q&A systems, especially location-based social Q&A systems.

Exploring and Analysing the African Web Ecosystem

It is well known that Africas Internet infrastructure is progressing at a rapid pace. A flurry of recent research has quantified this, highlighting the expansion of its underlying connectivity network. However, improving the infrastructure is not useful without appropriately provisioned services to exploit it. This paper measures the availability and utilisation of web infrastructure in Africa. Whereas others have explored web infrastructure in developed regions, we shed light on practices in developing regions. To achieve this, we apply a comprehensive measurement methodology to collect data from a variety of sources. We first focus on Google to reveal that its content infrastructure in Africa is, indeed, expanding. We, however, find that much of its web content is still served from the US and Europe, despite being the most popular website in many African countries. We repeat the same analysis across a number of other regionally popular websites to find that even national African websites prefer to host their content abroad. To explore the reasons for this, we evaluate some of the major bottlenecks facing content delivery networks (CDNs) in Africa. Amongst other things, we find a lack of peering between the networks hosting our probes, preventing the sharing of CDN servers, as well as poorly configured DNS resolvers. We conclude the work with a number of suggestions for alleviating the issues observed.

Extracting and Summarizing Situational Information from the Twitter Social Media during Disasters

Microblogging sites like Twitter have become important sources of real-time information during disaster events. A large amount of valuable situational information is posted in these sites during disasters; however, the information is dispersed among hundreds of thousands of tweets containing sentiments and opinion of the masses. To effectively utilize microblogging sites during disaster events, it is necessary to not only extract the situational information from the large amounts of sentiment and opinion, but also to summarize the large amounts of situational information posted in real-time. During disasters in countries like India, a sizeable number of tweets are posted in local resource-poor languages besides the normal English-language tweets. For instance, in the Indian subcontinent, a large number of tweets are posted in Hindi / Devanagari (the national language of India), and some of the information contained in such non-English tweets are not available (or available at a later point of time) through English tweets. In this work, we develop a novel classification-summarization framework which handles tweets in both English and Hindi -- we first extract tweets containing situational information, and then summarize this information. Our proposed methodology is developed based on the understanding of how several concepts evolve in Twitter during disaster. This understanding helps us achieve superior performance compared to the state-of-the-art tweet classifiers and summarization approaches on English tweets. Additionally, to our knowledge, this is the first attempt to extract situational information from non-English tweets.

Completeness Management for RDF Data Sources

The Semantic Web is commonly interpreted under the open-world assumption. Under this setting, available information only captures a subset of the reality, thus hindering certainty as to whether the reality is fully described (e.g., in the answer to a query). While there are several aspects of the reality where one can observe complete information, there is currently no way to assert meta-information about completeness in a machine-readable form. The aim of this paper is to fill this gap and to contribute a (formal) study of how to describe the completeness of parts of the Semantic Web, and how to leverage this novel information for query answering. One immediate benefit is that now query answers can be complemented with information about their completeness. More specifically, we introduce a theoretical framework allowing to augment RDF data sources with statements, also expressed in RDF, about their completeness. We then study the impact of completeness statements on the complexity of query answering by considering different fragments of the SPARQL language, including the RDFS entailment regime, and the federated scenario. We implement an efficient method for reasoning about query completeness and provide an experimental evaluation in the presence of large sets of completeness statements.

A rule-based transducer for querying incompletely aligned datasets

A growing number of Linked Open Data sources (from diverse provenance and about different domains) are made available which can be freely browsed and searched to find and extract useful information. However, access to them is difficult for the users due to different aspects. This paper is mainly concerned with the heterogeneity aspect. It is quite common for datasets to describe the same or overlapped domains but using different vocabularies. This paper presents a transducer that transforms a SPARQL query, suitably expressed in terms of the vocabularies used in a source Dataset, into another SPARQL query, suitably expressed for a target Dataset supported by different vocabularies. The transducer obtains an acceptable transformation of the original query in order to increase the opportunities of getting answers even in case of adverse situations (such as those when no direct translation of terms seems possible). Perhaps it does not always preserve the semantics of the query, although it does not refuse to obtain an equivalent translation if it is at hand. Transformation across datasets is achieved through the management of a wide range of transformation rules. The feasibility of our proposal has been validated with a prototype implementation that processes queries that appear in well known benchmarks and SPARQL endpoint logs. Results of the experiments show that the system is quite effective achieving adequate transformations.

TWEB Reviewer List

A Web Portal Study for High Performance Computing

This paper addresses web interfaces for High Performance Computing (HPC) simulation software. First, it presents a brief history, starting in the 90s with Java applets, of web interfaces used for accessing and making best possible use of remote HPC resources. Then this article reviews the present state of such HPC web-based portals. We identify and discuss the key features and constraints that characterize HPC portals. The design and development of Bull extreme factory Computing Studio v3 (XCS3) is chosen as a common thread for showing how these features can all be implemented in one software: multi-tenancy, multi-scheduler compatibility, HPC application template framework, complete control through an HTTP RESTful API, customizable user interface with Responsive Web Design, remote visualization, Role Base Access Control, and access through the Authentication, Authorization, and Accounting proven security framework. The paper concludes with the benefits of using such an HPC portal for both end-users and IT administrators.

Characterizing and Predicting User's Behavior on Local Search Queries

The use of queries to find products and services that are located nearby is increasing rapidly due mainly to the ubiquity of internet access and location services provided by smartphone devices. Local search engines help users by matching queries with a predefined geographical connotation (local queries) against a database of local business listings. Local search differs from traditional Web search because, to correctly capture users click behavior, the estimation of relevance between query and candidate results must be integrated with geographical signals, such as distance. The intuition is that users prefer businesses that are physically closer to them or in a convenient area (e.g. close to their home). However, this notion of closeness depends upon other factors, like the business category, the quality of the service provided, the density of businesses in the area of interest, the hour of the day or even the day of the week. In this work we perform an extensive analysis of online users interactions with a local search engine, investigating their intent, temporal patterns, and highlighting relationships between distance-to-business and other factors, such as business reputation, Furthermore, we investigate the problem of estimating the click-through rate on local search (LCTR) by exploiting the combination of standard retrieval methods with a rich collection of geo, user and business-dependent features. We validate our approach on a large log collected from a real-world local search service. Our evaluation shows that the non-linear combination of business and user information, geo-local and textual relevance features leads to a significant improvements over existing alternative approaches based on a combination of relevance, distance and business reputation.

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 are many applications where users express scoring queries against such databases that involve two elements: (i) a set of patterns describing relationships that a vertex of interest to the user must satisfy, and (ii) a scoring mechanism in which the user may use properties of the vertex in order to assign a score to that vertex. We define the concept of a partial pattern map query (partial PM-query) which intuitively allows us to prune, and show that finding an optimal partial PM-query is NP-hard. We then propose two algorithms, PScore_LP and PScore_NWST, to find the answer to a scoring (top-k) query. In PScore_LP, the optimal partial PM-query is found using a list-oriented pruning method. PScore_NWST leverages Node-Weighted Steiner Trees to quickly compute slightly sub-optimal solutions. We conduct detailed experiments comparing our algorithms with (i) an algorithm (PScore_Base) that computes all answers to the query, evaluates them according to the scoring method, and chooses the top-k, and (ii) two Semantic Web query processing systems (Jena and GraphDB). Our algorithms show better performance than PScore_Base and the Semantic Web query processing systems  moreover, PScore_NWST outperforms PScore_LP on large queries and on queries with a tree structure.

Bibliometrics

Publication Years 2007-2018
Publication Count 235
Citation Count 2453
Available for Download 235
Downloads (6 weeks) 1467
Downloads (12 Months) 14925
Downloads (cumulative) 190131
Average downloads per article 809
Average citations per article 10
First Name Last Name Award
Ricardo A Baeza-Yates ACM Fellows (2009)
Massimo Bernaschi ACM Gordon Bell Prize
National Research Council of Italy (2011) ACM Gordon Bell Prize
National Research Council of Italy (2011)
Elisa Bertino ACM Fellows (2003)
Maria Bielikova ACM Senior Member (2009)
Dan Boneh ACM Fellows (2016)
ACM Prize in Computing (2014)
Athman Bouguettaya ACM Distinguished Member (2012)
ACM Senior Member (2007)
Andrei Broder ACM Paris Kanellakis Theory and Practice Award (2012)
ACM Fellows (2007)
Carlos A. Castillo ACM Senior Member (2014)
Stefano Ceri ACM Fellows (2013)
Chen-Nee Chuah ACM Distinguished Member (2012)
ACM Senior Member (2006)
Lorrie Faith Cranor ACM Fellows (2014)
ACM Senior Member (2006)
Ernesto Damiani ACM Distinguished Member (2008)
Schahram Dustdar ACM Distinguished Member (2009)
Christos Faloutsos ACM Fellows (2010)
Elena Ferrari ACM Distinguished Member (2011)
Ophir Frieder ACM Fellows (2005)
Hector Garcia-Molina ACM Fellows (1997)
Lee Giles ACM Fellows (2006)
Vicki Hanson ACM Fellows (2004)
Simon Harper ACM Distinguished Member (2014)
ACM Senior Member (2009)
Monika Henzinger ACM Fellows (2016)
Djoerd Hiemstra ACM Senior Member (2009)
Eric Horvitz ACM AAAI Allen Newell Award (2015)
ACM Fellows (2014)
Bernard Jansen ACM Senior Member (2017)
Craig Knoblock ACM Fellows (2017)
ACM Distinguished Member (2008)
Ming Li ACM Fellows (2006)
Bing Liu ACM Fellows (2015)
Yiqun Liu ACM Senior Member (2016)
Dmitri Loguinov ACM Distinguished Member (2014)
ACM Senior Member (2007)
Filippo Menczer ACM Distinguished Member (2013)
Renee J Miller ACM Fellows (2009)
Mourad Ouzzani ACM Senior Member (2009)
Jian Pei ACM Fellows (2015)
ACM Senior Member (2007)
Ali Pinar ACM Distinguished Member (2015)
ACM Senior Member (2011)
Prabhakar Raghavan ACM Fellows (2001)
Naren Ramakrishnan ACM Distinguished Member (2009)
John T Riedl ACM Software System Award (2010)
ACM Fellows (2009)
ACM Distinguished Member (2007)
Michael Rung-Tsong Lyu ACM Fellows (2015)
Prashant J Shenoy ACM Distinguished Member (2009)
ACM Senior Member (2006)
Ingmar Weber ACM Senior Member (2017)
Xing Xie ACM Senior Member (2010)
Qiang Yang ACM Fellows (2017)
ACM Distinguished Member (2011)
Philip S Yu ACM Fellows (1997)
Lixia Zhang ACM Fellows (2006)
Ben Y. Zhao ACM Distinguished Member (2015)
Yu Zheng ACM Distinguished Member (2016)
ACM Senior Member (2011)
Yu Zheng ACM Distinguished Member (2016)
ACM Senior Member (2011)

First Name Last Name Paper Counts
İsmail Altıngövde 6
Ryen White 5
Wolfgang Nejdl 4
Xing Xie 4
Ingmar Weber 4
Weiying Ma 4
Berkant Cambazoglu 4
Fabio Casati 3
Weiyi Meng 3
Rifat Ozcan 3
Ling Liu 3
Anirban Mahanti 3
Ben Zhao 3
Ricardo Baeza-Yates 3
Özgür Ulusoy 3
Enhong Chen 3
Markus Strohmaier 3
Denis Helic 2
James Miller 2
Aphrodite Tsalgatidou 2
Marco Aiello 2
Stefano Calzavara 2
Michele Bugliesi 2
Philip YU 2
Alessandro Bozzon 2
Eric Horvitz 2
Phillipa Gill 2
Ziv Bar-Yossef 2
Piero Fraternali 2
Barry Smyth 2
Andreas Hotho 2
Monika Henzinger 2
Florian Daniel 2
Freddy Lécué 2
Yu Zheng 2
Haitao Zheng 2
Gang Wang 2
Marco Brambilla 2
Cristóbal Arellano 2
Sergiu Chelaru 2
Carey Williamson 2
Prashant Shenoy 2
Eepeng Lim 2
Nikolay Mehandjiev 2
Christo Wilson 2
Philipp Singer 2
Nikos Mamoulis 2
Andrei Broder 2
Clyde Giles 2
Clement Yu 2
Boualem Benatallah 2
Xiangye Xiao 2
Qiong Luo 2
Stefan Siersdorfer 2
Eda Baykan 2
Óscar Díaz 2
Cornelia Caragea 2
Bernard Jansen 2
Niklas Carlsson 2
Mudhakar Srivatsa 2
Renata Fortes 1
Sara Foresti 1
Pierangela Samarati 1
Rahul Singh 1
Mohammad Alrifai 1
Saikat Mukherjee 1
Valeria De Antonellis 1
Vanja Josifovski 1
Lance Riedel 1
Tong Zhang 1
Souneil Park 1
Aleksandar Matic 1
Giridhar Kumaran 1
Renan Cattelan 1
Hana Shepherd 1
Micah Dubinko 1
Ravi Kumar 1
Nicholas Jennings 1
Santo Fortunato 1
Alessandro Vespignani 1
Dmitri Loguinov 1
Gregorio Ponce 1
Jacobus Van Der Merwe 1
Haining Wang 1
Chris Cornelis 1
Houari Sahraoui 1
Xiaodi Huang 1
Rosa Alarcón 1
Hongmin Cai 1
Tianqiang Huang 1
Reza Sherkat 1
Ingo Weber 1
John Hurley 1
Mohamed Kaafar 1
Pablo Castells 1
Jeonhyung Kang 1
Flavio Lombardi 1
Cinzia Cappiello 1
Maristella Matera 1
Zhiyuan Su 1
Ming Li 1
Gabriele Tolomei 1
Alessandro Giuliani 1
Cevdet Aykanat 1
Xuanhieu Phan 1
Bruno Crispo 1
Krishna Puttaswamy 1
Youngjae Kim 1
Salvatore Orlando 1
Lidong Bing 1
Manolis Koubarakis 1
Hady Lauw 1
Guangyu Zhu 1
Brian Beirne 1
Ali Neyestani 1
Badr Atassi 1
Jens Eliasson 1
Jose Maz'on 1
Gregor Bochmann 1
Iosif Onut 1
Tanguy Urvoy 1
Pascal Filoche 1
Daniel Gmach 1
Alfons Kemper 1
Yun Chi 1
Sameh Elnikety 1
Michael Huemer 1
Khalid Belhajjame 1
Norman Paton 1
Régis Saint-Paul 1
Kweijay Lin 1
Peter Dolog 1
Fabrizio Silvestri 1
Hector Garcia-Molina 1
Alissa Cooper 1
Xuesong Wang 1
David Cheung 1
Christos Faloutsos 1
Pavel Serdyukov 1
Haitao Xu 1
Adam Wierzbicki 1
Elena Ferrari 1
Chunyan Miao 1
Zibin Zheng 1
Michael Lyu 1
Salima Benbernou 1
Jakub Marszałkowski 1
Bruno Ávila 1
Weiliang Zhao 1
Giuseppe Pirró 1
Xiuzhen Zhang 1
Giuseppe Psaila 1
Thomas Johnston 1
Vinod Muthusamy 1
Maja Pešić 1
Euiseong Seo 1
Federico Chesani 1
Daxin Jiang 1
Jing Jiang 1
Wenpeng Sha 1
Peng Huang 1
Sangkeun Lee 1
Sara Comai 1
Yiqun Liu 1
Shaoping Ma 1
Ali Mesbah 1
Stefan Lenselink 1
Akhmed Umyarov 1
Gilad Mishne 1
Pınar Karagöz 1
Irene Garrig'os 1
Xianchao Zhang 1
Mustafa Dincturk 1
Emmanuel Chauveau 1
Stefan Krompass 1
Belle Tseng 1
Giovanni Grasso 1
Christian Schallhart 1
Carole Goble 1
Halvard Skogsrud 1
Giovanni Pacifici 1
Frans Effendi 1
Keqiu Li 1
Juan Fernández-Luna 1
Wenting Tu 1
Alexey Drutsa 1
Haining Wang 1
Angelos Stavrou 1
Maria Rafalak 1
Simon Harper 1
Hengjie Song 1
Jianshu Weng 1
Xinyu Wang 1
Wenjie Song 1
Mourad Ouziri 1
Zaki Malik 1
Jan Mizgajski 1
Neil Yorke-Smith 1
Wenbin Cai 1
Barbara Poblete 1
Barbara Carminati 1
Jiaqian Gu 1
Huaqing Min 1
Dariusz Mokwa 1
Florian Geigl 1
Huiyuan Zheng 1
Greg Wiseman 1
Ram Gopal 1
Ram Ramesh 1
John Dunagan 1
Saher Esmeir 1
Uwe Zdun 1
Schahram Dustdar 1
Ahmet Sarıyüce 1
Comandur Seshadhri 1
Elad Kravi 1
Peter Bailey 1
Gonzalo Navarro 1
Hsintsang Lee 1
Xiaodong Wang 1
Marco Anisetti 1
Muhan Zhang 1
Sharon Goldberg 1
Mingdong Tang 1
Bing Liu 1
Ana Dias 1
Sushil Jajodia 1
Rossano Schifanella 1
Ciro Cattuto 1
Filippo Menczer 1
Shiva Ramanna 1
Yehoshua Sagiv 1
Fatemeh Vahedian 1
Yon Dourisboure 1
Evgeniy Gabrilovich 1
Kamini Garg 1
Hailun Lin 1
Stijn Vansummeren 1
Darko Kirovski 1
Ydo Wexler 1
Naren Ramakrishnan 1
Joseph Magnani 1
Ana Maguitman 1
Peter Moulder 1
Nathan Hurst 1
Alexander Lazovik 1
Dinh Phung 1
Miguel Serrano 1
Weifeng Su 1
Hejun Wu 1
Arjun Talwar 1
Amit Yadav 1
Derek Eager 1
Sakir Sezer 1
Partha Mukherjee 1
Djoerd Hiemstra 1
Kyungbaek Kim 1
Jianwei Gan 1
Aleksandar Kuzmanovic 1
Idit Keidar 1
Elena Demidova 1
José Menéndez 1
Dan Boneh 1
Christo Wilson 1
Soumi Chattopadhyay 1
Qian Lin 1
Stefano Guarino 1
Dominik Deja 1
Sukru Eraslan 1
Mikhail Bilenko 1
Jesus Bellido 1
Marco Comuzzi 1
Jing Zhao 1
Manishkumar Jha 1
Federica Paci 1
Mourad Ouzzani 1
Dimitris Zeginis 1
Vicente Pelechano 1
Boi Faltings 1
Chris Grier 1
Shuo Tang 1
Emi Garcia-Palacios 1
Rahul Balakavi 1
Jagdish Achara 1
Aojan Su 1
Simon Gottschalk 1
Dan Hong 1
Hongbo Fu 1
Massimo Bernaschi 1
Ivan Budiselić 1
Jeaho Hwang 1
Yukun Chen 1
Taklam Wong 1
Joonwon Lee 1
Wil Van Der Aalst 1
Stefano Ceri 1
Yafei Dai 1
Junghyun Lee 1
Sheelagh Carpendale 1
Yuval Merhav 1
Weize Kong 1
Lu Zhang 1
Fidel Cacheda 1
Pablo Pereira 1
Nan Mou 1
Wenxin Liang 1
Tim Finin 1
Yuru Lin 1
Bo Yang 1
Jiming Liu 1
James Thom 1
Seunghwan Ryu 1
Mike Spreitzer 1
Michael Schäfer 1
Zhisheng Li 1
Bernardo Huberman 1
Flavio Junqueira 1
Georgia Koutrika 1
Debra VanderMeer 1
Bolun Wang 1
B Prakash 1
Pasquale Minervini 1
Sergio Rojas-Galeano 1
Mohammad Rahman 1
Guangming Guo 1
Wei Wei 1
Xiaogang Han 1
Yuanhong Shen 1
Rafael Lins 1
Daniel Zoller 1
Thomas Niebler 1
Weifeng Su 1
Yaoyi Chiang 1
Yan Wang 1
Sujatha Gollapalli 1
Timothy Wood 1
Thomas Risse 1
Luca Aiello 1
Alain Barrat 1
Benjamin Markines 1
Helen Wang 1
Charles Reis 1
Ümit Çatalyürek 1
Stefano Tranquillini 1
Pavel Kucherbaev 1
Yuanzhuo Wang 1
Frank Neven 1
Francisco Claude 1
Cherian Mathew 1
Esther David 1
Weiming Hu 1
Francesco Saonara 1
Ehsan Warriach 1
Brett Adams 1
Svetha Venkatesh 1
Mauro Andreolini 1
Jian Yin 1
Massimo Mecella 1
Vassiliki Koutsonikola 1
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Mayank Agrawal 1
Prasant Mohapatra 1
Michael Sirivianos 1
Nilanjan Banerjee 1
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Sihyung Lee 1
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Mauro Conti 1
Arbnor Hasani 1
Jiawei Han 1
Paola Mello 1
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Davide Mazza 1
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Ahmed Hassan 1
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Paul Thomas 1
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Boanerges Aleman-Meza 1
Amit Sheth 1
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Einat Amitay 1
Claudia D'Amato 1
Daiping Liu 1
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Feida Zhu 1
Le Wu 1
Malik Magdon-Ismail 1
Yonghui Xu 1
Jialiang Shi 1
Matthias Bröcheler 1
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Quannan Li 1
Roberto Vivó 1
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A Vural 1
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Yuxiong He 1
Kaweh Naini 1
Karen Church 1
Matthew Richardson 1
Divya Sambasivan 1
Lei Li 1
Min Yang 1
Yongjae Lee 1
Qi Liu 1
Allen Lavoie 1
Leila Bahri 1
Qingyao Wu 1
Zhaoxing Li 1
Shaoping Zhu 1
Wenxin Liang 1
Iheb Amor 1
Cássio Prazeres 1
Jian Yang 1
Adam Senft 1
Peter Desnoyers 1
Raj Sharman 1
Carsten Hentrich 1
Ali Pınar 1
Devis Bianchini 1
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Yaron Kanza 1
Xin Zhang 1
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Nuria Oliver 1
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Han Liu 1
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Ying Hu 1
Alejandro Bellogín 1
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Tim Weninger 1
Hans Jacobsen 1
Liwei Liu 1
Huanhuan Cao 1
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Li Zhang 1
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Qi Liu 1
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Chennee Chuah 1
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Yeliz Yeşilada 1
Sanmay Das 1
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Stephan Doerfel 1
Ya Zhang 1
Xiaoqingfrank Liu 1
Simon Walk 1
Sabrina De Capitani Di Vimercati 1
Zan Sun 1
Jalal Mahmud 1
IV Ramakrishnan 1
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Ming He 1
Jinyoung Han 1
Gleb Gusev 1

Affiliation Paper Counts
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Siemens AG 1
IBM Zurich Research Laboratory 1
Temple University 1
University of Cambridge 1
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Korea Advanced Institute of Science & Technology 1
Pontifical Catholic University of Rio de Janeiro 1
Shanghai University of Finance and Economics 1
Intel Corporation 1
Nanjing University 1
China University of Mining Technology 1
Cyprus University of Technology 1
Nanyang Technological University School of Computer Engineering 1
Siemens USA 1
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 1
Computer Sciences Corporation in Deutschland 1
University of Applied Sciences and Arts of Southern Switzerland 1
Orange Labs 1
Turgut Ozal University 1
IBM Canada Ltd. 1
IBM Ireland Limited 1
Case Western Reserve University 2
University of Amsterdam 2
University of Ioannina 2
University of Bergamo 2
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Slovak University of Technology in Bratislava 2
Sungkyunkwan University 2
University of Sao Paulo 2
Tohoku University 2
Duke University 2
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Linkoping University 2
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Telefonica 2
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Universitat d'Alicante 2
IBM Research 2
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Aristotle University of Thessaloniki 2
University of Southern California, Information Sciences Institute 2
Sandia National Laboratories, California 2
Nankai University 2
Vienna University of Technology 2
Wright State University 2
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Simon Fraser University 2
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University of Twente 2
Kumamoto University 2
Indiana University 2
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Johannes Kepler University Linz 2
University of Delaware 2
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Indian Statistical Institute, Kolkata 2
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Tata Research Development and Design Centre 2
Huazhong University of Science and Technology 2
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University of California, Los Angeles 2
Northeastern University 2
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Rutgers, The State University of New Jersey 2
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Qatar Computing Research institute 2
CSIRO Data61 2
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Chinese University of Hong Kong 3
University of Modena and Reggio Emilia 3
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Delft University of Technology 3
Northwestern University 3
University of Zagreb 3
Purdue University 3
France Telecom Division Recherche et Developpement 3
INRIA Rhone-Alpes 3
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AT&T Inc. 3
University of Brescia 3
NEC Laboratories America, Inc. 3
University of Science and Technology Beijing 3
Monash University 3
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University of Bari 3
Shanghai Jiaotong University 3
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University of Oxford 3
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Swiss Federal Institute of Technology, Lausanne 3
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University of Hamburg 3
Technical University of Madrid 3
Universite Paris Descartes 3
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Indian Institute of Technology, Delhi 3
Eindhoven University of Technology 3
Ghent University 3
University of Luxembourg 3
Institute for Scientific Interchange Foundation 3
GESIS - Leibniz Institute for the Social Sciences 3
Zhejiang University 4
Korea University 4
Nanyang Technological University 4
Lulea University of Technology 4
University of California, Irvine 4
Poznan University of Technology 4
HP Labs 4
Universidad Politecnica de Valencia 4
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University of Massachusetts Amherst 4
Peking University 4
Graz University of Technology 4
Texas A and M University 4
Universidad de A Coruna 4
Stony Brook University 4
University of Ottawa, Canada 4
University of Southern California 4
Commonwealth Scientific and Industrial Research Organization 4
Institute of Computing Technology Chinese Academy of Sciences 4
University of Calgary 5
Arizona State University 5
University of Groningen 5
Hasselt University 5
University of California, Davis 5
Singapore Management University 5
Chinese Academy of Sciences 5
Macquarie University 5
Georgia Institute of Technology 5
Technical University of Munich 5
University of Maryland 5
Technion - Israel Institute of Technology 5
South China University of Technology 5
Virginia Tech 5
University of the Basque Country 5
University of Illinois at Urbana-Champaign 6
Middle East Technical University 6
The University of Hong Kong 6
RMIT University 6
University of Toronto 6
Italian National Research Council 6
University of Southampton 6
University of Milan 6
Bilkent University 7
IBM Thomas J. Watson Research Center 7
Carnegie Mellon University 7
University of New South Wales 7
University of Athens 7
University of Alberta 7
Ca' Foscari University of Venice 8
Microsoft Corporation 8
University of Manchester 9
Hong Kong University of Science and Technology 9
Pennsylvania State University 10
University of Trento 10
University of Science and Technology of China 10
University of Illinois at Chicago 10
Dalian University of Technology 11
Stanford University 11
Tsinghua University 11
Microsoft Research Asia 11
Yahoo Research Barcelona 12
Politecnico di Milano 15
Microsoft Research 17
University of California, Santa Barbara 17
Yahoo Research Labs 20
 
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