Showing papers in "ACM Transactions on The Web in 2012"

Friendship prediction and homophily in social media

Abstract: Social media have attracted considerable attention because their open-ended nature allows users to create lightweight semantic scaffolding to organize and share content. To date, the interplay of the social and topical components of social media has been only partially explored. Here, we study the presence of homophily in three systems that combine tagging social media with online social networks. We find a substantial level of topical similarity among users who are close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local similarity between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar interests are more likely to be friends, and therefore topical similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on several datasets, confirming that social networks constructed from topical similarity capture actual friendship accurately. When combined with topological features, topical similarity achieves a link prediction accuracy of about 92p.

Crawling Ajax-Based Web Applications through Dynamic Analysis of User Interface State Changes

Abstract: Using JavaScript and dynamic DOM manipulation on the client side of Web applications is becoming a widespread approach for achieving rich interactivity and responsiveness in modern Web applications. At the same time, such techniques---collectively known as Ajax---shatter the concept of webpages with unique URLs, on which traditional Web crawlers are based. This article describes a novel technique for crawling Ajax-based applications through automatic dynamic analysis of user-interface-state changes in Web browsers. Our algorithm scans the DOM tree, spots candidate elements that are capable of changing the state, fires events on those candidate elements, and incrementally infers a state machine that models the various navigational paths and states within an Ajax application. This inferred model can be used in program comprehension and in analysis and testing of dynamic Web states, for instance, or for generating a static version of the application. In this article, we discuss our sequential and concurrent Ajax crawling algorithms. We present our open source tool called Crawljax, which implements the concepts and algorithms discussed in this article. Additionally, we report a number of empirical studies in which we apply our approach to a number of open-source and industrial Web applications and elaborate on the obtained results.

A hybrid approach for efficient Web service composition with end-to-end QoS constraints

Abstract: Dynamic selection of Web services at runtime is important for building flexible and loosely-coupled service-oriented applications. An abstract description of the required services is provided at design-time, and matching service offers are located at runtime. With the growing number of Web services that provide the same functionality but differ in quality parameters (e.g., availability, response time), a decision needs to be made on which services should be selected such that the user's end-to-end QoS requirements are satisfied. Although very efficient, local selection strategy fails short in handling global QoS requirements. Solutions based on global optimization, on the other hand, can handle global constraints, but their poor performance renders them inappropriate for applications with dynamic and realtime requirements. In this article we address this problem and propose a hybrid solution that combines global optimization with local selection techniques to benefit from the advantages of both worlds. The proposed solution consists of two steps: first, we use mixed integer programming (MIP) to find the optimal decomposition of global QoS constraints into local constraints. Second, we use distributed local selection to find the best Web services that satisfy these local constraints. The results of experimental evaluation indicate that our approach significantly outperforms existing solutions in terms of computation time while achieving close-to-optimal results.

Beyond Social Graphs: User Interactions in Online Social Networks and their Implications

Abstract: Social networks are popular platforms for interaction, communication, and collaboration between friends. Researchers have recently proposed an emerging class of applications that leverage relationships from social networks to improve security and performance in applications such as email, Web browsing, and overlay routing. While these applications often cite social network connectivity statistics to support their designs, researchers in psychology and sociology have repeatedly cast doubt on the practice of inferring meaningful relationships from social network connections alone. This leads to the question: “Are social links valid indicators of real user interaction? If not, then how can we quantify these factors to form a more accurate model for evaluating socially enhanced applications?” In this article, we address this question through a detailed study of user interactions in the Facebook social network. We propose the use of “interaction graphs” to impart meaning to online social links by quantifying user interactions. We analyze interaction graphs derived from Facebook user traces and show that they exhibit significantly lower levels of the “small-world” properties present in their social graph counterparts. This means that these graphs have fewer “supernodes” with extremely high degree, and overall graph diameter increases significantly as a result. To quantify the impact of our observations, we use both types of graphs to validate several well-known social-based applications that rely on graph properties to infuse new functionality into Internet applications, including Reliable Email (RE), SybilGuard, and the weighted cascade influence maximization algorithm. The results reveal new insights into each of these systems, and confirm our hypothesis that to obtain realistic and accurate results, ongoing research on social network applications studies of social applications should use real indicators of user interactions in lieu of social graphs.

Identifying Web Spam with the Wisdom of the Crowds

Abstract: Combating Web spam has become one of the top challenges for Web search engines. State-of-the-art spam-detection techniques are usually designed for specific, known types of Web spam and are incapable of dealing with newly appearing spam types efficiently. With user-behavior analyses from Web access logs, a spam page-detection algorithm is proposed based on a learning scheme. The main contributions are the following. (1) User-visiting patterns of spam pages are studied, and a number of user-behavior features are proposed for separating Web spam pages from ordinary pages. (2) A novel spam-detection framework is proposed that can detect various kinds of Web spam, including newly appearing ones, with the help of the user-behavior analysis. Experiments on large-scale practical Web access log data show the effectiveness of the proposed features and the detection framework.

ClickRank: Learning Session-Context Models to Enrich Web Search Ranking

Abstract: User browsing information, particularly non-search-related activity, reveals important contextual information on the preferences and intents of Web users. In this article, we demonstrate the importance of mining general Web user behavior data to improve ranking and other Web-search experience, with an emphasis on analyzing individual user sessions for creating aggregate models. In this context, we introduce ClickRank, an efficient, scalable algorithm for estimating Webpage and Website importance from general Web user-behavior data. We lay out the theoretical foundation of ClickRank based on an intentional surfer model and discuss its properties. We quantitatively evaluate its effectiveness regarding the problem of Web-search ranking, showing that it contributes significantly to retrieval performance as a novel Web-search feature. We demonstrate that the results produced by ClickRank for Web-search ranking are highly competitive with those produced by other approaches, yet achieved at better scalability and substantially lower computational costs. Finally, we discuss novel applications of ClickRank in providing enriched user Web-search experience, highlighting the usefulness of our approach for nonranking tasks.

Modellus: Automated modeling of complex internet data center applications

Abstract: The rising complexity of distributed server applications in Internet data centers has made the tasks of modeling and analyzing their behavior increasingly difficult. This article presents Modellus, a novel system for automated modeling of complex web-based data center applications using methods from queuing theory, data mining, and machine learning. Modellus uses queuing theory and statistical methods to automatically derive models to predict the resource usage of an application and the workload it triggers; these models can be composed to capture multiple dependencies between interacting applications.Model accuracy is maintained by fast, distributed testing, automated relearning of models when they change, and methods to bound prediction errors in composite models. We have implemented a prototype of Modellus, deployed it on a data center testbed, and evaluated its efficacy for modeling and analysis of several distributed multitier web applications. Our results show that this feature-based modeling technique is able to make predictions across several data center tiers, and maintain predictive accuracy (typically 95p or better) in the face of significant shifts in workload composition; we also demonstrate practical applications of the Modellus system to prediction and provisioning of real-world data center applications.

Extracting information networks from the blogosphere

Abstract: We study the problem of automatically extracting information networks formed by recognizable entities as well as relations among them from social media sites. Our approach consists of using state-of-the-art natural language processing tools to identify entities and extract sentences that relate such entities, followed by using text-clustering algorithms to identify the relations within the information network. We propose a new term-weighting scheme that significantly improves on the state-of-the-art in the task of relation extraction, both when used in conjunction with the standard tf c idf scheme and also when used as a pruning filter. We describe an effective method for identifying benchmarks for open information extraction that relies on a curated online database that is comparable to the hand-crafted evaluation datasets in the literature. From this benchmark, we derive a much larger dataset which mimics realistic conditions for the task of open information extraction. We report on extensive experiments on both datasets, which not only shed light on the accuracy levels achieved by state-of-the-art open information extraction tools, but also on how to tune such tools for better results.

Quality and Leniency in Online Collaborative Rating Systems

Abstract: The emerging trend of social information processing has resulted in Web users’ increased reliance on user-generated content contributed by others for information searching and decision making. Rating scores, a form of user-generated content contributed by reviewers in online rating systems, allow users to leverage others’ opinions in the evaluation of objects. In this article, we focus on the problem of summarizing the rating scores given to an object into an overall score that reflects the object’s quality. We observe that the existing approaches for summarizing scores largely ignores the effect of reviewers exercising different standards in assigning scores. Instead of treating all reviewers as equals, our approach models the leniency of reviewers, which refers to the tendency of a reviewer to assign higher scores than other coreviewers. Our approach is underlined by two insights: (1) The leniency of a reviewer depends not only on how the reviewer rates objects, but also on how other reviewers rate those objects and (2) The leniency of a reviewer and the quality of rated objects are mutually dependent. We develop the leniency-aware quality, or LQ model, which solves leniency and quality simultaneously. We introduce both an exact and a ranked solution to the model. Experiments on real-life and synthetic datasets show that LQ is more effective than comparable approaches. LQ is also shown to perform consistently better under different parameter settings.

Integrating trust management and access control in data-intensive Web applications

Abstract: The widespread diffusion of Web-based services provided by public and private organizations emphasizes the need for a flexible solution for protecting the information accessible through Web applications. A promising approach is represented by credential-based access control and trust management. However, although much research has been done and several proposals exist, a clear obstacle to the realization of their benefits in data-intensive Web applications is represented by the lack of adequate support in the DBMSs. As a matter of fact, DBMSs are often responsible for the management of most of the information that is accessed using a Web browser or a Web service invocation.In this article, we aim at eliminating this gap, and present an approach integrating trust management with the access control of the DBMS. We propose a trust model with a SQL syntax and illustrate an algorithm for the efficient verification of a delegation path for certificates. Our solution nicely complements current trust management proposals allowing the efficient realization of the services of an advanced trust management model within current relational DBMSs. An important benefit of our approach lies in its potential for a robust end-to-end design of security for personal data in Web scenario, where vulnerabilities of Web applications cannot be used to violate the protection of the data residing on the database server. We also illustrate the implementation of our approach within an open-source DBMS discussing design choices and performance impact.

Navigating tomorrow's web: From searching and browsing to visual exploration

Abstract: We propose a new way of navigating the Web using interactive information visualizations, and present encouraging results from a large-scale Web study of a visual exploration system. While the Web has become an immense, diverse information space, it has also evolved into a powerful software platform. We believe that the established interaction techniques of searching and browsing do not sufficiently utilize these advances, since information seekers have to transform their information needs into specific, text-based search queries resulting in mostly text-based lists of resources. In contrast, we foresee a new type of information seeking that is high-level and more engaging, by providing the information seeker with interactive visualizations that give graphical overviews and enable query formulation. Building on recent work on faceted navigation, information visualization, and exploratory search, we conceptualize this type of information navigation as visual exploration and evaluate a prototype Web-based system that implements it. We discuss the results of a large-scale, mixed-method Web study that provides a better understanding of the potential benefits of visual exploration on the Web, and its particular performance challenges.

Exploiting External Collections for Query Expansion

Abstract: A persisting challenge in the field of information retrieval is the vocabulary mismatch between a user’s information need and the relevant documents. One way of addressing this issue is to apply query modeling: to add terms to the original query and reweigh the terms. In social media, where documents usually contain creative and noisy language (e.g., spelling and grammatical errors), query modeling proves difficult. To address this, attempts to use external sources for query modeling have been made and seem to be successful. In this article we propose a general generative query expansion model that uses external document collections for term generation: the External Expansion Model (EEM). The main rationale behind our model is our hypothesis that each query requires its own mixture of external collections for expansion and that an expansion model should account for this. For some queries we expect, for example, a news collection to be most beneficial, while for other queries we could benefit more by selecting terms from a general encyclopedia. EEM allows for query-dependent weighing of the external collections.We put our model to the test on the task of blog post retrieval and we use four external collections in our experiments: (i) a news collection, (ii) a Web collection, (iii) Wikipedia, and (iv) a blog post collection. Experiments show that EEM outperforms query expansion on the individual collections, as well as the Mixture of Relevance Models that was previously proposed by Diaz and Metzler [2006]. Extensive analysis of the results shows that our naive approach to estimating query-dependent collection importance works reasonably well and that, when we use “oracle” settings, we see the full potential of our model. We also find that the query-dependent collection importance has more impact on retrieval performance than the independent collection importance (i.e., a collection prior).

Cache-Based Query Processing for Search Engines

Abstract: In practice, a search engine may fail to serve a query due to various reasons such as hardware/network failures, excessive query load, lack of matching documents, or service contract limitations (e.g., the query rate limits for third-party users of a search service). In this kind of scenarios, where the backend search system is unable to generate answers to queries, approximate answers can be generated by exploiting the previously computed query results available in the result cache of the search engine. In this work, we propose two alternative strategies to implement this cache-based query processing idea. The first strategy aggregates the results of similar queries that are previously cached in order to create synthetic results for new queries. The second strategy forms an inverted index over the textual information (i.e., query terms and result snippets) present in the result cache and uses this index to answer new queries. Both approaches achieve reasonable result qualities compared to processing queries with an inverted index built on the collection.

Workload Characterization and Performance Implications of Large-Scale Blog Servers

Abstract: With the ever-increasing popularity of Social Network Services (SNSs), an understanding of the characteristics of these services and their effects on the behavior of their host servers is critical. However, there has been a lack of research on the workload characterization of servers running SNS applications such as blog services. To fill this void, we empirically characterized real-world Web server logs collected from one of the largest South Korean blog hosting sites for 12 consecutive days. The logs consist of more than 96 million HTTP requests and 4.7TB of network traffic. Our analysis reveals the following: (i) The transfer size of nonmultimedia files and blog articles can be modeled using a truncated Pareto distribution and a log-normal distribution, respectively; (ii) user access for blog articles does not show temporal locality, but is strongly biased towards those posted with image or audio files. We additionally discuss the potential performance improvement through clustering of small files on a blog page into contiguous disk blocks, which benefits from the observed file access patterns. Trace-driven simulations show that, on average, the suggested approach achieves 60.6p better system throughput and reduces the processing time for file access by 30.8p compared to the best performance of the Ext4 filesystem.

FoXtrot: Distributed structural and value XML filtering

Abstract: Publish/subscribe systems have emerged in recent years as a promising paradigm for offering various popular notification services. In this context, many XML filtering systems have been proposed to efficiently identify XML data that matches user interests expressed as queries in an XML query language like XPath. However, in order to offer XML filtering functionality on an Internet-scale, we need to deploy such a service in a distributed environment, avoiding bottlenecks that can deteriorate performance. In this work, we design and implement FoXtrot, a system for filtering XML data that combines the strengths of automata for efficient filtering and distributed hash tables for building a fully distributed system. Apart from structural-matching, performed using automata, we also discuss different methods for evaluating value-based predicates. We perform an extensive experimental evaluation of our system, FoXtrot, on a local cluster and on the PlanetLab network and demonstrate that it can index millions of user queries, achieving a high indexing and filtering throughput. At the same time, FoXtrot exhibits very good load-balancing properties and improves its performance as we increase the size of the network.

A model-driven methodology to the content layout problem in web applications

Abstract: This article presents a model-driven approach for the design of the layout in a complex Web application, where large amounts of data are accessed. The aim of this work is to reduce, as much as possible, repetitive tasks and to factor out common aspects into different kinds of rules that can be reused across different applications. In particular, exploiting the conceptual elements of the typical models used for the design of a Web application, it defines presentation and layout rules at different levels of abstraction and granularity. A procedure for the automatic layout of the content of a page is proposed and evaluated, and the layout of advanced Web applications is discussed.

A Methodology for SIP and SOAP Integration Using Application-Specific Protocol Conversion

Abstract: In recent years, the ubiquitous demands for cross-protocol application access are driving the need for deeper integration between SIP and SOAP. In this article we present a novel methodology for integrating these two protocols. Through an analysis of properties of SIP and SOAP we show that integration between these protocols should be based on application-specific converters. We describe a generic SIP/SOAP gateway that implements message handling and network and storage management while relying on application-specific converters to define session management and message mapping for a specific set of SIP and SOAP communication nodes. In order to ease development of these converters, we introduce an XML-based domain-specific language for describing application-specific conversion processes. We show how conversion processes can be easily specified in the language using message sequence diagrams of the desired interaction. We evaluate the presented methodology through performance analysis of the developed prototype gateway and high-level comparison with other solutions.