We examine the lifecycle requirements of intermittently connected web-applications (ICWAs) and investigate whether such applications can be developed as an "always connected" web-application combined with middleware that address ICWA requirements. We show that this is difficult to do because ICWAs require application-specific logic that is not easily combined with a middleware API. We therefore propose the use of "middleware components" in the areas of data-provisioning and change-set propagation. Combined with application-specific logic, these components make it easier to develop an ICWA by reducing the amount of required developer code. We show how our prototype UNTETHER system implements these components and reduces the burden on the application developer.

Reuse of existing presentation materials is prevalent among knowledge workers. However, finding the most appropriate material for reuse is challenging. Existing information management and search tools provide inadequate support for reuse due to their dependence on users' ability to effectively categorize, recall, and recognize existing materials. Based on our findings from an online survey and contextual interviews, we designed and implemented a slide-based contextual recommender, ConReP, for supporting reuse of presentation materials. ConReP utilizes a user-selected slide as a search-key, recommends materials based on similarity to the selected slide, and provides a local-context-based visual representation of the recommendations. Users input provides new insight into presentation reuse and reveals that slide-based search is more effective than keyword-based search, local-context-based visual representation helps in better recall and recognition, and shows the promise of this general approach of exploiting individual slides and local-context for better presentation reuse.

Diversified ranking on graphs is a fundamental mining task and has a variety of high-impact applications. There are two important open questions here. The first challenge is the measure - how to quantify the goodness of a given top-k ranking list that captures both the relevance and the diversity? The second challenge lies in the algorithmic aspect - how to find an optimal, or near-optimal, top-k ranking list that maximizes the measure we defined in a scalable way? In this paper, we address these challenges from an optimization point of view. Firstly, we propose a goodness measure for a given top-k ranking list. The proposed goodness measure intuitively captures both (a) the relevance between each individual node in the ranking list and the query; and (b) the diversity among different nodes in the ranking list. Moreover, we propose a scalable algorithm (linear wrt the size of the graph) that generates a provably near-optimal solution. The experimental evaluations on real graphs demonstrate its effectiveness and efficiency.

This work aims at discovering community structure in rich media social networks through analysis of time-varying, multirelational data. Community structure represents the latent social context of user actions. It has important applications such as search and recommendation. The problem is particularly useful in the enterprise domain, where extracting emergent community structure on enterprise social media can help in forming new collaborative teams, in expertise discovery, and in the long term reorganization of enterprises based on collaboration patterns. There are several unique challenges: (a) In social media, the context of user actions is constantly changing and coevolving; hence the social context contains time-evolving multidimensional relations. (b) The social context is determined by the available system features and is unique in each social media platform; hence the analysis of such data needs to flexibly incorporate various system features. In this article we propose MetaFac (MetaGraph Factorization), a framework that extracts community structures from dynamic, multidimensional social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multirelational and multidimensional social data; (2) an efficient multirelational factorization method for community extraction on a given metagraph; (3) an online method to handle time-varying relations through incremental metagraph factorization. Extensive experiments on real-world social data collected from an enterprise and the public Digg social media Web site suggest that our technique is scalable and is able to extract meaningful communities from social media contexts. We illustrate the usefulness of our framework through two prediction tasks: (1) in the enterprise dataset, the task is to predict users’ future interests on tag usage, and (2) in the Digg dataset, the task is to predict users’ future interests in voting and commenting on Digg stories. Our prediction significantly outperforms baseline methods (including aspect model and tensor analysis), indicating the promising direction of using metagraphs for handling time-varying social relational contexts.

Presentation material is a commonly-performed task. Yet current tools provide inadequate support - search tools are unable to return individual slides, and the linear model employed by presentation creation tools lacks structure and context. We propose a novel method for presentation creation, implemented in a tool called Outline Wizard, which enables outline-based composition and search. An Outline Wizard user enters a hierarchically-structured outline of a presentation; using that structure, the tool extracts user requests to formulate contextual queries, matches them against presentations within a repository, taking into account both content and structures of the presentations, and presents the user with sets of slides that are appropriate for each outline topic. At the heart of Outline Wizard is an outline-based search technique, which conducts content search within the context derived from the hierarchical structures of both user requests and presentations. We present a heuristic outline-extraction technique, which is used to reverse engineer the structures of presentations, thereby making the structures available for our search engine. Evaluations show that the outline extraction technique and outline-based search both perform well, and that users report a satisfying experience when using Outline Wizard to compose presentations from libraries of existing material.

This paper aims at discovering community structure in rich media social networks, through analysis of time-varying, multi-relational data. Community structure represents the latent social context of user actions. It has important applications in information tasks such as search and recommendation. Social media has several unique challenges. (a) In social media, the context of user actions is constantly changing and co-evolving; hence the social context contains time-evolving multi-dimensional relations. (b) The social context is determined by the available system features and is unique in each social media website. In this paper we propose MetaFac (MetaGraph Factorization), a framework that extracts community structures from various social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multi-relational and multi-dimensional social data; (2) an efficient factorization method for community extraction on a given metagraph; (3) an on-line method to handle time-varying relations through incremental metagraph factorization. Extensive experiments on real-world social data collected from the Digg social media website suggest that our technique is scalable and is able to extract meaningful communities based on the social media contexts. We illustrate the usefulness of our framework through prediction tasks. We outperform baseline methods (including aspect model and tensor analysis) by an order of magnitude.

Social media websites promote diverse user interaction on media objects as well as user actions with respect to other users. The goal of this work is to discover community structure in rich media social networks, and observe how it evolves over time, through analysis of multi-relational data. The problem is important in the enterprise domain where extracting emergent community structure on enterprise social media, can help in forming new collaborative teams, aid in expertise discovery, and guide long term enterprise reorganization. Our approach consists of three main parts: (1) a relational hypergraph model for modeling various social context and interactions; (2) a novel hypergraph factorization method for community extraction on multi-relational social data; (3) an on-line method to handle temporal evolution through incremental hypergraph factorization. Extensive experiments on real-world enterprise data suggest that our technique is scalable and can extract meaningful communities. To evaluate the quality of our mining results, we use our method to predict users' future interests. Our prediction outperforms baseline methods (frequency counts, pLSA) by 36-250% on the average, indicating the utility of leveraging multi-relational social context by using our method.

Execution behavior of a Java application can be nondeterministic due to concurrent threads of execution, thread scheduling, and variable network delays. This nondeterminism in Java makes the understanding and debugging of multi-threaded distributed Java applications a difficult and a laborious process. It is well accepted that providing deterministic replay of application execution is a key step towards programmer productivity and program under-standing. Towards this goal, we developed a replay framework based on logical thread schedules and logical intervals. An application of this framework was previously published in the context of a system called Deja Vu that provides deterministic replay of multi-threaded Java programs on a single Java Virtual Machine (JVM). In contrast, this paper focuses on distributed Deja Vu that provides deterministic replay of distributed Java applications running on multiple JVMs. We describe the issues and present the design, implementation and preliminary performance results of distributed Deja Vu that supports both multi-threaded and distributed Java applications.

Language-supported synchronization is a source of serious performance problems in many Java programs. Even single-threaded applications may spend up to half their time performing useless synchronization due to the thread-safe nature of the Java libraries. We solve this performance problem with a new algorithm that allows lock and unlock operations to be performed with only a few machine instructions in the most common cases. Our locks only require a partial word per object, and were implemented without increasing object size. We present measurements from our implementation in the JDK 1.1.2 for AIX, demonstrating speedups of up to a factor of 5 in micro-benchmarks and up to a factor of 1.7 in real programs.

Adaptive load distribution is necessary for parallel applications to co-exist effectively with other jobs in a network of shared, heterogeneous workstations. We present three methods that provide such support for PVM applications. Two of these methods, MPVM (migratable PVM) and UPVM (user-level PVM), adapt to changes in the workstation environment by transparently migrating the virtual processors (VPs) of the parallel application. A VP in MPVM is a Unix process, while UPVM defines lightweight process-like VPs. The third method, ADM (adaptive data movement), is a programming methodology for writing programs that perform adaptive load distribution through data movement. These methods are discussed and compared in terms of effectiveness, usability and performance.>