28375225
User-centered indexing for adaptive information access
We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Learning and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments.
7673579
Managing personal and group collections of information
The internet revolution has dramatically increased the amount of information available to users. Various tools such as search engines have been developed to help users find the information they need from this vast repository. Users often also need tools to help manipulate the growing amount of useful information they have discovered. Current tools available for this purpose are typically local components of web browsers designed to manage URL bookmarks. They provide limited functionalities to handle high information complexities.
12280706
A distributed multi-agent system for collaborative information management and sharing
In this paper, we present DIAMS, a system of distributed, collaborative agents to help users access, manage, share and exchange information. A DIAMS personal agent helps its owner find information most relevant to current needs. It provides tools and utilities for users to manage their information repositories with dynamic organization and virtual views. Flexible hierarchical display is integrated with indexed query search to support effective information access. Automatic indexing methods are employed to support user queries and communication between agents.
2426986
Quantifying Privacy Leakage through Answering Database Queries
We assume a database consists of records of individuals with private or sensitive fields. Queries on the distribution of a sensitive field within a selected population in the database can be submitted to the data center. The answers to the queries leak private information of individuals though no identification information is provided. Inspired by decision theory, we present a quantitative model for the privacy protection problem in such a database query or linkage environment in this paper. In the model, the value of information is estimated from the viewpoint of the querier.To estimate the value, we define the information state of the data user by a class of probability distributions on the set of possible confidential values. We further define the usefulness of information based on how easy the data user can locate individuals that fit the description given in the queries. These states and the usefulness of information can be modified and refined by the user's knowledge acquisition actions. The value of information is then defined as the expected gain of the privacy receiver and the privacy is protected by imposing costs on the answers of the queries for balancing the gain.
119479
Visualizing and discovering web navigational patterns
Web site structures are complex to analyze. Cross-referencing the web structure with navigational behaviour adds to the complexity of the analysis. However, this convoluted analysis is necessary to discover useful patterns and understand the navigational behaviour of web site visitors, whether to improve web site structures, provide intelligent on-line tools or offer support to human decision makers. Moreover, interactive investigation of web access logs is often desired since it allows ad hoc discovery and examination of patterns not a priori known. Various visualization tools have been provided for this task but they often lack the functionality to conveniently generate new patterns. In this paper we propose a visualization tool to visualize web graphs, representations of web structure overlaid with information and pattern tiers. We also propose a web graph algebra to manipulate and combine web graphs and their layers in order to discover new patterns in an ad hoc manner.
193728
Feedback-based Scheduling for Back-end Databases in Shared Dynamic Content Server Clusters
This paper introduces a self-configuring architecture for scaling the database tier of dynamic content Web servers. We use a unified approach to load and fault management based on dynamic data replication and feedback-based scheduling. While replication provides scaling and high availability, feedback scheduling dynamically allocates tasks to commodity databases across workloads in response to peak loads or failure conditions thus providing quality of service. By augmenting the feedback loop with state awareness, we avoid oscillations in resource allocation. We investigate our transparent provisioning mechanisms in the database tier using the TPC-W e-commerce and the on-line auction Rubis benchmarks. We demonstrate that our techniques provide quality of service under load bursts and failure scenarios
39684197
A sound and complete proof theory for the generalized logic of only knowing
We present a sound and complete proof theory for the generalized logic of only knowing (GOL). The GOL logic is a modal logic proposed recently (Chen 1997) as a unified logical framework for non-monotonic reasoning, which extends the logic of only knowing (OL) of Levesque (1990), and covers the important notion of epistemic specification (Gelfond 1991, Gelfond and Przymusinska 1993) which is very useful for knowledge representation. The OL logic has been shown to have close connections with Lifschitz's minimal belief and negation as failure (Lifschitz 1994) and various other major non-monotonic formalisms. The GOL logic is more general and powerful than OL. The completeness result for the GOL proof theory establishes the theoretical foundation for answering queries in epistemic theories. This work is restricted to the propositional case only.
34159049
Approximation algorithms for scheduling real-time jobs with multiple feasible intervals
Time-critical jobs in many real-time applications have multiple feasible intervals. Such a job is constrained to execute from start to completion in one of its feasible intervals. A job fails if the job remains incomplete at the end of the last feasible interval. Earlier works developed an optimal off-line algorithm to schedule all the jobs in a given job set and on-line heuristics to schedule the jobs in a best effort manner. This paper is concerned with how to find a schedule in which the number of jobs completed in one of their feasible intervals is maximized. We show that the maximization problem is $${\cal N}{\cal P}$$-hard for both non-preemptible and preemptible jobs. This paper develops two approximation algorithms for non-preemptible and preemptible jobs. When jobs are non-preemptible, Algorithm Least Earliest Completion Time First (LECF) is shown to have a 2-approximation factor; when jobs are preemptible, Algorithm Least Execution Time First (LEF) is proved being a 3-approximation algorithm. We show that our analysis for the two algorithms are tight. We also evaluate our algorithms by extensive simulations. Simulation results show that Algorithms LECF and LEF not only guarantee the approximation factors but also outperform other multiple feasible interval scheduling algorithms in average.