34700716
Local computation of Gaussian belief functions
5950046
A theory of Gaussian belief functions
Abstract A Gaussian belief function can be intuitively described as a Gaussian distribution over a hyperplane, whose parallel subhyperplanes are the focal elements. This paper elaborates on the idea of Dempster and Shafer and formally represents a Gaussian belief function as a wide-sense inner product and a linear functional over a variable space, and as their duals over a hyperplane in a sample space. By adapting Dempster's rule to the continuous case, it derives a rule of combination and proves its equivalence to its geometric description by Dempster. It illustrates by examples how mixed knowledge involving linear equations, multivariate Gaussian distributions, and partial ignorance can be represented and combined as Gaussian belief functions.
8923918
A Linear Belief Function Approach to Portfolio Evaluation
We show how to use linear belief functions to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, distributional assumptions, linear relations, and empirical asset pricing models. We then appeal to Dempster's rule of combination to integrate the knowledge for assessing an overall belief on portfolio performance, and to update this belief by incorporating additional information.
14735447
Representing asymmetric decision problems using coarse valuations
A valuation-based system approach to knowledge representation has shown its advantages in improving computational efficiency and in allowing many decision models including belief networks. This study applies the Dempster-Shafer theory of belief functions and extends its framework to allow coarse valuations, which admit incomplete specification of probabilities and utilities and, therefore, are more flexible in representing asymmetric decision problems. It presents an algorithm for making inferences and decisions in systems using coarse valuations. It shows that a coarse valuation-based system provides a most natural and compact representation of decision problems.
18129308
PeerTrust: supporting reputation-based trust for peer-to-peer electronic communities
Peer-to-peer (P2P) online communities are commonly perceived as an environment offering both opportunities and threats. One way to minimize threats in such communities is to use community-based reputations to help estimate the trustworthiness of peers. We present PeerTrust - a reputation-based trust supporting framework, which includes a coherent adaptive trust model for quantifying and comparing the trustworthiness of peers based on a transaction-based feedback system, and a decentralized implementation of such a model over a structured P2P network. PeerTrust model has two main features. First, we introduce three basic trust parameters and two adaptive factors in computing trustworthiness of peers, namely, feedback a peer receives from other peers, the total number of transactions a peer performs, the credibility of the feedback sources, transaction context factor, and the community context factor. Second, we define a general trust metric to combine these parameters. Other contributions of the paper include strategies used for implementing the trust model in a decentralized P2P environment, evaluation mechanisms to validate the effectiveness and cost of PeerTrust model, and a set of experiments that show the feasibility and benefit of our approach.
31477
Surface parameterization via aligning optimal local flattening
This paper presents a novel parameterization method for a non-closed triangular mesh. For every flattened 1-ring neighbors, we choose a local coordinate frame, and the local geometry structure is represented as local parametric coordinates. Then the global optimal parametric coordinates are attained by aligning all the local parametric planes while preserving the local structure as much as possible. The boundary conditions are not necessary in our method, thus no high distortion appears around the boundary, and distortion is uniformly distributed over parametric domain. In addition, our method can operate directly on mesh surface which has holes without any preprocessing of surface partition. Furthermore, linear constraints are allowed in the parameterization in a least squares sense.
10153352
Trigger grouping: a scalable approach to large scale information monitoring
Information change monitoring services are becoming increasingly useful as more and more information is published on the Web. A major research challenge is how to make the service scalable to serve millions of monitoring requests. Such services usually use soft triggers to model users' monitoring requests. We have developed an effective trigger grouping scheme to optimize the trigger processing. The main idea behind this scheme is to reduce repeated computation by grouping monitoring requests of similar structures together. In this paper, we evaluate our approach using both measurements on real systems and simulations. The study shows significant performance gains using the trigger grouping approach. Moreover, the gains are critically dependent on group size and group size distribution (e.g., Zipf). We also discuss the benefit, trade-off, and runtime characteristics of the proposed approach.
2715587
A scalable peer-to-peer architecture for distributed information monitoring applications
We present PeerCQ, a decentralized architecture for Internet scale information monitoring using a network of heterogeneous peer nodes. PeerCQ uses continual queries (CQs) as its primitives to express information-monitoring requests. The PeerCQ development has three unique characteristics. First, we develop a systematic and serverless approach to large scale information monitoring, aiming at providing a fully distributed, highly scalable, and self-configurable architecture for scalable and reliable processing of a large number of CQs over a network of loosely coupled, heterogeneous, and possibly unreliable nodes (peers). Second, we introduce an effective service partitioning scheme at the P2P protocol layer to distribute the processing of CQs over a peer-to-peer information monitoring overlay network while maintaining a good balance between system utilization and load balance in the presence of peer joins, departures, and failures. A unique feature of our service partitioning scheme is its ability to incorporate strategies for handling hot spot monitoring requests and peer heterogeneity into the load balancing scheme in PeerCQ. Third, but not least, we develop a dynamic passive replication scheme to enable reliable processing of long-running information monitoring requests in an environment of inherently unreliable peers, including an analytical model to discuss its fault tolerance properties. We report a set of experiments demonstrating the feasibility and the effectiveness of the PeerCQ approach to large-scale peer-to-peer information monitoring.
326701
A modeling and execution environment for distributed scientific workflows
We illustrate how a domain scientist can perform a complex scientific task by interleaving data access, querying, and manipulation, as well as analytical steps and computations in complex, problem specific ways. We show how our system is used by a geneticist for solving the problem of discovering the so-called "co-regulated" genes by interlinking data and computation from several Web sites, local computations, as well as local and remote databases. The main distinctive features of our system (compared, e.g., to the ZOO environment (Ioannidis et al., 1996)) include: (i) executable workflows run as Web services; (ii) abstract workflows employ concept names and semantic types that are higher-level (and thus more "scientist friendly") than executable workflows; and (iii) our system supports automatic translation of the latter into the former.