256,761 research outputs found
Towards defining semantic foundations for purpose-based privacy policies
We define a semantic model for purpose, based on which purpose-based privacy policies can be meaningfully expressed and enforced in a business system. The model is based on the intuition that the purpose of an action is determined by its situation among other inter-related actions. Actions and their relationships can be modeled in the form of an action graph which is based on the business processes in a system. Accordingly, a modal logic and the corresponding model checking algorithm are developed for formal expression of purpose-based policies and verifying whether a particular system complies with them. It is also shown through various examples, how various typical purpose-based policies as well as some new policy types can be expressed and checked using our model
DEEP: a provenance-aware executable document system
The concept of executable documents is attracting growing interest from both academics and publishers since it is a promising technology for the dissemination of scientific results. Provenance is a kind of metadata that provides a rich description of the derivation history of data products starting from their original sources. It has been used in many different e-Science domains and has shown great potential in enabling reproducibility of scientific results. However, while both executable documents and provenance are aimed at enhancing the dissemination of scientific results, little has been done to explore the integration of both techniques. In this paper, we introduce the design and development of DEEP, an executable document environment that generates scientific results dynamically and interactively, and also records the provenance for these results in the document. In this system, provenance is exposed to users via an interface that provides them with an alternative way of navigating the executable document. In addition, we make use of the provenance to offer a document rollback facility to users and help to manage the system's dynamic resources
A framework for proving the self-organization of dynamic systems
This paper aims at providing a rigorous definition of self- organization, one
of the most desired properties for dynamic systems (e.g., peer-to-peer systems,
sensor networks, cooperative robotics, or ad-hoc networks). We characterize
different classes of self-organization through liveness and safety properties
that both capture information re- garding the system entropy. We illustrate
these classes through study cases. The first ones are two representative P2P
overlays (CAN and Pas- try) and the others are specific implementations of
\Omega (the leader oracle) and one-shot query abstractions for dynamic
settings. Our study aims at understanding the limits and respective power of
existing self-organized protocols and lays the basis of designing robust
algorithm for dynamic systems
The Impact of Global Clustering on Spatial Database Systems
Global clustering has rarely been investigated in
the area of spatial database systems although dramatic
performance improvements can be
achieved by using suitable techniques. In this paper,
we propose a simple approach to global clustering
called cluster organization. We will demonstrate
that this cluster organization leads to considerable
performance improvements without any
algorithmic overhead. Based on real geographic
data, we perform a detailed empirical performance
evaluation and compare the cluster organization
to other organization models not using global
clustering. We will show that global clustering
speeds up the processing of window queries as
well as spatial joins without decreasing the performance
of the insertion of new objects and of selective
queries such as point queries. The spatial
join is sped up by a factor of about 4, whereas
non-selective window queries are accelerated by
even higher speed up factors
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