45847616
EROICA: A Rule-Based Approach to Organizational Policy Management in Workflow Systems
Conventional workflow management systems have been criticized for being too rigid in dealing with changes in organizational policies, in particular for the lack of flexible mechanisms for handling complex role resolution. In this paper we describe a rule-based approach (EROICA) for managing organizational policies in workflow systems. Our approach extends the well-known ECA (event-condition-action) approach used in database systems by elevating roles, objects and time intervals to first class citizens, hence the term EROICA (event-role-object-interval-condition-action). In this paper, we focus on the development of a flexible role resolution mechanism that operates under various business constraints specified with EROICA rules. We further discuss the issues of architecture and implementation and demonstrate that the EROICA framework is promising in making next generation of workflow systems more powerful and flexible.
17013178
Smart supply web: an application of web-based data and workflow mediation
This paper describes our vision of a Supply Web as the next major advance in the e-commerce world in terms of how e-services are delivered. After highlighting shortcomings of existing supply chain solutions, we give an architecture to show how three important technologies, namely, workow systems, data integration and on-line decision support, will have to come together to enable seamless deployment of e-service supply web solutions. We rst discuss research eorts (namely, XRL, YAT and DecisionFlows) that support the three technologies separately, and then describe the challenges of integrating them eectively.
1888002
An Analysis and Taxonomy of Unstructured Workflows
Most workflow tools support structured workflows despite the fact that unstructured workflows can be more expressive. The reason for this is that unstructured workflows are more prone to errors. In this paper, we describe a taxonomy that serves as a framework for analyzing unstructured workflows. The taxonomy organizes unstructured workflows in terms of two considerations: improper nesting and mismatched split-join pairs. Based on this taxonomy we characterize situations that are well-behaved and others that are not. We also discuss well-behaved unstructured workflows that have equivalent structured mappings. Finally, we also introduce a relaxed notion of correctness called quasi-equivalence that is based on one-directional bisimulation. The results of our research will be useful for researchers investigating expressiveness and correctness issues in unstructured workflows.
1890977
W-RBAC - A Workflow Security Model Incorporating Controlled Overriding of Constraints
This paper presents a pair of role-based access control models for workflow systems, collectively known as the W-RBAC models. The first of these models, W0-RBAC is based on a framework that couples a powerful RBAC-based permission service and a workflow component with clear separation of concerns for ease of administration of authorizations. The permission service is the focus of the work, providing an expressive logic-based language for the selection of users authorized to perform workflow tasks, with preference ranking. W1-RBAC extends the basic model by incorporating exception handling capabilities through controlled and systematic overriding of constraints.
16012186
A fine-grained, controllable, user-to-user delegation method in RBAC
This paper addresses the issues surrounding user-to-user delegation in RBAC. We show how delegations can be incorporated into the RBAC model in a simple and straightforward manner. A special feature of the model is that it allows fine-grained control over what rights a user wishes to delegate as opposed to delegation at the role level where all the rights of a role must be delegated. In addition, the model provides a rich set of controls regarding further delegations of a right, generic constraints that further control delegations, and an innovative model for revocations. Properties of both delegation and revocation are discussed, and our work is compared with other related research.
206778468
A performance study of general grid structures for replicated data
Recently, there has been considerable interest in the study of replica-control protocols which are based on organizing several copies of an object into logical structures, such as rectangular grids. In addition to high availability, another objective in exploiting such structures is to improve the degree of load sharing in a system. The authors extend the scope of grid structures to general grids, which allow holes in various positions of a rectangular structure and are useful to consider because they often produce availabilities that are higher than solid grids, where every position must be occupied by a node. In addition to proposing an improvement to the existing grid protocol, new insights are offered into the performance of the grids, from both availability and load sharing points of view. Algorithms for designing grids to maximize availability independently and also in conjunction with a load sharing constraint are given.<<ETX>>
8461087
Articulatory class based spectral envelope representation for voice fonts
Voice fonts are used to represent and transform the personality of speech. The spectral envelope is one of the main descriptors of voice fonts. We present a new technique to represent the spectral envelope in voice fonts which makes it possible to create a universal voice font for a given speaker which can be used for multiple languages. It enables voice fonts to be used for translingual personality transformation of speech. We evaluate the performance of the proposed approach through monolingual and translingual personality transformation experiments. We also compare the performance with the phoneme class based spectral envelope representation in voice fonts through various objective and subjective tests.
10669659
Study of Canada/US Dollar Exchange Rate Movements Using Recurrent Neural Network Model of FX-Market
Understanding exchange rate movements has long been an extremely challenging and important task. Unsatisfactory results produced by time series regression models have led to the claim by several authors that in foreign exchange markets, past movements of the price of a given currency have no predictive power in forecasting future movements of the currency price. In this paper, we build a recurrent neural network model for FX-market to explain exchange rate movements. Asset prices are discovered in the marketplace by the interaction of market design and agents’ behaviour. The interaction is simulated by integrating 1) the FX-market mechanism; 2) an economic framework; and 3) the embedding of both tasks in neural network architectures. The results indicate that both macroeconomic and microeconomic variables are useful to forecast exchange rate changes. Results from regression model based on neural-fuzzy forecasting system are also included for comparison.
33509544
Multiscale rough set data analysis with application to stock performance modeling
Multiscale Rough Set Data Analysis (MSRSDA) combines the feature selection and feature extraction ability of RSDA with the multiresolution property of the wavelet transform. RSDA removes the irrelevant (having no impact on prediction performance) and dependent (having no discriminatory power) market variables from the multivariate time series data matrix, thereby improving generalization capability of the forecasting engine (neural network, fuzzy inference system). A wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in details and approximation. Significance of market variables at various wavelet scales representing "detailed" short-term history and "general" long-term history of the time series, is computed using RSDA in wavelet space. Irrelevant and redundant variables at each scale are removed and a forecasting engine is then trained on each of the relevant resolution scales (i.e., those scales where significant events are detected); individual wavelets forecasts are recombined to form the overall forecast. The superior performance of MSRSDA is illustrated through stock performance modeling example. For prediction of S&P500 index, a data matrix of 21 variables has been used. MSRSDA gives 69% accuracy on stock-trend prediction.
14165673
Ontology for Task-Based Clinical Guidelines and the Theory of Granular Partitions
The theory of granular partitions (TGP) is a new approach to the understanding of ontologies and other classificatory systems. The paper explores the use of this new theory in the treatment of task-based clinical guidelines as a means for better understanding the relations between different clinical tasks, both within the framework of a single guideline and between related guidelines. We used as our starting point a DAML+OIL-based ontology for the WHO guideline for hypertension management, comparing this with related guidelines and attempting to show that TGP provides a flexible and highly expressive basis for the manipulation of ontologies of a sort which might be useful in providing more adequate Computer Interpretable Guideline Models (CIGMs) in the future.
18222685
XCHOKe: malicious source control for congestion avoidance at Internet gateways
The paper describes an algorithm to control flows from nonadaptive sources in the Internet in a simple and lightweight fashion. The algorithm, called XCHOKe, is an extension to CHOKe and RED. The paper presents experimental results from simulations that demonstrate that the algorithm outperforms current well-known algorithms for buffer management in providing better protection for TCP flows under varying degrees of attack from non-adaptive flows.