An agent-based workflow system for enterprise based on FIPA-OS framework

The paper describes what we have learned from implementing a multi-agent architecture used to support enterprise notions and principles for intelligent systems design. In the last couple of years, agent-based management systems have been widely used to monitor and control business design processes. In this paper, multi-agent architecture is proposed and applied to the workflow management system in ASE¹ (Advance Semiconductor Electronic) Inc., which is the world’s well-known largest provider of independent semiconductor for manufacturing services in assembly and test. The proposed FIPA-OS autonomous workflow management system uses a workflow co-ordination mechanism and an agent integration mechanism to enable the routine daily jobs error handle. Our vision for extending the FIPA-OS architectural elements to cover the development and implementation of generic web-Centric collaborative applications concludes the paper.     

An incremental analysis for resource conflicts to workflow specifications

Workflow management technology helps modulizing and controlling complex business processes within an enterprise. Generally speaking, a workflow management system (WfMS) is composed of two primary components, a design environment and a run-time system. Structural, timing and resource verifications of a workflow specification are required to assure the correctness of the specified system. In this paper, an incremental methodology is constructed to analyze resource consistency and temporal constraints after each edit unit defined on a workflow specification. The methodology introduces several algorithms for general and temporal analyses. The output returned right away can improve the judgment and thus the speed and quality on designing.     

An overview of workflow management: From process modeling to workflow automation infrastructure

Today's business enterprises must deal with global competition, reduce the cost of doing business, and rapidly develop new services and products. To address these requirements enterprises must constantly reconsider and optimize the way they do business and change their information systems and applications to support evolving business processes. Workflow technology facilitates these by providing methodologies and software to support (i) business process modeling to capture business processes as workflow specifications, (ii) business process reengineering to optimize specified processes, and (iii) workflow automation to generate workflow implementations from workflow specifications. This paper provides a high-level overview of the current workflow management methodologies and software products. In addition, we discuss the infrastructure technologies that can address the limitations of current commercial workflow technology and extend the scope and mission of workflow management systems to support increased workflow automation in complex real-world environments involving heterogeneous, autonomous, and distributed information systems. In particular, we discuss how distributed object management and customized transaction management can support further advances in the commercial state of the art in this area. Recomended by: Omran Bukhres and e. Kühn     

POESIA: An ontological workflow approach for composing Web services in agriculture

This paper describes the POESIA approach to systematic composition of Web services. This pragmatic approach is strongly centered in the use of domain-specific multidimensional ontologies. Inspired by applications needs and founded on ontologies, workflows, and activity models, POESIA provides well-defined operations (aggregation, specialization, and instantiation) to support the composition of Web services. POESIA complements current proposals for Web services definition and composition by providing a higher degree of abstraction with verifiable consistency properties. We illustrate the POESIA approach using a concrete application scenario in agroenvironmental planning.Received: 15 December 2002, Accepted: 16 April 2003, Published online: 30 September 2003Edited by: V. AtluriRenato Fileto: fileto@ic.unicamp.br     

Global transaction support for workflow management systems: from formal specification to practical implementation

In this paper, we present an approach to global transaction management in workflow environments. The transaction mechanism is based on the well-known notion of compensation, but extended to deal with both arbitrary process structures to allow cycles in processes and safepoints to allow partial compensation of processes. We present a formal specification of the transaction model and transaction management algorithms in set and graph theory, providing clear, unambiguous transaction semantics. The specification is straightforwardly mapped to a modular architecture, the implementation of which is first applied in a testing environment, then in the prototype of a commercial workflow management system. The modular nature of the resulting system allows easy distribution using middleware technology. The path from abstract semantics specification to concrete, real-world implementation of a workflow transaction mechanism is thus covered in a complete and coherent fashion. As such, this paper provides a complete framework for the application of well-founded transactional workflows. Received: 16 November 1999 / Accepted 29 August 2001 Published online: 6 November 2001     

HarVis: An integrated social media content analysis framework for YouTube platform

YouTube (owned by Google Inc.) is arguably among most popular social media platforms used by millions across the globe. It provides an ever-growing, unique and rich source of content which presents new opportunities and challenges for information discovery and analysis. It is pertinent to explore and understand a topic via YouTube content to discover interesting information about public opinions and sentiments. This paper presents an integrated framework to facilitate the acquisition, storage, management, processing, and visualization of relevant content with the objective to assist in such analysis. It not only collects a significant portion of content, relevant to a given topic, in short time but also offers tools for visual exploratory analysis such as; (i) temporal evolution, (ii) vocabulary network, (iii) authors relative popularity and influence (iv) categories and (v) user communities and influencers. The utility and effectiveness is demonstrated through content analysis of a famous YouTube entertainment topic, the “Gangnam Style”.     

Conceptual framework for document semantic modelling: an application to document and knowledge management in the legal domain

The main contribution of this paper is to lay down a conceptual framework for document semantics modeling. This framework provides a generic graphical knowledge representation model based on Sowa’s conceptual structures. Modeling primitives are introduced to represent factual and ontological knowledge that can be expressed in electronic documents. Binding features are proposed so as to keep knowledge representation and knowledge formulation linked together.

This framework may be applied to various domains and may accept, for this purpose, many different ontological extensions. Thus an extension is provided so as to properly handle the particular kind of knowledge encountered in the legal domain.     

Global consistency,local sparsity and pixel correlation: A unified framework for face hallucination

In this paper, a novel two-phase framework is presented to deal with the face hallucination problem. In the first phase, an initial high-resolution (HR) face image is produced in patch-wise. Each input low-resolution (LR) patch is represented as a linear combination of training patches and the corresponding HR patch is estimated by the same combination coefficients. Realizing that training patches similar with the input may provide more appropriate textures in the reconstruction, we regularize the combination coefficients by a weighted _?2-norm${?}_2\mathrm{-}\mathrm{norm}$ minimization term which enlarges the coefficients for relevant patches. The HR face image is then initialized by integrating all the HR patches. In the second phase, three regularization models are introduced to produce the final HR face image. Different from most previous approaches which consider global and local priors separately, the proposed algorithm incorporates the global reconstruction model, the local sparsity model and the pixel correlation model into a unified regularization framework. Initializing the regularization problem with the HR image obtained in the first phase, the final output HR image can be optimized through an iterative procedure. Experimental results show that the proposed algorithm achieves better performances in both reconstruction error and visual quality.     

A global supply chain risk management framework: An application of text-mining to identify region-specific supply chain risks

Nowadays global supply chains enable companies to enhance competitive advantages, increase manufacturing flexibility and reduce costs through a broader selection of suppliers. Despite these benefits, however, insufficient understanding of uncertain regional differences and changes often increases risks in supply chain operations and even leads to a complete disruption of a supply chain. This paper addresses this issue by proposing a text-mining based global supply chain risk management framework involving two phases. First, the extant literature about global supply chain risks was collected and analyzed using a text-based approaches, including term frequency, correlation, and bi-gram analysis. The results of these analyses revealed whether the term-related content is important in the studied literature, and correlated topic model clustering further assisted in defining potential supply chain risk factors. A risk categorization (hierarchy) containing a total of seven global supply chain risk types and underlying risk factors was developed based on the results. In the second phase, utilizing these risk factors, sentiment analysis was conducted on online news articles, selected according to the specific type of risk, to recognize the pattern of risk variation. The risk hierarchy and sentiment analysis results can improve the understanding of regional global supply chain risks and provide guidance in supplier selection.     

A-PIE: An algorithm for preserving privacy,quality of information,and energy consumption in Participatory Sensing Systems

We present A-PIE, a hybrid privacy-preserving mechanism for Participatory Sensing Systems that provides a high level of privacy protection as well as a high quality of information while minimizing the energy consumption. A-PIE takes into consideration the variability of the variable of interest to identify clusters, and divides the target area in cells of different sizes. A-PIE applies anonymization or double-encryption to balance privacy protection, quality of information and energy consumption based on the cell’s size. Extensive experimentation, using a real air monitoring system, shows the superior performance of the proposed mechanism when compared with most important privacy-preserving mechanisms.     

Integrating intrusion alert information to aid forensic explanation: An analytical intrusion detection framework for distributive IDS

The objective of this research is to show an analytical intrusion detection framework (AIDF) comprised of (i) a probability model discovery approach, and (ii) a probabilistic inference mechanism for generating the most probable forensic explanation based on not only just the observed intrusion detection alerts, but also the unreported signature rules that are revealed in the probability model. The significance of the proposed probabilistic inference is its ability to integrate alert information available from IDS sensors distributed across subnets. We choose the open source Snort to illustrate its feasibility, and demonstrate the inference process applied to the intrusion detection alerts produced by Snort. Through a preliminary experimental study, we illustrate the applicability of AIDF for information integration and the realization of (i) a distributive IDS environment comprised of multiple sensors, and (ii) a mechanism for selecting and integrating the probabilistic inference results from multiple models for composing the most probable forensic explanation.     

Degrees of conditional (in)dependence: A framework for approximate Bayesian networks and examples related to the rough set-based feature selection

Bayesian networks provide the means for representing probabilistic conditional independence. Conditional independence is widely considered also beyond the theory of probability, with linkages to, e.g. the database multi-valued dependencies, and at a higher abstraction level of semi-graphoid models. The rough set framework for data analysis is related to the topics of conditional independence via the notion of a decision reduct, to be considered within a wider domain of the feature selection. Given probabilistic version of decision reducts equivalent to the data-based Markov boundaries, the studies were also conducted for other criteria of the rough-set-based feature selection, e.g. those corresponding to the multi-valued dependencies. In this paper, we investigate the degrees of approximate conditional dependence, which could be a topic corresponding to the well-known notions such as conditional mutual information and polymatroid functions, however, with many practically useful approximate conditional independence models unmanageable within the information theoretic framework. The major paper’s contribution lays in extending the means for understanding the degrees of approximate conditional dependence, with appropriately generalized semi-graphoid properties formulated and with the mathematical soundness of the Bayesian network-like representation of the approximate conditional independence statements thoroughly proved. As an additional contribution, we provide a case study of the approximate conditional independence model, which would not be manageable without the above-mentioned extensions.     

Graph based knowledge models for capitalizing,predicting and learning: A proof of concept applied to the dam systems

The capitalization and the analysis of historical information is nowadays a prerequisite for any effective risk management and assessment in a wide range of domains. Despite the development of mathematical models, procedures, support decision systems and databases, some engineering disciplines, such as civil engineering, remain resistant to the use of new digital technology due to the gap between the expectations of the engineers and the support that the tools may really provide. It is essential to propose a tool able to process both cross disciplinary and interdisciplinary knowledge flux and feedback from experience in a common and convenient unifying framework. The aim is to assist and to support engineering work and to make the task of knowledge modelling easier. The domain of dam systems is no exception to the rule. Dam failures are still commonplace. These failures stem from a lack of understanding about the complex relationships between three different factors: random hazards, the limit states of dam structures along with human activities and decisions. No generic and holistic approach is currently available that permits the processing of both knowledge and data, performs inferences and is easily usable for all types of users. This paper proposes the basic principles of a convenient design methodology for capitalizing, learning and predicting based on the formalism of conceptual graphs. The aim is to provide an easily usable tool able to (1) capitalise heterogeneous knowledge and store a database about dams, (2) issue alerts on current projects, (3) draw lessons from past dam failures and (4) tackle key issues in forensic civil engineering.     

Development of PCA-based cluster quantile regression (PCA-CQR) framework for streamflow prediction: Application to the Xiangxi river watershed,China

In this study, a PCA-based cluster quantile regression (PCA-CQR) method was proposed through integrating principal component analysis and quantile regression approaches into a stepwise cluster analysis framework. In detail, the principal component analysis was adopted to overcome the multicollinearity among the explanatory variables, while the quantile regression approach was used to provide probabilistic information in prediction. The proposed PCA-CQR method can effectively capture discrete and nonlinear relationships between explanatory and response variables. The applicability of PCA-CQR was demonstrated by a case study of monthly streamflow prediction in the Xiangxi River, China. The nonlinearity between the hydro-meteorological variables and the streamflow measurements was characterized through the measure of maximal information coefficient (MIC), which demonstrated the need of the proposed PCA-CQR method. The results showed that the previous monthly streamflow and precipitation, as well as potential evapotranspiration in current month posed significant nonlinear impacts on the streamflow in current month. Three components could well reflect the total variance of the input variables. Comparison between traditional SCA and PCA-CQR showed that the proposed approach could provide more accurate predictions than traditional SCA methods. Moreover, probabilistic forecasts could be provided by PCA-CQR, and the 90% predictive intervals could well bracket the observations in both calibration and validation periods. Also, sensitivity analysis was performed to identify the impacts of the control parameters in PCA-CQR on the performance of the proposed model. The results showed the proposed PCA-CQR improved the robustness of traditional SCA. Finally, comparison among PCA-CQR, GRNN and MLR also showed the effectiveness of the proposed method.