17412122
Exploring Alternatives during Requirements Analysis
Goal-oriented requirements analysis techniques provide ways to refine organizational and technical objectives, to more effectively explore alternatives during requirements definition. After selecting a set of alternatives to achieve these objectives, you can elaborate on them during subsequent phases to make them more precise and complete. The authors argue that goal-oriented analysis complements and strengthens traditional requirements analysis techniques by offering a means for capturing and evaluating alternative ways of meeting business goals. They detail the five main steps that comprise goal-oriented analysis. These steps include goal analysis, softgoal analysis, softgoal correlation analysis, goal correlation analysis, and evaluation of alternatives. To illustrate the main elements of the proposed analysis technique, they explore a typical scenario that involves defining requirements for a meeting scheduling system.
14482795
Collaborative multi-agents for workflow management
Workflow management helps organizations to organize their work on different activities in such a way that the predictable underlying processes are carried out effectively and efficiently in a distributed environment. We describe a business workflow management system with intelligent multi-agent support in a collaborative organization environment. With the help from multi-agent collaboration, our workflow management system can organize the organization's work processes and provide resource control, data mediation, semantic mapping, and knowledge visualization processing. Such systems are able to manage the organizational workflow more effectively and more efficiently.
270876
Feature Extraction with Genetic Algorithms Based Nonlinear Principal Component Analysis for Face Recognition
Principal component analysis (PCA) and linear discriminant analysis (LDA) are two commonly used feature extraction techniques. In this paper, a nonlinear evolutionary weighted principal component analysis (EWPCA) based on genetic algorithms is proposed. Similar to LDA, the EWPCA maximizes the ratio of between-class variations to that of within-class variations, and achieves better classification performance than that of traditional PCA. Genetic algorithms are chosen as the searching method to select optimal weights for the EWPCA. In face recognition, evolutionary facial feature obtained by performing EWPCA is used as the representation of original face images. Experimental results on ORL and combo face databases prove that EWPCA outperforms both PCA, kernel PCA and LDA
548885
Landmarks: a new model for similarity-based pattern querying in time series databases
In this paper we present the landmark model, a model for time series that yields new techniques for similarity-based time series pattern querying. The landmark model does not follow traditional similarity models that rely on pointwise Euclidean distance. Instead, it leads to landmark similarity, a general model of similarity that is consistent with human intuition and episodic memory. By tracking different specific subsets of features of landmarks, we can efficiently compute different landmark similarity measures that are invariant under corresponding subsets of six transformations; namely, shifting, uniform amplitude scaling, uniform time scaling, uniform bi-scaling, time warping and non-uniform amplitude scaling. A method of identifying features that are invariant under these transformations is proposed. We also discuss a generalized approach for removing noise from raw time series without smoothing out the peaks and bottoms. Beside these new capabilities, our experiments show that landmark indexing is considerably fast.
17954201
Dynamic Supply Chain Integration through Intelligent Agents
The success of supply chains depends on the match between the requirements and offerings that deliver the services. It can be ensured by separating requirements from the means of realization as well as dynamically assigning available resources to requests. To achieve this, we need coordinate the flow of information among the services and link their business processes under various constraints. Existing approaches to this problem have relied on complete information of services and resources, and have failed to adequately address the dynamics and uncertainties of the operating environments. The problem is complicated as a result of undetermined requirements of decomposed individual services and unpredictable solutions contributed by service providers. This paper examines an agent-mediated coordination approach to dynamic supply chain integration. Each agent works as a broker for each service involved in the chain, exploring service requests as well as identifying suitable resources for achieving compatibility and coherence among the decisions of individual services. Based on the approach, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach
5516437
Compact reachability labeling for graph-structured data
Testing reachability between nodes in a graph is a well-known problem with many important applications, including knowledge representation, program analysis, and more recently, biological and ontology databases inferencing as well as XML query processing. Various approaches have been proposed to encode graph reachability information using node labeling schemes, but most existing schemes only work well for specific types of graphs. In this paper, we propose a novel approach, HLSS(Hierarchical Labeling of Sub-Structures), which identifies different types of substructures within a graph and encodes them using techniques suitable to the characteristics of each of them. We implement HLSS with an efficient two-phase algorithm, where the first phase identifies and encodes strongly connected components as well as tree substructures, and the second phase encodes the remaining reachability relationships by compressing dense rectangular submatrices in the transitive closure matrix. For the important subproblem of finding densest submatrices, we demonstrate the hardness of the problem and propose several practical algorithms. Experiments show that HLSS handles different types of graphs well, while existing approaches fall prey to graphs with substructures they are not designed to handle.
8456748
Friends Troubleshooting Network: Towards Privacy-Preserving, Automatic Troubleshooting
Content sharing is a popular usage of peer-to-peer systems for its inherent scalability and low cost of maintenance. In this paper, we leverage this nature of peer-to-peer systems to tackle a new problem: automatic misconfiguration troubleshooting. In this setting, machine configurations from the peers are “shared” to diagnose the misconfigurations on a sick machine. A key challenge for such a troubleshooting system is privacy preservation. To this end, we construct Friends Troubleshooting Network (FTN), a peer-to-peer overlay network, where the links between peer machines reflect the friendship of their owners. To preserve privacy, we use historyless and futureless random-walk in the FTN, during which search along with parameter aggregation are carried out for the purpose of troubleshooting. Many of our techniques can be applied to other application scenarios that require privacy-preserving distributed computing and information aggregation. We have also identified a number of open challenges that remain to be addressed.