6738575
Agent-oriented software modeling
Due to the increased applications of agents, agent-oriented software becomes large and complex. To support systematic developments of such software, the agent-oriented software development methodology needs to be developed. This paper focuses on the modeling phase of agent-oriented software development. For agent-oriented software modeling, the Agent Elicitation method, Intra and Inter Agent modeling method are proposed. Agent Elicitation shows how to extract agents from classes in the real world. Modeling methods of agent's characteristics-Goal, Belief, Plan and Capability-are proposed for Intra Agent modeling. Methods of agent's mobility and communication in multiagent systems are proposed for Inter Agent modeling.
16878992
Improving use case driven analysis using goal and scenario authoring: A linguistics-based approach
Use case driven analysis does not facilitate effective requirements elicitation or provide rationale for the various artifacts that get generated during the requirements analysis phase. On the other hand, goal and scenario based approach is considered to be effective for elicitation but it does not lead to use cases. This paper discusses how to combine goal and scenario based requirements elicitation technique with use case driven analysis using natural language concepts. In our proposed approach, four levels of goals, scenario authoring rules, and linguistic techniques have been developed to identify use cases from text based goal and scenario descriptions. The artifacts resulting from our approach could be used as input to a use case diagramming tool to automate the process of use case diagram generation.
28523270
Goal based agent-oriented software modeling
This paper presents a goal based modeling method for an agent-oriented software system. In modeling research for agent-oriented software, problems often emerged in that the properties of an agent system were not efficiently reflected in the development phase (analysis, design, etc.), because the techniques based on the other software paradigm are adopted to develop agent-oriented software. The result of analysis differs depending on which paradigm is used. An agent is goal-oriented, which necessitates a shift in paradigm. Therefore, the modeling method for an agent system should start from the concept of goal.
35666646
A Linguistics-Based Approach for Use Case Driven Analysis Using Goal and Scenario Authoring
Although Use Case driven analysis has been widely used in requirements analysis, it does not facilitate effective requirements elicitation or provide rationale for the various artifacts that get generated. On the other hand, goal and scenario based approach is considered to be effective for elicitation but it does not lead to use cases. This paper discusses how to combine goal and scenario based requirements elicitation technique with use case driven analysis using natural language concepts. In our proposed approach, four levels of goals, scenario authoring rules, and linguistic techniques have been developed to identify use cases from text based goal and scenario descriptions. The artifacts resulting from our approach could be used as input to a use case diagramming tool to automate the process of use case diagram generation.
5105796
Two-stage normalization using background intensities in cDNA microarray data
BackgroundIn the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the measured gene expression levels. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. One major source of variation is the background intensities. Recently, some methods have been employed for correcting the background intensities. However, all these methods focus on defining signal intensities appropriately from foreground and background intensities in the image analysis. Although a number of normalization methods have been proposed, no systematic methods have been proposed using the background intensities in the normalization process.ResultsIn this paper, we propose a two-stage method adjusting for the effect of background intensities in the normalization process. The first stage fits a regression model to adjust for the effect of background intensities and the second stage applies the usual normalization method such as a nonlinear LOWESS method to the background-adjusted intensities. In order to carry out the two-stage normalization method, we consider nine different background measures and investigate their performances in normalization. The performance of two-stage normalization is compared to those of global median normalization as well as intensity dependent nonlinear LOWESS normalization. We use the variability among the replicated slides to compare performance of normalization methods.ConclusionsFor the selected background measures, the proposed two-stage normalization method performs better than global or intensity dependent nonlinear LOWESS normalization method. Especially, when there is a strong relationship between the background intensity and the signal intensity, the proposed method performs much better. Regardless of background correction methods used in the image analysis, the proposed two-stage normalization method can be applicable as long as both signal intensity and background intensity are available.
5155977
A Hybrid Keyword Search across Peer-to-Peer Federated Databases
The need for Keyword search in databases is suggested both by Web integration with legacy database management system and by dynamic Web publication. However, it sacrifices the inherent meaning of database schema. Web search engines provide clues for resource location on the Web, but have similar semantic problems. The Semantic Web suggests an ideal solution for the semantic problem on the Web. But due to the need for sophisticated domain definition and lack of unified definitions, many Web pages are not part of the Semantic Web. We define a hybrid search to be a search combining semantic metadata and keywords. A hybrid search on P2P based federated databases provides meaningful and scalable search on an overlay network across the Internet. This paper describes the design of the combined search for unstructured data with associated metadata, information retrieval from the repository, peer-to-peer based communication layer, and data integration hub.
18360879
Scalable Hybrid Search on Distributed Databases
We have previously described a hybrid keyword search that combines metadata search with a traditional keyword search over unstructured context data. This hybrid search paradigm provides the inquirer additional options to narrow the search with some semantic aspect from the XML metadata query. But in earlier work, we experienced the scalability limitations of a single-machine implementation. In this paper, we describe a scalable hybrid search on distributed databases. This scalable hybrid search provides a total query result from the collection of individual inquiries against independent data fragments distributed in a computer cluster. We demonstrate our architecture extends the scalability of a native XML query limited in a single machine and improves the performance for some queries.
16116503
Evolutionary programming techniques for constrained optimization problems
Two evolutionary programming (EP) methods are proposed for handling nonlinear constrained optimization problems. The first, a hybrid EP, is useful when addressing heavily constrained optimization problems both in terms of computational efficiency and solution accuracy. But this method offers an exact solution only if both the mathematical form of the objective function to be minimized/maximized and its gradient are known. The second method, a two-phase EP (TPEP) removes these restrictions. The first phase uses the standard EP, while an EP formulation of the augmented Lagrangian method is employed in the second phase. Through the use of Lagrange multipliers and by gradually placing emphasis on violated constraints in the objective function whenever the best solution does not fulfill the constraints, the trial solutions are driven to the optimal point where all constraints are satisfied. Simulations indicate that the TPEP achieves an exact global solution without gradient information, with less computation time than the other optimization methods studied here, for general constrained optimization problems.
3492450
A framework for data prefetching using off-line training of Markovian predictors
An important technique for alleviating the memory bottleneck is data prefetching. Data prefetching solutions ranging from pure software approach by inserting prefetch instructions through program analysis to purely hardware mechanisms have been proposed. The degrees of success of those techniques are dependent on the nature of the applications. The need for innovative approach is rapidly growing with the introduction of applications such as object-oriented applications that show dynamically changing memory access behavior In this paper, we propose a novel framework for the use of data prefetchers that are trained off-line using smart learning algorithms to produce prediction models which captures hidden memory access patterns. Once built, those prediction models are loaded into a data prefetching unit in the CPU at the appropriate point during the runtime to drive the prefetching. On average by using table size of about 8KB size, we were able to achieve prediction accuracy of about 68% through our own proposed learning method and performance was boosted about 37% on average on the benchmarks we tested. Furthermore, we believe our proposed framework is amenable to other predictors and can be done as a phase of the profiling-optimizing-compiler.