Software architecture is widely available today to define the high-level design methodology of large software systems. The growth has become a progressively significant part of the software lifecycle, due to the incre...
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Software architecture is widely available today to define the high-level design methodology of large software systems. The growth has become a progressively significant part of the software lifecycle, due to the increasing complexity of the software being constructed. Software designers need to carefully define and reason the structural design of complex, distributed information systems, which are often encompassed of a mix of innovative and reused modules. A good, extensible and maintainable architecture often makes the difference between successful and failed projects. A good architectural representation holds the key to the efficiency of the software architecture usage and description. In this paper, an accurate software architecture model is developed using the concept of risk evaluation and prediction. In existing software architecture models, the tree-based model is not implemented. The requirements are not in a structured manner which leads to a greater computation time for execution. In the proposed method, the tree-based software architecture helps to formulate the requirements in a structured way of representation and hence it leads to a lesser computation time for execution. The module prediction strategy is applied to split up the entire requirements into sub-groups to handle the large number of stakeholder requirements. Finally, the risk assessment stage is performed to identify the risk factors of the software model and minimize the impact of the risk. The proposed software architecture results in lesser memory utilization and execution time with better performance, reliability and flexibility than the existing neuro-fuzzy performance evaluation model.
Web Mining is an important sub-branch of Data Mining. The Data Mining technology normally adopts data integration method to generate Data warehouse, on which to dig the Relation Rules, Cluster Characters and get the u...
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ISBN:
(纸本)0769519571
Web Mining is an important sub-branch of Data Mining. The Data Mining technology normally adopts data integration method to generate Data warehouse, on which to dig the Relation Rules, Cluster Characters and get the useful module prediction and knowledge evaluation. For Web Data's semi-structure and heterogeneous (mixed media) character, The Web Mining technology is different to the pure mining based on database. In this paper we introduce a Multi-Agent cooperation module, which play as Knowledge Crawler, Exaction Machine, Generalization Machine and Analysis Machine. Through the communication between Multi-Agent system and Agent Naming Server, these agents acquire useful Knowledge..
Background: One of the important goals in the post-genomic era is to determine the regulatory elements within the non-coding DNA of a given organism's genome. The identification of functional cis-regulatory module...
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Background: One of the important goals in the post-genomic era is to determine the regulatory elements within the non-coding DNA of a given organism's genome. The identification of functional cis-regulatory modules has proven difficult since the component factor binding sites are small and the rules governing their arrangement are poorly understood. However, the genomes of suitably diverged species help to predict regulatory elements based on the generally accepted assumption that conserved blocks of genomic sequence are likely to be functional. To judge the efficacy of strategies that prefilter by sequence conservation it is important to know to what extent the converse assumption holds, namely that functional elements common to both species will fall within these conserved blocks. The recently completed sequence of a second Drosophila species provides an opportunity to test this assumption for one of the experimentally best studied regulatory networks in multicellular organisms, the body patterning of the fly embryo. Results: We find that 50%-70% of known binding sites reside in conserved sequence blocks, but these percentages are not greatly enriched over what is expected by chance. Finally, a computational genome-wide search in both species for regulatory modules based on clusters of binding sites suggests that genes central to the regulatory network are consistently recovered. Conclusions: Our results indicate that binding sites remain clustered for these "core modules" while not necessarily residing in conserved blocks. This is an important clue as to how regulatory information is encoded in the genome and how modules evolve.
Background: Regulation of gene transcription is crucial for the function and development of all organisms. While gene prediction programs that identify protein coding sequence are used with remarkable success in the a...
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Background: Regulation of gene transcription is crucial for the function and development of all organisms. While gene prediction programs that identify protein coding sequence are used with remarkable success in the annotation of genomes, the development of computational methods to analyze noncoding regions and to delineate transcriptional control elements is still in its infancy. Results: Here we present novel algorithms to detect cis-regulatory modules through genome wide scans for clusters of transcription factor binding sites using three levels of prior information. When binding sites for the factors are known, our statistical segmentation algorithm, Ahab, yields about 150 putative gap gene regulated modules, with no adjustable parameters other than a window size. If one or more related modules are known, but no binding sites, repeated motifs can be found by a customized Gibbs sampler and input to Ahab, to predict genes with similar regulation. Finally using only the genome, we developed a third algorithm, Argos, that counts and scores clusters of overrepresented motifs in a window of sequence. Argos recovers many of the known modules, upstream of the segmentation genes, with no training data. Conclusions: We have demonstrated, in the case of body patterning in the Drosophila embryo, that our algorithms allow the genome-wide identification of regulatory modules. We believe that Ahab overcomes many problems of recent approaches and we estimated the false positive rate to be about 50%. Argos is the first successful attempt to predict regulatory modules using only the genome without training data. Complete results and module predictions across the Drosophila genome are available at [http://***/similar tosiggia/].
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