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Analysis Pattern

The Experts below are selected from a list of 8574 Experts worldwide ranked by ideXlab platform

Mohamed E. Fayad – 1st expert on this subject based on the ideXlab platform

  • EuroPLoP – The Negotiation Analysis Pattern.
    , 2020
    Co-Authors: Haitham S. Hamza, Mohamed E. Fayad

    Abstract:

    Negotiation is a general concept that has wide range of applications that span various contexts. This paper introduces the Negotiation Analysis Pattern. This Pattern aims to provide a model that analyzes the core concept of the negotiation. In order to achieve this goal, Negotiation Pattern is built based on the concepts of Stable Analysis Patterns we have introduced before in [2, 3,4]. The paper provides detailed documentation of the proposed Pattern. In addition, it demonstrates the usage of the Pattern through the mean of examples.

  • A Pattern Language for Building Stable Analysis Patterns
    , 2020
    Co-Authors: Haitham S. Hamza, Mohamed E. Fayad

    Abstract:

    Software Analysis Patterns are believed to play a major role in reducing the cost and condensing the time of software product lifecycles. However, Analysis Patterns have not realized their full potential. One of the common problems with today”s Analysis Patterns is the lack of stability. In many cases, Analysis Pattern that model specific problems fail to model the same problem when it appears in different context, forcing software developers to analyze the problem from scratch. As a result, the reusability of the Pattern will diminish. This paper presents a Pattern language for building stable Analysis Patterns. The objective of this language is to propose a way of achieving stability while constructing Analysis Patterns.

  • IRI – Accessibility Stable Analysis Pattern (Stable Pattern for Model Based Software Reuse)
    2015 IEEE International Conference on Information Reuse and Integration, 2015
    Co-Authors: Mohamed E. Fayad, Siddharth Jindal

    Abstract:

    The Accessibility Stable Analysis Pattern intends to describe the core knowledge behind the concept of Accessibility. Accessibility finds an extensive range of usages in various applications. The Pattern also gives an excellent start to software developers, by defining the core knowledge of any accessibility problem. Any developer can build on, extend or reuse the Pattern to model any specific application by involving the factor of Accessibility.

Yuan Lu – 2nd expert on this subject based on the ideXlab platform

  • Tree Analysis Pattern of mass spectral urine profiles in differential diagnosis of bladder transitional cell carcinoma
    Chinese journal of oncology, 2007
    Co-Authors: Deng-long Wu, Ming Guan, Yuanfang Zhang, Yue-min Xu, Jiong Zhang, Yuan Lu

    Abstract:

    OBJECTIVE: To develope a tree Analysis Pattern of mass spectral urine profiles to discriminate bladder transitional cell carcinoma (TCC) from non-cancer lesions using surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology. METHODS: Urine samples from 61 bladder transitional cell carcinoma (TCCs) patients, 53 healthy volunteers and 42 patients with other urogenital diseases were analyzed using IMAC-Cu-3 ProteinChip. Proteomic spectra were generated by SELDI-TOF- MS. A preliminary “training” set of spectra derived from Analysis of urine from 46 TCC patients, 32 patients with benign urogenital diseases (BUD), and 40 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm which identified a fine-protein mass Pattern that discriminated cancers from non-cancers effectively. A blinded test set including 38 cases was used to determine the sensitivity and specificity of the classification system. RESULTS: The algorithm identified a cluster Pattern that, in the training set, segregated cancer from non-cancer with a sensitivity of 84.8% and specificity of 91.7%. The discriminatory Pattern was correctly identified. A sensitivity of 93.3% and a specificity of 87% for the blinded test were obtained when compared the TCC versus non-cancers. CONCLUSION: SELDI-TOF-MS technology is a rapid, convenient and high-throughput analyzing method. The urine tree Analysis proteomic Pattern as a screening tool is effective for differential diagnosis of bladder cancer. More detailed studies are needed to further evaluate the clinical value of this Pattern.

  • Using Tree Analysis Pattern and SELDI-TOF-MS to Discriminate Transitional Cell Carcinoma of the Bladder Cancer from Noncancer Patients
    European Urology, 2004
    Co-Authors: Ming Guan, Deng-long Wu, Yuanfang Zhang, Zhong Wu, Ming Xu, Yuan Lu

    Abstract:

    OBJECTIVE: To determine whether SELDI protein profiling of urine coupled with a tree Analysis Pattern could differentiate TCC from noncancer patients. METHODS: The ProteinChip Arrays were performed on a ProteinChip PBS II reader of the ProteinChip Biomarker System. The study was divided into two phases: a preliminary phase with construction of tree Analysis Pattern, and a testing phase with test urine samples. Generation of the tree Analysis Pattern was performed by a training data set consisting of 104 samples. The validity of the tree Analysis Pattern was then challenged with a test set of 68 samples. RESULTS: Average of 187 mass peaks was detected in the urine samples, and five of these peaks were used to construct the tree Analysis Pattern. The classification Pattern correctly predicted 91.67-94.64% of the samples for both of the two groups in the training set, for an overall correct classification of about 93%. The Pattern correctly predicted 72.0% (49 of 68) of the test samples, with 71.4% (25 of 35) of the TCC samples, 72.7% (24 of 33) of the noncancer samples. CONCLUSIONS: The high sensitivity and specificity obtained by the urine protein profiling approach demonstrate that SELDI-TOF-MS combined with a tree Analysis Pattern can both facilitate discriminate TCC bladder cancer with noncancer and provide an innovative clinical diagnostic platform improve the detection of TCC bladder cancer patients.

Rong Peng – 3rd expert on this subject based on the ideXlab platform

  • An Analysis Pattern Driven Analytical Requirements Modeling Method
    Communications in Computer and Information Science, 2020
    Co-Authors: Jingjing Ji, Rong Peng

    Abstract:

    Analytical requirements are the basis for building the enterprise data models that are used to develop the IT assets that deliver the analytical requirements to business users [10]. Due to the difficulties existed in the modeling and Analysis process, reusing existing Analysis experiences becomes a good choice to find an optimal way from problem domain to solution domain efficiently. To help data analysts use previous experience to elicit and model analytical requirements and find satisfactory solutions, an Analysis Pattern driven analytical requirements modeling method is proposed. It utilizes Analysis Patterns to help analysts model the relationships between data domains and machine domains, and select available Analysis models under the guidance of measurable analytical goals. The modeling process is an interactive and iterative process, which uses the feedbacks from analysts to adjust its Analysis behavior on real time. To illustrate the method more specifically, we apply it on requirements tracing.

  • RE Workshops – An Analysis Pattern Driven Requirements Modeling Method
    2016 IEEE 24th International Requirements Engineering Conference Workshops (REW), 2016
    Co-Authors: Jingjing Ji, Rong Peng

    Abstract:

    Enormous commercial value brought by big data Analysis promotes the vigorous development of big data Analysis industry. Due to the difficulties existed in the modeling process, reusing existing Analysis experiences becomes a good choice to find an optimal way from problem domain to solution domain efficiently. To help data analysts reuse previous experiences to elicit and model Analysis requirements and find satisfactory solutions, an Analysis Pattern driven Analysis requirements modeling method is proposed. It utilizes Analysis Patterns to help analysts model the relationships between data domains and machine domains, and select available Analysis models under the guidance of measurable Analysis goals. The modeling process is an interactive and iterative process, which uses the feedbacks from analysts to adjust its Analysis behavior.

  • An Analysis Pattern Driven Requirements Modeling Method
    2016 IEEE 24th International Requirements Engineering Conference Workshops (REW), 2016
    Co-Authors: Jingjing Ji, Rong Peng

    Abstract:

    Enormous commercial value brought by big data Analysis promotes the vigorous development of big data Analysis industry. Due to the difficulties existed in the modeling process, reusing existing Analysis experiences becomes a good choice to find an optimal way from problem domain to solution domain efficiently. To help data analysts reuse previous experiences to elicit and model Analysis requirements and find satisfactory solutions, an Analysis Pattern driven Analysis requirements modeling method is proposed. It utilizes Analysis Patterns to help analysts model the relationships between data domains and machine domains, and select available Analysis models under the guidance of measurable Analysis goals. The modeling process is an interactive and iterative process, which uses the feedbacks from analysts to adjust its Analysis behavior.