Christian Saad

A Unified Framework for Enterprise Architecture Analysis

Enterprise Architecture (EA) analysis is an important tool for leveraging EA models. However, due to the diverse nature of the EA field, analysis techniques must be able to deal with variability caused by different modeling standards as well as their adaption to specific organizational needs. Additionally, the definitions of the relevant measures often vary between different stakeholders and organizations. To address these challenges we propose a (meta) model independent framework for the analysis of architecture models. Based on a generic representation of architectural data, we employ a data-flow based analysis approach to enable a context-sensitive evaluation of organization specific measures. We demonstrate the generic applicability of this framework through a re-implementation of three different analyses from literature.

Adaptive Approach for Impact Analysis in Enterprise Architectures

Due to the large size of typical Enterprise Architecture models, it is often difficult for humans to fully grasp their contents. Moreover, because of their inherent complexity, the task of generating additional value from these models is very challenging without suitable analysis methods. Impact analysis, which determines the effects of changes or failures on other architectural elements, can therefore provide valuable information for enterprise architects. Whether an element is affected depends on its context, i.e. its (transitive) connections to other elements and their semantics with respect to the analysis. In this paper, we propose a generic, context-sensitive approach to the implementation of impact analyses. This method relies on the technique of data-flow analysis to propagate the effects through the model. Since the analysis specification only relies on a set of relationship classes, it can be easily adapted to the needs of organization-specific EA meta models by providing custom mappings for the respective types.

Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction

The relationship between metabolism and methylation is considered to be an important aspect of cancer development and drug efficacy. However, it remains poorly defined how to apply this aspect to improve preclinical disease characterization and clinical treatment outcome. Using available molecular information from Kyoto Encyclopedia of Genes and Genomes (KEGG) and literature, we constructed a large-scale knowledge-based metabolic in silico model. For the purpose of model validation, we applied data from the Cancer Cell Line Encyclopedia (CCLE) to investigate computationally the impact of metabolism on chemotherapy efficacy. In our model, different metabolic components such as MAT2A, ATP6V0E1, NNMT involved in methionine cycle correlate with biologically measured chemotherapy outcome (IC50) that are in agreement with findings of independent studies. These proteins are potentially also involved in cellular methylation processes. In addition, several components such as 3,4-dihydoxymandelate, PAPSS2, UPP1 from metabolic pathways involved in the production of purine and pyrimidine correlate with IC50. This study clearly demonstrates that complex computational approaches can reflect findings of biological experiments. This demonstrates their high potential to grasp complex issues within systems medicine such as response prediction, biomarker identification using available data resources.

Parallelizing highly complex engine management systems: EMS Parallelization

The automotive industry seeks to include more and more features in its vehicles. For this purpose, the necessary policy shift towards multi‐core technology is in full swing. To eventually exploit the extra processing power, there is much additional effort needed for coping with the tremendously increased complexity. This is largely due to the elaborate parallelization process that spans a vast search space. Consequently, there is a strong need for innovative methods and appropriate tools for the migration of legacy single‐core software. We use the results of a data dependency analysis performed on AUTOSAR system descriptions to determine advantageous partitions as well as initial task‐to‐core mappings. Afterwards, the extracted information serves as input for the simulation within a multi‐core timing tool suite. Here, the initial solution is evaluated with respect to proper scheduling and metrics like cross‐core communication rates, communication latencies, or core load distribution. A subsequent optimization process improves the initial solution and enables a comparative assessment. To demonstrate the benefit, we substantially expand a previous case study by applying our approach to two complex engine management systems and by showing the advantages compared to a parallelization process without preceding dependency analysis and initial partition/mapping suggestions.

A Flow Analysis Approach for Service-Oriented Architectures

The discipline of SOA (Service-oriented Architecture) provides concepts for designing the structural and behavioral aspects of application landscapes that rely on the interaction of self-contained services. To assess an architecture’s quality and validate its conformance to behavioral requirements, those models must be subjected to sophisticated static analyses. We propose a comprehensive methodology that relies on data flow analysis for a context-sensitive evaluation of service-oriented system designs. The approach employs a model-based format for SOA artifacts which acts as a uniform basis for the specification and execution of various analyses. Using this methodology, we implement two analyses which reveal blocking calls and assess performance metrics. These applications are evaluated in the context of two case studies that have been developed in the SENSORIA and the ENVIROFI projects.

Context-Sensitive Impact Analysis for Enterprise Architecture Management

Since Enterprise Architecture (EA) models are typically very large, it is often difficult for humans to fully grasp their contents. Due to this inherent complexity, the task of generating additional value from these models is very challenging without a suitable analysis method. Impact analysis, which is able to determine the effects which changes have on other architectural elements, can therefore provide valuable information for an enterprise architect. Whether an element is affected by a change depends on its context, i.e. its (transitive) connections to other elements and their status with respect to the analysis. In this paper we propose a contextsensitive approach to the implementation of impact analyses. This method relies on the technique of data-flow analysis to propagate the effects of changes throughout the model. As a consequence, the specification can be defined in a very generic fashion, which only relies on relationship classes. Therefore it can be easily adapted to organization-specific EA meta models as only the relationship types have to be mapped to the respective

Applying data-flow analysis to models a novel approach for model analysis

Using modeling techniques, the structure of an application domain can be captured in an easy and highly expressive way. However, while the use of meta models for the definition of modeling languages is a common and well-understood activity, extracting information about behavioral properties as well as the validation of static semantics is still a challenge. In this paper we present a novel approach for model analysis that addresses these issues by applying the method of dataflow analysis to the modeling domain. By approximating the dynamic behavior of a model, this allows for an abstract interpretation of its runtime characteristics.