# Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis
## 3. The Architecture and the Core Components
### SOA Based Soft Compute Platform Supporting WAAF Matrix
Consequently, the BIMA framework has emerged to address the challenges faced by volume, velocity, and veracity of data being received and analyzed in real time. Hence, an SOA model is used to tackle the required speed and accuracy of data. The BIMA framework has become a reality for businesses. Examples include (1) Google's Gmail service, which has been operating in an Exabyte of data; (2) Amazon web services that have streaming media globally every second; and (3) other real-time life sensitive data such as medial, weather forecasts, Earth monitoring, and space applications data. As a result, there is a growing need for a structured framework to select, process, and monitor highly relevant data. In this context, [PERSON] [4; 54] describes a lightweight with a risk reduction approach to significant data analytics process and architecture, which is known as Lightweight Evaluation and Architecture Prototyping for BIMA framework (LEAP4 BD). This provides a good starting point for organizations to use a semantic knowledge-based tool for data selection and acquisition. It is yet another tool for data extraction, which is not SOA-based and lacks a systematic approach. Similarly, [PERSON] et al. [55] have proposed a framework for a geoscience big data framework, which consists of multidimensional data where Hadoop (Hbase) has been adopted for storing and managing multidimensional geoscientific data and the MapReduce-based parallel algorithm has been used for processing such data. Although the Hadoop framework does propose large-scale data extraction, it lacks in the generalization and application of SOA architecture. The proposed SOA based on a soft computing framework for big data (SoftBD) layer is part of our overall BIMA framework with soft compute intelligence algorithms for decision making when collecting, selecting, extracting, validating, and evaluating the data. Additionally, soft computing algorithms such as the neuro-fuzzy and Bayesian theorem that allows data prediction based on historical data collected. The SoftBD framework is shown in Figure 5.  
SoftBD starts receiving data continuously with cloud platforms such as Amazon EC2, Windows Azure, Google, and Salesforce. The data sources can be from multiple sources including geoscience, entertainment monitoring, selection and prediction (Home and Cinema), aircraft monitoring, IoT devices, healthcare, mobile, financial market integrity analysis, and forecastings such as the BIMA framework, wireless sensor networks, and natural disaster areas such as fire, flood, and earthquake, and epidemics. The cloud is the best suitable platform as it provides elastic services to store and maintain data globally. The next layer is a data clustering layer using the Hadoop platform, which can be part of the chosen cloud platform. This layer does the data processing, categorizing the data into entertainment, geosciences, and WSN (wireless sensor network). The next layer is a data analytics layer, which could include the Hadoop/MapReduce algorithm to speed up analyzing the data parallelization. The big challenge in Hadoop is resource allocation and scheduling for parallel high-performance computing. The final layer on the vertical services is our new soft compute intelligence layer, which includes algorithms and models on fuzzy-logic, neuro-fuzzy, and Bayesian theorem. The primary purpose of this service layer is to offer prediction models on the categorized and analyzed data. This also covers data evaluation, assessment, and validation. All of the vertical segments are based on service-oriented architecture provisioning, which consists of loosely coupled services within the BIMA framework.