Big Data Preparation and Exploration
Nowadays, the most tedious thing is to get accurate and precise data. So when it comes to huge amount of data, it is difficult to segregate them. The above-mentioned problems are solved by big data technology. The methodology of data preparation is an effective tool for decision-making and capturing of genuine data. Query processing and streaming of social network data can be efficiently processed by data preprocessing mechanism. The ensemble analysis of large sets of data is associated with different techniques involved in big data analysis. In the medical field, the authenticity of data is crucial. Every report that to be processed and analyzed should be accurate and genuine so the data preparation is an essential need for the big data processing.
Understanding Original Data Analysis
The lack of original data and understanding of the processing of current data was a real threat in medical big data processing. The major factor is the ability to understand whether the data to be segregated has to be categorized for privacy and authenticity. The originality of data is crucial, and for segregating it, plenty of information should be collected. Following are some information to be considered .
- 1. The foremost thing is to answer for all relevant question raised in suspect ability of data.
- 2. Accuracy of medical data in all aspects should be evaluated.
- 3- The decision-making based on the information should be precise and unambiguous (Figure 2.1).
There are certain steps to be followed for understanding original data analysis. They are described below.
(i) Designing of Questions and Problems
Data should be understood by designing various questionnaires and problems to be answered and solved. The framed questions should be clear
Figure 2.1 Understanding process of raw data.
and concise. The questions can be of querying or non-querying type depending on the information and scenario. The main constraint are the data should be measurable and the unit of measurement should be judgable. For example, a medical diagnosis of a patient having asthma problem shows a real scenario of querying. Sometimes the disease occurs due to food intake or by some habitual behaviors. The genetic behavior also becomes a factor for it or environmental disorders also a reason, so a real query should be asked with the chances and relevance of the data. But a question like how long asthma can be sustained with above factor is an ambiguity because all the above parameters cannot perfectly be chances for it or a constant reason for the genetic background can have a chance of getting cured by ageing. These parameters can be measurable and precise. So for each query to be answered, a large set of data has to be collected and organized.
(ii) Collection of Data
Data collection is a huge task for designing a questioner. The data collected has to be well organized and informative. The primary task is to collect existing available data before collecting new data. On the basis of existing data, missing data and parameters should be measured. The observation of the data is a very critical task for the formation of genuine data. Getting data is not a big task, but acquiring the right data is very important.
(iii) Analysis of the Data
After getting the right data, a deeper data analysis should be done. The data should be sorted in a particular order and filtered based on observable parameters to be analyzed. On the basis of overall data, the maximum, minimum, and standard deviation should be analyzed and evaluated. Any relevant tools can be used for the data analysis and for getting an accurate result from the analyzed dataset. The fluctuation of the result depends on the analysis of data. A perfect analysis provides accurate and precise results; imperfection can lead to a failure of the whole process.
(iv) Interpretation of Outcome from Analysis
The result can be altered by any mismatch in any of the above steps. The result of interpretation of outcome from analysis only depends on whether the hypothesis assumed is true or false. Rejection or failure should also be considered rather than accepting an uninterrupted data analysis.
Benefits of Big Data Pre-Processing
Big data have grown massively and been evaluated and processed in the recent years. The problems faced by traditional database systems are remedied by big data processing. Ensuring the high quality of data and cleaning of data are major tasks. Here are some fascinating benefits of big data pre-processing.
(i) Noise Filtering
In this technological era, it’s very difficult to get data without noise. Most part of data processing involves the problem of missing data and outlier data management. The noise in digital data is due to the padded or unwanted bits embedded with the data in digital form. There are different methods for removal of noise from ordinary data, but in case of big data, it’s a difficult task. A large amount of data need to be analyzed to reproduce the high quality of data. Big data need to be processed efficiently and accurately within prescribed time limit. But the alteration of this type of data is called as noise in big data. The imperfection of data can be effected by different factors like distribution of unmanaged data, transmission, and integration of data. Intelligent decision-making of big data can be influenced by the noise barrier effected by the above factors. The futuristic techniques in big data have the limitation of imperfection occurring during data processing. Big data can be transformed to a new form which has less noise, and this form is referred to as smart data. There are different methods for smart data processing. Here processing is a solution to noise problems in big data analysis which intense the formation of Smart data are as follows. Apache Spark framework is used to eliminate the noisy information from the clustered data. The two main methods are described in the following .
(a) Univariate Ensemble Method
The Univariate Ensemble method uses a single classifier for distinguishing and filtering the actual data with an outlier. It uses Random Forest algorithm for classification in different division of training set and undergo decision-making for analysis. The method also consists of a cross-validated committee filter which manipulates the data through a partition based on homogenous sections . Each section in the tree undergoes voting mechanism in which the misclassified instance for the particular section is eliminated in such a way that each section iterates and results in noise-free information. It also involves a prediction mechanism for noise filtering (Figure 2.2).
Figure 2.2 illustrates the process of retrieval of smart data. There are some decision-making processes to iterate the data again and again for getting smart data. The partitioning method will analyze each part of the data units for accuracy and for making the validation process simple. The final product of this analysis will produce smart data which will be noise free and precise.
(b) Multivariate Ensemble Method
Figure 2.2 illustrates the process of retrieval of smart data. There are some decision-making processes to iterate the data again and again for getting smart data. The Multivariate Ensemble method uses multivariate Random Forest algorithm with gradient-boosted machine . Tlie multivariate algorithm uses a prediction mechanism with multiple
Figure 2.2 Univariate Ensemble Method diagram.
response data instead of a single data for noise filtering. The analysis involves the time factor of data as well as its clustering property. The splitting process or partitioning process will undergo regressive tree partition. The boosting will yield high performance since it can eliminate the errors or residual errors of the previous iteration. A better result will be obtained by this methodology since it involves parallel processing of a large amount of data. It is more complex than the Univariate Ensemble Method, so for processing of a single data, it yields better performance and poor time utilization.
Figure 2.3 shows a variant behavior  which involves partitioning of data followed by the gradient-boosting method for avoiding the residual errors so that prediction power will be improved and accuracy will be increased. The voting process will improve the precision iterates till noise-free data is obtained to form a set of smart data. As mentioned earlier, it is suitable for an ensemble of noisy data to be filtered in parallel (Figure 2.3).
(ii) Agile Big Data
The agile capability of big data analysis  helps in overcoming the unpredictable changes and handling large amount of information. The seal- ability of data can be attained through agility. The main purpose is to adapt to new changes in the methodology and framework. So for that, it involves distributed systems with a cloud computing platform for iteratively processing the data. Data transformation can be done through this agile property of big data pre-processing. It also involves joint ownership where there will be
Figure 2.3 Multivariate Ensemble Method diagram.
interaction between different organizations and business partners. The joint ownership will help to improve the validation process and data migration very quickly and accurately. The agile business lab will comprise a variety of stakeholders like business and IT experts.
(a) Understanding Agile Data
The main feature of agile data is its digital transformation and migration management. In different domains, the data as well as its features will vary. This process involves the identification of data through its characteristics and activities. It will increase the quality of services and reduce the cost and reliability of data.
(b) Benefits of Agile Data
The main advantage of agile data  is the clarity of information. In business, enterprises will frequently change their framework and processes, so they need to manage the data to adapt to the new environment. So this capability can be easily achieved through agile data. The agile management team will make decisions in every crucial situation. Tlie integration and transformation of data in a new platform will be reconsidered by this agile team before processing. These data contain mostly transactional data which can automate the process with higher operational efficiency. Some other benefits include the focus toward realtime results, feature engineering, better validation, and machine learning capability. The achievement of target metrics in data exploration and cleaning processes will be quite simple with agile data. The changes in scope and requirements because of different partners can be processed by this agile data methodology.
(c) Agile Data Processing
Agile processing  involves a set of data partners such as IT companies, business organizations, and marketing firms. In agile processing data is captured from different applications, processed for extraction, and then driven to data structuring for end user needs. Data can be categorized as snapshots and modular components. The snapshots involve information for transmission and consumption. The modular components can be processed faster by splitting them into different smaller units which involves real-time transmission of data. After structuring, data is transformed to be published for end user consumption (Figure 2.4).
Figure 2.4 Agile data process diagram.