Overview: This section explains the research philosophy, the literature survey for this research, data collection technique, data analysis approach and the overall process of the research. It started by gather the general insights that were relevant to the research; derived the information into a research topic; brief explanation about how the research was conducted, how the data was collected through experiment, how the data was analysed and concluded.
3.1. Research Philosophy
The main idea of the research was built upon a general insight that was gathered by reviewing a number relevant literature and research papers. Therefore the perspective of the research was an objective view, instead of subjective view, which was built on a personal
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It started by define the hypotheses; define relevant variables and set up a testing condition; data was collected by an experiment in controlled environments and using controlled variables (Dawson, 2009). The other testing technique was eliminated (such as action research, case study and survey) because those techniques were not suitable to this research.
Through the experiment, a quantitative and a numeric data were collected and treated as the primary data (Rosalia, 2015). Data analysis was presented in a statistical model along with the detail descriptions based on the experiment outcomes in order to answer the research question (Bryman, 2012). The secondary data was collected through the literature survey (Kumar, 2014).
A qualitative data collection (such as an interview, focus group and participant observation) was not applicable in this research because of these following reasons:
1. This research was about testing an existing theory, while qualitative approach is about establishing new theory (Bryman,
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Research Process
This research adopted the sequential process model, therefore the research was executed in a fixed and continuous sequences (Dawson, 2009).
1. Identify the wider knowledge
The wider insight about relational and non-relational database performance, particularly MySQL and Hadoop was gathered through the literature survey. By read textbooks, reviewing academic journals and research papers, I founded a gap in the performance of relational database compare to the non-relational.
2. Determine research topic
Research topic was derived from the understanding of query processing in MySQL and Hadoop, the database performance issues, performance tuning and the importance of database performance. Thus, it was decided to develop a comparative analysis to observe the effectiveness of the performance of MySQL (non cluster) and Hadoop in structured and unstructured dataset (Rosalia, 2015). Furthermore, the analysis included a comparison between those two platforms in two variance of data size.
3. Define research approach
A deductive reasoning was applied in a controlled experiment to verify the theory: In read and write operation, MySQL has a better performance compare to Hadoop in structured dataset, while Hadoop has a better performance compare to MySQL in unstructured dataset (Rosalia,
Over many ago relational databases reside most of the data but after the introduction of NoSQL database had changed this procedure. Most of the unstructured data had been sent to NoSQL database. Relational database systems, which showed good performance before the birth of internet and cloud computing era is now unable to control the heat of new technologies. To stabilize this situation new requirements were set to design by RDBMS. To meet these challenges they need highly scalable and unstructured data model with high performance; so they choose NoSQL database (Muhammad Mughees, 2013).
RESEARCH METHODOLOGY: The research design was descriptive in nature. The data was gathered through personal interview and observation method.
This study is organized into five distinct chapters. Chapter one deals with the introduction which is made up of background information of the research, statement of the research problem, objectives, conceptual definition of terms, limitations and scope of the research and finally the
In this chapter, describes the research method that was followed. It including the research design and participants, and the techniques and data analysis methods that are used for research and detected. Moreover, it is also discussing the techniques used to carry out the experimental work.
In Nowadays, there are two major of database management systems which are used to deal with data, the first one called Relational Database Management System (RDBMS) which is the traditional relational databases, it deals with structured data and have been popular since decades since 1970, while the second one called Not only Structure Query Language databases (NoSQL), they are dealing with semi-structured and unstructured data; the NoSQL types are gaining their popularity with the development of the internet and the social media since April 2009. NoSQL are intending to override the cons of RDBMs, such as fixed
This article presents the outline of the research project, including the different sections and summary descriptions of the information contained in each section. Overall, the research comprises six different sections. These include the introduction, literature review, analysis approach, results, discussion, and conclusion. It is important that these sections are well knit to ensure a logical flow of ideas and clarity in the presentation of the research report. The following section shows the research outline.
Hadoop is a great data storage choice and Hadoop Distributed File System (HDFS) or Hive is often used to store transactional data in its raw state. The map-reduce processing supported by these Hadoop frameworks can deliver great performance, but it does not support the same specialized query optimization that mature relational database technologies do. Improving query performance, at this time, requires acquiring query accelerators or writing code. Every company who chose to use Hadoop needs to optimize their architecture in a way compatible to Hadoop.
The proposed research is based on a combination of qualitative and quantitative methods. Qualitative research involves iterative, logical and exploratory process that gathers the views of participants with the objective of analysing the facts that relate to the study. On the other hand quantitative methods comprise of deductive ways of studying the data collected (Bryman, 2004 cited in Heath and Tyna, 2010; pg 10).
NoSQL databases are designed to expand transparently and horizontally to take advantage of new nodes, and designed with low-cost hardware. SQL have problems in Scalability.
Data Analysis is defined as "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data. (Statistician John Tukey, 1961)” In general, Data Analysis is used to check the validation of the vaguely collected data in terms of variations and profitability. However, if Data Analysis is not used we cannot obtain the accurate, factual and optimum solution of the given challenge. Data Analysis tools helps us to understand the question and produces answer in a detailed space. It also helps to find the gaps between data such
The quality of qualitative research is of great importance because as the data obtained from the research can be used to form the basis in quantitative research. Several researchers have criticized the fact that qualitative research does not have a method that can be used to measure its quality. However, there are various concepts that can be used to measure the quality of qualitative research. These methods are the school of thoughts of Dixon-Woods et al. and the Lincoln et al. and the measurement of reliability, validity, and generalizability. According to various researches, these methods have been efficient in computing the quality of qualitative researches.
Commentary: This article focused on qualitative research, for both experimental and non-experimental data. Such as surveys, questionnaires, and case studies. This would be useful when designing a qualitative study to know researchers may have to go back and re think their design even in the data collection part of the study.
It consist of research method, data collection method, data measurement method, method of analysis that carried out for the reseach.
This chapter presents the background of the study, the statement of the problem, the assumptions made in accordance with the design of the project, the scope and delimitation, the significance of the study, the research design and methodology, and the definition of terms used in the study.
A general definition of research is the systematic investigation into and study of materials and sources in order to establish facts and reach new conclusions. Research is an important part of many people’s lives both personally and professionally. What kind of car to buy? What are the most recent therapies to combat breast cancer? What is the best stock to invest in? Research needs to continually and systematically be conducted for society to move forward. Qualitative research is one type of research “that encompasses a number of philosophical orientations and approaches.” Early in the 20th century, scientists, such as anthropologists and sociologists went out into the world to ask the questions of how and why the societal and cultural world was the way that it was. Inquiry was done to better understand social phenomenon. As time went on, different professional fields such as education, law, health, and social work also embraced qualitative research. Today hundreds of books and journals exist on qualitative research, as well as various strategies, paradigms, and methods for conducting such research.