ITC571 - Project Proposal and Plan



How to use Big Data as a Service (BDaaS) on Cloud?


Most of the recent innovations within the IT industry are related to Big Data and Cloud Computing. The data is analyzed in the way of studying the operations pertaining to each of the industries so as to maximize the utilization of efficiency. With the increasing use of cloud computing and service computing, several services are emerging over the internet which in turn is generating a large amount of data, which is raising a demand for the combination of Cloud computing and big data in the way of creating new technologies which can help to solve the problem of big data. Big data is specifically based on the cloud computing which is an idea that is being used by several vendors [1]. The Data as a Service (DaaS) facilitates sharing the data among the systems, clouds, and applications. DaaS helps the data architects to be able to select data from a variety of different pools, filtering out the data that is sensitive, and making the remaining data to be available whenever demanded. The key benefit of DaaS is that it allows the elimination of the burdens and the risks of the management of a data to a 3rd party cloud service provider. The overall combination of cloud computing platforms and big data technologies has been able to introduce a new category of technology which is known as Big Data as a Service. The primary reason behind undertaking this research is that the Big Data all around is increasing at a faster pace and using it as a service while storing it into Cloud Service is going to be the next big thing.


Mell & Grance (2011) have highlighted that Cloud Computing is typically built around a whole series of the hardware and the software which can be accessed remotely using any web browser [2]. Once a system has established a cloud, how the services pertaining to cloud computing will be deployed in regard of the business models are likely to differ on the basis of requirements.

Buyya, Vecchiola, & Selvi (2013) provided that DaaS is an information distributing and providing model where the required data such as the sounds, images, texts and videos can be made available for the consumers over the internet. DaaS is typically based on the concept where the required data can be overall on demand to the users irrespective of their geographic separation of the provider and the receiver [3].

Mell & Grance (2011) posits that Daas Can be regarded as a solution of the increasing variety and volume of the data along with the competencies which are required to manage the increasing amount of data. Big data is used by the organizations in the way of generating competitive advantage [2]. Data would turn out to be worthless if the data cannot be interpreted, understood, analyzed, and the results thereof can be applied within the context. The poor quality of the data cannot be trusted and it is likely to result in reducing the ability to undertake intelligent business decisions. The overall capability to be able to capture, format, process and distributing the data within the real-time or faster will require huge investment in the area of architecture and infrastructure of data management.

The overall amount of data that is consumed and produced has been growing at a faster page within the digital world. The increasing use of social media and the increasing use of smart devices have been increasing a large amount of data on regular intervals. If the data is not managed effectively it is likely that it will lead to the development of invaluable information. Different new technologies have been emerging within the market in the way of addressing the overall problem of the management and the analysis of the Big Data [4]. The organizations have been finding it difficult to be able to implement these types of Big Data technologies in an effective manner because of the problems such as the lack of available expertise. Use of Big Data as a Service will offer common big data related services to the consumers in the way of enhancing efficiency and reducing the overall costs.

The primary aim or objective of this research would be to identify that how Big Data can be used as a Service over cloud?


This particular project is likely to help identify the needs of Big Data as a Service, and how can it turn out to be useful when cloud computing is associated with the big data.

The above image represents the use of cloud computing in the field of big data management. The sources for large data from the Web and cloud are stored within a distributed database which is fault-tolerant and is processed using a programming model for the large datasets along with the distributed algorithm within a cluster. The primary purpose of the data visualization as shown in the above figure is that it allows to view the analytical results which presented visually with the use of different graphs in the area of decision making. The big data makes use of distributed storage technology which is based on cloud computing instead of a local storage which is attached with an electronic device or computed. The evaluation of Big Data is typically driven by the fast growing cloud based applications which developed using virtualized technologies. Thus, cloud computing will not only offer facilities for the processing and computation of big data but it will also turn out to be a service model.



Research Method

The research method that will be used in this particular research will be qualitative research method where the research will consist of an investigation of seeking answers to different questions while systematically using a set of different procedures which are predefined in the way of answering the questions [5]. This research method is likely to help collect the required evidence and produces different findings which are not determined. It helps to produce findings which are applicable far way beyond the boundaries of the study.

Data Collection

This project will make use of the case study method where different research articles will be identified and the overall need of DaaS and the association of cloud computing will be evaluated.

Ethical Issues

The data will be collected from different case studies whereby the articles will be obtained from the university library and the information which will be taken from these articles will not be tampered in any manner and will be effectively used in the project while referencing the sources properly.

Analysis of Data

The data will be evaluated and analyzed while comparing the information from different sources and the data in these articles are further analyzed on the basis of different case studies. 

 Expected Outcome

  1. The project will help to identify the overall importance of DaaS
  2. The project will identify that how DaaS can be associated with cloud computing
  3. What is the future of Big Data as a Service with Cloud Computing





Obtaining knowledge of the evaluation of different papers


Identifying different topics and selecting an innovative topic for the project


Planning for the project


Defining the deliverables of the project


Identification of suitable methodology and preparing a proposal


Understanding the need for annotated bibliography


Identification of different articles for annotated bibliography


Preparation for project


Preparation for project


Preparation for project


Preparation for project


Project Report Submission



[1] M. A. Job, "BIG DATA-AS-A- SERVICE (BDAAS) IN CLOUD COMPUTING ENVIRONMENTS," IPASJ International Journal of Computer Science, vol. 5, no. 1, pp. 29-35, January 2017.

[2] P. Mell and T. Grance, "The NIST definition of cloud computing," Gaithersburg, 2011.

[3] R. Buyya, C. Vecchiola and T. Selvi, "Mastering in Cloud Computing – Foundations and Applications Programming," USA, 2013.

[4] I. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani and S. U. Khan, "The rise of “big data” on cloud computing: Review and open research issues," Information Systems, vol. 47, no. 1, pp. 98-115, 2015.

[5] S. Jamshed, "Qualitative research method-interviewing and observation," J Basic Clin Pharm, vol. 5, no. 4, pp. 87-88, 2014.


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