Big Data A Roadmap to Trust

Question: Explore "Trust in Big Data". Answer: 1. Introduction The current world incorporates the cheaper communication and storage on the digital zone. The advancement of the new sensor technologies or online social networks has been the main reason of the explosion of huge amount of data (Kaisler et al., 2013). The term, which has emerged, for catching the phenomenon is known as Big Data. The application of the Big Data analytics or privacy and security encompass the various fields of technologies including the medicine, healthcare, business, finance, law, transportation, education, and telecommunication. 1.1 Background The use of Big Data has been very sensitive in nature, which is very much dependent on the personal data of the constantly changing technological environment. There has been evading of the new aspects of the data privacy and data protection that are currently governed by a number of complex regulations that are constantly changing. At present, the approaches to privacy have been different in the different sectors depending on the industries (Adelola, Dawson and Batmaz 2015). As a result, the exploitation of the private information is exposed to the risk to the businesses. 1.2 Aims The project aims at providing a proposal related to the Big Data for assessing the trust and computation that the world is perception upon the Big Data system. The research proposal aims at elaborating the various challenges to create trust on the Big Data. 1.3 Significance of the research There has been evidence of the new aspects of the data privacy and data protection that are currently governed by a number of complex regulations that are constantly changing. They proposal would enable the researcher to illustrate on the implications of the Big Data such that to allow the confidence of the customers against the Big Data system to grow and prosper in the long run. 1.4 Problem statement The huge diversity and amount of the data sources and information helps in providing a lot of new opportunities and at the same time poses various challenges for online trust (Yaqoob et al. 2016). The questioning about the trust has always been there since the era when Big Data came into existence. The consumers are, thus, required to be convinced that their private and confidential information are adequately protected and are being accessed in a fairly manner. This implication would help in generating trust and confidence in the individuals relating to the Big Data. 1.5 Research questions The research quotations related to the research proposal of the related topic can be: How can Big Data be useful for assessing the trust? How can the Big Data can be communicated transparently? Can Big Data lead to better results? How can trust be increased on the result information? How can we measure trust in Big Data? How can the trustworthiness of the Big Data analytics be ensured? 1.6 Hypothesis The hypothesis relates to the research topic can be given by the following: The Big Data system does not provide useful outcome to gain trust The trust related to the Big Data cannot be measured The application of the Big Data fails to gain the trustworthiness of the customers 2. Literature Review Big Data Big Data has been around since a few years and the applications of which have been extended to various fields of technology. According to Katal, Wazid and Goudar (2013), the most self-evident characteristics of the Big Data has been its dimension of the volume of data. Moreover, the Big Data has describes as the phenomenon which utilizes the dimensions of the velocity and variety. Franks (2012) believes that taming of the Big Data would be helping the industries not only to master the massive quantities of information but would also help in tackling the multitude and variety of heterogeneous types of data. According to Raghupathi and Raghupathi (2014), Big Data can be defined as the innovative form of data processing system that incorporates the high volume, high variety and high velocity information assets that demand highly enhanced decision making methodology. Notion of trust The term Trust has been extensively the word for extensive research in the various fields as there is still no satisfactory response that could define the same. However, Chen, Rau and Kolz (2013) defined the trust as the specific level of probability which helps an individual to identify the performance of the capacity of a particular action such that to be able to monitor the same. Ruohomaa and Kutvonen (2013), postulate that there are two basic models of trust viz., policy based trust and reputation based trust. The policy-based trust is based on the evidence exchange of the credentials. On the other hand, the reputation-based trust is an estimation of the trustworthiness. This reputation-based trust related to the Big Data relies on the reviewing, referrals and ratings from the community members who are accustomed with the Big Data. 2.1 Review Techniques The literature review discusses about the various concerts related to the Big Data and the trust being perceived by the individuals related to the research topic. The review should be dealing in illustrating the definitions and focal issues of the terms related to the Big Data system and the trust such that to find a co-relation between them. The review should be evaluating the entire ideas and concepts related to the research topic and formulate a research framework such that to illustrate on the core idea of the research study. This reviewing technique would be helpful for the researcher to structure the problem domain and present the current research direction and interdependencies as well. 2.2 Limitations The literature review lacks the implementation of any specific model that could illustrate the Big Data and the trust related to the system .Moreover, the review is also unable to relate the Big Data system with the trustworthiness in the individuals related to the same. Thus, the literature should be incorporating relevant models that could further illustrate the relation between the Big Data and trust. 2.3 Focus The focus of the research study would be: To find the significance of the Big Data in the industries To found the reaction between the Big Data and trust To establish data driven approach for the trust management based on reputation To define the focal issues for serving as the future research directions 3. Research design 3.1 Conceptual framework Trust in Big Data Trust and reputation systems have a huge opportunity as the number, viloloci8ty, and variety if data has been increasing on a daily basis. The process relates to the reputation system comprises of the two important steps viz., (i) Collection and preparation and (ii) storage and communication related to the Big Data applications (Snger and Pernul 2014). Input Output Big Data Applications Collection and Preparation Storage and Communication Trust relation Trust or Trustee (i) Collection and preparation: In the current digital world, vast number of reputation data is being created every day pawing to the expansion in the number of web applications including the e-Commerce platform and networking communities online (Snger and Pernul 2014). The reputation system incorporates the inclusion of both the implicit and explicit information. The trust information can be created for the rating of the trustee as for example on ecommerce websites like eBay. On the other hand, the implicit trust information care derived from the potential data sources, which are interoperable in the reputation system. (ii) Storage and communication: The scores as provided by the customers can evaluate the reputation. These reputation scores would be helping in offering the information for supporting the users in understanding the values of the scores (Snger and Pernul 2014). These scores would also helping in accomplishing the transparency in the Big Data analytics. 3.2 Addressing the research questions How can Big Data be useful for assessing the trust? Rationale: This question would help in clarifying the usefulness of the Big Data in managing huge amount of information. This attribute would be helping in signifying the trustworthiness or data quality of the sources as well. How can the computational process can be communicated transparently? Rationale: This question would enable the researcher to acquire some useful information related to the illustration of appropriate techniques for making the outcome of the Big Data management to be transparent. Can Big Data lead to better results? Rationale: This question would help the researcher to focus on the realistic approach to the research topic. The question would also help in gaining the knowledge related to the data quantity and data quant. How can trust be increased on the result data? Rationale: The question would help in addressing the privacy preserving method and techniques. This concept of the privacy preserving techniques would enable in infusing the trust in the individuals related to the Big Data. How can we measure trust in Big Data? Rationale: The question would enable the researcher to quantify the trustworthiness of the data items and the data sources such that to develop a system for the accurate measurement of to the trustworthiness of Big Data for measuring the same. How can the trustworthiness of the Big Data analytics be ensured? Rationale: These questions would enable the researcher to provide accurate directions to research process for solutions including the accountability and befits of the Big Data system. 3.3 Expected outcomes The Big Data has been the most talked about topic that is a very significant research area in the field of innovation and technology as relevant to both the business and the individuals. The search process would enable the illustration of the trust that the individuals perceive in respective of the Big Data. The research process incorporates the research questions that would help the researcher to acquire some useful information related to the Big Data. The research study would help in evaluating the trust in the Big Data, which is a prerequisite for the assessment of the trust. 4. Proposed project plan Time-line Outline Number Task Name Duration Start Finish Predecessors 1 Project plan of research proposal on Big Data and Trust 48 days Tue 5/24/16 Thu 7/28/16 1.1 Finding up of topic 2 days Tue 5/24/16 Wed 5/25/16 1.1.1 Conducting research on background information on the topic 1 day Tue 5/24/16 Tue 5/24/16 1.1.2 Strengthening initial proposal 1 day Wed 5/25/16 Wed 5/25/16 3 1.2 Defining the Project 3 days Thu 5/26/16 Mon 5/30/16 4 1.2.1 Identifying the sensitivity of Big Data in the industries 1 day Thu 5/26/16 Thu 5/26/16 4 1.2.2 Assessing the trust and computation from the perceptions of the individuals 1 day Fri 5/27/16 Fri 5/27/16 6 1.2.3 Setting the objectives of conducting the research 1 day Mon 5/30/16 Mon 5/30/16 7 1.3 Defining the Project Objective 3 days Tue 5/31/16 Thu 6/2/16 8 1.3.1 Identifying the effectiveness of Big Data 1 day Tue 5/31/16 Tue 5/31/16 8 1.3.2 Evaluating its effects in the market 1 day Wed 6/1/16 Wed 6/1/16 10 1.3.3 Identifying the Innovative technologies 1 day Thu 6/2/16 Thu 6/2/16 11 1.4 Setting up of Research Questionnaires 3 days Fri 6/3/16 Tue 6/7/16 12 1.4.1 Effectiveness of the Big Data in evaluating trust 1 day Fri 6/3/16 Fri 6/3/16 12 1.4.2 Identification of trustworthiness of the Big Data 1 day Mon 6/6/16 Mon 6/6/16 14 1.4.3 Innovated technologies to be implemented for successful operations 1 day Tue 6/7/16 Tue 6/7/16 15 1.5 Identifying the Problem Statements 4 days Wed 6/8/16 Mon 6/13/16 16 1.5.1 Identifying the issues in the industries related to Big Data management 1 day Wed 6/8/16 Wed 6/8/16 16 1.5.2 Identifying various challenges for online trust 1 day Thu 6/9/16 Thu 6/9/16 18 1.5.3 Issues related while the expansion of business 1 day Fri 6/10/16 Fri 6/10/16 19 1.5.4 Evaluation of the survey to evaluate the level of protection of the private data of the customers 1 day Mon 6/13/16 Mon 6/13/16 20 1.6 literature review 4 days Tue 6/14/16 Fri 6/17/16 21 1.6.1 Conducting research on the available journals 1 day Tue 6/14/16 Tue 6/14/16 21 1.6.2 Setting the appropriate theories and models for discussing 1 day Wed 6/15/16 Wed 6/15/16 23 1.6.3 Preparing different views in literature 1 day Thu 6/16/16 Thu 6/16/16 24 1.6.4 Summarizing the reviews 1 day Fri 6/17/16 Fri 6/17/16 25 1.7 Identifying the importance of Big Data 7 days Mon 6/20/16 Tue 6/28/16 26 1.7.1 Factors affecting Business achievements in Global market 1 wk Mon 6/20/16 Fri 6/24/16 26 1.7.2 Innovation effects on the evolution of companies 1 day Mon 6/27/16 Mon 6/27/16 28 1.7.3 Role of Bog data analytics in the industries 1 day Tue 6/28/16 Tue 6/28/16 29 1.8 Setting up of Research Hypothesis 2 days Wed 6/29/16 Thu 6/30/16 30 1.8.1 Assessing the trust and computation that the world is perception on the Big Data 1 day Wed 6/29/16 Wed 6/29/16 30 1.8.2 Elaborating the various challenges to create trust on the Big Data 1 day Thu 6/30/16 Thu 6/30/16 32 1.9 Research methodologies 20 days Thu 6/30/16 Thu 7/28/16 33 1.9.1 Describing the methodologies to be applied in this research 1 day Fri 7/1/16 Fri 7/1/16 33 1.9.2 Tools and techniques of the research selected 1 day Mon 7/4/16 Mon 7/4/16 35 1.9.3 procedure of research is prepared 1 day Tue 7/5/16 Tue 7/5/16 36 1.9.4 Selection of data collection method 1 day Wed 7/6/16 Wed 7/6/16 37 1.9.5 Selection of analysis method 1 day Thu 7/7/16 Thu 7/7/16 38 1.9.6 Survey 2 days Fri 7/8/16 Mon 7/11/16 39 1.9.6.1 Conducting a pilot research through historical information 1 day Fri 7/8/16 Fri 7/8/16 39 1.9.6.2 tentative outcome of the research analyzed 1 day Mon 7/11/16 Mon 7/11/16 41 1.9.7 Implementation 1 mon Fri 7/1/16 Thu 7/28/16 1.9.8 Literature Review due 0 days Thu 6/30/16 Thu 6/30/16 26 1.9.9 Data Collection and Analysis Report due 0 days Mon 7/11/16 Mon 7/11/16 42 1.9.10 Final Business Research Thesis due 0 days Thu 7/28/16 Thu 7/28/16 43 References Adelola, T., Dawson, R. and Batmaz, F., 2015. Privacy and data protection in e-commerce in developing nations: evaluation of different data protection approaches. Chen, N., Rau, P.L.P. and Kolz, D., 2013. Comparison of Trust on Group Buying Websites between American and Chinese Young Adults. InCross-Cultural Design. Cultural Differences in Everyday Life(pp. 367-372). Springer Berlin Heidelberg. Franks, B., 2012.Taming the big data tidal wave: Finding opportunities in huge data streams with advanced analytics(Vol. 49). John Wiley Sons. Kaisler, S., Armour, F., Espinosa, J.A. and Money, W., 2013, January. Big data: Issues and challenges moving forward. InSystem Sciences (HICSS), 2013 46th Hawaii International Conference on(pp. 995-1004). IEEE. Katal, A., Wazid, M. and Goudar, R.H., 2013, August. Big data: issues, challenges, tools and good practices. InContemporary Computing (IC3), 2013 Sixth International Conference on(pp. 404-409). IEEE. Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and potential.Health Information Science and Systems,2(1), p.3. Ruohomaa, S. and Kutvonen, L., 2013. Behavioural Evaluation of reputation-based trust systems. InEnterprise Interoperability(pp. 158-171). Springer Berlin Heidelberg. Snger, J. and Pernul, G., 2014. Reusability for trust and reputation systems. InTrust Management VIII(pp. 28-43). Springer Berlin Heidelberg. Yaqoob, I., Chang, V., Gani, A., Mokhtar, S., Hashem, I.A.T., Ahmed, E., Anuar, N.B. and Khan, S.U., 2016. Information fusion in social big data: Foundations, state-of-the-art, applications, challenges, and future research directions.International Journal of Information Management.