The role of cloud computing architecture - MyAssignmenthelp.com

Question: Discuss aboutThe role ofcloudcomputing architecture. Answer: Introduction The paper mainly reflects on information usage experience and data value management, as well as on gaining target of big data analytics. It is opined by Kwon Lee and Shin (2014) that searching, data mining as well as analysis is related with the big data analytics which are generally comprehended as a new IT ability. This is quite helpful in improving the performance of the firm. It is identified that even some of the organizations are accepting the big data analytics for firming their competition market and for opening up various innovative trade opportunities however it is identified that there are still number of firms those are still not adopting the new technology due to lack of knowledge as well as improper information on big data. The paper highlights one of the research models that are generally proposed for clarifying the achievement of big data analytics as of various hypothetical perspective of information usage experience as well as data quality management. The empirical investigation helps in revealing the purpose for big data analytics that positively impact by marinating quality of the information which is associated with corporate. In addition to this, the paper elaborates that the experience of the firm in using internal source of data can hamper the intention of big data analytics adoption. The paper mainly emphases on the growth of big data on cloud computing. According to Hashem et al. (2015), in present days the cloud computing is considered as one of the powerful tool that helps in performing massive scale as well as complex computing. It generally helps in eliminating the need of maintaining various types of expensive hardware, software as well as dedicated space. It is identified that massive growth in big data is mainly generated with the help of cloud computing. The paper elaborates that big data is one of the challenging as well as time-demanding job that generally needs very large computational infrastructure for ensuring proper analysis as well as data processing. The paper reviews the big data rise in context to cloud computing with the intention of illustrate the characteristics, classification of big data with respect to cloud computing. In addition to this, it is identified that the author focuses on various types of research challenges in context to scal ability, data transformation, data integrity, regulatory issues as well as governance. The paper mainly focuses on big data and management which is a major functionality for future generation application. According to George, Haas Pentland (2014), the emphasis on big data is increasing as well as the rate of using business analytics and smart living environment is also increases. The modern world organizations have jumped in to the big data and management system for using ever increasing volumes of data. The data for big data is collected from various data collection source such as various types of user generated content, mobile Trans actions as well as social media. The data generally needs powerful computational techniques for unveiling various patterns as well as trends between big socioeconomic datasets. Moreover, new visions usually garnered from various information value abstraction which can evocatively accompaniment official surveys, information as well as archival data sources. The paper mainly focuses on the trends of big data analytics which is one of the major future generation applications. According to Kambatla et al. (2014), data repositories for big data analytics are currently exceeding Exabyte which are mainly increasing in size. It is identified that away from the sheer magnitude, the datasets and its various associated applications poses different types of challenges for software development. The datasets are mainly distributed and therefore the sizes as well as privacy are generally considered based on various types warrant distributed methods or techniques. Data generally exists on various platforms with different computational as well as network capabilities. Considerations of security, fault tolerance as well as access control are found critical in different applications. It is reviewed that for most of the emerging applications, data driven methods some points are net not known. Moreover, it is found that data analytics is impacted by the ch aracteristics of software stack as well as hardware platform. The paper also elaborates some of the emerging trends that are helpful in highlighting software, hardware as well as application landscape of big data analytics. The paper mainly reviews on the background as well as on the state of the big data. It is identified that the paper mainly focuses on the four different phases of the value chain that mainly includes data centers, internet of things as well as Hadoop. It is identified that in each of the phase, proper discussion about the background, technical challenges as well as review on various latest trends are generally provided (Chen, Mao Liu, 2014).The paper also examines several types of representative applications like internet of things, online social networks, medical applications, smart grid as well as collective intelligence that are mainly associated with big data. In addition to this, the paper elaborates number of challenges that are associated with big data. The paper mainly reflects on the role of cloud computing architecture in big data. It is identified that in the data driven society, large amount of data are generally collected from different actions, people as well as algorithm however it is analyzed that handling of big data has become one of the major challenge before the companies. In this paper, the challenges that the companies faces due to handling of the architecture of big data are generally explained. The paper also addresses the function of cloud computing architecture as one of the significant solution for various types of issues that are associated with big data (Bahrami Singhal, 2015). The challenges that are related with storing, maintaining, analyzing, recovering as well as retrieving big data are discussed. It is elaborated in this paper that cloud computing can be helpful in providing proper explanation for big data with proper with open source as well as cloud software tools in order to handle different types of big data issues. The paper reflects on the technologies as well as challenges that are mainly related with big data. It is stated by Chen et al. (2014) that the term of big data was mainly coined under the explosion of global data which was mainly utilized for describing various types of datasets. The paper introduces number of features of big data as well as its various characteristics that include velocity, value, variety as well as volume. Various challenges that are associated with big data are also elaborated. Big data faces number of challenges which includes analytical mechanism, data representation, redundancy reduction, data life cycle management, data confidentiality, as well as energy management. The challenges as well as issues are explained on a detail basis so that the issues can be resolved easily. The paper reflects on big data provenance which mainly elaborates information about the origin as well as formation procedure of data. It is identified that such information are quite useful for debugging transformation, auditing as well as evaluating the data quality. The paper illustrates that provenance is generally studied by the workflow, database as well as distributed system communities. The paper mainly reviews various types of approaches for large scale provenance that helps in discussing different types of potential issues of big data benchmark that generally aims to integrate provenance management (Glavic, 2014). Moreover, the paper examines how the concept of big data benchmarking would get benefit from provenance information and it is analyze that provenance are generally utilized for analyzing as well as identifying performance bottlenecks for testing the ability of the system for exploiting commonalities in processing as well as data. Additionally, it is identified tha t provenance are generally utilized for data centric performance metrics, for computing fine grained as well as for measuring the ability of the system for exploiting communalities of data and for profiling various types of systems. The paper focuses on the opportunities as well as big data challenges. Zhou et al. (2014) sated that the big data is one of the term that is considered as one of the major trends in the last few years that generally enhances the rate of research as well as various types of administration applications. It is identified that data is one of the powerful raw material that generally helps in creating multidisciplinary research events for business and government performance. The main goal of the paper is to share various types of data analytics opinions as well as perspectives that are mainly related with the opportunities as well as challenges that are brought forth by the movement of big data. It is identified that the author brings various types of diverse perspectives that come from different geographical locations. In addition to this, it is identified that the paper generally evokes discussion rather providing comprehensive survey of big data research. The paper reflects that in the era of big data, data is mainly generated, analyzed as well as collected at an unprecedented scale for making data driven decisions. It is found that poor quality of data is quite prevalent on web as well as on large databases. As poor quality of data can create serious consequences on the outcome of data analysis it is identified that veracity of big data is highly recognized (Saha Srivastava, 2014).The paper elaborates that due to sheer velocity as well as volume of data it is quite important for an individual to understand as well as repair in a quite scalable as well as timely manner. The paper mainly focuses on two major dimensions that generally discover various types of quality issues for trading off accuracy which identifies number of problems of the community. In addition to this, the paper elaborates the factors that are helpful in discovering as well as learning various types of data quality semantics. The paper reflects on big data investment, skills as well as on value of the firm. It is identified that the paper mainly considers various factors that are helpful in shaping early returns of investment in context to big data technologies. It generally tests various types of hypothesis that generally returns to early investment in Hadoop (Tambe, 2014). The analysis utilizes sources of new data like the LinkedIn that generally helps in enabling direct measurement of firms into number of emerging technical skills which mainly include Map/reduce Apache as well as Hadoop. The paper analyzed that evidence for the labor market generally disappears for the investment that is made on mature data technologies like SQL database. In addition to this, it is found that the skills are generally diffused and are mainly available through number of channels. The findings of big data underscore the significance of corporate investment for acquisition of various type of technical skills as well as for explaining various types of difference in productivity growth rate. The paper mainly focuses on big data research in information system. According to Abbasi, Sarker Chiang (2016), big data has got considerable attention due to the information system discipline. It is identified that the paper mainly helps in presenting research topics for highlighting some of the specific challenges that is generally posed by big data. It is identified that number of steps on the research agenda of big data are generally discussed by focusing on number of interplays between various characteristics of big data. The paper mainly highlights big data as one of the disruption to the value chain that helps in creating widespread impact which helps in limiting the way that is changed due to various types of scholarly work. The paper reflects on proper critical discussion that is made on the opportunities as well as challenges for design science, economics on research as well as on various types of emerging implications for various types of methodologies and theories that g enerally arises due to disruptive effects within the big data. References Abbasi, A., Sarker, S., Chiang, R. H. (2016). Big Data Research in Information Systems: T varying perspectives onoward an Inclusive Research Agenda.Journal of the Association for Information Systems,17(2). Bahrami, M., Singhal, M. (2015). The role of cloud computing architecture in big data. InInformation granularity, big data, and computational intelligence(pp. 275-295). Springer International Publishing. Chen, M., Mao, S., Liu, Y. (2014). Big data: A survey.Mobile Networks and Applications,19(2), 171-209. Chen, M., Mao, S., Zhang, Y., Leung, V. C. M. (2014).Big data: related technologies, challenges and future prospects(pp. 2-9). Heidelberg: Springer. George, G., Haas, M. R., Pentland, A. (2014). Big data and management.Academy of Management Journal,57(2), 321-326 Glavic, B. (2014). Big data provenance: Challenges and implications for benchmarking. InSpecifying big data benchmarks(pp. 72-80). Springer, Berlin, Heidelberg Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., Khan, S. U. (2015). The rise of big data on cloud computing: Review and open research issues.Information Systems,47, 98-115. Kambatla, K., Kollias, G., Kumar, V., Grama, A. (2014). Trends in big data analytics.Journal of Parallel and Distributed Computing,74(7), 2561-2573. Kwon, O., Lee, N., Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics.International Journal of Information Management,34(3), 387-394. Saha, B., Srivastava, D. (2014, March). Data quality: The other face of big data. InData Engineering (ICDE), 2014 IEEE 30th International Conference on(pp. 1294-1297). IEEE. Tambe, P. (2014). Big data investment, skills, and firm value.Management Science,60(6), 1452-1469. Zhou, Z. H., Chawla, N. V., Jin, Y., Williams, G. J. (2014). Big data opportunities and challenges: Discussions from data analytics perspectives [discussion forum].IEEE Computational Intelligence Magazine,9(4), 62-74.