Annotated Bibliography - PDF
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Added on 2020-05-28
Annotated Bibliography - PDF
Added on 2020-05-28
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Running head: ANNOTATED BIBLIOGRAPHYBig DataName of the StudentName of the UniversityAuthor’s Note
ANNOTATED BIBLIOGRAPHY11. Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experienceand acquisition intention of big data analytics.International Journal of InformationManagement,34(3), 387-394. 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) thatsearching, data mining as well as analysis is related with the big data analytics which aregenerally comprehended as a new IT ability. This is quite helpful in improving the performanceof the firm. It is identified that even some of the organizations are accepting the big dataanalytics for firming their competition market and for opening up various innovative tradeopportunities however it is identified that there are still number of firms those are still notadopting the new technology due to lack of knowledge as well as improper information on bigdata. The paper highlights one of the research models that are generally proposed for clarifyingthe achievement of big data analytics as of various hypothetical perspective of information usageexperience as well as data quality management. The empirical investigation helps in revealingthe purpose for big data analytics that positively impact by marinating quality of the informationwhich is associated with corporate. In addition to this, the paper elaborates that the experience ofthe firm in using internal source of data can hamper the intention of big data analytics adoption.2. 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.InformationSystems,47, 98-115.The paper mainly emphases on the growth of big data on cloud computing. According toHashem et al. (2015), in present days the cloud computing is considered as one of the powerful
ANNOTATED BIBLIOGRAPHY2tool that helps in performing massive scale as well as complex computing. It generally helps ineliminating the need of maintaining various types of expensive hardware, software as well asdedicated space. It is identified that massive growth in big data is mainly generated with the helpof 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 properanalysis as well as data processing. The paper reviews the big data rise in context to cloudcomputing with the intention of illustrate the characteristics, classification of big data withrespect to cloud computing. In addition to this, it is identified that the author focuses on varioustypes of research challenges in context to scalability, data transformation, data integrity,regulatory issues as well as governance.3. George, G., Haas, M. R., & Pentland, A. (2014). Big data and management.Academy ofManagement Journal,57(2), 321-326The paper mainly focuses on big data and management which is a major functionality forfuture generation application. According to George, Haas & Pentland (2014), the emphasis onbig data is increasing as well as the rate of using business analytics and smart living environmentis also increases. The modern world organizations have jumped in to the big data andmanagement system for using ever increasing volumes of data. The data for big data is collectedfrom various data collection source such as various types of user generated content, mobileTran’s actions as well as social media. The data generally needs powerful computationaltechniques for unveiling various patterns as well as trends between big socioeconomic datasets.Moreover, new visions usually garnered from various information value abstraction which canevocatively accompaniment official surveys, information as well as archival data sources.
ANNOTATED BIBLIOGRAPHY34. Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big dataanalytics.Journal of Parallel and Distributed Computing,74(7), 2561-2573. The paper mainly focuses on the trends of big data analytics which is one of the majorfuture generation applications. According to Kambatla et al. (2014), data repositories for big dataanalytics are currently exceeding Exabyte which are mainly increasing in size. It is identified thataway from the sheer magnitude, the datasets and its various associated applications posesdifferent types of challenges for software development. The datasets are mainly distributed andtherefore the sizes as well as privacy are generally considered based on various types warrantdistributed methods or techniques. Data generally exists on various platforms with differentcomputational as well as network capabilities. Considerations of security, fault tolerance as wellas access control are found critical in different applications. It is reviewed that for most of theemerging applications, data driven methods some points are net not known. Moreover, it is foundthat data analytics is impacted by the characteristics of software stack as well as hardwareplatform. The paper also elaborates some of the emerging trends that are helpful in highlightingsoftware, hardware as well as application landscape of big data analytics.5. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey.Mobile Networks andApplications,19(2), 171-209.The paper mainly reviews on the background as well as on the state of the big data. It isidentified that the paper mainly focuses on the four different phases of the value chain thatmainly includes data centers, internet of things as well as Hadoop. It is identified that in each ofthe phase, proper discussion about the background, technical challenges as well as review onvarious latest trends are generally provided (Chen, Mao & Liu, 2014).The paper also examines
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