Big Data Advantages And Disadvantages

937 Words4 Pages
1.1 Big Data Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making. And better decisions can mean greater operational efficiency, cost reduction and reduced risk. Analysis of data sets can find new correlations, to spot business trends, prevent diseases, combat crime and so on. Data sets grow in size in part because they…show more content…
The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers". What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. Thus, what is considered "big" one year becomes ordinary later. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration." Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data is a set of techniques and technologies that require new forms of integration to uncover large hidden values from large datasets that are diverse, complex, and of a massive scale. 1.2 Overview of the…show more content…
1.4 Objectives of the project • An innovative architectural model for context-aware monitoring known as knowledge discovery based Big Data for context-aware monitoring is proposed that uses cloud computing platforms. Every generated context of AAL systems are sent to the cloud. A number of distributed servers in the cloud store and process those contexts to extract required information for decision-making using this novel technique. • A 2-step learning methodology is developed. In the first step, the system identifies the correlations between context attributes and the threshold values of vital signs. Using Map Reduce Apriori algorithm, over a long term context data of a particular patient, the system generates a set of association rules that are specific to that patient. In the second step, the system uses supervised learning over anew large set of context data generated using the rules discovered in the first step. In this way, the system becomes more robust to accurately predict any patient
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