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Data Mining
Published in Richard J. Roiger, Data Mining, 2017
Hadoop (http://hadoop.apache.org/) is the most recognized open-source distributed computing environment for handling big data. The Hadoop framework is made up of two major components, the Hadoop Distributed File System (HDFS) for storing data and MapReduce for data processing (Mone, 2013). HDFS divides and redistributes data to the servers representing the cluster nodes, while MapReduce handles the distribution of tasks throughout the nodes and combines results for a final solution. Although native Hadoop requires complex programming skills, several application packages that run on top of Hadoop have been developed (Zikopoulos et al., 2012).
Blockchain with Corona Virus: Moving Together to Prevent Future Pandemics
Published in Vineet Kansal, Raju Ranjan, Sapna Sinha, Rajdev Tiwari, Nilmini Wickramasinghe, Healthcare and Knowledge Management for Society 5.0, 2021
P M Srinivas, Supriya B Rao, Shailesh Shetty S, Shiji Abraham, Harisha
All the related parties of the current blockchain network come to a mutual agreement (consensus) on the current data state of the ledger by allowing consensus protocols. They develop trust between unknown peers during a distributed computing environment. If a person adds some data to blockchain, it's crucial for the distributed peers of blockchain to agree and analyze all additions before they are incorporated for good into the blockchain (Marmorstein, 2019), and the peers must know when to add a block and when to walk across the whole network. So, a consensus protocol is similar to the bicentennial tolerance example; the challenge for the generals was to understand which command to listen to: whether to attack or retreat.
IoT and the Need for Data Rationalization
Published in Diego Galar Pascual, Pasquale Daponte, Uday Kumar, Handbook of Industry 4.0 and SMART Systems, 2019
Diego Galar Pascual, Pasquale Daponte, Uday Kumar
Middleware is a software layer interposed between software applications to make it easier for software developers to perform communication and input/output. Its feature of hiding the details of different technologies is fundamental to free IoT developers from software services that are not directly relevant to the specific IoT application. Middleware gained popularity in the 1980s due to its major role in simplifying the integration of legacy technologies into new ones. It also facilitated the development of new services in the distributed computing environment.
Data provenance collection and security in a distributed environment: a survey
Published in International Journal of Computers and Applications, 2021
Wolali Ametepe, Changda Wang, Selasi Kwame Ocansey, Xiaowei Li, Fida Hussain
Distributed computing environment does not only provide flexibility of communication but also mitigates management and IT infrastructural costs. It raises safety challenges of keeping the confidentiality of the data. Therefore, distributed computing users might not have faith in their service provider that stores their data in isolated geographical localities. The great concern for distributed computing users is the data security which is an obstacle in adopting distributed computing services especially for most of those tasks with sensitive data in banking and healthcare environment. Unfortunately, distributed computing infrastructures such as cloud computing are lacking data provenance management so far. Provenance shows how a peculiar piece of data is produced or generated. As soon as the data are processed the provenance is generated. Data provenance is one of the key elements that reflect the information quality. Provenance defines the history of the derivation of the data items. Provenance involves tracing the source of an information and the subsequent changes performed on it. Hasan et al. [13] were among the first to define the secure provenance problem and argued that it is of vital importance in numerous applications. They emphasized the protection of the contents of provenance records. The data provenance plays an important role in the forensic analysis by providing a digital proof for investigation. Usually, provenance considers who performed an action, where, and when an action occurs on the data [14].