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Citations
... Now a days, great attention has been given to large data (e.g. [2,9,16]), largely due to a wide range of research problems associated with practical systems and plans such as modeling and the Mass. Distributed, large-scale storage database and mining industry. ...
... sharing and invasion (e.g. [16] )?. ...
In this paper, the Big Data challenges and the processing is analyzed, recently great attention has been paid to the challenges for great data, largely due to the wide spread of applications and systems used in real life, such as presentation, modeling, processing and large (often unlimited) data storage. Mass Data Survey, OLAP Mass Data, Mass Data Dissemination and Mass Data Protection. Consequently, we focus on further research trends and, as a default, we will explore a future research challenge research project in this area of research.
Cloud computing is a most powerful technology which performs massive-scale and complex computing. It eliminates the requirement to maintain costly computing hardware, dedicated space requirement and related software. Massive growth in the scale of data or big data generated through cloud computing has been identified. Concept of big data is a challenging and time-demanding task that requires a large computational space to ensure successful data processing and analysis. This paper includes definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced. The similarities between big data and cloud computing, big data storage systems, several big data processing techniques and Hadoop technology are also discussed. The term 'Big Data' defines innovative techniques and technologies to capture, store, distribute, manage and analyze petabyte-or larger-sized datasets with high-velocity and different structures. Big data may be structured, unstructured or semi-structured, resulting in incapability of conventional data management methods. Data can be generated from various relevant sources and can store in the system at various rates. In order to analyze these large amounts of data in an inexpensive and efficient way, parallelism technique is used. 2015 was the year that Big Data went from being something that a majority of organizations were either doing or at the very least actively considering. The growth of cloud-based Big Data services has made Big Data analytics a feasible reality for organizations of all sizes.
