Big Data: Related Technologies, Challenges and Future by Min Chen

By Min Chen

This Springer short presents a finished evaluate of the historical past and up to date advancements of massive info. the worth chain of massive facts is split into 4 levels: information new release, information acquisition, info garage and information research. for every part, the booklet introduces the final history, discusses technical demanding situations and stories the newest advances. applied sciences below dialogue comprise cloud computing, net of items, facts facilities, Hadoop and extra. The authors additionally discover numerous consultant purposes of huge facts equivalent to company administration, on-line social networks, healthcare and clinical purposes, collective intelligence and clever grids. This e-book concludes with a considerate dialogue of attainable learn instructions and improvement tendencies within the box. gigantic information: comparable applied sciences, demanding situations and destiny customers is a concise but thorough exam of this intriguing zone. it truly is designed for researchers and pros attracted to colossal info or similar examine. Advanced-level scholars in laptop technology and electric engineering also will locate this ebook useful.

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3 Storage Mechanism for Big Data Considerable research on big data promotes the development of storage mechanisms for big data. Existing storage mechanisms of big data may be classified into three bottom-up levels: (a) file systems, (b) databases, and (c) programming models. File systems are the foundation of the applications at upper levels. Google’s GFS is an expandable distributed file system to support large-scale, distributed, data-intensive applications [5]. GFS uses cheap commodity servers to achieve faulttolerance and provides customers with high-performance services.

It would be desirable if the entire system is not serious affected with respect to serving the reading and writing requests from customer terminals. This property is called availability. • Partition Tolerance: multiple servers in a distributed storage system are connected by a network. The network could have link/node failures or temporary congestion. The distributed system should have a certain level of tolerance to problems caused by network failures. It would be desirable that the distributed storage still works well when the network is partitioned.

This approach greatly reduces the times that big tables are modified, since only small tables are frequently modified. The cost of data modification is thus reduced to a great extent. Therefore, this method mitigates the problem of high cost for data changes and increases the look-up speed for recently modified data. AP systems,also ensure partition tolerance. However, AP systems are different from CP systems in that AP systems also ensure availability. However, AP systems only ensure eventual consistency rather than strong consistency in the previous two systems.

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