By Marozzo, Fabrizio; Talia, Domenico; Trunfio, Paolo
Data research within the Cloud introduces and discusses versions, tools, thoughts, and structures to research the massive variety of electronic information assets to be had on the web utilizing the computing and garage amenities of the cloud.
Coverage contains scalable info mining and data discovery recommendations including cloud computing strategies, versions, and structures. particular sections specialize in map-reduce and NoSQL versions. The booklet additionally comprises thoughts for accomplishing high-performance allotted research of huge facts on clouds. ultimately, the booklet examines examine developments similar to huge information pervasive computing, data-intensive exascale computing, and large social community analysis.
- Introduces facts research ideas and cloud computing concepts
- Describes cloud-based versions and platforms for giant info analytics
- Provides examples of the state of the art in cloud facts analysis
- Explains tips to advance large-scale info mining purposes on clouds
- Outlines the most study traits within the quarter of scalable immense info analysis
Read or Download Data Analysis in the Cloud : Models, Techniques and Applications PDF
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Additional info for Data Analysis in the Cloud : Models, Techniques and Applications
Sample text
John Wiley & Sons, New York. , 2008. Top 10 algorithms in data mining. Knowledge Inform. Syst. 14, 1–37. , 1999. Parallel and distributed association mining: a survey. IEEE Concurrency 7 (4), 14–25. , 2000. Scalable algorithms for association mining. IEEE Trans. Knowledge Data Eng. 12 (3), 372–390. CHAPTER 2 Introduction to Cloud Computing This chapter introduces the basic concepts of cloud computing, which provides scalable storage and processing services that can be used for extracting knowledge from big data repositories.
Openness and extensibility: The architecture should be open to the integration of new knowledge discovery tools and services. Moreover, existing services should be open for extension, but closed for modification, according to the open-closed principle. • Independence from infrastructure: The architecture should be designed to be as independent as possible from the underlying infrastructure; in other terms, the system services should be able to exploit the basic functionalities provided by different infrastructures.
When all the tasks are complete, the master node returns the result to the user node. For years, grid and distributed computing systems have been widely used for data processing. These systems work well with compute-intensive jobs, but require a lot of network bandwidth to handle huge amounts of distributed data. , 2012). In contrast to RDBMS, that is ideal for storing and processing structured data, MapReduce can be used to process semistructured or unstructured data in parallel, since data is evaluated at processing time.