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- W2462775963 abstract "Today’s high-dimensional data, which is mostly unstructured, makes data patterns discovery (a.k.a. data mining) challenging and difficult for services engineers. Unstructured data mining deviates from existing information extraction methodologies that have been previously put forward due to the fact that recent data formation and storage has no standard schema; and the data is heterogeneous. At the storage level, the NoSQL database has been proposed as a preferred technology to accommodate the high-dimensional data, and the technology has received significant enterprise adoption. At the technology level, the query style of NoSQL databases differ from schema-based storages such as the RDBMS. Currently, there is lack of tools, technologies, and methodologies that can aid the community to support data patterns discovery in the big data epoch. Previously, an Analytics-as-a-Service (AaaS) framework is proposed for terms mining in document-based NoSQL systems. In this chapter, we provide comprehensive views about the performance of several algorithms that have been employed to achieve the topics and terms mining tasks. This chapter is a reproduction of several proposed algorithms which can enable the software engineering community to realize what has been done regarding the enhancement of accuracy of terms mining form document-based NoSQL systems." @default.
- W2462775963 created "2016-07-22" @default.
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- W2462775963 date "2016-01-01" @default.
- W2462775963 modified "2023-09-28" @default.
- W2462775963 title "Unstructured Data, NoSQL, and Terms Analytics" @default.
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- W2462775963 doi "https://doi.org/10.1007/978-3-319-30146-4_6" @default.
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