Titlebook: Big Data Analytics; First International Srinath Srinivasa,Vasudha Bhatnagar Conference proceedings 2012 Springer-Verlag Berlin Heidelberg

metamorphose

書目名稱Big Data Analytics影響因子(影響力)




書目名稱Big Data Analytics影響因子(影響力)學(xué)科排名




書目名稱Big Data Analytics網(wǎng)絡(luò)公開度




書目名稱Big Data Analytics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Big Data Analytics被引頻次




書目名稱Big Data Analytics被引頻次學(xué)科排名




書目名稱Big Data Analytics年度引用




書目名稱Big Data Analytics年度引用學(xué)科排名




書目名稱Big Data Analytics讀者反饋




書目名稱Big Data Analytics讀者反饋學(xué)科排名

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發(fā)牢騷

overrule

Cloud Computing and Big Data Analytics: What Is New from Databases Perspective?ness insights by querying and analyzing such massive amounts of data is becoming the need of the hour. The warehouses and solutions built around them are unable to provide reasonable response times in handling expanding data volumes. One can either perform analytics on big volume once in days or one

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caldron

Exploiting Schema and Documentation for Summarizing Relational Databasesbase is a challenge which involves identifying semantically correlated elements in a database schema. Research efforts are being made to propose schema summarization approaches by exploiting database schema and data stored in the database. In this paper, we have made an effort to propose an efficien

原告

Faceted Browsing over Social Mediasystem that can return posts published on social media covering various aspects of a concept being searched. We present a faceted model for navigating social media that provides a consistent, usable and domain-agnostic method for extracting information from social media. We present a set of domain i

槍支

Analog Textual Entailment and Spectral Clustering (ATESC) Based Summarizationth analysis has revealed that LTTs performance is limited by unfair entailment calculation, weak segmentation and assignment of equal importance to each segment produced. It seems that because of these drawbacks, the summary produced from experimentation on articles collected from New York Times web

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隱藏

An Efficient Method of Building an Ensemble of Classifiers in Streaming Dataselected or not. The exiting methods refine a classifier based on a regular time interval. They refine a classifier even if the classification accuracy of the classifier is high. Also it uses a classifier even if the classification accuracy is low. In this paper, our ensemble method selects data in