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

metamorphose

书目名称Big Data Analytics影响因子(影响力)




书目名称Big Data Analytics影响因子(影响力)学科排名




书目名称Big Data Analytics网络公开度




书目名称Big Data Analytics网络公开度学科排名




书目名称Big Data Analytics被引频次




书目名称Big Data Analytics被引频次学科排名




书目名称Big Data Analytics年度引用




书目名称Big Data Analytics年度引用学科排名




书目名称Big Data Analytics读者反馈




书目名称Big Data Analytics读者反馈学科排名

defile

发牢骚

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

metropolitan

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

枪支

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

意外

隐藏

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