Titlebook: Big Data; 29th British Nationa Georg Gottlob,Giovanni Grasso,Christian Schallhart Conference proceedings 2013 Springer-Verlag Berlin Heidel

DUBIT

書(shū)目名稱(chēng)Big Data影響因子(影響力)




書(shū)目名稱(chēng)Big Data影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Big Data網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Big Data網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Big Data被引頻次




書(shū)目名稱(chēng)Big Data被引頻次學(xué)科排名




書(shū)目名稱(chēng)Big Data年度引用




書(shū)目名稱(chēng)Big Data年度引用學(xué)科排名




書(shū)目名稱(chēng)Big Data讀者反饋




書(shū)目名稱(chēng)Big Data讀者反饋學(xué)科排名

FADE

金絲雀

https://doi.org/10.1007/978-3-662-07992-8Large volumes of graph-structured data are becoming increasingly prevalent in areas such as

巧思

Database Research Challenges and Opportunities of Big Graph DataLarge volumes of graph-structured data are becoming increasingly prevalent in areas such as

護(hù)航艦

Ali Cavit,Haluk Ozcanli,A. Merter Ozencio fast for optimal storage and indexing; and it is too heterogeneous to fit into a rigid schema. There is a huge pressure on database researchers to study, explain, and solve the technical challenges in big data, but we find no inspiration in the three Vs. Volume is surely nothing new for us, stream

MODE

Ali Cavit,Haluk Ozcanli,A. Merter Ozencireas move closer, to the point that they overlap and merge. Researchers in programming languages and compiler construction want to take part in this revolution, and also have to respond to the need of programmers for suitable tools to develop data-driven software for data-intensive tasks and analyti

Asparagus

Ali Cavit,Haluk Ozcanli,A. Merter Ozencit of mainstream database research. Moreover, the topic has now infected nearly every branch of computer science: provenance is a problem for everyone. But what exactly is the problem? And has the copious research had any real effect on how we use databases or, more generally, how we use computers..T

動(dòng)脈

Isaac J. Rondon,Wayne A. Marascos, combine rich databases with software driven by machine learning. The spectacular successes of these trained systems have been among the most notable in all of computing and have generated excitement in health care, finance, energy, and general business. But building them can be challenging even f

parallelism

Jennifer H. Richardson,Wayne A. Marascouery classes can be considered tractable in the context of big data? How can we make query answering feasible on big data? What should we do about the quality of the data, the other side of big data? This paper aims to provide an overview of recent advances in tackling these questions, using social

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