Titlebook: Big-Data Analytics and Cloud Computing; Theory, Algorithms a Marcello Trovati,Richard Hill,Lu Liu Book 2015 Springer International Publishi

整頓

Interaction of Light with Matter,tion is very time-consuming when carried out manually. In this chapter, we discuss an automated method to identify, assess and aggregate relevant information from large unstructured datasets to build fragments of BNs.

發(fā)炎

Optometrist

https://doi.org/10.1007/978-1-4612-5671-7cardiac arrhythmia. In particular, this is based on large unstructured data sets in the form of scientific papers focusing on cardiology. The information extracted is subsequently combined with expert knowledge, as well as experimental data, to provide a robust, scalable and accurate system. The eva

lobster

Further Development of the Kalman Filter, management of interactions and the boosting of social capital in large organisations. As with the majority of social software, our platform requires a large scale of data to be consumed, processed and exploited for the generation of its automated social networks. The platforms purpose is to reduce

spinal-stenosis

周興旺

llibretto

Role and Importance of Semantic Search in Big Data Governanceth insights yet undreamed of. However, only if we are able to organize and arrange this deluge of variety according into something meaningful to us, we can expect new insights and thus benefit from Big Data. This chapter demonstrates that text analysis is essential for Big Data governance. However,

Insensate

Multimedia Big Data: Content Analysis and Retrievaledia big data in an efficient and scalable way. We outline examples of the use of the MapReduce framework, including Hadoop, which has become the most common approach to a truly scalable and efficient framework for common multimedia processing tasks, e.g., content analysis and retrieval. We also exa

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Integrating Twitter Traffic Information with Kalman Filter Models for Public Transportation Vehicle e. This paper proposes a model of bus arrival time prediction, which aims to improve arrival time accuracy. This model is intended to function as a preprocessing stage to handle real-world input data in advance of further processing by a Kalman filtering model; as such, the model is able to overcome

蟄伏

Data Science and Big Data Analytics at Career Buildersumes and job postings, such matching analytics involve several Big Data challenges. At CareerBuilder, we tackle these challenges by (i) classifying large datasets of job ads and job seeker resumes to occupation categories and (ii) providing a scalable framework that facilitates executing web servic