This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora.
This study demonstrates how the use of large-scale, text-based data can provide insights into students' learning processes.
While a single winner hasn't emerged in this, the trend feels pretty firmly set, and the overall "SQL-on-Hadoop" feels more and more like the new default for large-scale analytics data warehouses.
To get a large, experimentally grounded data set, we used data from a large-scale text-mining project [12], [13] that provided access to experimental results described in hundreds of thousands of published research articles.
Currently, many biologists prefer using a spreadsheet such as Excel (Microsoft Corp., Seattle, WA, USA) or a simple text to organize their experimental results, and biology databases only provide full-text searches to access large-scale, unstructured text data.
The summaries were then imported into QSR NVivo7, a useful data management tool for large scale text information [ 20].
Ram Rajagopal is an Associate Professor of Civil and Environmental Engineering at Stanford University, where he directs the Stanford Sustainable Systems Lab (S3L), focused on large-scale monitoring, data analytics and stochastic control for infrastructure networks, in particular, power networks.
To discuss in deep the big data analytics, this paper gives not only a systematic description of traditional large-scale data analytics but also a detailed discussion about the differences between data and big data analytics framework for the data scientists or researchers to focus on the big data analytics.
His focus areas include networks, large scale data analytics (big data), and digital products and services.
Large-scale data analytics can aid in discovering patterns in these data to gain new scientific insights; however, the domain of biology is very different from most other domains in this aspect.
Topics include OLAP data cube techniques for exploring multi-dimensional datasets, data provenance, causality and explanations for query answers, handling uncertain data (probabilistic, incomplete, and inconsistent databases), data analysis with humans in the loop, and systems for large-scale data analytics and visualization.
