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Big data analysis is often shallow compared to analysis of smaller data sets. [225] In many big data projects, there is no large data analysis happening, but the challenge is the extract, transform, load part of data pre-processing. [225]
Data about applicant's family and various other factors included. 12,960 Text Classification 1997 [480] [481] V. Rajkovic et al. University Dataset Data describing attributed of a large number of universities. None. 285 Text Clustering, classification 1988 [482] S. Sounders et al. Blood Transfusion Service Center Dataset
The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files. [2] In the open data discipline, data set is the unit to measure the information released in a public open data repository. The European data ...
Overhead Imagery Research Data Set: Annotated overhead imagery. Images with multiple objects. Over 30 annotations and over 60 statistics that describe the target within the context of the image. 1000 Images, text Classification 2009 [166] [167] F. Tanner et al. SpaceNet SpaceNet is a corpus of commercial satellite imagery and labeled training data.
As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before ...
Data-parallelism applied computation independently to each data item of a set of data, which allows the degree of parallelism to be scaled with the volume of data. The most important reason for developing data-parallel applications is the potential for scalable performance, and may result in several orders of magnitude performance improvement.
A very large database, (originally written very large data base) or VLDB, [1] is a database that contains a very large amount of data, so much that it can require specialized architectural, management, processing and maintenance methodologies. [2] [3] [4] [5]
Data science is an interdisciplinary field [10] focused on extracting knowledge from typically large data sets and applying the knowledge and insights from that data to solve problems in a wide range of application domains.