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Importantly, systematic errors cannot be removed by increasing the data because potential systematic errors also grow with increased size of data sets (Rodríguez-Ezpeleta et al. 2007).
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We learned that with the increasing size of data, the improvement of execution increases.
SemPoD was evauluated both in terms of systematic user survey and scalability for queries with different levels of complexity over increasing size of data.
The increasing size of data sets in bioinformatics in many cases necessitates downloading and carrying out analysis on local computing resources.
In this post genomics era, the most important challenge is to analyze and present the increasing size of data in a meaningful way to make it usable for all the researchers.
To cope with the increased size of high bit-depth image/video data compared to those of conventional LDR applications, it is necessary to develop appropriate compression techniques.
The recent development of Next-Generation Sequencing technologies gives rise to new applications along with new difficulties: (a) the increased size of profiles (up to 10 data-points when micro-array signals were closer to 10), and (b) the discrete nature of the output (number of reads starting at each position of the genome).
Although storing a few files on a file system is no longer a challenge for a small laboratory, with the increasing size of the data generated in each average experiment, it is crucial to organize and annotate data within local laboratory information management systems (LIMS), and/or in a public data repository.
The increasing size of RDF data requires efficient systems to store and query them.
The increasing size of RDF data requires a solution with good scale-out characteristics.
We encountered memory overflow problems when handling a large number of result subspace clusters and that was due to the increasing size of Clusters data structure used in Algorithm ??
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