Sentence examples for static dataset from inspiring English sources

As most of the related approaches, we assume a static dataset with mostly read-only accesses.

The use of a bulk load operation based on a static dataset to build an optimized Slim-tree is proposed in [17].

In order to have more rigor test on the method, actually another test is done with the COSMIC satellite-borne GNSS dataset and one static dataset (at CHUR station).

SGD curation and loading of InterPro predictions from the GOA project are both constantly ongoing, so in order to work with a static dataset, we analyzed the complete sets of literature-based and computational GO annotations from SGD, as well as the GO ontology file, dating from October 2009.

Dynamic and static datasets were acquired preoperatively and every 4-weeks postoperatively until sacrifice.

However, current Slope-one-based algorithms are all designed for static datasets, which are contradictory to real situations where dynamic datasets are mostly involved.

In addition, existing clustering approaches usually analyze static datasets in which objects are kept unchanged after being processed; however many practical datasets are dynamically modified which means some previously learned patterns have to be updated accordingly.

They're defining 'big data' as referring to situations where "high volumes of different types of data produced with high velocity from a high number of various types of sources are processed, often in real time, by IT tools (powerful processors, software and algorithms)" — as opposed to "traditional" (and often manual) data mining of low-variety, small scale and static datasets.

Keeping these static datasets and intermediate messages in memory and making them readily available in the subsequent iterations avoids unnecessary I/O overhead and thus significantly speed up the computation.

This creates a major computational challenge when attempting to integrate these dynamic and static datasets.

They discuss that VFDT has many advantages over other methods (e.g., rule based, neural networks, other decision trees, Bayesian networks) such as VFDT can make prediction both diagnostically and prognostically, can handle a changing non-static dataset, not using rigid rules (can be difficult for experts to put their knowledge into rules).