large data sets

Neither of these approaches are appealing when you're dealing with large data sets or trying to build user-friendly Web applications.

Given large data sets, whether they are text or numeric, it is often useful to group together, or cluster, similar items automatically.

Large data sets are no longer esoteric concerns for a few big companies, but are becoming commonplace.

As the name suggests, this is a ready-made Zend Framework component that you can use to paginate large data sets.

Large data sets are often the result of multiple data feeds.

When working with large data sets, this analysis can negatively impact the performance of the control when automatic resizing occurs.

Some statisticians argue for a tougher standard of proof when researchers are fishing in large data sets.

However, in a production environment with large data sets, lazy loading always provides advantages.

In a cloud environment, the MapReduce structure increases the efficiency of throughput for large data sets.

Hadoop can be used in many applications beyond simply computing word counts of large data sets.

The efficiency of the proposed approach is shown by the experimental results on very large data sets.

Large data sets with escalating complexity have pushed traditional data mining techniques to new levels of processing demand.

We conclude that cache- oblivious algorithms do outperform traditional RAM-model algorithms when working on large data sets.

These fields tend to manipulate large data sets to assist the user in visualizing, estimating, or actually solving non-trivial problems.

For very large data sets, a scalable recommendation algorithm must perform the most expensive calculations offline.

This allows for the creation of truly enterprise scalable applications with large data sets via Domino.

Initial vector-space creation times can be long, especially for large data sets.

The decision tree technique is a very effective method for classifying large data sets.

Data Mining, which extracts useful information from large data sets or databases, is a rising cross subject.

Such rules can be discovered efficiently from even very large data sets.

We need large data sets that provide detailed environmental, demographic, and genetic data to find answers.

But in large data sets, on average, an asymmetry what you find.

Processing of data as batch or real-time, including handling large volumes of large data sets.

Navigating through large data sets efficiently.

Although in search engine we mainly process large data sets, in other applications we also need to process a large mount of small files.

However, in most case, it can meet production requirements without large data sets for a specific application.

This is the simplest synchronization method, but can involve unnecessarily large data sets being exchanged, which can limit performance.

Similarly, the use of "predictive analytics" on the basis of large data sets may transform health care.

Finally, Pig is a platform on Hadoop for analyzing large data sets.

However, the SSNN models required offline training with large data sets of input-output data.

If you are working with large data sets, consider using a dedicated buffer pool for the table space.

For very large data sets it quickly becomes impractical, or even impossible, to use JavaScript for tasks such as sorting by a column.

Handle very large data sets using virtual mode.

Reading large data sets into memory.

Do you see or foresee an increasing need for working with large data sets?

These kinds of large data sets are poised to become one of the most important resources the Internet creates.

It can help you process large data sets faster (by balancing workloads where and when needed).

One area that is ripe for parallelization is sorting and searching in large data sets.

On the identification of representative samples from large data sets, with application to synoptic climatology

A GA-Based Clustering Algorithm for Large Data Sets with Mixed Numerical and Categorical Values

A CSA-Based Clustering Algorithm for Large Data Sets with Mixed Numeric and Categorical Values

Dojo's approach to dealing with large data sets is simple, elegant, and boils down to two things

disjoint clustering of large data sets;

Mixed EM algorithm for large data sets