Sentence examples for structured data processing from inspiring English sources

Spark supports in-memory computing, that enables it to query data much faster compared to diskbased engines such as Hadoop, and supports a rich set of other tools such as Spark SQL (for SQL and structured data processing), MLLib (for machine learning) and Spark Streaming.

This paper reviews recent developments in DSNs from four aspects: network structure, data processing paradigm, sensor fusion algorithm with emphasis on fault-tolerant algorithm design, and optimal sensor deployment strategy.

Although stream processing performance has been studied [12, 14], comparative feasibility analysis of the technologies has not been extensively performed in the context of semi-structured data processing.

Some fundamental concepts and important issues in relation to TRR have previously been discussed, mainly in the context of processing structured data in biomedical informatics; however, it is important that these concepts be re-examined in the context of processing narrative data using MLP.

Data are acquired through a search protocol, which is required for processing structured data.

Reporting clinical trial results in text-based articles rather than properly structured data sets makes computational processing of the results practically impossible [ 15].

The use of queries expressed in natural language can, it is believed, overcome these hurdles [ 13], yet computers are good at processing structured data but much less effective in handling natural language that is inherently unstructured.

To analyse numerical or structured data, techniques like "online analytical processing" and data mining methods are used.

We recognize that regular expressions are only appropriate for highly structured, machine-produced data and span the gap between structured data entry and natural language processing techniques for semantically interpreting text such as clinical notes.

Current knowledge processing technology limits the acquisition and especially the processing of comprehensive EHR records which incorporate structured data, images, video-clips, bio-signals, genomic data, unstructured textual data covering clinical findings, detailed patient history, etc.

Traditional data processing technology mainly refers to the analysis and synthesis of structured data, which is logical and expressed through a two-dimensional table structure.