The startup developed a set of algorithms to crawl and parse the Web's biggest travel information databases (like Wikitravel and Open Street Maps), before ranking the data to determine which pieces have the most relevance to its users and serving them up as rich travel guides.
As the model is parsed, the database is accessed using the Data Access Library that contains the object-oriented wrapper classes and their functions.
To test this strategy, a mixture of known ESTs (18,490 pea and 17,198 S. sclerotiorum ESTs) from public databases were pooled and parsed; the tBLASTx method successfully separated 90.1% of the artificial EST mix with 99.9% accuracy.
The CPU has a copy of the database and the data structures, and, instead of waiting idle for the GPU to parse the entire database, carries out part of the alignment task, thus reducing the total execution time.
Search functions were implemented at the aggregate client level to allow users to search and parse information from the database according to user-defined parameters.
The second part of the test measured the time required to download and parse all repository databases for the virtual machines (each machine had between 2 and 4 repositories configured) without using the repository cache.
Since the construction file archives were stored as electronic files in a central file repository, a file parser could be developed in the future to extract the meta-data from the file and parse this information into the database automatically.
The simplest way to find this sort of data was to fetch all the articles from our database and then parse the returned JSON.
The project transforms the world's largest collection of knowledge into book form by writing software that parses the Wikipedia database in English and lays out thousands of volumes, complete with covers, and then uploads them for print-on-demand.
That's because it re-parses the SaaS database once a day or when the customer first signs up, and in between, usage is fairly low beyond interacting with the data.
