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    <blockquote> <p>1. Large sample web server logs that have been anonymized.</p> </blockquote> <p>These work to start with:</p> <ul> <li><a href="http://archive.ics.uci.edu/ml/datasets.html" rel="noreferrer">UCI Machine Learning Repository</a> <ul> <li><a href="http://archive.ics.uci.edu/ml/datasets/Anonymous+Microsoft+Web+Data" rel="noreferrer">Anonymous Microsoft Web Data</a></li> <li><a href="http://archive.ics.uci.edu/ml/datasets/MSNBC.com+Anonymous+Web+Data" rel="noreferrer">MSNBC.com Anonymous Web Data</a></li> <li><a href="http://archive.ics.uci.edu/ml/datasets/Syskill+and+Webert+Web+Page+Ratings" rel="noreferrer">Syskill and Webert Web Page Ratings</a></li> </ul></li> </ul> <p>There are many, many more data sets available than these (see the gamut of other answers), but this is the lowest hanging fruit that meets your original criteria. As a bonus, they have <a href="http://archive.ics.uci.edu/ml/contact.html" rel="noreferrer">a contact link</a> if you have specific needs they may know of.</p> <blockquote> <p>2. Datasets used for database performance benchmarking.</p> </blockquote> <p>This sounds like a misnomer, because you're asking for empirical data sets that describe <a href="http://en.wikipedia.org/wiki/B+_tree" rel="noreferrer">well-defined</a> <a href="http://en.wikipedia.org/wiki/Divide_and_conquer_algorithm" rel="noreferrer">algorithmic</a> <a href="http://en.wikipedia.org/wiki/Database_normalization" rel="noreferrer">problems</a>. Specifically, it sounds like you're trying to find sets of data that you can use to test and benchmark various database systems in real time, using well-defined, normalized relational data that can be used as a set of test cases for determining the most efficient solution that meets your needs.</p> <p>I don't agree with this approach. Instead of finding a litany of database systems and their canned implementations, it's far better to explore the <a href="http://en.wikipedia.org/wiki/B+_tree" rel="noreferrer">algorithmic</a> <a href="http://en.wikipedia.org/wiki/Graph_database" rel="noreferrer">guarantees</a> of these systems as your first port of call. Once you've determined the algorithmic constraints that meet your needs, you can hone in on a set of canned solutions that you can benchmark on efficiency of, for example, indexing, sorting, searching, insertion, deletion, and retrieval.</p> <p>Wikipedia provides <a href="http://en.wikipedia.org/wiki/Database_testing" rel="noreferrer">a terse article on database testing concepts</a> that you can use to determine and write test cases for benchmarking performance. For example, you might use an agnostic data access interface like <a href="http://en.wikipedia.org/wiki/Java_Database_Connectivity" rel="noreferrer">JDBC</a> and <a href="http://www.inetsoftware.de/products/jdbc-driver/oracle/documentation/benchmark" rel="noreferrer">JDBC Benchmark</a> to determine the relative timings of each operation. From here, you can hone in on a correct solution.</p> <p><strong>In short,</strong> go to <a href="http://en.wikipedia.org/wiki/Analysis_of_algorithms" rel="noreferrer">the research</a> first for determining database guarantees. Once a set of candidate solutions has been identified, you can select amongst those by testing (or otherwise determining) the constant time performance of each desired operation.</p>
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    1. POLarge public datasets?
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    1. COYes, it's a stale thread, but it's deserving of a more current answer for both parts of the question. Best of luck to whomever finds this to be a useful signpost to their right solution in the future.
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    2. COcriteria used for bounty: most effort. none of the answers were particularly good on their own (and I didn't expect there to be good answers), but I am happy that there were more answers on the thread in total.
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