Experimental Data for log-k-decomp

Raw Data for Experiments, parsed data from Experiments, and SQLite database with the data
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For details see: 

Georg Gottlob, Matthias Lanzinger, Cem Okulmus, Reinhard Pichler: Fast Parallel Hypertree Decompositions in Logarithmic Recursion Depth; accepted for PODS'22.

We provide the following items:

•  the complete raw output of all our experiments, directly from our test machine and run via HTCondor.
   (rawdata_from_HTCondor.zip)
• we also provide the instances from HyberBench that we ran our experiments against.
  (hyperbench.zip)
•  to make this more useful, we also provide the extracted information from each run of each decomposition method. (parseddata_csv.zip and/or parseddata_sql.zip)
  The scripts used to parse the output is also included. (scripts_to_parse_raw_data.zip)
  
  Each test run is broken down into the graph from HyperBench its run on, the width parameter that was used (where applicable), the time the instance took to run, whether a timeout was encountered (1 hour for all runs, except for a specific test run with htdLEO as indicated in the paper where a timeout of 10 hours was used). In addition to this, the column "Correct" indicates whether the test produced an HD ("True") or whether the decomposition method determined that the graph has an hypertree width higher than the sought width and couldn't find an HD ("False"). We also provide the exact command line arguments for each test in the "RunInfo" column. All this information is contained in the "Run" table.
•  To allow easy analysis of the data, an SQLite3 database is included, already including all the parsed data. (logkExperiments.sqlite)
  Furthermore, we also include annotated queries, used to produced any of the statistics shown in the paper, such as the data for the main table, and any of the figures. (queries.zip)

We hope this allows any interested party to easily reproduce our experiments.

Links to source code of used decomposition methods:

• htdLEO: https://github.com/ASchidler/htdsmt
• NewDetKDecomp: https://github.com/TUfischl/newdetkdecomp
• The anonymised link to the source code of log-k-decomp is provided in the submission.