public void TestWeightedSum_DataTable()
{
string[] strPrefSQL = GetPreferences();
for (int i = 0; i <= strPrefSQL.GetUpperBound(0); i++)
{
SQLCommon common = new SQLCommon();
common.SkylineType = new SkylineSQL();
DataTable dt = common.ParseAndExecutePrefSQL(Helper.ConnectionString, Helper.ProviderName, strPrefSQL[i]);
//Check tuples (every algorithm should deliver the same amount of tuples)
Assert.AreEqual(dt.Rows.Count, 55208, 0, "WeightedSum Sorting failed");
}
}
public void GeneratePerformanceQueries()
{
if (MaxDimensions < MinDimensions)
{
Debug.WriteLine("Max Dimensions must be >= Min Dimensions!");
return;
}
//Open DBConnection --> Otherwise first query is slower as usual, because DBConnection is not open
SQLCommon parser = new SQLCommon();
DataTable dt = parser.ParseAndExecutePrefSQL(Helper.ConnectionString, Helper.ProviderName, "SELECT cars.id FROM cars SKYLINE OF cars.price LOW");
//Use the correct line, depending on how incomparable items should be compared
ArrayList listPreferences = new ArrayList();
SqlConnection cnnSQL = new SqlConnection(Helper.ConnectionString); //for CLR performance tets
ArrayList preferencesMode = new ArrayList();
if (UseCLR)
{
cnnSQL.Open();
}
switch (Set)
{
case PreferenceSet.ArchiveComparable:
preferencesMode = GetArchiveComparablePreferences();
break;
case PreferenceSet.ArchiveIncomparable:
preferencesMode = GetArchiveIncomparablePreferences();
break;
case PreferenceSet.Jon:
preferencesMode = GetJonsPreferences();
break;
case PreferenceSet.Mya:
preferencesMode = GetMyasPreferences();
break;
case PreferenceSet.Barra:
preferencesMode = GetBarrasPreferences();
break;
case PreferenceSet.All:
preferencesMode = GetAllPreferences();
break;
case PreferenceSet.Numeric:
preferencesMode = GetNumericPreferences();
break;
case PreferenceSet.NumericIncomparable:
preferencesMode = GetNumericIncomparablePreferences();
break;
case PreferenceSet.Categoric:
preferencesMode = GetCategoricalPreferences();
break;
case PreferenceSet.CategoricIncomparable:
preferencesMode = GetCategoricalIncomparablePreferences();
break;
case PreferenceSet.MinCardinality:
preferencesMode = GetMinimalCardinalityPreferences();
break;
case PreferenceSet.LowCardinality:
preferencesMode = GetLowCardinalityPreferences();
break;
case PreferenceSet.HighCardinality:
preferencesMode = GetHighCardinalityPreferences();
break;
case PreferenceSet.LowAndHighCardinality:
preferencesMode = GetLowAndHighCardinalityPreferences();
break;
case PreferenceSet.ForRandom10:
preferencesMode = Get10ForRandomPreferences();
break;
case PreferenceSet.ForRandom17:
preferencesMode = Get17ForRandomPreferences();
break;
}
//Calculate correlationmatrix and cardinality from the preferences
ArrayList correlationMatrix = GetCorrelationMatrix(preferencesMode);
ArrayList listCardinality = GetCardinalityOfPreferences(preferencesMode);
//Depending on the mode create the sets from the preferences
if (Mode == PreferenceChooseMode.Combination)
{
//Tests every possible combination with y preferences from the whole set of preferences
if (MaxDimensions > preferencesMode.Count)
{
Debug.WriteLine("Combination with more dimensions than preferences. Please reduce dimensions!");
return;
}
//create all possible combinations and add it to listPreferences
for (int i = MinDimensions; i <= MaxDimensions; i++)
{
GetCombinations(preferencesMode, i, 0, new ArrayList(), ref listPreferences);
}
}
else if (Mode == PreferenceChooseMode.SameOrder)
{
int draws = MaxDimensions - MinDimensions + 1;
//Tests x times randomly y preferences
for (int iChoose = 0; iChoose < draws; iChoose++)
{
ArrayList preferencesRandom = new ArrayList();
ArrayList preferencesChoose = (ArrayList)preferencesMode.Clone();
//First define define randomly how many dimensions
int sampleDimensions = iChoose + MinDimensions;
//Choose x preferences randomly
for (int i = 0; i < sampleDimensions; i++)
{
preferencesRandom.Add(preferencesChoose[i]);
}
//add random preferences to listPreferences
listPreferences.Add(preferencesRandom);
}
}
else if (Mode == PreferenceChooseMode.Shuffle)
{
//Tests x times randomly y preferences
for (int iChoose = 0; iChoose < _randomDraws; iChoose++)
{
ArrayList preferencesRandom = new ArrayList();
ArrayList preferencesChoose = (ArrayList)preferencesMode.Clone();
//First define define randomly how many dimensions
int differentDimensions = MaxDimensions - MinDimensions + 1;
int sampleDimensions = Rnd.Next(differentDimensions) + MinDimensions;
//Choose x preferences randomly
for (int i = 0; i < sampleDimensions; i++)
{
int r = Rnd.Next(preferencesChoose.Count);
preferencesRandom.Add(preferencesChoose[r]);
preferencesChoose.RemoveAt(r);
}
//add random preferences to listPreferences
listPreferences.Add(preferencesRandom);
}
}
else if (Mode == PreferenceChooseMode.Correlation)
{
if (MaxDimensions > 2)
{
Debug.WriteLine("This test mode only works for 2 dimensions!");
return;
}
//Sort correlations to find the strongest
correlationMatrix.Sort(new CorrelationModel());
//Sort correlations ascending
CorrelationModel model = (CorrelationModel)correlationMatrix[0];
preferencesMode.Clear();
preferencesMode.Add(model.ColA);
preferencesMode.Add(model.ColB);
listPreferences.Add(preferencesMode);
}
else if (Mode == PreferenceChooseMode.AntiCorrelation)
{
if (MaxDimensions > 2)
{
Debug.WriteLine("This test mode only works for 2 dimensions!");
return;
}
//Sort correlations ascending
correlationMatrix.Sort(new CorrelationModel());
//Take only the two preferences with the worst correlation
CorrelationModel model = (CorrelationModel)correlationMatrix[correlationMatrix.Count - 1];
preferencesMode.Clear();
preferencesMode.Add(model.ColA);
preferencesMode.Add(model.ColB);
listPreferences.Add(preferencesMode);
}
else if (Mode == PreferenceChooseMode.Independent)
{
if (MaxDimensions > 2)
{
Debug.WriteLine("This test mode only works for 2 dimensions!");
return;
}
//Sort correlations to find the strongest
correlationMatrix.Sort(new CorrelationModel());
//Find the most independent atributes (closest to zero)
CorrelationModel modelBefore = new CorrelationModel();
CorrelationModel modelAfter = new CorrelationModel();
for (int i = 0; i <= correlationMatrix.Count; i++)
{
CorrelationModel model = (CorrelationModel)correlationMatrix[i];
if (model.Correlation > 0)
{
//continue until the correlation turnaround
modelBefore = model;
}
else
{
modelAfter = model;
//Leave the function, because now the correlation is getting worse
break;
}
}
//Add the two preferences to the list, that are closer to zero
preferencesMode.Clear();
if (Math.Abs(modelBefore.Correlation) > Math.Abs(modelAfter.Correlation))
{
preferencesMode.Add(modelAfter.ColA);
preferencesMode.Add(modelAfter.ColB);
}
else
{
preferencesMode.Add(modelBefore.ColA);
preferencesMode.Add(modelBefore.ColB);
}
listPreferences.Add(preferencesMode);
}
List<SkylineStrategy> listStrategy = new List<SkylineStrategy>();
if (Strategy == null)
{
//If no strategy is defined --> Take all possible algorithms
//listStrategy.Add(new SkylineSQL());
listStrategy.Add(new SkylineBNLSort());
listStrategy.Add(new SkylineBNLSort());
listStrategy.Add(new SkylineDQ());
listStrategy.Add(new SkylineHexagon());
listStrategy.Add(new SkylineDecisionTree());
}
else
{
listStrategy.Add(Strategy);
}
//Generates the R-Commands for the rpref package (for testig exactly the same statements in rpref)
if (WriteRCommand)
{
StringBuilder sb = new StringBuilder();
sb.AppendLine("---------------------------------------------");
sb.AppendLine("-----------------R-Commands------------------");
sb.AppendLine("---------------------------------------------");
sb.AppendLine("library(rPref)");
sb.AppendLine("setwd(\"E:\\\\Projekte\\\\prefSQL_Paper\\\\Test Scalagon\")");
sb.AppendLine("mydata = read.csv(\"cars_all.csv\", sep=\";\")");
sb.AppendLine("skylinesize <- array(" + listPreferences.Count + ":1)");
sb.AppendLine("timeins <- array(" + listPreferences.Count + ":1)");
for (int iPreferenceIndex = 0; iPreferenceIndex < listPreferences.Count; iPreferenceIndex++)
{
ArrayList preferences = (ArrayList)listPreferences[iPreferenceIndex];
ArrayList subPreferences = preferences; //.GetRange(0, i);
sb.Append("time = system.time(sky1 <- psel(mydata, ");
foreach(String pref in preferences) {
String rCommand = "";
if(pref.IndexOf("cars.") == -1) {
//Categorical preferences
//String tableName = pref.Substring(0, pref.IndexOf("."));
rCommand = "low(" + pref.Substring(0, pref.IndexOf(" (")).Replace(".name", "") + ")";
/*preferences.Add("colors.name ('red' >> 'blue' >> 'green' >> 'gold' >> 'black' >> 'gray' >> 'bordeaux' >> OTHERS EQUAL)");
preferences.Add("bodies.name ('bus' >> 'cabriolet' >> 'limousine' >> 'coupé' >> 'van' >> 'estate car' >> OTHERS EQUAL)");
preferences.Add("fuels.name ('petrol' >> 'diesel' >> 'bioethanol' >> 'electro' >> 'gas' >> 'hybrid' >> OTHERS EQUAL)");
preferences.Add("makes.name ('BENTLEY' >> 'DAIMLER' >> 'FIAT'>> 'FORD' >> OTHERS EQUAL)");
preferences.Add("conditions.name ('new' >> 'occasion' >> 'demonstration car' >> 'oldtimer' >> OTHERS EQUAL)");
preferences.Add("drives.name ('front wheel' >> 'all wheel' >> 'rear wheel' >> OTHERS EQUAL)");
preferences.Add("transmissions.name ('manual' >> 'automatic' >> OTHERS EQUAL)");*/
}
else
{
//Numeric preference
if (pref.IndexOf("LOW") > 0)
{
//LOW preference
rCommand = "low(" + pref.Substring(0, pref.IndexOf(" ")).Replace("cars.", "") +")";
}
else
{
//HIGH preferences
rCommand = "high(" + pref.Substring(0, pref.IndexOf(" ")).Replace("cars.", "") + ")";
}
}
sb.Append(rCommand);
//Don't add * on last record
if (pref != (string)preferences[preferences.Count-1])
{
sb.Append(" * ");
}
}
sb.AppendLine("))");
sb.AppendLine("timeins[" + (iPreferenceIndex + 1) + "] = time[3]");
sb.AppendLine("skylinesize[" + (iPreferenceIndex + 1) + "] = nrow(sky1)");
sb.AppendLine("skylinesize[" + (iPreferenceIndex + 1) + "]");
sb.AppendLine("timeins[" + (iPreferenceIndex + 1) + "]");
//string strSkylineOf = "SKYLINE OF " + string.Join(",", (string[])subPreferences.ToArray(Type.GetType("System.String")));
//sb.AppendLine(strSkylineOf);
}
sb.AppendLine("mean(skylinesize)");
sb.AppendLine("min(skylinesize)");
sb.AppendLine("max(skylinesize)");
sb.AppendLine("var(skylinesize)");
sb.AppendLine("sd(skylinesize)");
sb.AppendLine("mean(timeins)");
sb.AppendLine("min(timeins)");
sb.AppendLine("max(timeins)");
sb.AppendLine("var(timeins)");
sb.AppendLine("sd(timeins)");
sb.AppendLine("---------------------------------------------");
sb.AppendLine("---------------------------------------------");
sb.AppendLine("---------------------------------------------");
sb.AppendLine("");
sb.AppendLine("");
Debug.Write(sb.ToString());
//create filename
string strFileName = Path + "AllShuffle_RCommands" + DateTime.Now.Ticks + ".txt";
StreamWriter outfile = new StreamWriter(strFileName);
outfile.Write(sb.ToString());
outfile.Close();
}
int iStrategy = 0;
foreach(SkylineStrategy currentStrategy in listStrategy)
{
//Take all strategies
//BNL with attributeposition sorting and bnl with entropy function sorting
//For testing the BNL automatically (shuffle mode), once with attributeposition and once with entropy function
if (iStrategy == 1)
{
WindowSort = SQLCommon.Ordering.AttributePosition;
}
iStrategy++;
StringBuilder sb = new StringBuilder();
string strSeparatorLine;
if (Sampling)
{
strSeparatorLine = PerformanceSampling.GetSeparatorLine(ExcessiveTests);
}
else
{
strSeparatorLine = FormatLineString('-', "", "", "", "", "", "", "", "", "", "", "");
}
if (GenerateScript == false)
{
//Header
sb.AppendLine(" Path: " + Path);
sb.AppendLine(" Algorithm: " + currentStrategy);
sb.AppendLine(" Use CLR: " + UseCLR);
sb.AppendLine(" Preference Set: " + Set.ToString());
sb.AppendLine(" Preference Mode: " + Mode.ToString());
sb.AppendLine(" Host: " + Environment.MachineName);
sb.AppendLine(" Set of Preferences: " + listPreferences.Count);
sb.AppendLine(" Trials: " + Trials);
sb.AppendLine(" Table size: " + TableSize.ToString());
sb.AppendLine(" Dimension from: " + MinDimensions.ToString());
sb.AppendLine(" Dimension to: " + MaxDimensions.ToString());
sb.AppendLine(" BNL Initial Sort: " + WindowSort.ToString());
sb.AppendLine(" BNL Window Handling: " + WindowHandling.ToString());
//sb.AppendLine("Correlation Coefficients:" + string.Join(",", (string[])preferences.ToArray(Type.GetType("System.String"))));
//sb.AppendLine(" Cardinalities:" + string.Join(",", (string[])preferences.ToArray(Type.GetType("System.String"))));
if (Sampling)
{
sb.AppendLine(" Sampling: true");
sb.AppendLine(" Subsets Count: " + SamplingSubsetsCount);
sb.AppendLine(" Subset Dimension: " + SamplingSubsetDimension);
sb.AppendLine(" Sampling Runs: " + SamplingSamplesCount);
}
sb.AppendLine("");
if (Sampling)
{
sb.AppendLine(PerformanceSampling.GetHeaderLine());
}
else
{
sb.AppendLine(FormatLineString(' ', "preference set", "trial", "dimensions", "skyline size", "time total", "time algorithm", "min correlation", "max correlation", "product cardinality", "number of moves", "number of comparisons"));
}
sb.AppendLine(strSeparatorLine);
Debug.Write(sb);
}
List<long> reportDimensions = new List<long>();
List<long> reportSkylineSize = new List<long>();
List<long> reportTimeTotal = new List<long>();
List<long> reportTimeAlgorithm = new List<long>();
List<long> reportNumberOfMoves = new List<long>();
List<long> reportNumberOfComparisons = new List<long>();
List<double> reportMinCorrelation = new List<double>();
List<double> reportMaxCorrelation = new List<double>();
List<double> reportCardinality = new List<double>();
var perfSampling = new PerformanceSampling(SamplingSubsetsCount, SamplingSubsetDimension, SamplingSamplesCount, ExcessiveTests);
//For each preference set in the preference list
for (int iPreferenceIndex = 0; iPreferenceIndex < listPreferences.Count; iPreferenceIndex++)
{
ArrayList preferences = (ArrayList)listPreferences[iPreferenceIndex];
//Go only down two 3 dimension (because there are special algorithms for 1 and 2 dimensional skyline)
//for (int i = MinDimensions; i <= preferences.Count; i++)
//{
//ADD Preferences to SKYLINE
ArrayList subPreferences = preferences; //.GetRange(0, i);
string strSkylineOf = "SKYLINE OF " + string.Join(",", (string[])subPreferences.ToArray(Type.GetType("System.String")));
//SELECT FROM
string strSQL = "SELECT cars.id FROM ";
if (TableSize == Size.Small)
{
strSQL += "cars_small";
}
else if (TableSize == Size.Medium)
{
strSQL += "cars_medium";
}
else if (TableSize == Size.Large)
{
strSQL += "cars_large";
}
if (!UseNormalizedValues)
{
strSQL += " cars ";
}
else
{
strSQL += "cars_normalized cars ";
}
//Add Joins
strSQL += GetJoinsForPreferences(strSkylineOf);
//Add Skyline-Clause
strSQL += strSkylineOf;
//Convert to real SQL
parser = new SQLCommon();
parser.SkylineType = currentStrategy;
parser.ShowInternalAttributes = true;
parser.WindowHandling = WindowHandling;
parser.WindowSort = WindowSort;
if (GenerateScript == false)
{
for (int iTrial = 0; iTrial < Trials; iTrial++)
{
Stopwatch sw = new Stopwatch();
try
{
double minCorrelation = 0;
double maxCorrelation = 0;
SearchCorrelation(subPreferences, correlationMatrix, ref minCorrelation, ref maxCorrelation);
double cardinality = SearchCardinality(subPreferences, listCardinality);
long timeAlgorithm = 0;
long skylineSize = 0;
long numberOfMoves = 0;
long numberOfComparisons = 0;
if (Sampling)
{
string strTrial = iTrial + 1 + " / " + _trials;
string strPreferenceSet = iPreferenceIndex + 1 + " / " + listPreferences.Count;
Console.WriteLine(strPreferenceSet);
string strLine = perfSampling.MeasurePerformance(iTrial, iPreferenceIndex, listPreferences, preferences, parser, sw, reportDimensions, reportSkylineSize, reportTimeTotal, reportTimeAlgorithm, reportMinCorrelation, reportMaxCorrelation, minCorrelation, maxCorrelation, reportCardinality, cardinality, strSQL, strPreferenceSet, strTrial, reportNumberOfMoves, numberOfMoves, reportNumberOfComparisons, numberOfComparisons);
Debug.WriteLine(strLine);
sb.AppendLine(strLine);
}
else
{
sw.Start();
ArrayList clauseID = new ArrayList();
String strIDs = "";
for (int skylineLevel = 1; skylineLevel <= SkylineUpToLevel; skylineLevel++)
{
if (UseCLR)
{
string strSP = parser.ParsePreferenceSQL(strSQL);
SqlDataAdapter dap = new SqlDataAdapter(strSP, cnnSQL);
dt.Clear(); //clear datatable
dap.Fill(dt);
}
else
{
parser.Cardinality = (long)cardinality;
string strSQLWithWHERE = strSQL;
strSQLWithWHERE = strSQL.Substring(0, strSQL.IndexOf("SKYLINE OF"));
strSQLWithWHERE += strIDs;
strSQLWithWHERE += strSQL.Substring(strSQL.IndexOf("SKYLINE OF"));
dt = parser.ParseAndExecutePrefSQL(Helper.ConnectionString, Helper.ProviderName, strSQLWithWHERE);
}
timeAlgorithm += parser.TimeInMilliseconds;
numberOfMoves += parser.NumberOfMoves;
numberOfComparisons += parser.NumberOfComparisons;
skylineSize += dt.Rows.Count;
//only if more queries are requested
if (skylineLevel < SkylineUpToLevel && currentStrategy.GetType() != typeof(prefSQL.SQLSkyline.MultipleSkylineBNL))
{
//Add ids to WHERE clause
foreach (DataRow row in dt.Rows)
{
clauseID.Add((int)row[0]);
}
//Add WHERE clause with IDs that were already in the skyline
strIDs = "";
foreach (int id in clauseID)
{
strIDs += id + ",";
}
if (strIDs.Length > 0)
{
strIDs = "WHERE cars.id NOT IN (" + strIDs.TrimEnd(',') + ") ";
}
}
else
{
skylineLevel = SkylineUpToLevel;
}
}
sw.Stop();
reportDimensions.Add(preferences.Count);
reportSkylineSize.Add(skylineSize);
reportTimeTotal.Add(sw.ElapsedMilliseconds);
reportTimeAlgorithm.Add(timeAlgorithm);
reportNumberOfMoves.Add(numberOfMoves);
reportNumberOfComparisons.Add(numberOfComparisons);
reportMinCorrelation.Add(minCorrelation);
reportMaxCorrelation.Add(maxCorrelation);
reportCardinality.Add(cardinality);
//trial|dimensions|skyline size|time total|time algorithm
string strTrial = iTrial + 1 + " / " + _trials;
string strPreferenceSet = iPreferenceIndex + 1 + " / " + listPreferences.Count;
Console.WriteLine(strPreferenceSet);
string strLine = "";
//Was there an error?
if (dt.Rows.Count == 0)
{
strLine = FormatLineString("Error! " + strPreferenceSet, strTrial, preferences.Count, dt.Rows.Count, sw.ElapsedMilliseconds, timeAlgorithm, minCorrelation, maxCorrelation, cardinality, numberOfMoves, numberOfComparisons);
}
else
{
strLine = FormatLineString(strPreferenceSet, strTrial, preferences.Count, dt.Rows.Count, sw.ElapsedMilliseconds, timeAlgorithm, minCorrelation, maxCorrelation, cardinality, numberOfMoves, numberOfComparisons);
}
Debug.WriteLine(strLine);
sb.AppendLine(strLine);
}
}
catch (Exception e)
{
Debug.WriteLine(e.Message);
return;
}
}
}
else
{
strSQL = parser.ParsePreferenceSQL(strSQL);
string[] sizes = { "small", "medium", "large", "superlarge" };
//Format for each of the customer profiles
sb.AppendLine("PRINT '----- -------------------------------------------------------- ------'");
sb.AppendLine("PRINT '----- " + (preferences.Count + 1) + " dimensions ------'");
sb.AppendLine("PRINT '----- -------------------------------------------------------- ------'");
foreach (string size in sizes)
{
sb.AppendLine("GO"); //we need this in order the profiler shows each query in a new line
sb.AppendLine(strSQL.Replace("cars", "cars_" + size));
}
}
//}
}
////////////////////////////////
//Summary
///////////////////////////////
if (GenerateScript == false)
{
if (Sampling)
{
perfSampling.AddSummary(sb, strSeparatorLine, reportDimensions, reportSkylineSize, reportTimeTotal, reportTimeAlgorithm, reportMinCorrelation, reportMaxCorrelation, reportCardinality, reportNumberOfMoves, reportNumberOfComparisons);
perfSampling.AddPreferenceSetInformation(sb, listPreferences, strSeparatorLine);
}
else
{
AddSummary(sb, strSeparatorLine, reportDimensions, reportSkylineSize, reportTimeTotal, reportTimeAlgorithm, reportMinCorrelation, reportMaxCorrelation, reportCardinality, reportNumberOfMoves, reportNumberOfComparisons);
}
}
//Write some empty lines (clarification in output window)
Debug.WriteLine("");
Debug.WriteLine("");
Debug.WriteLine("");
//Write in file
string strFiletype;
if (GenerateScript == false)
{
strFiletype = ".txt";
}
else
{
strFiletype = ".sql";
}
//create filename
string strFileName = Path + Set.ToString() + "_" + Mode.ToString() + "_" + MinDimensions + "_" + MaxDimensions + "_" + currentStrategy + DateTime.Now.Ticks + strFiletype;
StreamWriter outfile = new StreamWriter(strFileName);
outfile.Write(sb.ToString());
outfile.Close();
}
//close connection
if (UseCLR)
{
cnnSQL.Close();
}
}
public void TestSkylineAmountOfTupelsDataTable()
{
string skylineSampleSql = TestContext.DataRow["skylineSQL"].ToString();
SQLCommon common = new SQLCommon();
common.SkylineType = new SkylineSQL();
PrefSQLModel model = common.GetPrefSqlModelFromPreferenceSql(skylineSampleSql);
DataTable dtNative = common.ExecuteFromPrefSqlModel(Helper.ConnectionString, Helper.ProviderName, model);
common.SkylineType = new SkylineBNL();
DataTable dtBNL = common.ParseAndExecutePrefSQL(Helper.ConnectionString, Helper.ProviderName, skylineSampleSql);
common.SkylineType = new SkylineBNLSort();
DataTable dtBNLSort = common.ParseAndExecutePrefSQL(Helper.ConnectionString, Helper.ProviderName, skylineSampleSql);
DataTable dtHexagon = new DataTable();
if (model.ContainsOpenPreference == false)
{
common.SkylineType = new SkylineHexagon();
dtHexagon = common.ParseAndExecutePrefSQL(Helper.ConnectionString, Helper.ProviderName, skylineSampleSql);
}
DataTable dtDQ = new DataTable();
//D&Q does not work with incomparable tuples
if (model.WithIncomparable == false)
{
common.SkylineType = new SkylineDQ();
dtDQ = common.ParseAndExecutePrefSQL(Helper.ConnectionString, Helper.ProviderName, skylineSampleSql);
}
int currentDataRowIndex = TestContext.DataRow.Table.Rows.IndexOf(TestContext.DataRow);
//Check tuples (every algorithm should deliver the same amount of tuples)
Assert.AreEqual(dtNative.Rows.Count, dtBNL.Rows.Count, 0,
"BNL Amount of tupels in query " + currentDataRowIndex + " do not match");
Assert.AreEqual(dtNative.Rows.Count, dtBNLSort.Rows.Count, 0,
"BNLSort Amount of tupels in query " + currentDataRowIndex + " do not match");
//Hexagon cannot handle Categorical preference that have no explicit OTHERS
if (model.ContainsOpenPreference == false)
{
Assert.AreEqual(dtNative.Rows.Count, dtHexagon.Rows.Count, 0,
"Hexagon Amount of tupels in query " + currentDataRowIndex + " do not match");
}
//D&Q does not work with incomparable tuples
if (model.WithIncomparable == false)
{
Assert.AreEqual(dtNative.Rows.Count, dtDQ.Rows.Count, 0,
"D&Q Amount of tupels in query " + currentDataRowIndex + " do not match");
}
}
public void TestIssue54()
{
string prefSQL = "SELECT c.Title AS Name, c.Price, c.Consumption, m.Name AS Manufacturer, b.Name AS Body "
+ "FROM Cars c "
+ "LEFT OUTER JOIN Makes m ON c.Make_Id = m.Id "
+ "LEFT OUTER JOIN Bodies b ON c.Body_Id = b.Id "
+ "WHERE m.Name = 'VW' AND b.Name = 'Bus' "
+ "SKYLINE OF c.Price LOW 1000 EQUAL, c.Consumption LOW";
var common = new SQLCommon
{
SkylineType = new SkylineBNL(),
ShowInternalAttributes = true
};
try
{
//If there is no exception in the execution of this query the test is successful
DataTable dt = common.ParseAndExecutePrefSQL(Helper.ConnectionString, Helper.ProviderName, prefSQL);
Assert.IsTrue(true);
}
catch
{
Assert.IsFalse(true);
}
}
public void Run()
{
try
{
//SKYLINE Queries
//string strPrefSQL = "SELECT t.id FROM cars t SKYLINE OF t.price LOW, t.mileage LOW";
//string strPrefSQL = "select cars.id from cars";
//string strPrefSQL = "SELECT * FROM cars t WHERE t.doors = 5 SKYLINE OF t.price LOW, t.mileage LOW, t.horsepower HIGH, t.consumption LOW, t.registrationnumeric HIGH";
//string strPrefSQL = "SELECT t.title AS Modellname, t.price AS Preis, t.consumption AS Verbrauch FROM cars t LEFT OUTER JOIN Makes m ON t.make_id = m.id LEFT OUTER JOIN Bodies b ON t.body_id = b.id WHERE m.name = 'VW' AND b.name = 'Bus' SKYLINE OF t.price LOW 1000 EQUAL, t.consumption LOW";
//string strPrefSQL = "SELECT t.id, t.title, t.price, c.name, t.enginesize FROM cars t LEFT OUTER JOIN colors c ON t.color_id = c.id SKYLINE OF t.price LOW, c.name ('pink' >> 'black' >> OTHERS INCOMPARABLE), t.enginesize HIGH ORDER BY BEST_RANK()";
//string strPrefSQL = "SELECT t.id, t.title, t.price, c.name, t.enginesize FROM cars t LEFT OUTER JOIN colors c ON t.color_id = c.id SKYLINE OF t.price LOW, c.name (OTHERS EQUAL >> 'pink' >> 'red'), t.enginesize HIGH ORDER BY SUM_RANK()";
//string strPrefSQL = "SELECT t.id, t.title, t.price FROM cars t SKYLINE OF t1.price LOW, t1.mileage LOW ORDER BY BEST_RANK()";
//string strPrefSQL = "SELECT t.id, t.price, t.mileage FROM cars t SKYLINE OF t1.price LOW, t1.mileage LOW ORDER BY SUM_RANK()";
//string strPrefSQL = "SELECT t.id, t.title, bodies.name AS Chassis, t.price, fuels.name FROM cars t LEFT OUTER JOIN fuels ON t.fuel_id = fuels.ID LEFT OUTER JOIN bodies ON t.body_id = bodies.ID SKYLINE OF bodies.name ('bus' >> OTHERS EQUAL) IS MORE IMPORTANT THAN t.price LOW, fuels.name ('petrol' >> OTHERS EQUAL)";
//string strPrefSQL = "SELECT t.id, t.title, t.price, t.mileage, t.enginesize FROM cars t SKYLINE OF t.mileage LOW IS MORE IMPORTANT THAN t.price LOW, t.engineszie HIGH ";
//SKYLINE Queries with JOINS
//string strPrefSQL = "SELECT c.id, c.price, b.name FROM cars t LEFT OUTER JOIN bodies b ON c.body_id = b.ID SKYLINE OF c.price LOW, b.name ('Bus' >> 'Kleinwagen')";
//string strPrefSQL = "SELECT c.id, c.price FROM cars t LEFT OUTER JOIN colors cc ON c.color_id = cc.id SKYLINE OF c.horsepower HIGH, cc.name ('red' >> 'blue' >> 'yellow')";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price FROM cars t LEFT OUTER JOIN colors ON t1.color_id = colors.ID SKYLINE OF t1.price LOW, colors.name ('red' >> 'blue' >> OTHERS INCOMPARABLE)";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price FROM cars t LEFT OUTER JOIN colors ON t1.color_id = colors.ID SKYLINE OF t1.price LOW, t1.mileage LOW, t1.horsepower HIGH, t1.enginesize HIGH, t1.doors HIGH, t1.consumption LOW, t1.cylinders HIGH, colors.name ('red' >> 'blue' >> 'yellow' >> OTHERS INCOMPARABLE)";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price FROM cars t LEFT OUTER JOIN colors ON t1.color_id = colors.ID SKYLINE OF t1.price LOW, t1.mileage LOW, t1.horsepower HIGH, t1.enginesize HIGH, t1.doors HIGH, t1.consumption LOW, t1.cylinders HIGH";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.pric FROM cars t LEFT OUTER JOIN colors ON t1.color_id = colors.ID WHERE t1.price < 10000 SKYLINE OF t1.price LOW, colors.name ('red' >> 'blue' >> OTHERS INCOMPARABLE)";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price FROM cars t LEFT OUTER JOIN colors ON t1.color_id = colors.ID WHERE t1.price < 10000 SKYLINE OF t1.price LOW, colors.name (OTHERS INCOMPARABLE >> 'blue' >> 'red')";
//string strPrefSQL = "SELECT c.id, c.price FROM cars t LEFT OUTER JOIN colors cc ON c.color_id = cc.id LEFT OUTER JOIN fuels f ON f.id = c.fuel_id SKYLINE OF c.price LOW 1000 INCOMPARABLE, cc.name ('red' == 'blue' >> OTHERS INCOMPARABLE >> 'gray'), f.name ('petrol' >> OTHERS INCOMPARABLE >> 'diesel')";
//string strPrefSQL = "SELECT c.title AS Name, c.Price FROM cars t LEFT OUTER JOIN colors co ON c.color_id = co.ID LEFT OUTER JOIN bodies b ON c.body_id = b.ID SKYLINE OF c.Price LOW, c.Mileage LOW";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price FROM cars t LEFT OUTER JOIN colors ON t1.color_id = colors.ID WHERE t1.price < 10000 SKYLINE OF t1.price LOW, colors.name ('silver' >> 'yellow' >> OTHERS INCOMPARABLE)";
//string strPrefSQL = "SELECT t.id, t.title, t.price FROM cars t LEFT OUTER JOIN colors ON t.color_id = colors.id SKYLINE OF t.price LOW, t.mileage LOW, colors.name ('red' >> {'blue', 'yellow'} >> OTHERS INCOMPARABLE)";
//string strPrefSQL = "SELECT t.id FROM cars t LEFT OUTER JOIN bodies ON t.body_id = bodies.ID LEFT OUTER JOIN colors ON colors.id = t.color_id SKYLINE OF t.price AROUND 10000, colors.name ('red' >> OTHERS EQUAL), bodies.name ('van' >> 'compact car' >> OTHERS EQUAL) ORDER BY BEST_RANK()";
//RANKING Queries
string strPrefSQL = "SELECT t.id, t.title FROM cars t ORDER BY WEIGHTEDSUM (t.price LOW 0.8, t.mileage LOW 0.2)";
//string strPrefSQL = "SELECT t.id, t.title FROM cars t LEFT OUTER JOIN colors c ON t.color_id = c.id ORDER BY WEIGTHEDSUM (t.price LOW 0.5, c.name ('brown' >> 'green' >> OTHERS EQUAL) 0.5)";
//Playground --> Test here your queries
//string strPrefSQL = "SELECT c.id, c.price, b.name FROM cars_small c LEFT OUTER JOIN bodies b ON c.body_id = b.ID SKYLINE OF c.price LOW, b.name ('Bus' >> 'Kleinwagen')";
//string strPrefSQL = "SELECT c.id, c.price FROM cars_small c LEFT OUTER JOIN colors cc ON c.color_id = cc.id ORDER BY WEIGHTEDSUM (c.price LOW 0.5, cc.name ('brown' >> 'green') 0.5)";
//string strPrefSQL = "SELECT c.id, c.price FROM cars_small c LEFT OUTER JOIN colors cc ON c.color_id = cc.id SKYLINE OF c.horsepower HIGH, cc.name ('red' >> 'blue' >> 'yellow')";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price FROM cars_small t1 LEFT OUTER JOIN colors ON t1.color_id = colors.ID SKYLINE OF t1.price LOW, colors.name ('red' >> 'blue' >> OTHERS INCOMPARABLE)";
//string strPrefSQL = "SELECT t1.id FROM cars t1 ORDER BY WEIGHTEDSUM (t1.price HIGH 0.5, t1.mileage HIGH 0.5, t1.horsepower LOW 0.5, t1.enginesize LOW 0.5, t1.consumption HIGH 0.5, t1.doors LOW 0.5, t1.cylinders LOW 0.5)";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price FROM cars t1 LEFT OUTER JOIN colors ON t1.color_id = colors.ID SKYLINE OF t1.price LOW, t1.mileage LOW, t1.horsepower HIGH, t1.enginesize HIGH, t1.doors HIGH, t1.consumption LOW, t1.cylinders HIGH, colors.name ('red' >> 'blue' >> 'yellow' >> OTHERS INCOMPARABLE)";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price FROM cars t1 SKYLINE OF t1.price LOW, t1.mileage LOW ORDER BY BEST_RANK()";
//string strPrefSQL = "SELECT t1.id FROM cars t1 SKYLINE OF t1.price LOW, t1.mileage LOW";
//string strPrefSQL = "SELECT cars.id, cars.consumption FROM cars SKYLINE OF cars.consumption LOW, cars.enginesize HIGH, cars.price LOW";
//string strPrefSQL = "SELECT * FROM cars SKYLINE OF cars.registrationnumeric HIGH, cars.mileage LOW, cars.horsepower HIGH 100 EQUAL";
//string strPrefSQL = "SELECT cars.id, cars.horsepower FROM cars SKYLINE OF cars.horsepower HIGH, cars.mileage LOW";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price FROM cars t1 LEFT OUTER JOIN colors ON t1.color_id = colors.ID SKYLINE OF t1.price LOW, t1.mileage LOW, t1.horsepower HIGH, t1.enginesize HIGH, t1.doors HIGH, t1.consumption LOW, t1.cylinders HIGH";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price, t1.mileage, t1.enginesize FROM cars t1 SKYLINE OF t1.price LOW, t1.mileage LOW, t1.enginesize HIGH ORDER BY SUM_RANK()";
//string strPrefSQL = "SELECT t1.id FROM cars t1 SKYLINE OF t1.price LOW, t1.mileage LOW, t1.horsepower HIGH, t1.enginesize HIGH, t1.doors HIGH, t1.consumption LOW, t1.cylinders HIGH";
//string strPrefSQL = "SELECT t1.id FROM cars_norm t1 SKYLINE OF t1.price LOW, t1.mileage LOW, t1.horsepower LOW, t1.enginesize LOW, t1.doors LOW, t1.consumption LOW, t1.cylinders LOW";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price FROM cars t1 SKYLINE OF t1.price LOW, t1.mileage LOW ORDER BY BEST_RANK()";
//string strPrefSQL = "SELECT t1.id AS ID, t1.title, t1.price FROM cars_small t1 SKYLINE OF t1.price LOW, t1.title ('hans' >> OTHERS INCOMPARABLE)";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price, t1.mileage, colors.name FROM cars_small t1 LEFT OUTER JOIN colors ON t1.color_id = colors.ID WHERE t1.price < 10000 SKYLINE OF t1.price LOW, colors.name ('red' >> 'blue' >> OTHERS INCOMPARABLE)";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price, t1.mileage, colors.name FROM cars_small t1 LEFT OUTER JOIN colors ON t1.color_id = colors.ID WHERE t1.price < 10000 SKYLINE OF t1.price LOW, colors.name (OTHERS INCOMPARABLE >> 'blue' >> 'red')";
//string strPrefSQL = "SELECT * FROM cars_small cs SKYLINE OF cs.price LOW, cs.mileage LOW SAMPLE BY RANDOM_SUBSETS COUNT 2 DIMENSION 1";
//string strPrefSQL = "SELECT t1.id, t1.price, t1.mileage FROM cars_small t1 SKYLINE OF t1.price LOW, t1.mileage LOW";
//string strPrefSQL = "SELECT t1.id, t1.price, t1.mileage FROM cars_small t1 SKYLINE OF t1.price LOW, t1.mileage LOW ORDER BY SUM_RANK()";
//string strPrefSQL = "SELECT t1.id FROM cars t1 SKYLINE OF t1.price LOW, t1.mileage LOW, t1.horsepower HIGH, t1.enginesize HIGH, t1.doors HIGH, t1.consumption LOW";
//string strPrefSQL = "SELECT c.id, c.price FROM cars_medium c LEFT OUTER JOIN colors cc ON c.color_id = cc.id LEFT OUTER JOIN fuels f ON f.id = c.fuel_id SKYLINE OF c.price LOW 1000 INCOMPARABLE, cc.name ('red' == 'blue' >> OTHERS INCOMPARABLE >> 'gray'), f.name ('petrol' >> OTHERS INCOMPARABLE >> 'diesel')";
//string strPrefSQL = "SELECT c.title AS Name, c.Price, c.Mileage, co.Name AS Color, b.Name AS Body FROM Cars AS c LEFT OUTER JOIN colors co ON c.color_id = co.ID LEFT OUTER JOIN bodies b ON c.body_id = b.ID SKYLINE OF c.Price LOW, c.Mileage LOW";
//string strPrefSQL = "SELECT t.id FROM cars t SKYLINE OF t.price LOW, t.mileage LOW";
//string strPrefSQL = "SELECT t1.id, t1.title, t1.price, colors.name FROM cars t1 LEFT OUTER JOIN colors ON t1.color_id = colors.ID WHERE t1.price < 10000 SKYLINE OF t1.price LOW, colors.name ('silver' >> 'yellow' >> OTHERS INCOMPARABLE)";
//string strPrefSQL = "SELECT cars.id, cars.title FROM cars SKYLINE OF cars.price LOW,cars.mileage LOW,cars.horsepower HIGH,cars.enginesize HIGH,cars.consumption LOW,cars.doors HIGH,cars.seats HIGH";
//string strPrefSQL = "SELECT t.id, t.title, t.price, colors.name AS colour FROM cars t LEFT OUTER JOIN colors ON t.color_id = colors.id SKYLINE OF t.price LOW, t.mileage LOW, colors.name ('red' >> {'blue', 'yellow'} >> OTHERS INCOMPARABLE)";
//string strPrefSQL = "SELECT t.id, t.title FROM cars t ORDER BY WEIGHTEDSUM (t.price LOW 0.8, t.mileage LOW 0.2)";
//string strPrefSQL = "SELECT t.id, t.title FROM cars t ORDER BY WEIGHTEDSUM( t.price LOW 0.8, t.mileage LOW 0.2)";
//Query that results in more than 4000 Characters
/*string strPrefSQL = "SELECT t1.id , t1.title , t1.price , t1.mileage , colors.name FROM cars_small t1 " +
"LEFT OUTER JOIN colors ON t1.color_id = colors.ID " +
"LEFT OUTER JOIN bodies ON t1.body_id = bodies.id " +
"LEFT OUTER JOIN Conditions ON t1.Condition_Id = Conditions.Id " +
"LEFT OUTER JOIN Models ON t1.Model_Id = Models.Id " +
"LEFT OUTER JOIN Makes ON t1.Make_Id = Makes.Id " +
"LEFT OUTER JOIN Drives ON t1.Drive_Id = Drives.Id " +
"LEFT OUTER JOIN Efficiencies ON t1.Efficiency_Id = Efficiencies.Id " +
"LEFT OUTER JOIN Pollutions ON t1.Pollution_Id = Pollutions.Id " +
"LEFT OUTER JOIN Transmissions ON t1.Transmission_Id = Transmissions.Id " +
"LEFT OUTER JOIN Fuels ON t1.Fuel_Id = Fuels.Id " +
"WHERE t1.price < 10000 " +
"SKYLINE OF " +
"t1.price LOW " +
", colors.name " +
"(" +
"'anthracite' >> 'beige' >> 'blue' >> 'bordeaux' >> " +
" 'brown' >> 'yellow' >> 'gold' >> 'gray' >> 'green' >> " +
" 'orange' >> 'pink' >> 'red' >> 'black' >> 'silver' >> " +
" 'turquoise' >> 'violet' >> 'white'" +
") " +
", bodies.name " +
"(" +
" 'bus' >> 'cabriolet' >> 'coupé' >> 'van' >> " +
" 'compact car' >> 'estate car' >> 'minivan' >> " +
" 'limousine' >> 'pick-up' >> 'scooter' >> 'suv' " +
")" +
", fuels.name " +
"(" +
" 'hybrid' >> 'bioethanol' >> 'diesel' >> 'gas' >> " +
" 'electro' >> 'petrol' " +
")";*/
//Query with 2 id spalten
//string strPrefSQL = "SELECT t.id, colors.id as colorid, t.title, t.price, colors.name AS colour FROM cars t LEFT OUTER JOIN colors ON t.color_id = colors.id SKYLINE OF t.price LOW, t.mileage LOW, colors.name ('red' >> 'blue' >> OTHERS EQUAL)";
Debug.WriteLine(strPrefSQL);
SQLCommon parser = new SQLCommon();
//Choose here your algorithm
parser.SkylineType = new SkylineSQL();
//parser.SkylineType = new SkylineBNL();
//parser.SkylineType = new SkylineBNLSort();
//parser.SkylineType = new SkylineHexagon();
//parser.SkylineType = new SkylineDQ();
//parser.SkylineType = new MultipleSkylineBNL();
//parser.SkylineType = new SkylineDecisionTree();
//Some other available properties
parser.ShowInternalAttributes = true;
parser.SkylineUpToLevel = 1;
//First parse only (to get the parsed string for CLR)
Debug.WriteLine(parser.ParsePreferenceSQL(strPrefSQL));
//Now parse and execute
Stopwatch sw = new Stopwatch();
sw.Start();
DataTable dt = parser.ParseAndExecutePrefSQL(Helper.ConnectionString, Helper.ProviderName, strPrefSQL);
sw.Stop();
StringBuilder sb = new StringBuilder();
sb.AppendLine("------------------------------------------");
sb.AppendLine("STATISTIC");
sb.AppendLine("------------------------------------------");
sb.AppendLine(" skyline size:" + dt.Rows.Count.ToString().PadLeft(6));
sb.AppendLine(" fields:" + dt.Columns.Count.ToString().PadLeft(6));
sb.AppendLine("algo time elapsed ms:" + parser.TimeInMilliseconds.ToString().PadLeft(6));
sb.AppendLine("total time elapsed ms:" + sw.ElapsedMilliseconds.ToString().PadLeft(6));
Debug.Write(sb);
}
catch (Exception ex)
{
Debug.WriteLine("ERROR: " + ex);
}
Environment.Exit(0);
}
internal string MeasurePerformance(int iTrial, int iPreferenceIndex, ArrayList listPreferences,
ArrayList preferences,
SQLCommon parser, Stopwatch sw, List<long> reportDimensions, List<long> reportSkylineSize,
List<long> reportTimeTotal,
List<long> reportTimeAlgorithm, List<double> reportMinCorrelation, List<double> reportMaxCorrelation, double minCorrelation, double maxCorrelation,
List<double> reportCardinality, double cardinality,
string strSQL, string strPreferenceSet, string strTrial, List<long> reportNumberOfMoves, long numberOfMoves, List<long> reportNumberOfComparisons, long numberOfComparisons)
{
Dictionary<ClusterAnalysis, List<List<double>>> clusterAnalysis;
Dictionary<ClusterAnalysis, List<List<double>>> clusterAnalysisMedian;
Dictionary<ClusterAnalysis, Dictionary<BigInteger, List<double>>> clusterAnalysisTopBuckets;
Dictionary<ClusterAnalysis, Dictionary<BigInteger, List<double>>> clusterAnalysisMedianTopBuckets;
List<IEnumerable<CLRSafeHashSet<int>>> producedSubsets =
ProduceSubsets(preferences);
InitClusterAnalysisDataStructures(out clusterAnalysis);
InitClusterAnalysisDataStructures(out clusterAnalysisMedian);
InitClusterAnalysisTopBucketsDataStructures(
out clusterAnalysisTopBuckets);
InitClusterAnalysisTopBucketsDataStructures(
out clusterAnalysisMedianTopBuckets);
var entireSkylineDataTable = new DataTable();
if (ExcessiveTests)
{
entireSkylineDataTable =
parser.ParseAndExecutePrefSQL(Helper.ConnectionString, Helper.ProviderName,
strSQL);
}
else
{
entireSkylineDataTable =
parser.ParseAndExecutePrefSQL(Helper.ConnectionString, Helper.ProviderName,
strSQL+ " SAMPLE BY RANDOM_SUBSETS COUNT " + SubsetsCount +
" DIMENSION " + SubsetDimension);
}
List<long[]> entireDataTableSkylineValues =
parser.SkylineType.Strategy.SkylineValues;
int[] skylineAttributeColumns =
SkylineSamplingHelper.GetSkylineAttributeColumns(entireSkylineDataTable);
IReadOnlyDictionary<long, object[]> entireSkylineDatabase =
prefSQL.SQLSkyline.Helper.GetDatabaseAccessibleByUniqueId(
entireSkylineDataTable, 0);
IReadOnlyDictionary<long, object[]> entireSkylineNormalized =
prefSQL.SQLSkyline.Helper.GetDatabaseAccessibleByUniqueId(
entireSkylineDataTable, 0);
SkylineSamplingHelper.NormalizeColumns(entireSkylineNormalized,
skylineAttributeColumns);
DataTable entireDataTable;
IReadOnlyDictionary<long, object[]> entireDatabaseNormalized =
GetEntireDatabaseNormalized(parser, strSQL, skylineAttributeColumns,
out entireDataTable);
IReadOnlyDictionary<long, object[]> entireDatabase =
prefSQL.SQLSkyline.Helper.GetDatabaseAccessibleByUniqueId(
entireDataTable, 0);
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
entireDatabaseBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetBuckets(entireDatabaseNormalized,
skylineAttributeColumns);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedEntireDatabaseBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(entireDatabaseBuckets);
foreach (
KeyValuePair<BigInteger, List<IReadOnlyDictionary<long, object[]>>> s in
entireDatabaseBuckets.OrderByDescending(l => l.Value.Count)
.ThenBy(l => l.Key).Take(5))
{
double percent = (double) s.Value.Count / entireDatabaseNormalized.Count;
clusterAnalysisTopBuckets[ClusterAnalysis.EntireDb].Add(s.Key,
new List<double>());
for (var i = 0; i < producedSubsets.Count; i++)
// to enable generalized average calculation
{
clusterAnalysisTopBuckets[ClusterAnalysis.EntireDb][s.Key]
.Add(percent);
}
}
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
entireSkylineBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetBuckets(entireSkylineNormalized,
skylineAttributeColumns);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedEntireSkylineBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(entireSkylineBuckets);
FillTopBuckets(clusterAnalysisTopBuckets,
ClusterAnalysis.EntireSkyline, entireSkylineBuckets,
entireSkylineNormalized.Count, entireDatabaseNormalized.Count,
entireSkylineNormalized.Count);
foreach (
KeyValuePair<BigInteger, List<double>> bucket in
clusterAnalysisTopBuckets[ClusterAnalysis.EntireSkyline])
{
double percent =
clusterAnalysisTopBuckets[ClusterAnalysis.EntireSkyline][
bucket.Key][0];
for (var i = 1; i < producedSubsets.Count; i++)
// to enable generalized average calculation
{
clusterAnalysisTopBuckets[ClusterAnalysis.EntireSkyline][
bucket.Key].Add(percent);
}
}
var clusterAnalysisForMedian = new prefSQL.Evaluation.ClusterAnalysis(entireDatabaseNormalized,
skylineAttributeColumns);
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
entireDatabaseMedianBuckets =
clusterAnalysisForMedian.GetBuckets(entireDatabaseNormalized,
skylineAttributeColumns, true);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedEntireDatabaseMedianBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(entireDatabaseMedianBuckets);
foreach (
KeyValuePair<BigInteger, List<IReadOnlyDictionary<long, object[]>>> s in
entireDatabaseMedianBuckets.OrderByDescending(l => l.Value.Count)
.ThenBy(l => l.Key).Take(5))
{
double percent = (double) s.Value.Count / entireDatabaseNormalized.Count;
clusterAnalysisMedianTopBuckets[ClusterAnalysis.EntireDb].Add(
s.Key,
new List<double>());
for (var i = 0; i < producedSubsets.Count; i++)
// to enable generalized average calculation
{
clusterAnalysisMedianTopBuckets[ClusterAnalysis.EntireDb][
s.Key]
.Add(percent);
}
}
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
entireSkylineMedianBuckets =
clusterAnalysisForMedian.GetBuckets(entireSkylineNormalized,
skylineAttributeColumns, true);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedEntireSkylineMedianBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(entireSkylineMedianBuckets);
FillTopBuckets(clusterAnalysisMedianTopBuckets,
ClusterAnalysis.EntireSkyline, entireSkylineMedianBuckets,
entireSkylineNormalized.Count, entireDatabaseNormalized.Count,
entireSkylineNormalized.Count);
foreach (
KeyValuePair<BigInteger, List<double>> bucket in
clusterAnalysisMedianTopBuckets[
ClusterAnalysis.EntireSkyline])
{
double percent =
clusterAnalysisMedianTopBuckets[
ClusterAnalysis.EntireSkyline][bucket.Key][0];
for (var i = 1; i < producedSubsets.Count; i++)
// to enable generalized average calculation
{
clusterAnalysisMedianTopBuckets[
ClusterAnalysis.EntireSkyline][bucket.Key].Add(percent);
}
}
strSQL += " SAMPLE BY RANDOM_SUBSETS COUNT " + SubsetsCount +
" DIMENSION " + SubsetDimension;
string strQuery;
string operators;
int numberOfRecords;
string[] parameter;
PrefSQLModel prefSqlModel = parser.GetPrefSqlModelFromPreferenceSql(strSQL);
string ansiSql = parser.GetAnsiSqlFromPrefSqlModel(prefSqlModel);
prefSQL.SQLParser.Helper.DetermineParameters(ansiSql, out parameter,
out strQuery, out operators,
out numberOfRecords);
var subsetObjects = new List<long>();
var subsetTime = new List<long>();
var subsetTimeElapsed = new List<long>();
var setCoverageSecondRandom = new List<double>();
var setCoverageSample = new List<double>();
var setCoverageBestRank = new List<double>();
var setCoverageSumRank = new List<double>();
var representationErrorSecondRandom = new List<double>();
var representationErrorSample = new List<double>();
var representationErrorBestRank = new List<double>();
var representationErrorSumRank = new List<double>();
var representationErrorSumSecondRandom = new List<double>();
var representationErrorSumSample = new List<double>();
var representationErrorSumBestRank = new List<double>();
var representationErrorSumSumRank = new List<double>();
var dominatedObjectsCountSecondRandom = new List<double>();
var dominatedObjectsCountSample = new List<double>();
var dominatedObjectsCountBestRank = new List<double>();
var dominatedObjectsCountSumRank = new List<double>();
var dominatedObjectsOfBestObjectSecondRandom = new List<double>();
var dominatedObjectsOfBestObjectSample = new List<double>();
var dominatedObjectsOfBestObjectBestRank = new List<double>();
var dominatedObjectsOfBestObjectSumRank = new List<double>();
var subsetCount = 1;
foreach (IEnumerable<CLRSafeHashSet<int>> subset in producedSubsets)
{
Console.WriteLine(strPreferenceSet + " (" + subsetCount + " / " +
producedSubsets.Count + ")");
sw.Restart();
var subsetsProducer = new FixedSkylineSamplingSubsetsProducer(subset);
var utility = new SkylineSamplingUtility(subsetsProducer);
var skylineSample = new SkylineSampling(utility)
{
SubsetCount = prefSqlModel.SkylineSampleCount,
SubsetDimension = prefSqlModel.SkylineSampleDimension,
SelectedStrategy = parser.SkylineType
};
DataTable sampleSkylineDataTable = skylineSample.GetSkylineTable(strQuery,
operators);
sw.Stop();
subsetObjects.Add(sampleSkylineDataTable.Rows.Count);
subsetTime.Add(skylineSample.TimeMilliseconds);
subsetTimeElapsed.Add(sw.ElapsedMilliseconds);
IReadOnlyDictionary<long, object[]> sampleSkylineDatabase =
prefSQL.SQLSkyline.Helper.GetDatabaseAccessibleByUniqueId(
sampleSkylineDataTable, 0);
IReadOnlyDictionary<long, object[]> sampleSkylineNormalized =
prefSQL.SQLSkyline.Helper.GetDatabaseAccessibleByUniqueId(
sampleSkylineDataTable, 0);
SkylineSamplingHelper.NormalizeColumns(sampleSkylineNormalized,
skylineAttributeColumns);
IReadOnlyDictionary<long, object[]> secondRandomSampleDatabase = new Dictionary<long, object[]>();
IReadOnlyDictionary<long, object[]> secondRandomSampleNormalized = new Dictionary<long, object[]>();
IReadOnlyDictionary<long, object[]> entireSkylineDataTableBestRankDatabase = new Dictionary<long, object[]>();
IReadOnlyDictionary<long, object[]> entireSkylineDataTableSumRankDatabase = new Dictionary<long, object[]>();
IReadOnlyDictionary<long, object[]> entireSkylineDataTableBestRankNormalized = new Dictionary<long, object[]>();
IReadOnlyDictionary<long, object[]> entireSkylineDataTableSumRankNormalized = new Dictionary<long, object[]>();
if (ExcessiveTests)
{
secondRandomSampleDatabase =
SkylineSamplingHelper.GetRandomSample(entireSkylineDatabase,
sampleSkylineDataTable.Rows.Count);
var secondRandomSampleNormalizedToBeCreated = new Dictionary<long, object[]>();
foreach (KeyValuePair<long, object[]> k in secondRandomSampleDatabase)
{
var newValue = new object[k.Value.Length];
k.Value.CopyTo(newValue, 0);
secondRandomSampleNormalizedToBeCreated.Add(k.Key, newValue);
}
secondRandomSampleNormalized =
new ReadOnlyDictionary<long, object[]>(
secondRandomSampleNormalizedToBeCreated);
SkylineSamplingHelper.NormalizeColumns(secondRandomSampleNormalized,
skylineAttributeColumns);
entireSkylineDataTableBestRankNormalized =
GetEntireSkylineDataTableRankNormalized(entireSkylineDataTable.Copy(),
entireDataTableSkylineValues, skylineAttributeColumns,
sampleSkylineDataTable.Rows.Count, 1,
out entireSkylineDataTableBestRankDatabase);
entireSkylineDataTableSumRankNormalized =
GetEntireSkylineDataTableRankNormalized(entireSkylineDataTable.Copy(),
entireDataTableSkylineValues, skylineAttributeColumns,
sampleSkylineDataTable.Rows.Count, 2,
out entireSkylineDataTableSumRankDatabase);
IReadOnlyDictionary<long, object[]> baseRandomSampleNormalized =
SkylineSamplingHelper.GetRandomSample(entireSkylineNormalized,
sampleSkylineDataTable.Rows.Count);
double setCoverageCoveredBySecondRandomSample = SetCoverage.GetCoverage(
baseRandomSampleNormalized,
secondRandomSampleNormalized, skylineAttributeColumns) * 100.0;
double setCoverageCoveredBySkylineSample = SetCoverage.GetCoverage(
baseRandomSampleNormalized,
sampleSkylineNormalized, skylineAttributeColumns) * 100.0;
double setCoverageCoveredByEntireBestRank = SetCoverage.GetCoverage(
baseRandomSampleNormalized,
entireSkylineDataTableBestRankNormalized, skylineAttributeColumns) * 100.0;
double setCoverageCoveredByEntireSumRank = SetCoverage.GetCoverage(baseRandomSampleNormalized,
entireSkylineDataTableSumRankNormalized, skylineAttributeColumns) *
100.0;
setCoverageSecondRandom.Add(setCoverageCoveredBySecondRandomSample);
setCoverageSample.Add(setCoverageCoveredBySkylineSample);
setCoverageBestRank.Add(setCoverageCoveredByEntireBestRank);
setCoverageSumRank.Add(setCoverageCoveredByEntireSumRank);
Dictionary<long, double>.ValueCollection baseRepresentationErrorSecondRandomSample = SetCoverage
.GetRepresentationError(
GetReducedSkyline(entireSkylineNormalized, secondRandomSampleNormalized),
secondRandomSampleNormalized, skylineAttributeColumns);
Dictionary<long, double>.ValueCollection baseRepresentationErrorSkylineSample = SetCoverage.GetRepresentationError(
GetReducedSkyline(entireSkylineNormalized, sampleSkylineNormalized),
sampleSkylineNormalized, skylineAttributeColumns);
Dictionary<long, double>.ValueCollection baseRepresentationErrorEntireBestRank =
SetCoverage.GetRepresentationError(
GetReducedSkyline(entireSkylineNormalized,
entireSkylineDataTableBestRankNormalized),
entireSkylineDataTableBestRankNormalized, skylineAttributeColumns);
Dictionary<long, double>.ValueCollection baseRepresentationErrorEntireSumRank =
SetCoverage.GetRepresentationError(
GetReducedSkyline(entireSkylineNormalized,
entireSkylineDataTableSumRankNormalized),
entireSkylineDataTableSumRankNormalized, skylineAttributeColumns);
representationErrorSecondRandom.Add(baseRepresentationErrorSecondRandomSample.Max()*100.0);
representationErrorSample.Add(baseRepresentationErrorSkylineSample.Max() * 100.0);
representationErrorBestRank.Add(baseRepresentationErrorEntireBestRank.Max() * 100.0);
representationErrorSumRank.Add(baseRepresentationErrorEntireSumRank.Max() * 100.0);
representationErrorSumSecondRandom.Add(baseRepresentationErrorSecondRandomSample.Sum() * 100.0);
representationErrorSumSample.Add(baseRepresentationErrorSkylineSample.Sum() * 100.0);
representationErrorSumBestRank.Add(baseRepresentationErrorEntireBestRank.Sum() * 100.0);
representationErrorSumSumRank.Add(baseRepresentationErrorEntireSumRank.Sum() * 100.0);
var dominatedObjectsCountRandomSample =
new DominatedObjects(entireDatabase,
secondRandomSampleDatabase,
skylineAttributeColumns);
var dominatedObjectsCountSampleSkyline =
new DominatedObjects(entireDatabase, sampleSkylineDatabase,
skylineAttributeColumns);
var dominatedObjectsCountEntireSkylineBestRank =
new DominatedObjects(entireDatabase,
entireSkylineDataTableBestRankDatabase, skylineAttributeColumns);
var dominatedObjectsCountEntireSkylineSumRank =
new DominatedObjects(entireDatabase,
entireSkylineDataTableSumRankDatabase, skylineAttributeColumns);
dominatedObjectsCountSecondRandom.Add(
dominatedObjectsCountRandomSample.NumberOfDistinctDominatedObjects);
dominatedObjectsCountSample.Add(
dominatedObjectsCountSampleSkyline.NumberOfDistinctDominatedObjects);
dominatedObjectsCountBestRank.Add(
dominatedObjectsCountEntireSkylineBestRank.NumberOfDistinctDominatedObjects);
dominatedObjectsCountSumRank.Add(
dominatedObjectsCountEntireSkylineSumRank.NumberOfDistinctDominatedObjects);
dominatedObjectsOfBestObjectSecondRandom.Add(
dominatedObjectsCountRandomSample
.NumberOfObjectsDominatedByEachObjectOrderedByDescCount.First().Value);
dominatedObjectsOfBestObjectSample.Add(
dominatedObjectsCountSampleSkyline
.NumberOfObjectsDominatedByEachObjectOrderedByDescCount.First().Value);
dominatedObjectsOfBestObjectBestRank.Add(
dominatedObjectsCountEntireSkylineBestRank
.NumberOfObjectsDominatedByEachObjectOrderedByDescCount.First().Value);
dominatedObjectsOfBestObjectSumRank.Add(
dominatedObjectsCountEntireSkylineSumRank
.NumberOfObjectsDominatedByEachObjectOrderedByDescCount.First().Value);
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
sampleBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetBuckets(sampleSkylineNormalized,
skylineAttributeColumns);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedSampleBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(sampleBuckets);
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
randomBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetBuckets(secondRandomSampleNormalized,
skylineAttributeColumns);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedRandomBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(randomBuckets);
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
bestRankBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetBuckets(
entireSkylineDataTableBestRankNormalized,
skylineAttributeColumns);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedBestRankBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(
bestRankBuckets);
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
sumRankBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetBuckets(
entireSkylineDataTableSumRankNormalized,
skylineAttributeColumns);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedSumRankBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(
sumRankBuckets);
FillTopBuckets(clusterAnalysisTopBuckets,
ClusterAnalysis.SampleSkyline, sampleBuckets,
sampleSkylineNormalized.Count, entireDatabaseNormalized.Count,
entireSkylineNormalized.Count);
FillTopBuckets(clusterAnalysisTopBuckets,
ClusterAnalysis.RandomSkyline, randomBuckets,
secondRandomSampleNormalized.Count, entireDatabaseNormalized.Count,
entireSkylineNormalized.Count);
FillTopBuckets(clusterAnalysisTopBuckets,
ClusterAnalysis.BestRank, bestRankBuckets,
entireSkylineDataTableBestRankNormalized.Count,
entireDatabaseNormalized.Count, entireSkylineNormalized.Count);
FillTopBuckets(clusterAnalysisTopBuckets,
ClusterAnalysis.SumRank, sumRankBuckets,
entireSkylineDataTableSumRankNormalized.Count,
entireDatabaseNormalized.Count, entireSkylineNormalized.Count);
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
sampleMedianBuckets =
clusterAnalysisForMedian.GetBuckets(sampleSkylineNormalized,
skylineAttributeColumns, true);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedSampleMedianBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(sampleMedianBuckets);
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
randomMedianBuckets =
clusterAnalysisForMedian.GetBuckets(secondRandomSampleNormalized,
skylineAttributeColumns, true);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedRandomMedianBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(randomMedianBuckets);
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
bestRankMedianBuckets =
clusterAnalysisForMedian.GetBuckets(
entireSkylineDataTableBestRankNormalized,
skylineAttributeColumns, true);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedBestRankMedianBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(
bestRankMedianBuckets);
IReadOnlyDictionary<BigInteger, List<IReadOnlyDictionary<long, object[]>>>
sumRankMedianBuckets =
clusterAnalysisForMedian.GetBuckets(
entireSkylineDataTableSumRankNormalized,
skylineAttributeColumns, true);
IReadOnlyDictionary<int, List<IReadOnlyDictionary<long, object[]>>>
aggregatedSumRankMedianBuckets =
prefSQL.Evaluation.ClusterAnalysis.GetAggregatedBuckets(
sumRankMedianBuckets);
FillTopBuckets(clusterAnalysisMedianTopBuckets,
ClusterAnalysis.SampleSkyline, sampleMedianBuckets,
sampleSkylineNormalized.Count, entireDatabaseNormalized.Count,
entireSkylineNormalized.Count);
FillTopBuckets(clusterAnalysisMedianTopBuckets,
ClusterAnalysis.RandomSkyline, randomMedianBuckets,
secondRandomSampleNormalized.Count, entireDatabaseNormalized.Count,
entireSkylineNormalized.Count);
FillTopBuckets(clusterAnalysisMedianTopBuckets,
ClusterAnalysis.BestRank, bestRankMedianBuckets,
entireSkylineDataTableBestRankNormalized.Count,
entireDatabaseNormalized.Count, entireSkylineNormalized.Count);
FillTopBuckets(clusterAnalysisMedianTopBuckets,
ClusterAnalysis.SumRank, sumRankMedianBuckets,
entireSkylineDataTableSumRankNormalized.Count,
entireDatabaseNormalized.Count, entireSkylineNormalized.Count);
var caEntireDbNew = new List<double>();
var caEntireSkylineNew = new List<double>();
var caSampleSkylineNew = new List<double>();
var caRandomSkylineNew = new List<double>();
var caBestRankNew = new List<double>();
var caSumRankNew = new List<double>();
for (var ii = 0; ii < skylineAttributeColumns.Length; ii++)
{
int entireSkyline = aggregatedEntireSkylineBuckets.ContainsKey(ii)
? aggregatedEntireSkylineBuckets[ii].Count
: 0;
int sampleSkyline = aggregatedSampleBuckets.ContainsKey(ii)
? aggregatedSampleBuckets[ii].Count
: 0;
int randomSkyline = aggregatedRandomBuckets.ContainsKey(ii)
? aggregatedRandomBuckets[ii].Count
: 0;
double entireSkylinePercent = (double)entireSkyline /
entireSkylineNormalized.Count;
double sampleSkylinePercent = (double) sampleSkyline /
sampleSkylineNormalized.Count;
double randomSkylinePercent = (double)randomSkyline /
secondRandomSampleNormalized.Count;
int entireDb = aggregatedEntireDatabaseBuckets.ContainsKey(ii)
? aggregatedEntireDatabaseBuckets[ii].Count
: 0;
double entireDbPercent = (double) entireDb /
entireDatabaseNormalized.Count;
int bestRank = aggregatedBestRankBuckets.ContainsKey(ii)
? aggregatedBestRankBuckets[ii].Count
: 0;
int sumRank = aggregatedSumRankBuckets.ContainsKey(ii)
? aggregatedSumRankBuckets[ii].Count
: 0;
double bestRankPercent = (double) bestRank /
entireSkylineDataTableBestRankNormalized.Count;
double sumRankPercent = (double) sumRank /
entireSkylineDataTableSumRankNormalized.Count;
caEntireDbNew.Add(entireDbPercent);
caEntireSkylineNew.Add(entireSkylinePercent);
caSampleSkylineNew.Add(sampleSkylinePercent);
caRandomSkylineNew.Add(randomSkylinePercent);
caBestRankNew.Add(bestRankPercent);
caSumRankNew.Add(sumRankPercent);
}
var caMedianEntireDbNew = new List<double>();
var caMedianEntireSkylineNew = new List<double>();
var caMedianSampleSkylineNew = new List<double>();
var caMedianRandomSkylineNew = new List<double>();
var caMedianBestRankNew = new List<double>();
var caMedianSumRankNew = new List<double>();
for (var ii = 0; ii < skylineAttributeColumns.Length; ii++)
{
int entireSkyline = aggregatedEntireSkylineMedianBuckets.ContainsKey(ii)
? aggregatedEntireSkylineMedianBuckets[ii].Count
: 0;
int sampleSkyline = aggregatedSampleMedianBuckets.ContainsKey(ii)
? aggregatedSampleMedianBuckets[ii].Count
: 0;
int randomSkyline = aggregatedRandomMedianBuckets.ContainsKey(ii)
? aggregatedRandomMedianBuckets[ii].Count
: 0;
double entireSkylinePercent = (double)entireSkyline /
entireSkylineNormalized.Count;
double sampleSkylinePercent = (double) sampleSkyline /
sampleSkylineNormalized.Count;
double randomSkylinePercent = (double)randomSkyline /
secondRandomSampleNormalized.Count;
int entireDb = aggregatedEntireDatabaseMedianBuckets.ContainsKey(ii)
? aggregatedEntireDatabaseMedianBuckets[ii].Count
: 0;
double entireDbPercent = (double) entireDb /
entireDatabaseNormalized.Count;
int bestRank = aggregatedBestRankMedianBuckets.ContainsKey(ii)
? aggregatedBestRankMedianBuckets[ii].Count
: 0;
int sumRank = aggregatedSumRankMedianBuckets.ContainsKey(ii)
? aggregatedSumRankMedianBuckets[ii].Count
: 0;
double bestRankPercent = (double) bestRank /
entireSkylineDataTableBestRankNormalized.Count;
double sumRankPercent = (double) sumRank /
entireSkylineDataTableSumRankNormalized.Count;
caMedianEntireDbNew.Add(entireDbPercent);
caMedianEntireSkylineNew.Add(entireSkylinePercent);
caMedianSampleSkylineNew.Add(sampleSkylinePercent);
caMedianRandomSkylineNew.Add(randomSkylinePercent);
caMedianBestRankNew.Add(bestRankPercent);
caMedianSumRankNew.Add(sumRankPercent);
}
clusterAnalysis[ClusterAnalysis.EntireDb].Add(caEntireDbNew);
clusterAnalysis[ClusterAnalysis.EntireSkyline].Add(
caEntireSkylineNew);
clusterAnalysis[ClusterAnalysis.SampleSkyline].Add(
caSampleSkylineNew);
clusterAnalysis[ClusterAnalysis.RandomSkyline].Add(
caRandomSkylineNew);
clusterAnalysis[ClusterAnalysis.BestRank].Add(
caBestRankNew);
clusterAnalysis[ClusterAnalysis.SumRank].Add(
caSumRankNew);
clusterAnalysisMedian[ClusterAnalysis.EntireDb].Add(
caMedianEntireDbNew);
clusterAnalysisMedian[ClusterAnalysis.EntireSkyline].Add(
caMedianEntireSkylineNew);
clusterAnalysisMedian[ClusterAnalysis.SampleSkyline].Add(
caMedianSampleSkylineNew);
clusterAnalysisMedian[ClusterAnalysis.RandomSkyline].Add(
caMedianRandomSkylineNew);
clusterAnalysisMedian[ClusterAnalysis.BestRank].Add(
caMedianBestRankNew);
clusterAnalysisMedian[ClusterAnalysis.SumRank].Add(
caMedianSumRankNew);
}
subsetCount++;
}
Dictionary<ClusterAnalysis, string> clusterAnalysisStrings =
GetClusterAnalysisStrings(skylineAttributeColumns, clusterAnalysis);
Dictionary<ClusterAnalysis, string> clusterAnalysisMedianStrings =
GetClusterAnalysisStrings(skylineAttributeColumns, clusterAnalysisMedian);
Dictionary<ClusterAnalysis, string> clusterAnalysisTopBucketsStrings =
GetClusterAnalysisTopBucketsStrings(clusterAnalysisTopBuckets, ExcessiveTests);
Dictionary<ClusterAnalysis, string> clusterAnalysisMedianTopBucketsStrings =
GetClusterAnalysisTopBucketsStrings(clusterAnalysisMedianTopBuckets, ExcessiveTests);
var time = (long) (subsetTime.Average() + .5);
var objects = (long) (subsetObjects.Average() + .5);
var elapsed = (long) (subsetTimeElapsed.Average() + .5);
Console.WriteLine("subsetTime");
foreach (var i in subsetTime)
{
Console.WriteLine(i);
}
Console.WriteLine("");
Console.WriteLine("subsetObjects");
foreach (var i in subsetObjects)
{
Console.WriteLine(i);
}
Console.WriteLine("");
reportDimensions.Add(preferences.Count);
reportSkylineSize.Add(objects);
reportTimeTotal.Add(elapsed);
reportTimeAlgorithm.Add(time);
reportMinCorrelation.Add(minCorrelation);
reportMaxCorrelation.Add(maxCorrelation);
reportCardinality.Add(cardinality);
var setCoverageSingle =
new Dictionary<SkylineTypesSingle, List<double>>
{
{SkylineTypesSingle.Random, setCoverageSecondRandom},
{SkylineTypesSingle.Sample, setCoverageSample},
{SkylineTypesSingle.BestRank, setCoverageBestRank},
{SkylineTypesSingle.SumRank, setCoverageSumRank}
};
var representationErrorSingle =
new Dictionary<SkylineTypesSingle, List<double>>
{
{SkylineTypesSingle.Random, representationErrorSecondRandom},
{SkylineTypesSingle.Sample, representationErrorSample},
{SkylineTypesSingle.BestRank, representationErrorBestRank},
{SkylineTypesSingle.SumRank, representationErrorSumRank}
};
var representationErrorSumSingle =
new Dictionary<SkylineTypesSingle, List<double>>
{
{SkylineTypesSingle.Random, representationErrorSumSecondRandom},
{SkylineTypesSingle.Sample, representationErrorSumSample},
{SkylineTypesSingle.BestRank, representationErrorSumBestRank},
{SkylineTypesSingle.SumRank, representationErrorSumSumRank}
};
var dominatedObjectsCountSingle =
new Dictionary<SkylineTypesSingle, List<double>>()
{
{
SkylineTypesSingle.Random,
dominatedObjectsCountSecondRandom
},
{
SkylineTypesSingle.Sample,
dominatedObjectsCountSample
},
{
SkylineTypesSingle.BestRank,
dominatedObjectsCountBestRank
},
{
SkylineTypesSingle.SumRank,
dominatedObjectsCountSumRank
}
};
var dominatedObjectsOfBestObjectSingle =
new Dictionary<SkylineTypesSingle, List<double>>
{
{
SkylineTypesSingle.Random,
dominatedObjectsOfBestObjectSecondRandom
},
{
SkylineTypesSingle.Sample,
dominatedObjectsOfBestObjectSample
},
{
SkylineTypesSingle.BestRank,
dominatedObjectsOfBestObjectBestRank
},
{
SkylineTypesSingle.SumRank,
dominatedObjectsOfBestObjectSumRank
}
};
AddToReports(_reportsLong, subsetObjects, subsetTime,
_reportsDouble);
if (ExcessiveTests)
{
AddToSetCoverage(_setCoverage, setCoverageSingle);
AddToSetCoverage(_representationError,
representationErrorSingle);
AddToSetCoverage(_representationErrorSum,
representationErrorSumSingle);
AddToSetCoverage(_dominatedObjectsCount,
dominatedObjectsCountSingle);
AddToSetCoverage(_dominatedObjectsOfBestObject,
dominatedObjectsOfBestObjectSingle);
}
string strLine = FormatLineString(strPreferenceSet, strTrial,
preferences.Count, objects,
elapsed, time, subsetTime.Min(), subsetTime.Max(),
MyMathematic.GetSampleVariance(subsetTime),
MyMathematic.GetSampleStdDeviation(subsetTime),
Mathematic.Median(subsetTime), Mathematic.LowerQuartile(subsetTime),
Mathematic.UpperQuartile(subsetTime), subsetObjects.Min(),
subsetObjects.Max(), MyMathematic.GetSampleVariance(subsetObjects),
MyMathematic.GetSampleStdDeviation(subsetObjects),
Mathematic.Median(subsetObjects), Mathematic.LowerQuartile(subsetObjects),
Mathematic.UpperQuartile(subsetObjects),
setCoverageSingle, representationErrorSingle,
representationErrorSumSingle, dominatedObjectsCountSingle,
dominatedObjectsOfBestObjectSingle,
clusterAnalysisStrings, clusterAnalysisMedianStrings,
clusterAnalysisTopBucketsStrings, clusterAnalysisMedianTopBucketsStrings,
minCorrelation, maxCorrelation,
cardinality, numberOfMoves, numberOfComparisons);
return strLine;
}
