/**
* Return an array of the distinct predicates in the KB ordered by their descending frequency of
* use. The {@link IV}s in the returned array will have been resolved to the corresponding {@link
* BigdataURI}s which can be accessed using {@link IV#getValue()}.
*
* @param kb The KB instance.
*/
protected static IVCount[] predicateUsage(final AbstractTripleStore kb) {
final SPORelation r = kb.getSPORelation();
if (r.oneAccessPath) {
// The necessary index (POS or POCS) does not exist.
throw new UnsupportedOperationException();
}
final boolean quads = kb.isQuads();
// the index to use for distinct predicate scan.
final SPOKeyOrder keyOrder = quads ? SPOKeyOrder.POCS : SPOKeyOrder.POS;
// visit distinct term identifiers for predicate position on that index.
@SuppressWarnings("rawtypes")
final IChunkedIterator<IV> itr = r.distinctTermScan(keyOrder);
// resolve term identifiers to terms efficiently during iteration.
final BigdataValueIterator itr2 = new BigdataValueIteratorImpl(kb /* resolveTerms */, itr);
try {
final Set<IV<?, ?>> ivs = new LinkedHashSet<IV<?, ?>>();
final Map<IV<?, ?>, IVCount> counts = new LinkedHashMap<IV<?, ?>, IVCount>();
while (itr2.hasNext()) {
final BigdataValue term = itr2.next();
final IV<?, ?> iv = term.getIV();
final long n = r.getAccessPath(null, iv, null, null).rangeCount(false /* exact */);
ivs.add(iv);
counts.put(iv, new IVCount(iv, n));
}
// Batch resolve IVs to Values
final Map<IV<?, ?>, BigdataValue> x = kb.getLexiconRelation().getTerms(ivs);
for (Map.Entry<IV<?, ?>, BigdataValue> e : x.entrySet()) {
final IVCount count = counts.get(e.getKey());
count.setValue(e.getValue());
}
final IVCount[] a = counts.values().toArray(new IVCount[counts.size()]);
// Order by descending count.
Arrays.sort(a);
return a;
} finally {
itr2.close();
}
}
/**
* Describe the default data set (the one identified by the namespace associated with the {@link
* AbstractTripleStore}.
*
* @param describeStatistics When <code>true</code>, the VoID description will include the {@link
* VoidVocabularyDecl#vocabulary} declarations, the property partition statistics, and the
* class partition statistics.
* @param describeNamedGraphs When <code>true</code>, each named graph will also be described in
* in the same level of detail as the default graph. Otherwise only the default graph will be
* described.
*/
public void describeDataSet(final boolean describeStatistics, final boolean describeNamedGraphs) {
final String namespace = tripleStore.getNamespace();
// This is a VoID data set.
g.add(aDataset, RDF.TYPE, VoidVocabularyDecl.Dataset);
// The namespace is used as a title for the data set.
g.add(aDataset, DCTermsVocabularyDecl.title, f.createLiteral(namespace));
// Also present the namespace in an unambiguous manner.
g.add(aDataset, SD.KB_NAMESPACE, f.createLiteral(namespace));
/**
* Service end point for this namespace.
*
* @see <a href="https://sourceforge.net/apps/trac/bigdata/ticket/689" > Missing URL encoding in
* RemoteRepositoryManager </a>
*/
for (String uri : serviceURI) {
g.add(
aDataset,
VoidVocabularyDecl.sparqlEndpoint,
f.createURI(uri + "/" + ConnectOptions.urlEncode(namespace) + "/sparql"));
}
// any URI is considered to be an entity.
g.add(aDataset, VoidVocabularyDecl.uriRegexPattern, f.createLiteral("^.*"));
if (!describeStatistics) {
// No statistics.
return;
}
// Frequency count of the predicates in the default graph.
final IVCount[] predicatePartitionCounts = predicateUsage(tripleStore);
// Frequency count of the classes in the default graph.
final IVCount[] classPartitionCounts = classUsage(tripleStore);
// Describe vocabularies based on the predicate partitions.
describeVocabularies(predicatePartitionCounts);
// defaultGraph description.
{
// Default graph in the default data set.
g.add(aDataset, SD.defaultGraph, aDefaultGraph);
// Describe the default graph using statistics.
describeGraph(aDefaultGraph, predicatePartitionCounts, classPartitionCounts);
} // end defaultGraph
// sb.append("termCount\t = " + tripleStore.getTermCount() + "\n");
//
// sb.append("uriCount\t = " + tripleStore.getURICount() + "\n");
//
// sb.append("literalCount\t = " + tripleStore.getLiteralCount() +
// "\n");
//
// /*
// * Note: The blank node count is only available when using the told
// * bnodes mode.
// */
// sb
// .append("bnodeCount\t = "
// + (tripleStore.getLexiconRelation()
// .isStoreBlankNodes() ? ""
// + tripleStore.getBNodeCount() : "N/A")
// + "\n");
/*
* Report for each named graph.
*/
if (describeNamedGraphs && tripleStore.isQuads()) {
final SPORelation r = tripleStore.getSPORelation();
// the index to use for distinct term scan.
final SPOKeyOrder keyOrder = SPOKeyOrder.CSPO;
// visit distinct IVs for context position on that index.
@SuppressWarnings("rawtypes")
final IChunkedIterator<IV> itr = r.distinctTermScan(keyOrder);
// resolve IVs to terms efficiently during iteration.
final BigdataValueIterator itr2 =
new BigdataValueIteratorImpl(tripleStore /* resolveTerms */, itr);
try {
while (itr2.hasNext()) {
/*
* Describe this named graph.
*
* Note: This is using the predicate and class partition
* statistics from the default graph (RDF merge) to identify
* the set of all possible predicates and classes within
* each named graph. It then tests each predicate and class
* partition against the named graph and ignores those which
* are not present in a given named graph. This is being
* done because we do not have a CPxx index.
*/
final BigdataResource graph = (BigdataResource) itr2.next();
final IVCount[] predicatePartitionCounts2 =
predicateUsage(tripleStore, graph.getIV(), predicatePartitionCounts);
final IVCount[] classPartitionCounts2 =
classUsage(tripleStore, graph.getIV(), classPartitionCounts);
final BNode aNamedGraph = f.createBNode();
// Named graph in the default data set.
g.add(aDataset, SD.namedGraph, aNamedGraph);
// The name of that named graph.
g.add(aNamedGraph, SD.name, graph);
// Describe the named graph.
describeGraph(aNamedGraph, predicatePartitionCounts2, classPartitionCounts2);
}
} finally {
itr2.close();
}
}
}
