@Override
public Cursor loadCursor() {
final ContentResolver cr = getContext().getContentResolver();
final String selection;
final String[] selectionArgs;
// Null represents the root filter
if (mRequest.getFilter() == null) {
selection =
DBUtils.concatenateWhere(HomeItems.DATASET_ID + " = ?", HomeItems.FILTER + " IS NULL");
selectionArgs = new String[] {mRequest.getDatasetId()};
} else {
selection =
DBUtils.concatenateWhere(HomeItems.DATASET_ID + " = ?", HomeItems.FILTER + " = ?");
selectionArgs = new String[] {mRequest.getDatasetId(), mRequest.getFilter()};
}
// XXX: You can use CONTENT_FAKE_URI for development to pull items from fake_home_items.json.
final Cursor c = cr.query(HomeItems.CONTENT_URI, null, selection, selectionArgs, null);
// SQLiteBridgeContentProvider may return a null Cursor if the database hasn't been created
// yet.
if (c != null) {
final Uri notificationUri = getDatasetNotificationUri(mRequest.getDatasetId());
c.setNotificationUri(cr, notificationUri);
}
return c;
}
private Cursor filterAllSites(
ContentResolver cr,
String[] projection,
CharSequence constraint,
int limit,
CharSequence urlFilter) {
String selection = "";
String[] selectionArgs = null;
// The combined history/bookmarks selection queries for sites with a url or title containing
// the constraint string(s), treating space-separated words as separate constraints
String[] constraintWords = constraint.toString().split(" ");
for (int i = 0; i < constraintWords.length; i++) {
selection =
DBUtils.concatenateWhere(
selection, "(" + Combined.URL + " LIKE ? OR " + Combined.TITLE + " LIKE ?)");
String constraintWord = "%" + constraintWords[i] + "%";
selectionArgs =
DBUtils.appendSelectionArgs(selectionArgs, new String[] {constraintWord, constraintWord});
}
if (urlFilter != null) {
selection = DBUtils.concatenateWhere(selection, "(" + Combined.URL + " NOT LIKE ?)");
selectionArgs =
DBUtils.appendSelectionArgs(selectionArgs, new String[] {urlFilter.toString()});
}
// Our version of frecency is computed by scaling the number of visits by a multiplier
// that approximates Gaussian decay, based on how long ago the entry was last visited.
// Since we're limited by the math we can do with sqlite, we're calculating this
// approximation using the Cauchy distribution: multiplier = 15^2 / (age^2 + 15^2).
// Using 15 as our scale parameter, we get a constant 15^2 = 225. Following this math,
// frecencyScore = numVisits * max(1, 100 * 225 / (age*age + 225)). (See bug 704977)
// We also give bookmarks an extra bonus boost by adding 100 points to their frecency score.
final String age =
"(" + Combined.DATE_LAST_VISITED + " - " + System.currentTimeMillis() + ") / 86400000";
final String sortOrder =
"(CASE WHEN "
+ Combined.BOOKMARK_ID
+ " > -1 THEN 100 ELSE 0 END) + "
+ Combined.VISITS
+ " * MAX(1, 100 * 225 / ("
+ age
+ "*"
+ age
+ " + 225)) DESC";
Cursor c =
cr.query(combinedUriWithLimit(limit), projection, selection, selectionArgs, sortOrder);
return new LocalDBCursor(c);
}