The problem itself sounds really challenging, but literally two point from the last question are:- - https://lucene.apache.org/solr/guide/6_6/other-parsers.html#OtherParsers-Scoring - find field in https://lucene.apache.org/solr/guide/6_6/function-queries.html#FunctionQueries-AvailableFunctions
On Thu, Jan 11, 2018 at 2:13 AM, Leila Deljkovic < leila.deljko...@koordinates.com> wrote: > Hi, > > https://lucene.apache.org/solr/guide/7_0/uploading-data- > with-index-handlers.html#UploadingDatawithIndexHandlers > -NestedChildDocuments <https://lucene.apache.org/ > solr/guide/7_0/uploading-data-with-index-handlers.html# > UploadingDatawithIndexHandlers-NestedChildDocuments> > > I have never used nested documents, but a bit of background on what I’m > doing is that a spatial data layer consisting of features (points, lines, > polygons, or an aerial image) is split up into sections (grid cells) based > on the density of these features over the layer; smaller grid cells > indicate high density of features in that area. > > I need to rank results based on density of features and whether dense > areas of the layer overlap with the region of space on a map I am searching > in. This is important because a layer could cover an entire country, for > example if I query for “roads”, the layer would be dense in urban areas as > there are more roads there, and less dense in rural areas, and if I am > searching for a particular city, this layer would be of interest to me even > though it covers the entire country. The idea is for the original layer to > be the parent document (which is what should be returned when a query is > made), and the child documents are the individual grid cells (which will > hold the geometry of the cell and a density field for the features inside > the cell). > > I would like to know if it is possible to rank the parent document based > on a function which aggregates fields from the child documents (in this > case, the density field). There is not much info on this that I could find > online. > > Thanks -- Sincerely yours Mikhail Khludnev
