Hey Mark, Thanks for your continued help. The output does include Schema Index as follows:
- ==> Filter(pred="(any(-_-INNER-_- in Collection(List(Literal(Oblivion Ring), Literal(Lightning Bolt), Literal(Mana Leak), Literal(Mulldrifter), Literal(Llanowar Elves), Literal(Counterspell), Literal(Rancor), Literal(Ponder), Literal(Doom Blade), Literal(Preordain), Literal(Evolving Wilds), Literal(Incinerate), Literal(Bonesplitter), Literal(Brainstorm), Literal(Vampire Nighthawk), Literal(Terramorphic Expanse), Literal(Acidic Slime), Literal(Path to Exile), Literal(Eternal Witness), Literal(Duress), Literal(Porcelain Legionnaire), Literal(Journey to Nowhere), Literal(Swords to Plowshares), Literal(Lingering Souls), Literal(Faith's Fetters), Literal(Hymn to Tourach), Literal(Qasali Pridemage), Literal(Sakura-Tribe Elder), Literal(Magma Jet), Literal(Lightning Helix), Literal(Rakdos Cackler), Literal(Shriekmaw), Literal(Cultivate), Literal(Bloodbraid Elf), Literal(Fact or Fiction), Literal(Faithless Looting), Literal(Birds of Paradise), Literal(Electrolyze), Literal(Man-o'-War), Literal(Kodama's Reach), Literal(Keldon Marauders), Literal(Flametongue Kavu), Literal(Dismember), Literal(Yavimaya Elder))) where Property(toCard,name(0)) == -_-INNER-_-) AND hasLabel(toCard:Card(0)))", _rows=45, _db_hits=1034) - ==> PatternMatch(g="(fromCard)-['r']-(toCard)", _rows=45, _db_hits= 201894) - ==> SchemaIndex(identifier="fromCard", _db_hits=0, _rows=1, label= "Card", query="Literal(Ultimate Price)", property="name") Does this indicate that my initial query should be modified somehow to include the index? On the general point about the domain not feeling graphy, I understand what you are saying. I'll explain my problem space a bit and perhaps you can see something that I've missed. What I'm doing here is collecting data on cards that people are "drafting". The process is very similar to when you were in P.E. at high school and the teacher put you and another person at the front of the class to pick players for your football team one at a time. What people do through my app is pick cards instead of football players. There are 30,000 possible cards that they can choose from but rather than offering all 30,000 at once the player is asked to make a choice from just 15 of the available card pool. Once they've made a choice they are asked again to make a decision from a DIFFERENT 14 cards, then 13, then 12 and so on. This process of picking from 15 to 1 cards is repeated 3 times until they end up with 45 cards. Each time the player makes a choice I increment the 'pickCount' on the relationship for the card that has been chosen against all of the cards that were previously chosen. In addition I increment the passCount for each card that was not chosen, against each card that has already been chosen, >From this I can infer the strength of the relationship between any two cards from the card pool. The data is coming from real "drafts" that are taking place on my site: www.cubetutor.com/draft/1. The idea behind gathering this data is to build an advanced AI that can draft cards using this relationship strength metric as one facet of a card picking algorithm. I hope that makes sense? So I'm gathering all of this data from real life drafts. I just can't fathom any other potential entities from my domain? It's entirely possible that a graph database just isn't appropriate for a domain where you have one node type and each node has one relationship type to potentially every other node. I suppose it would be sort of equivalent to a PERSON -[:KNOWS]-PERSON model if you assumed that every person knew almost every other person in the world! -- You received this message because you are subscribed to the Google Groups "Neo4j" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
