In general, any flexible query answering approach incurs a certain performance overhead compared to exact query answering.
In our case, the clustering and fragmentation have to be compute but also the query answering incurs some extra overhead due to the fact that the appropriate fragment has to be identified and multiple answers are returned whereas exact query answering would simply have returned an empty answer set.
If you see a headline that seems a little off topic or weird, followed by some very hastily written content, have a quick look at Google Trends and see if the exact query is in the title of the post or article.
It aims to deliver exact query answers by making the best effort through heterogeneous-grained adaptations with the goal to meet users' QoS requirements.
However, they only support exact query answering.
On the other hand, flexible query answering offers mechanisms to intelligently answer user queries going beyond conventional exact query answering.
Molecular data used in the benchmarks are publicly available from the PubChem database; exact query sets, datasets and CIDs of the molecules that were randomly selected for the benchmarks described in the paper are published on the project web site.
In this work, we used only exact query terms but not their translation(s) (due to PubMed's Automatic Term Mapping feature) in obtaining instances of click-words.
If a service requester is attempting an exact-match query for the repair shop functionality defined in Table 1, the query could be defined as q e = 〈 f ̂, κ ̂, ĉ, l ̂, E, Λ ̂ 〉 where: f ̂ = 〈 pr ̂, po ̂, D ̂, SF ̂ 〉, where pr ̂ = { ( CarBroken = = true ) }, po ̂ = { ( HasAppointment = = true ) }, D ̂ = ( CarDomain ), and SF ̂ = ( RepairShopFunctionality ).
The three following definitions formalize an exact-match query.
It deals with both exact queries and approximate ones, in which wildcards are used to match unspecified number of elements.
