Empty versus overabundant answers to flexible relational queries

Flexible data queries

“…According to [4] this approach has two main disadvantages. Firstly, the meaning of the initial query is diluted in order to capture neighbouring entities, but these entities satisfy a query with the same degree as all entities meeting the initial query (3). It means that the difference between original and neighbouring data (additional records selected by the relaxed query condition) does not exist.…”

“…But, when the user wants to know why an empty answer has appeared or how close entities to match he query condition are, user have to modify parameters in query conditions and process another, similar query. In theory this is known as "empty answer problem", that is, the problem of providing some alternative data when there is no data fitting the query [3].…”

“…On the other hand, in case of empty answer problems, one fuzzy query could solve this issue and therefore, leads to less computational and user burden. However, empty answer could also appear in fuzzy queries [3]. In order to solve this issue relaxation of the initial query has been suggested in [3].…”

“…However, empty answer could also appear in fuzzy queries [3]. In order to solve this issue relaxation of the initial query has been suggested in [3]. The relaxation is a continuous and time consuming process.…”

“…When query contains several fuzzy elementary conditions, then each elementary condition should be relaxed and tested whether empty answer disappeared. The querying process is repeated until the answer is not empty or the modified query becomes semantically far from the original one [3].…”