On saying “Enough already!” in SQL
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
An Enhanced Technique for k-Nearest Neighbor Queries with Non-Spatial Selection Predicates
Multimedia Tools and Applications
Fast Indexing and Visualization of Metric Data Sets using Slim-Trees
IEEE Transactions on Knowledge and Data Engineering
Processing Incremental Multidimensional Range Queries in a Direct Manipulation Visual Query
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Reducing the Braking Distance of an SQL Query Engine
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Query processing and optimization in Oracle Rdb
The VLDB Journal — The International Journal on Very Large Data Bases
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Efficient and self-tuning incremental query expansion for top-k query processing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
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An important functionality of the database management systems (DBMS) in the client/server architecture is the capacity of answer queries of client' applications. In this architecture, it is not always possible to foresee the quantity of results that may be generated from a specific query. So, a query may result in a large amount of records which not always is analyzed by the client, generating a waste of processing. To avoid this waste, the traditional DBMS use the technique of results pagination, which consists in sending the results in the proportion when client needs to analyze them. However, the traditional DBMS do not manipulate data where the similarity is the only relation between the domain elements. For this data domain, there is no support for the results pagination technique and a range query may return a very large list which will not be analyzed by the client. In order to minimize this problem, this paper presents a new query called "Next Range Query", that allows the pagination results in control in the client. The principle of the "Next Range Query" is to fragment the query in many queries allowing to forward and backward in the results list.