DBXplorer: A System for Keyword-Based Search over Relational Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Indexing Relational Database Content Offline for Efficient Keyword-Based Search
IDEAS '05 Proceedings of the 9th International Database Engineering & Application Symposium
Keyword Proximity Search in XML Trees
IEEE Transactions on Knowledge and Data Engineering
Effective keyword search in relational databases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Spark: top-k keyword query in relational databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Discover: keyword search in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient IR-style keyword search over relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Efficiently enumerating results of keyword search over data graphs
Information Systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A DHT-based infrastructure for ad-hoc integration and querying of semantic data
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Efficient keyword proximity search using a frontier-reduce strategy based on d-distance graph index
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Steiner Tree Problems In Computer Communication Networks
Steiner Tree Problems In Computer Communication Networks
Exploratory keyword search on data graphs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Towards an Effective XML Keyword Search
IEEE Transactions on Knowledge and Data Engineering
Keyword search in relational databases
Knowledge and Information Systems
Hadoop in Action
Finding Top-k Answers in Keyword Search over Relational Databases Using Tuple Units
IEEE Transactions on Knowledge and Data Engineering
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Most of the information on the Web can be currently classified according to its (information) structure in three different forms: unstructured (plain text), semi-structured (XML files) and structured (tables in a relational database). Currently Web search is the primary way to access massive information. Keyword search also becomes an alternative of querying over relational databases and XML documents, which is simple to people who are familiar with the use of Web search engines. There are several approaches to perform keyword search over relational databases such as Steiner Trees, Candidate Networks and Tuple Units. However these methods have some constraints. The Steiner Trees method is considered a NP-hard problem, moreover, a real databases can produce a large number of Steiner Trees, which are difficult to identify and index. The Candidate Network approach first needs to generate the candidate networks and then to evaluate them to find the best answer. The problem is that for a keyword query the number of Candidate Networks can be very large and to find a common join expression to evaluate all the candidate networks could require a big computational effort. Finally, the use of Tuple Units in a general conception produce very large structures that most of the time store redundant information. To address this problem we propose a novel approach for keywords search over structured data (KESOSD). KESOSD models the structured information as graphs and proposed the use of a keyword-structure-aware-index called KSAI that captures the implicit structural relationships of the information producing fast and accuracy search responses. We have conducted some experiments and the results show that KESOSD achieves high search efficiency and high accuracy for keyword search over structured data.