Using Semi-Joins to Solve Relational Queries
Journal of the ACM (JACM)
The SIFT information dissemination system
ACM Transactions on Database Systems (TODS)
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VLDB '05 Proceedings of the 31st international conference on Very large data bases
Approximating StreamingWindow Joins Under CPU Limitations
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
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Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Effective keyword search in relational databases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
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CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Spark: top-k keyword query in relational databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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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
Memory-limited execution of windowed stream joins
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Schema-Aware Keyword Search over XML Streams
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Authority-based keyword search in databases
ACM Transactions on Database Systems (TODS)
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Keyword proximity search in complex data graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Keyword search on external memory data graphs
Proceedings of the VLDB Endowment
Efficient Algorithms for Skyline Top-K Keyword Queries on XML Streams
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Scalable Keyword Search on Large Data Streams
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Querying Communities in Relational Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Efficient fuzzy full-text type-ahead search
The VLDB Journal — The International Journal on Very Large Data Bases
Scalable top-k keyword search in relational databases
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Future Generation Computer Systems
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It is widely recognized that the integration of information retrieval (IR) and database (DB) techniques provides users with a broad range of high quality services. Along this direction, IR-styled m-keyword query processing over a relational database in an rdbms framework has been well studied. It finds all hidden interconnected tuple structures, for example connected trees that contain keywords and are interconnected by sequences of primary/foreign key relationships among tuples. A new challenging issue is how to monitor events that are implicitly interrelated over an open-ended relational data stream for a user-given m-keyword query. Such a relational data stream is a sequence of tuple insertion/deletion operations. The difficulty of the problem is related to the number of costly joins to be processed over time when tuples are inserted and/or deleted. Such cost is mainly affected by three parameters, namely, the number of keywords, the maximum size of interconnected tuple structures, and the complexity of the database schema when it is viewed as a schema graph. In this paper, we propose new approaches. First, we propose a novel algorithm to efficiently determine all the joins that need to be processed for answering an m-keyword query. Second, we propose a new demand-driven approach to process such a query over a high speed relational data stream. We show that we can achieve high efficiency by significantly reducing the number of intermediate results when processing joins over a relational data stream. The proposed new techniques allow us to achieve high scalability in terms of both query plan generation and query plan execution. We conducted extensive experimental studies using synthetic data and real data to simulate a relational data stream. Our approach significantly outperforms existing algorithms.