Browsing by Author "GAO, Qiao"

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    Analyzing Temporal Keyword Queries for Interactive Search over Temporal Databases
    GAO, Qiao; LEE, Mong Li; LING, Tok Wang; DOBBIE, Gillian; ZENG, Zhong
    Querying temporal relational databases is a challenge for non-expert database users, since it requires users to understand the semantics of the database and apply temporal joins as well as temporal conditions correctly in SQL statements. Traditional keyword search approaches are not directly applicable to temporal relational databases since they treat time-related keywords as tuple values and do not consider the temporal joins between relations, which leads to missing answers, incorrect answers and missing query interpretations. In this work, we extend keyword queries to allow the temporal predicates, and design a schema graph approach based on the Object-Relationship-Attribute (ORA) semantics. This approach enables us to identify temporal attributes of objects/relationships and infer the target temporal data of temporal predicates, thus improving the completeness and correctness of temporal keyword search and capturing the various possible interpretations of temporal keyword queries.
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    A Semantic Framework for Designing Temporal SQL Databases
    GAO, Qiao; LEE, Mong Li; DOBBIE, Gillian; ZHONG, Zeng
    Many real world applications need to capture a mix of temporal and non-temporal entities, relationships and attributes. These concepts add complexity when designing database schemas and existing works are unable to capture the temporal semantics precisely. We propose a new framework for designing SQL databases that distinguishes between temporal and non-temporal concepts while also distinguishing between entities, relationships and attributes at every step. The framework rst utilizes an Entity-Relationship (ER) diagram to capture the real world semantics. Temporal constructs in the ER diagram are then annotated. Finally we map the temporal ER diagram to a normal form database schema that reduces redundant data by separating current data from historical data.We also describe how data consistency is maintained during updates. Experiment results show that we can generate database schemas that support efficientt access to both current and historical information, and enable better management of temporal data.