p385-enderle - Efficiently Processing Queries on...

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Efficiently Processing Queries on Interval-and-Value Tuples in Relational Databases Jost Enderle Nicole Schneider Thomas Seidl RWTH Aachen University, Germany Department of Computer Science 9 {enderle, schneider, seidl}@informatik.rwth-aachen.de Abstract With the increasing occurrence of temporal and spatial data in present-day database applications, the interval data type is adopted by more and more database systems. For an efficient support of queries that contain selections on interval attributes as well as simple-valued attributes (e. g. numbers, strings) at the same time, special index structures are required supporting both types of predicates in combination. Based on the Relational Interval Tree, we present various indexing schemes that support such combined queries and can be integrated in relational database systems with minimum effort. Experi- ments on different query types show superior performance for the new techniques in compari- son to competing access methods. 1. Introduction In recent years, many special-purpose data types got available in relational databases. Instead of providing ap- plication-specific systems such as spatial, multimedia, or XML databases for each conceivable domain, database implementors more and more introduce new data types and corresponding management functions directly into relational systems, either by a direct integration into the database kernel or – much more common – by providing extensible interfaces allowing users to define the required complex types together with appropriate operations. One of the simplest complex types is the interval data type. While intervals occur in various application areas, e. g. as tolerance ranges for imprecisely measured values in scientific databases, as line segments on a space-filling curve in spatial applications [1] [11], or as finite domain constraints in declarative systems [18] [26], the typical domain for intervals are temporal applications where they are used as transaction time and valid time ranges [30]. In a relational database system adopting the SQL:2003 [16] period data type (defined as a compound row object with starting and ending datetimes ), we could define, e. g., a contracts table storing the contract identifier c_no , the available budget c_budget and the interval-valued period c_period of a contract tuple by the following DDL state- ment: In practice, queries on such tables often contain selections on the interval-valued as well as on the simple attributes at the same time, as in the following query: Present-day relational database systems still provide a very limited selection of access structures, normally just indexes for simple data types (e. g. B-trees, hash tables) and sometimes also for spatial data types (e. g. R-trees). To support combined queries as in Figure 2, one could create a B-tree index on the simple attribute and an R-tree or composite index on the interval attribute. Unfortu- nately, interval queries are not very well supported by spatial access structures or composite indexes, especially
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p385-enderle - Efficiently Processing Queries on...

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