Extensible indexing: a framework for integrating domain-specific indexing schemes into Oracle8i

Srinivasan, Jagannathan; Murthy, Ravi; Sundara, Seema; Agarwal, Nipun; DeFazio, S.

doi:10.1109/icde.2000.839396

Cited by 22 publications

(15 citation statements)

References 4 publications

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“…Research in extensible relational database systems started more than two decades ago [Batory and Mannino 1986;Carey and Haas 1990] spanning academic system prototypes (e.g., EXODUS [Carey and DeWitt 1987;Carey et al 1991], Postgres [Stonebraker et al 1987;Stonebraker and Rowe 1986], GENESIS [Batory et al 1988]), and commercial products (e.g., IBM Starburst [Lohman et al 1991], Sybase [Olson et al 1998], Oracle [Srinivasan et al 2000]). Based on the extensible database components, previous work can be categorized into: (1) Extensibility in abstract data types [Linnemann et al 1988;Ong et al 1984;Osborn and Heaven 1986;Stonebraker 1986] where users can define new data types by specifying name, space allocation, and a set of functions to operate on the new data types, (2) Extensibility in query processing and optimization [Batory 1986;Graefe 1994;Graefe and DeWitt 1987;Haas et al 1989;Kabra and DeWitt 1999;Pirahesh et al 1992;Waas and Hellerstein 2009] where the idea is to use an extensible rule-based query optimizer to add user-defined rules, and (3) Extensibility in access methods [Hellerstein et al 1995;Lynch and Stonebraker 1988;Srinivasan et al 2000] where the idea is to generalize the execution of several index structures within one core implementation but allow extensible behavior based on the indexed data type, e.g., data-specific node splitting and merging strategies.…”

Section: Extensible Database Systemsmentioning

confidence: 99%

“…Based on the extensible database components, previous work can be categorized into: (1) Extensibility in abstract data types [Linnemann et al 1988;Ong et al 1984;Osborn and Heaven 1986;Stonebraker 1986] where users can define new data types by specifying name, space allocation, and a set of functions to operate on the new data types, (2) Extensibility in query processing and optimization [Batory 1986;Graefe 1994;Graefe and DeWitt 1987;Haas et al 1989;Kabra and DeWitt 1999;Pirahesh et al 1992;Waas and Hellerstein 2009] where the idea is to use an extensible rule-based query optimizer to add user-defined rules, and (3) Extensibility in access methods [Hellerstein et al 1995;Lynch and Stonebraker 1988;Srinivasan et al 2000] where the idea is to generalize the execution of several index structures within one core implementation but allow extensible behavior based on the indexed data type, e.g., data-specific node splitting and merging strategies. Compared to this previous work, our end goal in creating FlexPref is different in two main respects.…”

Section: Extensible Database Systemsmentioning

confidence: 99%

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Flexible and extensible preference evaluation in database systems

Levandoski

Eldawy

Mokbel

et al. 2013

TODS

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Personalized database systems give users answers tailored to their personal preferences. While numerous preference evaluation methods for databases have been proposed (e.g., skyline, top-k, k-dominance, kfrequency), the implementation of these methods at the core of a database system is a double-edged sword. Core implementation provides efficient query processing for arbitrary database queries, however this approach is not practical since each existing (and future) preference method requires implementation within the database engine. To solve this problem, this paper introduces FlexPref, a framework for extensible preference evaluation in database systems. FlexPref, implemented in the query processor, aims to support a wide-array of preference evaluation methods in a single extensible code base. Integration with FlexPref is simple, involving the registration of only three functions that capture the essence of the preference method. Once integrated, the preference method "lives" at the core of the database, enabling the efficient execution of preference queries involving common database operations. This paper also provides a query optimization framework for FlexPref, as well as a theoretical framework that defines the properties a preference method must exhibit to be implemented in FlexPref. To demonstrate the extensibility of FlexPref, this paper also provide case studies detailing the implementation of seven state-of-the-art preference evaluation methods within FlexPref. We also experimentally study the strengths and weaknesses of an implementation of FlexPref in PostgreSQL over a range of single -table and multi-table preference queries.

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Section: Extensible Database Systemsmentioning

confidence: 99%

Section: Extensible Database Systemsmentioning

confidence: 99%

Flexible and extensible preference evaluation in database systems

Levandoski

Eldawy

Mokbel

et al. 2013

TODS

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show abstract

“…Oracle's Extensibility Framework [5] has been used to implement the operators and the new indexing scheme. Specifically, ONT_RELATED, ONT_DISTANCE, and ONT_PATH operators are implemented as user-defined operators.…”

Section: Permission To Copy Without Fee All or Part Of This Materials mentioning

confidence: 99%

“…To speed up queries involving ONT_RELATED, a new indexing scheme ONT_INDEXTYPE is implemented using Oracle's Extensible Indexing Framework [5]. Users only need to create an index on the column holding ontology terms using ONT_INDEXTYPE as follows:…”

Section: Speeding Up Ont_related and Ont_expand Operationsmentioning

confidence: 99%

Supporting Ontology-based Semantic Matching in RDBMS

Das

Chong

Eadon

et al. 2004

Proceedings 2004 VLDB Conference

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Ontologies are increasingly being used to build applications that utilize domain-specific knowledge. This paper addresses the problem of supporting ontology-based semantic matching in RDBMS. Specifically, 1) A set of SQL operators, namely ONT_RELATED, ONT_EXPAND, ONT_DISTANCE, and ONT_PATH, are introduced to perform ontology-based semantic matching, 2) A new indexing scheme ONT_INDEXTYPE is introduced to speed up ontology-based semantic matching operations, and 3) System-defined tables are provided for storing ontologies specified in OWL. Our approach enables users to reference ontology data directly from SQL using the semantic match operators, thereby opening up possibilities of combining with other operations such as joins as well as making the ontology-driven applications easy to develop and efficient. In contrast, other approaches use RDBMS only for storage of ontologies and querying of ontology data is typically done via APIs. This paper presents the ontology-related functionality including inferencing, discusses how it is implemented on top of Oracle RDBMS, and illustrates the usage with several database applications.

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“…Database vendors have realized this need and have initiated efforts to support several non-traditional indexes, e.g., (Oracle [37], and IBM DB2 [1]). …”

Section: Introductionmentioning

confidence: 99%