No abstract

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.

The full integration of information retrieval

Partitioning is typically employed on large-scale data to improve manageability, availability, and performance. However, for tables connected by a referential constraint (capturing a parent-child relationship), the current approaches require individually partitioning each table thereby burdening the user with the task of maintaining the tables equi-partitioned, which not only is cumbersome but also error prone. This paper proposes a new partitioning method (partition by reference) that allows tables with a parent-child relationship to be logically equi-partitioned by inheriting the partition key from the parent table without duplicating the key columns. The partitioning key is resolved through an existing parent-child relationship, enforced by an active referential constraint. This logical dependency is used to automatically i) cascade partition maintenance operations performed on parent table to child tables, and ii) handle migration of child rows when partition key or parent key in parent table is updated, as a single atomic operation. This method has been introduced in Oracle Database 11gR1 with support for tables with both single level and composite partitioning methods. The paper describes the key concepts of table partitioning by reference method, discusses the design and implementation challenges, and presents an experimental study covering a usage scenario common in Information Life Cycle Management (ILM) applications.

No abstract

The concept of time-constrained SQL queries was introduced to address the problem of long-running SQL queries. A key approach adopted for supporting time-constrained SQL queries is to use sampling to reduce the amount of data that needs to be processed, thereby allowing completion of the query in the specified time constraint. However, sampling does make the query results approximate and hence requires the system to estimate the values of the expressions (especially aggregates) occurring in the select list. Thus, coming up with estimates for aggregates is crucial for time-constrained approximate SQL queries to be useful, which is the focus of this paper. Specifically, we address the problem of estimating commonly occurring aggregates (namely, SUM, COUNT, AVG, MEDIAN, MIN, and MAX) in timeconstrained approximate queries. We give both point and interval estimates for SUM, COUNT, AVG, and MEDIAN using Bernoulli sampling for various type of queries, including join processing with cross product sampling. For MIN (MAX), we give the confidence level that the proportion 100γ% of the population will exceed the MIN (or be less than the MAX) obtained from the sampled data.

The growth of RDF data makes it imperative that an efficient mechanism for bulk-loading RDF graphs be supported. Thus, the paper proposes a bulk-load scheme that allows fast loading of arbitrarily large RDF graphs into a database. Specifically, three modes of load are supported: i) loading into an empty RDF graph, ii) appending to a non-empty RDF graph, and iii) concurrent loads into multiple graphs. The bulk-load scheme is implemented as part of Oracle Database Semantic Technologies and the performance experiments conducted with a variety of RDF graphs (from UniProt and synthesized data of Lehigh University Benchmark) demonstrate the scalability of the approach. The paper outlines the challenges involved in bulkloading of large RDF graphs, describes the bulk-load scheme, discusses its implementation, and presents a performance study.

OdeVIew IS the graphlcal front end for Ode, an obJect-onented database system and envtronment Ode's data model supports data encapsulation, type mhentance, and complex obJects OdeVIew provides faclhtles for exammmg the database schema (I e , the obJect type or class hierarchy), exammmg class definmons, browsmg obJects, followmg chains of references startmg from an obJect, synchromzed browsmg, dlsplaymg selected portions of obJects (proJection), and retrieving obJects with spectfic characterlstlcs (selectton)OdeVIew does not need to know about the Internals of Ode obJects Consequently, the Internals of specific classes are not hardwlred mto OdeVIew and new classes can be added to the Ode database wlthout requumg any changes to or recompllatton of OdeView Just as OdeView does not know about the obJect Internals, class functions (methods) for dtsplaymg obJects are written without knowing about the specifics of the wmdowmg software used by OdeView or the graphical user interface provided by ItIn this paper, we present OdeView, and discuss Its design and lmplementatton