Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

In semistructured databases there is no schema fixed in advance. To provide the benefits of a schema in such environments, we introduce DataGuides: concise and accurate structural summaries of semistructured databases. DataGuides serve as dynamic schemas, generated from the database; they are useful for browsing database structure, formulating queries, storing information such as statistics and sample values, and enabling query optimization. This paper presents the theoretical foundations of DataGuides along with an algorithm for their creation and an overview of incremental maintenance. We provide performance results based on our implementation of DataGuides in the Lore DBMS for semistructured data. We also describe the use of DataGuides in Lore, both in the user interface to enable structure browsing and query formulation, and as a means of guiding the query processor and optimizing query execution.

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DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases

With the growth of the Web, there has been a rapid increase in the number of users who need to access online databases without having a detailed knowledge of the schema or of query languages; even relatively simple query languages designed for non-experts are too complicated for them. We describe BANKS, a system which enables keyword-based search on relational databases, together with data and schema browsing. BANKS enables users to extract information in a simple manner without any knowledge of the schema or any need for writing complex queries. A user can get information by typing a few keywords, following hyperlinks, and interacting with controls on the displayed results. BANKS models tuples as nodes in a graph, connected by links induced by foreign key and other relationships. Answers to a query are modeled as rooted trees connecting tuples that match individual keywords in the query. Answers are ranked using a notion of proximity coupled with a notion of prestige of nodes based on inlinks, similar to techniques developed for Web search. We present an efficient heuristic algorithm for finding and ranking query results.

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Keyword searching and browsing in databases using BANKS

Efficient management of RDF data is an important factor in realizing the Semantic Web vision. Performance and scalability issues are becoming increasingly pressing as Semantic Web technology is applied to real-world applications. In this paper, we examine the reasons why current data management solutions for RDF data scale poorly, and explore the fundamental scalability limitations of these approaches. We review the state of the art for improving performance for RDF databases and consider a recent suggestion, "property tables." We then discuss practically and empirically why this solution has undesirable features. As an improvement, we propose an alternative solution: vertically partitioning the RDF data. We compare the performance of vertical partitioning with prior art on queries generated by a Web-based RDF browser over a large-scale (more than 50 million triples) catalog of library data. Our results show that a vertical partitioned schema achieves similar performance to the property table technique while being much simpler to design. Further, if a column-oriented DBMS (a database architected specially for the vertically partitioned case) is used instead of a row-oriented DBMS, another order of magnitude performance improvement is observed, with query times dropping from minutes to several seconds.

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Scalable semantic web data management using vertical partitioning

Despite the intense interest towards realizing the Semantic Web vision, most existing RDF data management schemes are constrained in terms of efficiency and scalability Still, the growing popularity of the RDF format arguably calls for an effort to offset these drawbacks Viewed from a relational-database perspective, these constraints are derived from the very nature of the RDF data model, which is based on a triple format Recent research has attempted to address these constraints using a vertical-partitioning approach, in which separate two-column tables are constructed for each property However, as we show, this approach suffers from similar scalability drawbacks on queries that are not bound by RDF property value In this paper, we propose an RDF storage scheme that uses the triple nature of RDF as an asset This scheme enhances the vertical partitioning idea and takes it to its logical conclusion RDF data is indexed in six possible ways, one for each possible ordering of the three RDF elements Each instance of an RDF element is associated with two vectors; each such vector gathers elements of one of the other types, along with lists of the third-type resources attached to each vector element Hence, a sextuple-indexing scheme emerges This format allows for quick and scalable general-purpose query processing; it confers significant advantages (up to five orders of magnitude) compared to previous approaches for RDF data management, at the price of a worst-case five-fold increase in index space We experimentally document the advantages of our approach on real-world and synthetic data sets with practical queries

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Hexastore: sextuple indexing for semantic web data management

Devising a scheme for efficient and scalable querying of Resource Description Framework (RDF) data has been an active area of current research. However, most approaches define new languages for querying RDF data, which has the following shortcomings: 1) They are difficult to integrate with SQL queries used in database applications, and 2) They incur inefficiency as data has to be transformed from SQL to the corresponding language data format. This paper proposes a SQL based scheme that avoids these problems. Specifically, it introduces a SQL table function RDF_MATCH to query RDF data. The results of RDF_MATCH table function can be further processed by SQL's rich querying capabilities and seamlessly combined with queries on traditional relational data. Furthermore, the RDF_MATCH table function invocation is rewritten as a SQL query, thereby avoiding run-time table function procedural overheads. It also enables optimization of rewritten query in conjunction with the rest of the query. The resulting query is executed efficiently by making use of B-tree indexes as well as specialized subject-property materialized views. This paper describes the functionality of the RDF_MATCH table function for querying RDF data, which can optionally include user-defined rulebases, and discusses its implementation in Oracle RDBMS. It also presents an experimental study characterizing the overhead eliminated by avoiding procedural code at runtime, characterizing performance under various input conditions, and demonstrating scalability using 80 million RDF triples from UniProt protein and annotation data.

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An efficient SQL-based RDF querying scheme

This inference engines are an integral part of semantic data stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle semantic data store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include (i) inferencing based on semantics of RDFS/OWL constructs and user-defined rules, (ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and (iii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle database. The paper describes the inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach.

Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in Oracle

A method and apparatus for efficiently storing and retrieving data in a database using index-only tables is disclosed. Storing a row of data in a database using index-only tables involves storing in a leaf node an index entry that includes a key value along with all other values in the row of data. If the row of data exceeds a predetermined size, then a portion of the row of data is stored in a user specified overflow area. Retrieving a row of data from an index-only table for a user-supplied key involves identifying a leaf node for the key, and reading a row of data from the index entry and any remaining portion from the overflow area when the row exceeds the predetermined size.

Index with entries that store the key of a row and all non-key values of the row

A method for managing expressions includes receiving a first query that includes a first conditional expression. The first expression is then represented as data in a column of a table. A second query is received that specifies a first set of criteria, and the second query is executed to select data based at least on whether expressions in the column satisfy the first set of criteria. In an embodiment, the second query further specifies a second set of criteria, wherein executing the second query includes selecting data based on whether data in columns other than the expression column satisfy the second criteria. A special index is defined, which can be created on the column that stores the expressions as data, to filter large sets of expressions efficiently. A method of evaluating an expression set stored as data in a table classifies each predicate from each expression, and filters the expression set based on the predicate classification.

Managing expressions in a database system

The method for processing data in a relational database wherein ontology data that specifies terms and relationships between pairs of said terms expressed in an OWL document is stored in the database, database queries that include a semantic matching operator are formed which identify the ontology data and further specify a stated relationship between two input terms, and the query is executed to invoke the semantic matching operator to determine if the two input terms are related by the stated relationship by consulting said ontology data.

Jagannathan Srinivasan

Papers

An efficient SQL-based RDF querying scheme

Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in Oracle

Index with entries that store the key of a row and all non-key values of the row

Managing expressions in a database system

System for ontology-based semantic matching in a relational database system