The variety of data is one of the important issues in the era of Big Data. The data are naturally organized in different formats and models, including structured data, semi-structured data, and unstructured data. Prior research has envisioned an approach to abstract multi-model data with a schema category and an instance category by using category theory. In this paper, we demonstrate a system, called MultiCategory, which processes multi-model queries based on category theory and functional programming. This demo is centered around four main scenarios to show a tangible system. First, we show how to build a schema category and an instance category by loading different models of data, including relational, XML, key-value, and graph data. Second, we show a few examples of query processing by using the functional programming language Haskell. Third, we demo the flexible outputs with different models of data for the same input query. Fourth, to better understand the category theoretical structure behind the queries, we offer a variety of graphical hooks to explore and visualize queries as graphs with respect to the schema category, as well as the query processing procedure with Haskell.

Many RDF Knowledge Graph Stores use IDs to represent triples to save storage and for ease of maintenance. Oracle is no exception. While this design is good for a small footprint on disk, it incurs overhead in query processing, as it requires joins with the value table to return results or process aggregates/filter/order-by queries. It becomes especially problematic as the result size increases or the number of projected variables increases. Depending on queries, the value table join could take up most of the query processing time. In this paper, we propose to use in-memory virtual columns to avoid value table joins. It has advantages in that it does not increase the footprint on disk, and at the same time it avoids the value table joins by utilizing in-memory virtual columns. The idea is to materialize the values only in memory and utilize the compression and vector processing that come with Oracle Database In-Memory technology. Typically, the value table in RDF is small compared to the triples table. Therefore, its footprint is manageable in memory, especially with compression in columnar format. The same mechanism can be applied to any application where there exists a one-to-one mapping between an ID and its value, such as data warehousing or data marts. The mechanism has been implemented in Oracle 18c. Experimental results using the LUBM1000 benchmark show up to two orders of magnitude query performance improvement.

Graph Databases are gaining popularity, owing to pervasiveness of graph data in social networks, physical sciences, networking, and web applications. A majority of these databases are based on the property graph model, which is characterized as key/value-based, directed, and multi-relational. In this paper, we consider the problem of supporting property graphs as RDF in Oracle Database. We introduce a property graph to RDF transformation scheme. The main challenge lies in representing the key/value properties of property graph edges in RDF. We propose three models: 1) named graph based, 2) subproperty based, and 3) (extended) reification based, all of which can be supported with RDF capabilities in Oracle Database. These models are evaluated with respect to ease of SPARQL query formulation, join complexities, skewness in generated RDF data, query performance, and storage overhead. An experimental study with a real-life Twitter social network dataset on Oracle Database 12c demonstrates the feasibility of representing property graphs as RDF and presents a quantitative performance comparison of the proposed models.

This paper describes the Semantic Indexing feature introduced in Oracle Database for indexing unstructured text (document) columns. This capability enables searching for concepts (such as people, places, organizations, and events), in addition to words or phrases, with further options for sense disambiguation and term expansion by consulting knowledge captured in OWL/RDF ontologies. The distinguishing aspects of our approach are: 1) Indexing: Instead of building a traditional inverted index of (annotated) token and/or named entity occurrences, we extract the entities, associations, and events present in a text column data and store them as RDF named graphs in the Oracle Database Semantic Store. This base content can be further augmented with knowledge bases and inferred triples (obtained by applying domain-specific ontologies and rule bases). 2) Querying: Instead of relying on proprietary extensions for specifying a search, we allow users to specify a complete SPARQL query pattern that can capture arbitrarily complex relationships between query terms. We have implemented this feature by introducing a sem_contains SQL operator and the associated sem_indextype indexing scheme. The indexing scheme employs an extensible architecture that supports indexing of unstructured text using native as well as third party text extraction tools. The paper presents a model for the semantic index and querying, describes the feature, and outlines its implementation leveraging Oracle's native support for RDF/OWL storage, inferencing, and querying. We also report a study involving use of this feature on a TREC collection of over 130,000 news articles.

The paper addresses the problem of visualizing large scale RDF data via a 3-S approach, namely, by using, (1) Subsets: to present only relevant data for visualisation; both static and dynamic subsets can be specified, (2) Summaries: to capture the essence of RDF data being viewed; summarized data can be expanded on demand thereby allowing users to create hybrid (summary-detail) fisheye views of RDF data, and (3) Sampling: to further optimize visualization of large-scale data where a representative sample suffices. The visualization scheme works with both asserted and inferred triples (generated using RDF(S) and OWL semantics). This scheme is implemented in Oracle by developing a plug-in for the Cytoscape graph visualization tool, which uses functions defined in a Oracle PL/SQL package, to provide fast and optimized access to Oracle Semantic Store containing RDF data. Interactive visualization of a synthesized RDF data set (LUBM 1 million triples), two native RDF datasets (Wikipedia 47 million triples and UniProt 700 million triples), and an OWL ontology (eClassOwl with a large class hierarchy including over 25,000 OWL classes, 5,000 properties, and 400,000 class-properties) demonstrates the effectiveness of our visualization scheme.

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 bulk- loading of large RDF graphs, describes the bulk-load scheme, discusses its implementation, and presents a performance study.

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.

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.

Keywords are typically used to qualify rows in a table. However, the fact that a keyword denotes a concept, which belongs to a specific knowledge domain, is not semantically enforced in current database systems. This paper proposes defining ontology based referential constraint for such keyword columns. A query on ontology, specified as part of the referential constraint, is used to identify the domain for the keyword column. Furthermore, since ontology may evolve causing change to the domain of the keyword column, the paper proposes use of ontology based transformation functions to either automatically evolve or to recommend refinements for the values in the keyword column. Also, queries on a keyword column can perform semantic match, that is, match a keyword to related terms based on the associated ontology. Thus, the proposed approach of semantically connecting keyword columns to ontologies 1) enhances semantic data integrity, 2) facilitates evolution of keyword columns with the referenced ontology, and 3) enables semantic match queries on keyword columns.

Any auxiliary structure, such as a bitmap or a B+-tree index, that refers to rows of a table stored as a primary B+-tree (e.g., tables with clustered index in Microsoft SQL Server, or index-organized tables in Oracle) by their physical addresses would require updates due to inherent volatility of those addresses. To address this problem, we propose a mapping mechanism that 1) introduces a single mapping table, with each row holding one key value from the primary B+-tree, as an intermediate structure between the primary B+-tree and the associated auxiliary structures, and 2) augments the primary B+-tree structure to include in each row the physical address of the corresponding mapping table row. The mapping table row addresses can then be used in the auxiliary structures to indirectly refer to the primary B+-tree rows. The two key benefits are: 1) the mapping table shields the auxiliary structures from the volatility of the primary B+-tree row addresses, and 2) the method allows reuse of existing conventional table mechanisms for supporting auxiliary structures on primary B+-trees. The mapping mechanism is used for supporting bitmap indexes on index-organized tables in Oracle9i. The analytical and experimental studies show that the method is storage efficient, and (despite the mapping table overhead) provides performance benefits that are similar to those provided by bitmap indexes implemented on conventional tables.

Most commercial database systems support B/sup +/-tree indexes using either: physical row identifiers, for example, DB2; or logical row identifiers, for example, NonStop SQL. Physical row identifiers provide fast access to data. However, unlike logical row identifiers, they need to be updated whenever the row moves. This paper describes an alternate approach where hybrid row identifiers are used. A hybrid row identifier consists of two components: a logical component, namely, the primary key of the base table row; and a physical component, namely, the database block address (DBA) of the row. By treating the DBA as a guess regarding where the row may be found, performance comparable to physical B/sup +/-tree indexes is attained for valid guess-DBAs. This scheme retains the logical index advantage of avoiding an immediate index update when the base table row moves. Instead, an online utility can be used to lazily fix the invalid guess-DBAs. This scheme has been used to implement B/sup +/-tree indexes for Oracle8i index-organized tables (primary B/sup +/-tree like structure) which encounter both row movement and table reorganization.