Recently graph has been drawing lots of attention both as a natural data model that captures fine-grained relationships between data entities and as a tool for powerful data analysis that considers such relationships. In this paper, we present a new graph database system that integrates a robust graph storage with an efficient graph analytics engine. Primarily, our system adopts two domain-specific languages (DSLs), one for describing graph analysis algorithms and the other for graph pattern matching queries. Compared to the API-based approaches in conventional graph processing systems, the DSL-based approach provides users with more flexible and intuitive ways of expressing algorithms and queries. Moreover, the DSL-based approach has significant performance benefits as well, (1) by skipping (remote) API invocation overhead and (2) by applying high-level optimization from the compiler.

Triple stores implementing the RL profile of OWL 2 are becoming increasingly popular. In contrast to unrestricted OWL 2, the RL profile is known to enjoy favourable computational properties for query answering, and state-of-the-art RL reasoners such as OWLim and Oracle's native inference engine of Oracle Spatial and Graph have proved extremely successful in industry-scale applications. The expressive restrictions imposed by OWL 2 RL may, however, be problematical for some applications. In this paper, we propose novel techniques that allow us (in many cases) to compute exact query answers using an off-the-shelf RL reasoner, even when the ontology is outside the RL profile. Furthermore, in the cases where exact query answers cannot be computed, we can still compute both lower and upper bounds on the exact answers. These bounds allow us to estimate the degree of incompleteness of the RL reasoner on the given query, and to optimise the computation of exact answers using a fully-fledged OWL 2 reasoner. A preliminary evaluation using the RDF Semantic Graph feature in Oracle Database has shown very promising results with respect to both scalability and tightness of the bounds.

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A query under an object-oriented data model exhibits characteristics which one finds in a relational query and those which clearly separate it from a relational query. The differences in turn have interesting impacts on the complexity of and strategy for query evaluation in object-oriented databases. The authors develop a model of a query under an object-oriented data model, and analyze the fundamental differences in the semantics of queries in object-oriented databases and relational databases.>

There are two major issues to address to achieve integration of an object-oriented programming system with a database system. One is the language issue: an object-oriented programming language must be augmented with semantic data modeling concepts to provide a robust set of data modeling concepts to allow modeling of entities for important real-world applications. Another is the computational-model issue: application programmers should be able to access and manipulate objects as though the objects are in an infinite virtual memory; in other words, they should not have to be aware of the existence of a database system in their computations with the data structures the programming language allows. This paper discusses these issues and presents the solutions which we have incorporated into the ORION object-oriented database system at MCC.

A DAG (direct acyclic graph) is an important data structure which requires efficient support in CAD (computer-aided design) databases. It typically arise from the design hierarchy, which describes complex designs in terms of subdesigns. A study is made of the properties of the three types of clustered sequences of nodes for hierarchies and DAGs, and algorithms are developed for generating the clustered sequences, retrieving the descendants of a given node, and inserting new nodes into existing clustered sequences of nodes which preserve their clustering properties. The performance of the clustering sequences is compared.>

A model of a complex object is presented and a set of meaningful operations, both basic and advanced, on a single complex object and on a configuration of complex objects is defined. A set of requirements is presented for storage subsystems that support complex objects. Implementation of complex objects and operations on a single complex object are described, and a detailed performance analysis is provided which establishes the merit of complex objects. Finally, storage techniques are proposed for supporting advanced operations on a configuration of complex objects.>

Many applications in the CAD/CAM and AI domains require the capability to define and manipulate a structured collection of records as a single logical entity, called a complex object. A complex object is the unit of sharing among many configurations of higher level entities. In this paper, we present a model of complex object and define a set of meaningful operations, both basic and advanced, on a single complex object and on a configuration of complex objects. We then present a set of requirements for storage subsystems that support complex objects. We also describe our implementation of complex objects and operations on a single complex object; and provide a detailed performance analysis which establishes the merit of complex objects. Finally, we propose storage techniques for supporting advanced operations on a configuration of complex objects.

In this paper, we elaborate on three major enhancements to the conventional object-oriented data model, namely, schema evolution, composite objects, and versions. Schema evolution is the ability to dynamically make changes to the class definitions and the structure of the class lattice. Composite objects are recursive collections of exclusive components that are treated as units of storage, retrieval, and integrity enforcement. Versions are variations of the same object that are related by the history of their derivation. These additional features are strongly motivated by data management requirements of object-oriented applications from the AI, CAD/CAM, and OIS (office information systems with multimedia documents) domains. An object-oriented data model, with these enhancements, has been incorporated into ORION, a prototype database system developed at MCC as a vehicle of research into object-oriented databases.

Many applications in such domains as computer-aided design require the capability to define, store and retrieve as a single unit a collection of related objects known as a composite object. A composite object explicitly captures and enforces the IS-PART-OF integrity constraint between child and parent pairs of objects in a hierarchical collection of objects. Further, it can be used as a unit of storage and retrieval to enhance the performance of a database system.

Object-oriented programming is well-suited to such data-intensive application domains as CAD/CAM, AI, and OIS (office information systems) with multimedia documents. At MCC we have built a prototype object-oriented database system, called ORION. It adds persistence and sharability to objects created and manipulated in applications implemented in an object-oriented programming environment. One of the important requirements of these applications is schema evolution, that is, the ability to dynamically make a wide variety of changes to the database schema. In this paper, following a brief review of the object-oriented data model that we support in ORION, we establish a framework for supporting schema evolution, define the semantics of schema evolution, and discuss its implementation.

Presented in this paper is the data model for ORION, a prototype database system that adds persistence and sharability to objects created and manipulated in object-oriented applications. The ORION data model consolidates and modifies a number of major concepts found in many object-oriented systems, such as objects, classes, class lattice, methods, and inheritance. These concepts are reviewed and three major enhancements to the conventional object-oriented data model, namely, schema evolution, composite objects, and versions, are elaborated upon. Schema evolution is the ability to dynamically make changes to the class definitions and the structure of the class lattice. Composite objects are recursive collections of exclusive components that are treated as units of storage, retrieval, and integrity enforcement. Versions are variations of the same object that are related by the history of their derivation. These enhancements are strongly motivated by the data management requirements of the ORION applications from the domains of artificial intelligence, computer-aided design and manufacturing, and office information systems with multimedia documents.

VLSI layout systems perform various operations on design data describing the layouts of geometries (rectangles) on different mask layers. In this paper, we identify a basic set of such operations and show a formulation of these operations in terms of expressions of predicates that are suitable for processing by a database system. We then describe the operations and properties of a 4-D (four-dimensional) tree structure for efficiently supporting these operations. We also provide a qualitative analysis of the time complexity of the data structure, which compares favorably against various existing proposals.