Conceptual classes and system classes in object databases

Integration of object-oriented programming languages and database systems in KOPERNIK

Conventional data models are not satisfactory for modelling data base application systems. The features that they provide are too low level and representational to allow the semantics of a data base to be directly expressed in the schema. The semantic data model (SDM) has been designed as a natural application modelling mechanism that can capture and express the structure of an application environment. The features of the SDM correspond to the principal intensional structures naturally occurring in contemporary data base applications. Furthermore, facilities for expressing derived (redundant) information are an essential part of the SDM; derived information is as prominent in an SDM schema as is primitive data. The SDM is designed to enhance the effectiveness and usability of computerized data bases. It can serve as a formal specification and documentation mechanism for a data base, can support a variety of powerful user interface facilities, and can be used as a tool in the data base design process.

This paper attempts to design a user-friendly interface which enhances usability of relational database systems. Our approach to usability is to design a semantic data model suitable for a user interface and provide an easy-to-use query language DOMCALC (domain calculus language) which is based on the semantic data model. A query in DOMCALC can be described in terms of intuitive notions, namely domains, instead of relations and attributes. That is, DOMCALC requires for users no knowledge of a conceptual database such as relation names and attribute names. Therefore, the usability based on the semantic data model removes the requirement of knowledge of a conceptual database from users and protects user views from changes in the conceptual database. We also describe an outline of a database system PRIM (Prototype of Laboratory information Management System) which has been developed with the above considerations. This is a step toward the enhancement of usability in relational database systems.

This paper compares two approaches to meeting the data handling requirements of Intermedia, a hypermedia system developed at the Institute for Research in Information and Scholarship at Brown University. Intermedia, though written using an object-oriented programming language, relies on a traditional relational database management system for data storage and retrieval. We examine the ramifications of replacing the relational database with an object-oriented database. We begin by describing the important characteristics each database system. We then describe Intermedia and give an overview of its architecture and its data handling requirements. We explain why and how we used a relational database management system and the problems that we encountered with this system. We then present the design of an object-oriented database schema for Intermedia and compare the relational and object-oriented database management system approaches.

We investigate methods to improve the performance of algorithms for automatic storage reclamation of object databases. These algorithms are based on a technique called partitioned garbage collection, in which a subset of the entire database is collected independently of the rest. We evaluate how different application, database system, and garbage collection implementation parameters affect the performance of garbage collection in object database systems. We focus specifically on investigating the policy that is used to select which partition in the database should be collected. Three of the policies that we investigate are based on the intuition that the values of overwritten pointers provide good hints about where to find garbage. A fourth policy investigated chooses the partition with the greatest presence in the I/O buffer. Using simulations based on a synthetic database, we show that one of our policies requires less I/O to collect more garbage than any existing implementable policy. Furthermore, that policy performs close to a locally optimal policy over a wide range of simulation parameters, including database size, collection rate, and database connectivity. We also show what impact these simulation parameters have on application performance and investigate the expected costs and benefits of garbage collection in such systems.

The Inference Engine is an integral part of Semantic Data Stores. In this paper, we describe our experience of implementing a scalable inference engine for HBase 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, and ii) 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 the HBase 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 LUBM semantic datasets demonstrates the effectiveness of our approach.

This research presents a method for constructing a semantic data model for the field of material science based on ontology. The method involves designing the semantic data model and establishing mapping rules between the data model and the ontology. Data semantic analysis and annotation are carried out based on the semantic data model, supporting the integration and efficient retrieval of heterogeneous data in the material science field. Furthermore, the method utilizes the OWL-S technique to describe, issue, and acquire relevant data services, enabling higher-level service semantic synergy in the material science domain. The proposed method effectively enables semantic integration, intelligent inquiry, and personalized services for data in the material science field.

Object-oriented databases (OODBs) have been used for many advanced applications requiring advanced modeling power. In this paper, a locking-based concurrency control scheme is presented for OODBs. The proposed scheme reduces locking overhead for class hierarchy which is an important property in OODBs. While existing schemes show good performance only for specific applications, the proposed scheme can incur less locking overhead for any applications. For the performance evaluation of the proposed scheme, simulation is conducted using 007 benchmark. Through the simulation, the proposed scheme is compared with the two existing techniques. The performance studies show that the proposed scheme is superior to existing works.

A novel approach is described for building intelligent information systems (or knowledge-base management systems). The approach utilizes the knowledge data language, which is a schema specification language developed for the knowledge/data model. The model, referred to as a hypersemantic data model, captures both knowledge semantics, as specified in knowledge-based systems, and data semantics, as represented by semantic data models. Hypersemantic data models facilitate the incorporation of knowledge in the form of heuristics, uncertainty, constraints and other artificial intelligence concepts, together with object-oriented concepts found in semantic data models. The unified knowledge/data modeling features and constructs of the language are used to develop a prototype knowledge base management system, the KDL-advisor. >

This article treats the problem of vagueness in databases from a general point of view. Several kinds of attribute imprecise values are considered, including the case where such values are fuzzy set of objects. The possibility of managing uncertain data is also taken into account and both sources of the lack of information are studied jointly. All these vague elements are represented in a unified manner by using a semantic data model. The article shows how this representation is possible and opens the way for implementing this kind of information by using a classic object-oriented database system. © 1996 John Wiley & Sons, Inc.

There have been several efforts to use relational model and database to store and manipulate Resource Description Framework (RDF). They have one general disadvantage, i.e. one is forced to map the model of semantics of RDF into relational model, which will end up in constraints and additional properties, such as, validating each assertion against the RDF schema which also stored as a triplets table. In this paper, we introduce Semantic Data Model as a proposed data model language to store and manipulate Resource Description Framework. This study also tries to prescribe the procedure on transforming a semantic data model into a RDF data model. Keyworsd: Semantic Data Model, Resource Description Framework.

Impedance mismatch of data flow is the major problem in the relational database when one is using the web based application development on the computer system. It effects the development of scalable and reusable web application. Due to this, the research cost of development and maintenance increases. Therefore the present work is an attempt towards an object-oriented database system by using Db4o to overcome the impedance mismatch problem. In the current work, the development of web application for Indian Postal Services by using Db4o is demonstrated on dot NET platform. Before developing the application, the authors used a Unified Modeling Language (UML) model in the form of UML class, sequence and use-case diagrams. The Db4o is used to store the object-oriented database and the performance of Db4o is observed through several object-oriented queries and corresponding results are demonstrated.

Cyclic query processing issues in object-oriented databases are investigated. A data and cyclic query model is defined for an object-oriented database system, using a graph model. Then the efficient processing of a general object-oriented cyclic query is discussed. For efficient processing, a general cyclic query and the access plans generated for a given query are defined and a cost model is developed to determine the cost for each access plant generated. The retrieval algorithms used for actual data retrieval are also investigated. >

Mobility is the cause for new requirements on database systems. Database systems used in classical LAN- or host-based environments provide these new features deficiently. New concepts are needed in mobile environments. The conventional client-server architecture has to extended in a client-agent-server architecture. We describe new approaches in database technology to meet the new requirements like adaptive database system components and intensified use of query statistics. This includes the management of queries and query results. The access to the information will be done via an OQL-like interface. The OQL query will be transformed into queries of the underlying relational or object-oriented database systems used by the information servers.

In this paper, a Graph-based semantic Data Model (GDM) is proposed with the primary objective of bridging the gap between the human perception of an enterprise and the needs of computing infrastructure to organize information in some particular manner for efficient storage and retrieval. The Graph Data Model (GDM) has been proposed as an alternative data model to combine the advantages of the relational model with the positive features of semantic data models. The proposed GDM offers a structural representation for interacting to the designer, making it always easy to comprehend the complex relations amongst basic data items. GDM allows an entire database to be viewed as a Graph (V, E) in a layered organization. Here, a graph is created in a bottom up fashion where V represents the basic instances of data or a functionally abstracted module, called primary semantic group (PSG) and secondary semantic group (SSG). An edge in the model implies the relationship among the secondary semantic groups. The contents of the lowest layer are the semantically grouped data values in the form of primary semantic groups. The SSGs are nothing but the higher-level abstraction and are created by the method of encapsulation of various PSGs, SSGs and basic data elements. This encapsulation methodology to provide a higher-level abstraction continues generating various secondary semantic groups until the designer thinks that it is sufficient to declare the actual problem domain. GDM, thus, uses standard abstractions available in a semantic data model with a structural representation in terms of a graph. The operations on the data model are formalized in the proposed graph algebra. A Graph Query Language (GQL) is also developed, maintaining similarity with the widely accepted user-friendly SQL. Finally, the paper also presents the methodology to make this GDM compatible with the distributed environment, and a corresponding query processing technique for distributed environment is also suggested for the sake of completeness.