Database schema

Abstract: The present invention addresses the problem of implementing X.500 using an SQL product. The present application discloses an application of X.500 to a relational database, a database design and use of the database to perform X.500 services. Particularly, the disclosure relates to implementation using an RDBMS (Relational DataBase Management System). One invention disclosed resides around service modelling, the processing of arbitrary data using a fixed set of queries/services. Another invention resides in the implementation of a disk based model using relational queries to satisfy X.500 services and enables benefits of RDBMS to be exploited. Further, the invention provides an SQL based X.500 application that can perform at subsecond speed and is relatively unaffected by the size of database, DIT shape, type of data or complexity of service, including aliases.

Abstract: In order to integrate a wide variety of databases or many diverse sources of information, we need the ability to learn the similarities directly from instances of the data, which may be embodied within a database model, a conceptual schema, application programs, or data contents. The process of determining semantically equivalent data items can not be "pre-programmed" since the information to be accessed is heterogeneous. Intelligent information integration involves extracting semantics, expressing them as metadata, and matching semantically equivalent data elements. Semint (SEMantic INTegrator) is a system prototype for semantic integration being developed at Northwestern University. It provides a graphical user interface and supports access to a variety of database systems and utilizes both schema information and data contents to determine attribute equivalence. In Semint, the knowledge of how to match equivalent data elements is "discovered", not "pre-programmed".

Abstract: A key aspect of interoperation among data-intensive systems involves the mediation of metadata and ontologies across database boundaries. One way to achieve such mediation between a local database and a remote database is to fold remote metadata into the local metadata, thereby creating a common platform through which information sharing and exchange becomes possible. Schema implantation and semantic evolution, our approach to the metadata folding problem, is a partial database integration scheme in which remote and local (meta)data are integrated in a stepwise manner over time. We introduce metadata implantation and stepwise evolution techniques to interrelate database elements in different databases, and to resolve conflicts on the structure and semantics of database elements (classes, attributes, and individual instances). We employ a semantically rich canonical data model, and an incremental integration and semantic heterogeneity resolution scheme. In our approach, relationships between local and remote information units are determined whenever enough knowledge about their semantics is acquired. The metadata folding problem is solved by implanting remote database elements into the local database, a process that imports remote database elements into the local database environment, hypothesizes the relevance of local and remote classes, and customizes the organization of remote metadata. We have implemented a prototype system and demonstrated its use in an experimental neuroscience environment.

Abstract: A method for allowing chronologically overlapping database transactions in a multi-threaded environment without the need for explicit thread synchronization for database access. Literal database connections are managed on a per thread basis, thus allowing different chronologically overlapping transactions in different threads. Four basic objects are used to accomplish the chronologically overlapping transactions in a multi-threaded environment. The environment object is a static object that creates and maintains a pointer to the database application environment handle in addition to performing basic error recovery and initialization functionality. The database object encapsulates a logical database connection. The database connection object contains the functions that can be performed on a database connection, such as transaction management and query creation. The database statement object encapsulates a query that can be made to the database. This database statement object allows parameters to be attached to the query, and result objects to be returned.