SchemaSQL -- A Language for Interoperability in Relational Multi-database Systems

We provide a principled extension of SQL, called SchemaSQL , that offers the capability of uniform manipulation of data and meta-data in relational multi-database systems. We develop a precise syntax and semantics of SchemaSQL in a manner that extends traditional SQL syntax and semantics, and demonstrate the following. (1) SchemaSQL retains the flavour of SQL while supporting querying of both data and meta-data. (2) It can be used to represent data in a database in a structure substantially different from original database, in which data and meta-data may be interchanged. (3) It also permits the creation of views whose schema is dynamically dependent on the contents of the input instance. (4) While aggregation in SQL is restricted to values occurring in one column at a time, SchemaSQL permits "horizontal" aggregation and even aggregation over more general "blocks" of information. (5) SchemaSQL provides a great facility for interoperability and data/meta-data manage..

Modeling Uncertainty In Deductive Databases

. Information Source Tracking (IST) method has been developed recently for the modeling and manipulation of uncertain and inaccurate data in relational databases. In this paper we extend the IST method to deductive databases. We show that positive uncertain databases, i.e. IST-based deductive databases with only positive literals in the heads and the bodies of the rules, enjoy a least model/least fixpoint semantics. Query processing in this model is studied next. We extend the top-down and bottom-up evaluation techniques of logic programming and deductive databases to our model. Finally, we study negation for uncertain databases, concentrating on stratified uncertain databases. 1 Introduction Database systems are evolving into knowledge-base systems, and are increasingly used in applications where handling inaccurate data is essential. In a recent study, uncertainty management was listed as one of the important future challenges in database research. "Further research [in un..

On A Theory of Probabilistic Deductive Databases

We propose a framework for modeling uncertainty where both belief and doubt can be given independent, first-class status. We adopt probability theory as the mathematical formalism for manipulating uncertainty. An agent can express the uncertainty in her knowledge about a piece of information in the form of a confidence level, consisting of a pair of intervals of probability, one for each of her belief and doubt. The space of confidence levels naturally leads to the notion of a trilattice, similar in spirit to Fitting's bilattices. Intuitively, the points in such a trilattice can be ordered according to truth, information, or precision. We develop a framework for probabilistic deductive databases by associating confidence levels with the facts and rules of a classical deductive database. While the trilattice structure offers a variety of choices for defining the semantics of probabilistic deductive databases, our choice of semantics is based on the truth-ordering, which we find to be closest to the classical framework for deductive databases. In addition to proposing a declarative semantics based on valuations and an equivalent semantics based on fixpoint theory, we also propose a proof procedure and prove it sound and complete. We show that while classical Datalog query programs have a polynomial time data complexity, certain query programs in the probabilistic deductive database framework do not even terminate on some input databases. We identify a large natural class of query programs of practical interest in our framework, and show that programs in this class possess polynomial time data complexity, i.e. not only do they terminate on every input database, they are guaranteed to do so in a number of steps polynomial in the input database size

On A Theory of Probabilistic Deductive Databases

We propose a framework for modeling uncertainty where both belief and doubt can be given independent, first-class status. We adopt probability theory as the mathematical formalism for manipulating uncertainty. An agent can express the uncertainty in her knowledge about a piece of information in the form of a confidence level, consisting of a pair of intervals of probability, one for each of her belief and doubt. The space of confidence levels naturally leads to the notion of a trilattice, similar in spirit to Fitting's bilattices. Intuitvely, the points in such a trilattice can be ordered according to truth, information, or precision. We develop a framework for probabilistic deductive databases by associating confidence levels with the facts and rules of a classical deductive database. While the trilattice structure offers a variety of choices for defining the semantics of probabilistic deductive databases, our choice of semantics is based on the truth-ordering, which we find to be clo..

Information Integration and the Semantic Web

Introduction Information integration and interoperability among information sources are related problems that have received significant attention since early days of computer information processing. Initially, for a few decades, the focus was on integration/interoperability for a relatively small number of sources. This is the setting encountered in traditional business and service applications, for example when two companies merge or several services interoperate (which requires the integration of their information systems). Much of the work in this context of federated or multi-databases focused on integrating schemas by defining a global schema in an expressive data model and defining mappings from local schemas to the global one [19]. More recently, in the context of integration of data sources on the internet, the so-called global-as-view (GAV) and local-as-view (LAV) paradigms have emerged out of projects such as TSIMMIS [20] and Information Manifold (IM) [12]. Recently, the a

On Efficiently Implementing SchemaSQL on a SQL Database System

SchemaSQL is a recently proposed extension to SQL for enabling multi-database interoperability. Several recently identified applications for SchemaSQL, however, mainly rely on its ability to treat data and schema labels in a uniform manner, and call for an efficient implementation of it on a single RDBMS. We first develop a logical algebra for SchemaSQL by combining classical relational algebra with four restructuring operators -- unfold, fold, split, and unite -- originally introduced in the context of the tabular data model by Gyssens et al. [GLS96], and suitably adapted to fit the needs of SchemaSQL. We give an algorithm for translating SchemaSQL queries/views involving restructuring, into the logical algebra above. We also provide physical algebraic operators which are useful for query optimization. Using the various operators as a vehicle, we give several alternate implementation strategies for SchemaSQL queries/views. All the proposed strategies can be implemented non-intrusi..