Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate it: In order to manipulate large sets of complex objects as efficiently as today’s database systems manipulate simple records, query processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and post-relational database systems, including iterative execution of complex query evaluation plans, the duality of sort- and hash-based set matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.

We address the problem of providing integrated access to diverse and dynamic information sources. We explain how this problem differs from the traditional database integration problem and we focus on one aspect of the information integration problem, namely information exchange. We define an object-based information exchange model and a corresponding query language that we believe are well suited for integration of diverse information sources. We describe how, the model and language have been used to integrate heterogeneous bibliographic information sources. We also describe two general-purpose libraries we have implemented for object exchange between clients and servers.

The goal of the Tsimmis Project is to develop tools that facilitate the rapid integration of heterogeneous information sources that may include both structured and unstructured data. This paper gives an overview of the project, describing components that extract properties from unstructured objects, that translate information into a common object model, that combine information from several sources, that allow browsing of information, and that manage constraints across heterogeneous sites. Tsimmis is a joint project between Stanford and the IBM Almaden Research Center.

Advanced applications require construction, efficient access and management of large databases with rich data structures and inference mechanisms. However, such capabilities are not directly supported by the existing database systems. In this paper, we describe Relationlog, a persistent deductive database system that is able to directly support the storage, efficient access and inference of data with complex structures.

Knowledge discovery in databases, or data mining, is an important issue in the development of data- and knowledge-base systems. An attribute-oriented induction method has been developed for knowledge discovery in databases. The method integrates a machine learning paradigm, especially learning-from-examples techniques, with set-oriented database operations and extracts generalized data from actual data in databases. An attribute-oriented concept tree ascension technique is applied in generalization, which substantially reduces the computational complexity of database learning processes. Different kinds of knowledge rules, including characteristic rules, discrimination rules, quantitative rules, and data evolution regularities can be discovered efficiently using the attribute-oriented approach. In addition to learning in relational databases, the approach can be applied to knowledge discovery in nested relational and deductive databases. Learning can also be performed with databases containing noisy data and exceptional cases using database statistics. Furthermore, the rules discovered can be used to query database knowledge, answer cooperative queries and facilitate semantic query optimization. Based upon these principles, a prototyped database learning system, DBLEARN, has been constructed for experimentation.

yosikawaQkyoto-su.ac.jp Abstract: This paper introduces ILOG ( a declarative language in the style of (stratified) datalog ( which can be used for querying, schema translation, and schema augmentation in the context of object-based data models. The semantics of ILOG is based on the use of Skolem functors, and is closely related to semantics for object-based data manipulation languages which provide mechanisms for explicit creation of object identifiers (OIDs). A normal form is presented for ILOG ’ programs not involving recursion through OID creation, which identifies a precise correspondence between OIDs created in the target, and values and OIDs in the source. The expressive power of various sublanguages of ILOG ’ is shown to range from a natural generalization of the conjunctive queries to the object-based context, to a language which can specify all computable database translat.ions (up to duplicate copies). The issue of testing vuliilityof ILOG programs translat.ing one semantic schema to another is studied: cases are presented for which several-validity issues (e.g., functional and/or subset relationships in the

The goal of the Tsimmis Project is to develop tools that facilitate the rapid integration of heterogeneous information sources that may include both structured and unstructured data. This paper gives an overview of the project, describing components that extract properties from unstructured objects, that translate information into a common object model, that combine information from several sources, and that allow browsing of information.

XML has become the lingua franca for data exchange and integration across administrative and enterprise boundaries. Nearly all data providers are adding XML import or export capabilities, and standard XML Schemas and DTDs are being promoted for all types of data sharing. The ubiquity of XML has removed one of the major obstacles to integrating data from widely disparate sources -- namely, the heterogeneity of data formats. However, general-purpose integration of data across the wide area also requires a query processor that can query data sources on demand, receive streamed XML data from them, and combine and restructure the data into new XML output --- while providing good performance for both batch-oriented and ad-hoc, interactive queries. This is the goal of the Tukwila data integration system, the first system that focuses on network-bound, dynamic XML data sources. In contrast to previous approaches, which must read, parse, and often store entire XML objects before querying them, Tukwila can return query results even as the data is streaming into the system. Tukwila is built with a new system architecture that extends adaptive query processing and relational-engine techniques into the XML realm, as facilitated by a pair of operators that incrementally evaluate a query's input path expressions as data is read. In this paper, we describe the Tukwila architecture and its novel aspects, and we experimentally demonstrate that Tukwila provides better overall query performance and faster initial answers than existing systems, and has excellent scalability.

The set-height of a complex object type is defined to be its level of nesting of the set construct. In a query of the complex object calculus which maps a database D to an output type T,anintermediate type is a type which is used by some variable of the query, but which is not present in D or T.Foreachk, i ≥ 0 we define CALCk,i to be the family of calculus queries mapping from and to types with set-height ≤ k and using intermediate types with set-height ≤ i. In particular, CALC0,0 is the classical relational calculus, and CALC0,1 is equivalent to the family of secondorder (relational) queries. Several results concerning these families of languages are obtained. A primary focus is on the families CALC0,i, which map relations to relations. Upper and lower bounds in terms of hyper-exponential time and space on the complexity of these families are provided. The CALC0,i hierarchy does not collapse with respect to expressive power. The union ∪0≤iCALC0,i is exactly the family of elementary queries, i.e., queries with hyper-exponential complexity. The expressive power of queries from the complex object calculus interpreted using semantics based on the use of arbitrarily large finite or infinite set of invented values is studied. Under these semantics, the expressive power of the relational calculus is not increased, and the CALC0,i hierarchy collapses at CALC0,1. In general, queries with these semantics may not be computable. We also consider an alternative semantics which yields a family of queries equivalent to the computable queries. 1

) Mengchi Liu Department of Computer Science University of Regina Regina, Saskatchewan, Canada S4S 0A2 mliu@cs.uregina.ca Abstract This paper presents a novel logic-based language for nested relations. It stands in the same relationship to the nested relation model as Datalog stands to the relational model. The main novelties of the language are the mechanisms for representing both partial and complete information on sets and tuples, and the introduction of a new ordering on interpretations that captures the intended semantics for nested sets, tuples and relations. Under appropriate stratification restrictions, it is shown that the unique minimal and supported model, if it exists, can be computed bottom-up, and therefore used as the intended semantics of the program. 1 Introduction In the past decade, there has been a lot of interest in nested relations, whose tuple components may be sets, tuples or even relations [2, 8, 11, 12, 14, 15]. The extended relational algebra and cal...