We witness a rapid increase in the number of structured information sources that are available online, especially on the WWW. These sources include commercial databases on product information, stock market information, real estate, automobiles, and entertainment. We would like to use the data stored in these databases to answer complex queries that go beyond keyword searches. We face the following challenges: (1) Several information sources store interrelated data, and any query-answering system must understand the relationships between their contents. (2) Many sources are not full-featured database systems and can answer only a small set of queries over their data (for example, forms on the WWW restrict the set of queries one can ask). (3) Since the number of sources is very large, effective techniques are needed to prune the set of information sources accessed to answer a query. (4) The details of interacting with each source vary greatly. We describe the Information Manifold, an imp...

Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

The amount of data of all kinds available electronically has increased dramatically in recent years. The data resides in different forms, ranging from unstructured data in file systems to highly structured in relational database systems. Data is accessible through a variety of interfaces including Web browsers, database query languages, application-specific interfaces, or data exchange formats. Some of this data is raw data, e.g. images or sound. Some of it has structure even if the structure is often implicit, and not as rigid or regular as that found in standard database systems. Sometimes the structure exists but has to be extracted from the data. Sometimes also it exists but we prefer to ignore it for certain purposes such as browsing. We call here semi-structured data this data that is (from a particular viewpoint) neither raw data nor strictly typed, i.e., not table-oriented as in a relational model or sorted-graph as in object databases...

Abstract. A number of ideas concerning information-integration tools can be thought of as constructing answers to queries using views that represent the capabilities of information sources. We review the formal basis of these techniques, which are closely related to containment algo-rithms for conjunctive queries and/or Datalog programs. Then we com-pare the approaches taken by AT&T Labs ' "Information Manifold " and the Stanford "Tsimmis " project in these terms. 1 Theoretical Background Before addressing information-integration issues, let us review some of the basic ideas concerning conjunctive queries, Datalog programs, and their containment. To begin, we use the logical rule notation from [Ull88]. Example 1. The following: p(X,Z):- a(X,Y) & a(Y,Z). is a rule that talks about a, an EDB predicate ("Extensional DataBase, " or stored relation), and p, an IDB predicate ("Intensional DataBase, " or predicate whose relation is constructed by rules). In this and several other examples, it is useful to think of a as an "arc " predicate defining a graph, while other predicates define certain structures that might exist in the graph. That is, a(X, Y) means there is an arc from node X to node Y. In this case, the rule says "p(X, Z) is true if there is an arc from node X to node Y and also an arc from Y to Z." That is, p represents paths of length 2. In general, there is one atom, the head, on the left of the "if " sign,:- and zero of more atoms, called subgoals, on the right side (the body). The head always has an IDB predicate; the subgoals can have IDB or EDB predicates. Thus, here p(X, Z) is the head, while a(X, Y) and a(Y, Z) are subgoals. We assume that each variable appearing in the head also appears somewhere in the body. This "safety " requirement assures that when we use a rule, we are not left with undefined variables in the head when we try to infer a fact about the head's predicate. We also assume that atoms consist of a predicate and zero or more arguments. An argument can be either a variable or a constant. However, we exclude function symbols from arguments.

The problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a wide variety of data management problems. In query optimization, finding a rewriting of a query using a set of materialized views can yield a more efficient query execution plan. To support the separation of the logical and physical views of data, a storage schema can be described using views over the logical schema. As a result, finding a query execution plan that accesses the storage amounts to solving the problem of answering queries using views. Finally, the problem arises in data integration systems, where data sources can be described as precomputed views over a mediated schema. This article surveys the state of the art on the problem of answering queries using views, and synthesizes the disparate works into a coherent framework. We describe the different applications of the problem, the algorithms proposed to solve it and the relevant theoretical results.

In recent years there has been an increased interest in managing data which does not conform to traditional data models, like the relational or object oriented model. The reasons for this non-conformance are diverse. One one hand, data may not conform to such models at the physical level: it may be stored in data exchange formats, fetched from the Internet, or stored as structured les. One the other hand, it may not conform at the logical level: data may have missing attributes, some attributes may be of di erent types in di erent data items, there may be heterogeneous collections, or the data may be simply specified by a schema which is too complex or changes too often to be described easily as a traditional schema. The term semistructured data has been used to refer to such data. The data model proposed for this kind of data consists of an edge-labeled graph, in which nodes correspond to objects and edges to attributes or values. Figure 1 illustrates a semistructured database providing information about a city. Relational databases are traditionally queried with associative queries, retrieving tuples based on the value of some attributes. To answer such queries efciently, database management systems support indexes for translating attribute values into tuple ids (e.g. B-trees or hash tables). In object-oriented databases, path queries replace the simpler associative queries. Several data structures have been proposed for answering path queries e ciently: e.g., access support relations 14] and path indexes 4]. In the case of semistructured data, queries are even more complex, because they may contain generalized path expressions 1, 7, 8, 16]. The additional exibility is needed in order to traverse data whose structure is irregular, or partially unknown to the user.

In this paper we motivate and describe materialized views, their applications, and the problems and techniques for their maintenance. We present a taxonomy of view maintenanceproblems basedupon the class of views considered, upon the resources used to maintain the view, upon the types of modi#cations to the base data that areconsidered during maintenance, and whether the technique works for all instances of databases and modi#cations. We describe some of the view maintenancetechniques proposed in the literature in terms of our taxonomy. Finally, we consider new and promising application domains that are likely to drive work in materialized views and view maintenance. 1 Introduction What is a view? A view is a derived relation de#ned in terms of base #stored# relations. A view thus de#nes a function from a set of base tables to a derived table; this function is typically recomputed every time the view is referenced. What is a materialized view? A view can be materialized by storin...

We study the complexity of the problem of answering queries using materialized views. This problem has attracted a lot of attention recently because of its relevance in data integration. Previous work considered only conjunctive view definitions. We examine the consequences of allowing more expressive view definition languages. The languageswe consider for view definitions and user queries are: conjunctive queries with inequality, positive queries, datalog, and first-order logic. We show that the complexity of the problem depends on whether views are assumed to store all the tuples that satisfy the view definition, or only a subset of it. Finally, we apply the results to the view consistency and view self-maintainability problems which arise in data warehousing. 1 Introduction The notion of materialized view is essential in databases [34] and is attracting more and more attention with the popularity of data warehouses [28]. The problem of answering queries using materialized views [24...

The standard approach to integrating heterogeneous information sources is to build a global schema that relates all of the information in the different sources, and to pose queries directly against it. The problem is that schema integration is usually difficult, and as soon as any of the information sources change or a new source is added, the process mayhave to be repeated. The SIMS system uses an alternative approach. A domain model of the application domain is created, establishing a fixed vocabulary for describing data sets in the domain. Using this language, each available information source is described. Queries to SIMS against the collection of available information sources are posed using terms from the domain model, and reformulation operators are employed to dynamically select an appropriate set of information sources and to determine how to integrate the available information to satisfy a query. This approach results in a system that is more flexible than existing ones, more easily scalable, and able to respond dynamically to newly available or unexpectedly missing information sources.

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