Foundations of Data Exchange

Abstract

The problem of exchanging data between different databases with different schemas is an area of immense importance. Consequently data exchange has been one of the most active research topics in databases over the past decade. Foundational questions related to data exchange largely revolve around three key problems: how to build target solutions; how to answer queries over target solutions; and how to manipulate schema mappings themselves? The last question is also known under the name 'metadata management', since mappings represent metadata, rather than data in the database. In this book the authors summarize the key developments of a decade of research. Part I introduces the problem of data exchange via examples, both relational and XML; Part II deals with exchanging relational data; Part III focuses on exchanging XML data; and Part IV covers metadata management.

Correctness of SQL Queries on Databases with Nulls

“…Much of the following is standard in the literature on databases with incomplete information; see, e.g., [1,18,31]. The usual way of modeling missing values in a database is to use marked (or labeled) nulls, which often appear in applications such as data integration and exchange [3,22]. In this model, databases are populated by two types of elements: constants and nulls, coming from countably infinite sets denoted by Const and Null, respectively.…”

Deciding Definability by Deterministic Regular Expressions

“…Let D be a minimal DFA and S be its set of consistent symbols. Then D has -an Out-Consistency Violation, if there exist gates q 1 Notice that the first two violations focus on D S and the last one on D, as in the Bkw-Algorithm. In summary, we will also say that a DFA D has a violation if and only if it has at least one of the above violations.…”

“…Primarily, schema information is crucial for automatic error detection in the data itself (which is called validation, see, e.g., [5,26,2,20]) or in the procedures that transform the data [24,23,22]. Furthermore, schemas provide information for optimization of XML querying and processing [25,28], they are inevitable when integrating data through schema matching [1], and they provide users with a high-level overview of the structure of the data. From a software development point of view, schemas are very useful to precisely specify pre-and post-conditions of software routines that process XML data.…”

Executable schema mappings for statistical data processing

“…Although we propose some modifications to support our needs (introduction of mathematical and statistical operators), we do not alter the declarative essence of the tgd language, and the resolution methods (e.g., the chase) remain applicable. For data exchange, we refer to classical formulations [1].…”

“…A statistical program can be intuitively seen as an ETL job composed of a number of flows each representing a tgd statement. In this context all flows have the same structure and involve: data source steps, feeding data into the ETL stream; merge steps, combining streams coming from different sources; calculation steps, performing simple 1 The seasonal decomposition is an operator that decomposes a time series into various components, one of which is the trend, which, roughly speaking considers medium-or longterm "variations", ignoring seasonal, cyclic (and stochastic) ones [14,37].…”