40383321
Schema Mapping as Query Discovery
8958554
Nested mappings: schema mapping reloaded
Many problems in information integration rely on specifications, called schema mappings, that model the relationships between schemas. Schema mappings for both relational and nested data are well-known. In this work, we present a new formalism for schema mapping that extends these existing formalisms in two significant ways. First, our nested mappings allow for nesting and correlation of mappings. This results in a natural programming paradigm that often yields more accurate specifications. In particular, we show that nested mappings can naturally preserve correlations among data that existing mapping formalisms cannot. We also show that using nested mappings for purposes of exchanging data from a source to a target will result in less redundancy in the target data. The second extension to the mapping formalism is the ability to express, in a declarative way, grouping and data merging semantics. This semantics can be easily changed and customized to the integration task at hand. We present a new algorithm for the automatic generation of nested mappings from schema matchings (that is, simple element-to-element correspondences between schemas). We have implemented this algorithm, along with algorithms for the generation of transformation queries (e.g., XQuery) based on the nested mapping specification. We show that the generation algorithms scale well to large, highly nested schemas. We also show that using nested mappings in data exchange can drastically reduce the execution cost of producing a target instance, particularly over large data sources, and can also dramatically improve the quality of the generated data.
641485
Mapping XML and relational schemas with Clio
Merging and coalescing data from multiple and diverse sources into different data formats continues to be an important problem in modern information systems. Schema matching (the process of matching elements of a source schema with elements of a target schema) and schema mapping (the process of creating a query that maps between two disparate schemas) are at the heart of data integration systems. We demonstrate Clio, a semi-automatic schema mapping tool developed at the IBM Almaden Research Center. In this paper, we showcase Clio's mapping engine which allows mapping to and from relational and XML schemas, and takes advantage of data constraints in order to preserve data associations.
1321953
Data-driven understanding and refinement of schema mappings
At the heart of many data-intensive applications is the problem of quickly and accurately transforming data into a new form. Database researchers have long advocated the use of declarative queries for this process. Yet tools for creating, managing and understanding the complex queries necessary for data transformation are still too primitive to permit widespread adoption of this approach. We present a new framework that uses data examples as the basis for understanding and refining declarative schema mappings. We identify a small set of intuitive operators for manipulating examples. These operators permit a user to follow and refine an example by walking through a data source. We show that our operators are powerful enough both to identify a large class of schema mappings and to distinguish effectively between alternative schema mappings. These operators permit a user to quickly and intuitively build and refine complex data transformation queries that map one data source into another.
16826457
Creating Nested Mappings with Clio
Schema mappings play a central role in many data integration and data exchange scenarios. In those applications, users need to quickly and correctly specify how data represented in one format is converted into a different format. Clio (L. Popa et al., 2002) is a joint research project between IBM and the University of Toronto studying the creation, maintenance, and use of schema mappings. There have always been two goals in our work in Clio: 1) the automatic creation of logical assertions that capture the way one or more source schemas are mapped into a target schema, and 2) the generation of transformation queries or programs that transform a source data instance into a target data instance.
17723951
DIALOG: A Model Of Diagnostic Logic For Internal Medicine
A system for computer assisted medical diagnosis has been developed, which incorporates an innovative model of diagnostic logic. A supporting medical data base has also been assembled, now comprising approximately fifty percent of the major diseases of internal medicine. Using weighted associations between disease entitles and their manifestations, and employing a powerful attention focusing heuristic, the system has demonstrated competence In dealing with difficult clinical problems involving multiple diagnoses.
15552348
Some Alternative Formulations of the Event Calculus
The Event Calculus is a narrative based formalism for reasoning about actions and change originally proposed in logic programming form by Kowalski and Sergot. In this paper we summarise how variants of the Event Calculus may be expressed as classical logic axiomatisations, and how under certain circumstances these theories may be reformulated as "action description language" domain descriptions using the Language ?. This enables the classical logic Event Calculus to inherit various provably correct automated reasoning procedures recently developed for ?.
4964831
Information-theoretic tools for mining database structure from large data sets
Data design has been characterized as a process of arriving at a design that maximizes the information content of each piece of data (or equivalently, one that minimizes redundancy). Information content (or redundancy) is measured with respect to a prescribed model for the data, a model that is often expressed as a set of constraints. In this work, we consider the problem of doing data redesign in an environment where the prescribed model is unknown or incomplete. Specifically, we consider the problem of finding structural clues in an instance of data, an instance which may contain errors, missing values, and duplicate records. We propose a set of information-theoretic tools for finding structural summaries that are useful in characterizing the information content of the data, and ultimately useful in data design. We provide algorithms for creating these summaries over large, categorical data sets. We study the use of these summaries in one specific physical design task, that of ranking functional dependencies based on their data redundancy. We show how our ranking can be used by a physical data-design tool to find good vertical decompositions of a relation (decompositions that improve the information content of the design). We present an evaluation of the approach on real data sets.
44402607
SPHINX: A Framework for Creating Personal, Site-Specific Web Crawlers
Abstract Crawlers, also called robots and spiders, are programs that browse the World Wide Web autonomously. This paper describes SPHINX, a Java toolkit and interactive development environment for Web crawlers. Unlike other crawler development systems, SPHINX is geared towards developing crawlers that are Web-site-specific, personally customized, and relocatable. SPHINX allows site-specific crawling rules to be encapsulated and reused in content analyzers, known as classifiers: Personal crawling tasks can be performed (often without programming) in the Crawler Workbench, an interactive environment for crawler development and testing. For efficiency, relocatable crawlers developed using SPHINX can be uploaded and executed on a remote Web server.
18318781
Schema Discovery
Structured data is distinguished from unstructured data by the presence of a schema describing the logical structure and semantics of the data. The schema is th e means through which we understand and query the underlying data. The schema permits the more sophi ticated structured queries that are not possible over schema-less data. Most systems assume that th e schema is predefined and is an accurate reflection of the data. This assumption is often not valid in n etworked databases that may contain data originating from many sources and may not be valid within leg acy databases where the semantics of data have evolved over time. As a result, querying and tasks that d epend on structured queries (including data integration and schema mapping) may not be effective. I n this paper, we consider the problem of discovering schemas from data. We focus on discovering prop erties of data that can be exploited in querying and transforming data. Finally, we very briefly con sider the suitability of mining approaches to the task of schema discovery.