Schema Mapping Research Articles

Autonomous mapping of HL7 RIM and relational database schema

Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients. For this purpose, they take advantage of the full potential of current...

Bridging the data integration gap

The integration of multiple autonomous and heterogeneous data sources (both across the web and via a company intranet) has received much attention throughout the years, particularly due to its many applications in the fields of Artificial Intelligence and medical research data sharing. Data integration systems embody this work and have come very far in the past twenty years. The problem of designing such systems is characterized by a number of issues that are interesting from a theoretical point of view: answering queries using logical views, query containment and completeness, automatic integration of existing data sources via schema mapping tools, etc. In this work we discuss these issues, compare and contrast various proposed solutions (federated database systems and data warehouses), and finally propose a novel extension of the MVC (model, view, controller) web-based framework that allows for the rapid development and implementation of data integration systems solutions suitable for use on the web.

Reverse data exchange

An inverse of a schema mapping M is intended to undo what M does, thus providing a way to perform reverse data exchange. In recent years, three different formalizations of this concept have been introduced and studied, namely the notions of an inverse of a schema mapping, a quasi-inverse of a schema mapping, and a maximum recovery of a schema mapping. The study of these notions has been carried out in the context in which source instances are restricted to consist entirely of constants, while target instances may contain both constants and labeled nulls. This restriction on source instances is crucial for obtaining some of the main technical results about these three notions, but, at the same time, limits their usefulness, since reverse data exchange naturally leads to source instances that may contain both constants and labeled nulls. We develop a new framework for reverse data exchange that supports source instances that may contain nulls, and we thereby overcome the semantic mismatch between source and target instances of the previous formalizations. The development of this new framework requires a careful reformulation of all the important notions, including the notions of the identity schema mapping, inverse, and maximum recovery. To this effect, we introduce the notions of extended identity schema mapping, extended inverse, and maximum extended recovery, by making systematic use of the homomorphism relation on instances. We give results concerning the existence of extended inverses and of maximum extended recoveries, and results concerning their applications to reverse data exchange and query answering. Moreover, we show that maximum extended recoveries can be used to capture in a quantitative way, the amount of information loss embodied in a schema mapping specified by source-to-target tuple-generating dependencies.

Normalization and optimization of schema mappings

Schema mappings are high-level specifications that describe the relationship between database schemas. They are an important tool in several areas of database research, notably in data integration ...

Harvesting models from web 2.0 databases

Data rather than functionality are the sources of competitive advantage for Web2.0 applications such as wikis, blogs and social networking websites. This valuable information might need to be capitalized by third-party applications or be subject to migration or data analysis. Model-Driven Engineering (MDE) can be used for these purposes. However, MDE first requires obtaining models from the wiki/blog/website database (a.k.a. model harvesting). This can be achieved through SQL scripts embedded in a program. However, this approach leads to laborious code that exposes the iterations and table joins that serve to build the model. By contrast, a Domain-Specific Language (DSL) can hide these “how” concerns, leaving the designer to focus on the “what”, i.e. the mapping of database schemas to model classes. This paper introduces Schemol, a DSL tailored for extracting models out of databases which considers Web2.0 specifics. Web2.0 applications are often built on top of general frameworks (a.k.a. engines) that set the database schema (e.g., MediaWiki, Blojsom). Hence, table names offer little help in automating the extraction process. In addition, Web2.0 data tend to be annotated. User-provided data (e.g., wiki articles, blog entries) might contain semantic markups which provide helpful hints for model extraction. Unfortunately, these data end up being stored as opaque strings. Therefore, there exists a considerable conceptual gap between the source database and the target metamodel. Schemol offers extractive functions and view-like mechanisms to confront these issues. Examples using Blojsom as the blog engine are available for download.

Composition and inversion of schema mappings

A schema mapping is a specification that describes how data from a source schema is to be mapped to a target schema. Schema mappings have proved to be essential for data-interoperability tasks such as data exchange and data integration. The research on this area has mainly focused on performing these tasks. However, as Bernstein pointed out [7], many information-system problems involve not only the design and integration of complex application artifacts, but also their subsequent manipulation. Driven by this consideration, Bernstein proposed in [7] a general framework for managing schema mappings. In this framework, mappings are usually specified in a logical language, and high-level algebraic operators are used to manipulate them [7, 16, 33, 12, 8]. Two of the most fundamental operators in this framework are the composition and inversion of schema mappings. Intuitively, the composition can be described as follows. Given a mapping M1 from a schema A to a schema B, and a mapping M2 from B to a schema E, the composition of M1 and M2 is a new mapping that describes the relationship between schemas A and E. This new mapping must be semantically consistent with the relationships previously established by M1 and M2. On the other hand, an inverse of M1 is a new mapping that describes the reverse relationship from B to A, and is semantically consistent with M1. In practical scenarios, the composition and inversion of schema mappings can have several applications. In a data exchange context [13], if a mapping M is used to exchange data from a source to a target schema, an inverse of M can be used to exchange the data back to the source, thus reversing the application of M. As a second application, consider a peer-data management system (PDMS) [10, 24]. In a PDMS, a peer can act as a data source, a mediator, or both, and the system relates peers

Knowledge Representation and Information Management for Financial Risk Management: Report of a Workshop

The National Science Foundation and The Pew Charitable Trusts co‐sponsored a Workshop on Knowledge Representation and Information Management for Financial Risk Management on July 21 and 22, 2010 in Arlington, Virginia (see Flood, Kyle and Raschid, 2010). The goal of the workshop was to initiate a research discussion about the knowledge representation challenges for effective financial information management. Over fifty invited academic researchers, financial regulators and industry practitioners participated in the event. The participants brought diverse perspectives and expertise in economics, computer science, finance, and information science, resulting in an interdisciplinary exchange of ideas. The specific topics covered a broad range, including financial risk management, ontologies for knowledge representation, formal logics, schema mapping, systemic risk, constraint languages, networks, simulation techniques, data integrity, operational risk and data security, to name a few. This report describes the discussions flowing from the workshop and its immediate aftermath in greater detail. We hope, however, that this is only the beginning of a much longer conversation.

The structure of inverses in schema mappings

A schema mapping is a specification that describes how data structured under one schema (the source schema) is to be transformed into data structured under a different schema (the target schema). The notion of an inverse of a schema mapping is subtle, because a schema mapping may associate many target instances with each source instance, and many source instances with each target instance. In PODS 2006, Fagin defined a notion of the inverse of a schema mapping. This notion is tailored to the types of schema mappings that commonly arise in practice (those specified by “source-to-target tuple-generating dependencies”, or s-t tgds ). We resolve the key open problem of the complexity of deciding whether there is an inverse. We also explore a number of interesting questions, including: What is the structure of an inverse? When is the inverse unique? How many nonequivalent inverses can there be? When does an inverse have an inverse? How big must an inverse be? Surprisingly, these questions are all interrelated. We show that for schema mappings M specified by full s-t tgds (those with no existential quantifiers), if M has an inverse, then it has a polynomial-size inverse of a particularly nice form, and there is a polynomial-time algorithm for generating it. We introduce the notion of “essential conjunctions” (or “essential atoms” in the full case), and show that they play a crucial role in the study of inverses. We use them to give greatly simplified proofs of some known results about inverses. What emerges is a much deeper understanding about this fundamental and complex operator.

Identification of Carbonic Anhydrase I Immunodominant Epitopes Recognized by Specific Autoantibodies Which Indicate an Improved Prognosis in Patients with Malignancy after Autologous Stem Cell Transplantation

This work employs an epitope mapping of carbonic anhydrase (CA), isoform I (CA I), for detection of the main immunodominant epitopes. Our interest has arisen from an observed spontaneous tumor regression in patients who developed an aplastic anemia type syndrome after a high-dose therapy with autologous stem cell transplantation and whose sera contained high titer of anti carbonic anhydrase (anti-CA) autoantibodies. There are many indications that the presence of these autoantibodies may provide significant survival benefit for the patients. Western blot analysis confirmed strong immunoreactivity of the patients' sera with several CA isoforms and the CA I has been selected for our study as a highly abundant and widely distributed isoform. The applied analytical approach consists of specific fragmentation of CA I protein followed by immunospecific isolation of peptides reacting with polyclonal anti-CA I autoantibodies of patients in spontaneous remission. We improved the standard epitope mapping schema by incorporating the benefits of magnetic carriers and biomagnetic separation techniques. Mass spectrometry has been applied for detection and identification of epitopes and the acquired results were verified by bioinformatic tools. The candidate epitopes of CA I (NVGHS, DGLAV, SSEQL, and SLKPI) are discussed herein as potential therapeutic targets. This work highlights the usefulness of the epitope mapping technique based on magnetic microspheres for effective and rapid determination of immunodominant epitopes of the target protein.

MapMerge

One of the main steps towards integration or exchange of data is to design the mappings that describe the (often complex) relationships between the source schemas or formats and the desired target schema. In this paper, we introduce a new operator, called MapMerge, that can be used to correlate multiple, independently designed schema mappings of smaller scope into larger schema mappings. This allows a more modular construction of complex mappings from various types of smaller mappings such as schema correspondences produced by a schema matcher or pre-existing mappings that were designed by either a human user or via mapping tools. In particular, the new operator also enables a new "divide-and-merge" paradigm for mapping creation, where the design is divided (on purpose) into smaller components that are easier to create and understand, and where MapMerge is used to automatically generate a meaningful overall mapping. We describe our MapMerge algorithm and demonstrate the feasibility of our implementation on several real and synthetic mapping scenarios. In our experiments, we make use of a novel similarity measure between two database instances with different schemas that quantifies the preservation of data associations. We show experimentally that MapMerge improves the quality of the schema mappings, by significantly increasing the similarity between the input source instance and the generated target instance.

TRAMP

Though partially automated, developing schema mappings remains a complex and potentially error-prone task. In this paper, we present TRAMP (TRAnsformation Mapping Provenance), an extensive suite of tools supporting the debugging and tracing of schema mappings and transformation queries. TRAMP combines and extends data provenance with two novel notions, transformation provenance and mapping provenance, to explain the relationship between transformed data and those transformations and mappings that produced that data. In addition we provide query support for transformations, data, and all forms of provenance. We formally define transformation and mapping provenance, present an efficient implementation of both forms of provenance, and evaluate the resulting system through extensive experiments.

Scalable data exchange with functional dependencies

The recent literature has provided a solid theoretical foundation for the use of schema mappings in data-exchange applications. Following this formalization, new algorithms have been developed to generate optimal solutions for mapping scenarios in a highly scalable way, by relying on SQL. However, these algorithms suffer from a serious drawback: they are not able to handle key constraints and functional dependencies on the target, i.e., equality generating dependencies (egds). While egds play a crucial role in the generation of optimal solutions, handling them with first-order languages is a difficult problem. In fact, we start from a negative result: it is not always possible to compute solutions for scenarios with egds using an SQL script. Then, we identify many practical cases in which this is possible, and develop a best-effort algorithm to do this. Experimental results show that our algorithm produces solutions of better quality with respect to those produced by previous algorithms, and scales nicely to large databases.

Product Data Integration and Management Research Based on XML

Product information technology has been widely applied in manufacturing field, improved the rapid response ability of enterprises to the market demands, and to some extent enhanced the competitiveness of enterprise products. However, product data information generated by different information system has the characteristics of diversification and differentiation. The integration and management of these product heterogeneous data has become the bottleneck which restricts the development of enterprise information technology. Based on XML, this paper researches the integration and management of heterogeneous data, expounds integration structure of heterogeneous data, introduces integration process and schema mapping of heterogeneous data, and realizes schema translation of heterogeneous data. Finally, through the PLM XML schema which is provided by the product life-cycle management software, this paper verifies the integrated management of relational heterogeneous data on Teamcenter.

Data exchange and schema mappings in open and closed worlds

In the study of data exchange one usually assumes an open-world semantics, making it possible to extend instances of target schemas. An alternative closed-world semantics only moves ‘as much data as needed’ from the source to the target to satisfy constraints of a schema mapping. It avoids some of the problems exhibited by the open-world semantics, but limits the expressivity of schema mappings. Here we propose a mixed approach: one can designate different attributes of target schemas as open or closed, to combine the additional expressivity of the open-world semantics with the better behavior of query answering in closed worlds. We define such schema mappings, and show that they cover a large space of data exchange solutions with two extremes being the known open and closed-world semantics. We investigate the problems of query answering and schema mapping composition, and prove two trichotomy theorems, classifying their complexity based on the number of open attributes. We find conditions under which schema mappings compose, extending known results to a wide range of closed-world mappings. We also provide results for restricted classes of queries and mappings guaranteeing lower complexity.

Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration

For a number of years, librarians have heard that natural language processing (NLP) will revolutionize information management and retrieval in health care settings. The goal of the two editors, professors at the University Montpellier 2 in France, is to compile research that librarians and health information systems administrators and developers will find useful in incorporating data management systems solutions into their organizations. This book provides relevant theoretical frameworks and empirical research findings in NLP according to linguistic granularity and presents original applications. Both editors demonstrate expertise in the field of computer science. Prince headed the French National University Council for Computer Science and leads the NLP research team at the Laboratoire d'Informatique, de Robotique et de Microelectronique de Montpelier. She specializes in NLP and cognitive science. Roche's research interests include text mining, information retrieval (IR), terminology, and NLP for schema mapping. NLP is a subfield of computer science that addresses the operation and management of texts, as input or output of computational devices. The scientific community is interested in NLP for the following uses: IR and knowledge extraction, knowledge integration to existing devices, and use and application of existing knowledge structures for IR services. Scientific literature encompasses so much knowledge that only computer-based systems can browse and filter it. A regular search engine does not have the complexity to undertake complex scientific queries. Knowledge classifications, taxonomies with hierarchical ties between knowledge items, provide ontologies at a high cost in human labor and involvement. NLP tools reorganize artificial intelligence (AI) techniques to focus on linguistic-conceptual relationships, rather than primarily textual analysis. Knowledge integration translates synonymous terms using data and text mining to complete or correct existing knowledge structures. Medical literature contains a substantial number of acronyms. In NLP, unlike mathematical originated formalisms, a concept can be addressed through a variety of words and phrases that are not exactly equivalent. Retrieving the relevant set of texts from a complex query must not only rely on words, but also grasp ideas expressed by distinct strings of words or phrases and necessitates topical classification. More than half of the scientific literature is written in non-English languages. Key BioNLP domain resources include PubMed, ontologies and thesauri (e.g., GeneOntology), and the Medical Subject Headings (MeSH) Thesaurus. The extraction process for IR and knowledge management (KM) is the same. However, IR results in raw data, and KM results in machine operable data. The two properties that define this book are emphasizing NLP as the main methodological issue and studying the interaction between NLP and its application domains of science and medicine. NLP goes far beyond the concept of word computing to include sentence level and discourse, segment, or text level. Sentence meaning results from word interactions as well as word meanings. Paragraph positions and grammar demonstrate the intentions of the human authors and are generally ignored by most computational techniques. NLP theories provide an attempt to address how text organization shows the dependence of language on its nonlinguistic environment. Such theories include discourse relations theory, discourse rhetorical structures theory, and speech acts theory. BioNLP has developed its own characteristics to process the domain language of biology and medicine. The target audience includes graduate school professors and students, NLP researchers, AI researchers, terminologists, linguists, health information systems specialists, and the BioNLP community. Librarians in academic settings serving the aforementioned may find this book useful in their collections, particularly if they serve those interested in European research, because the majority of the twenty-two chapter authors are international. The book is divided into five sections: “Works at a Lexical Level,” “Crossroads between NLP and Ontological Knowledge Management”; “Going Beyond Words and NLP Approaches Involving the Sentence Level”; “Pragmatics, Discourse Structures and Segment Level as the Last Stage in the NLP Offer to Biomedicine”; “NLP Software for IR in Biomedicine”; and “Conclusion and Perspectives.” The book begins with a chapter on text mining for biomedicine that sets the stage. Following the preface and chapters are sections on compilation of resources, about the contributors, and the index. References are predominantly from the last ten years, with some as new as 2008, though most are older. Some are needlessly cited twice. Contributors represent ongoing research from across nations and disciplines. The index does not include cross-references, which is odd considering the topic of the book. There are some typos or misspellings dispersed throughout the text. Graphs and illustrations are relevant and enhance the reader's understanding of concepts. The numerous mathematical formulas will appeal to the target audience. Prince and Roche have succeeded in compiling an original work in the field of BioNLP. There appears to be no advantage to the purchase of perpetual access, although potential certainly exists for an enhanced electronic version.

Using Interval Cycles to Model Krumhansl's Tonal Hierarchies

The analyses presented in this paper use interval-cycle profiles to show that there is a statistical link between interval cycles and Krumhansl and Kessler's (1982) major- and minor-key tonal hierarchies. An interval cycle is the minimum number of additive iterations of an interval that are required for the original pitch classes to be restated—a formal property that we hypothesize leads to perceptual grouping referred to as interval-cycle proximity. The interval cycles that best explain the extent to which any chord's interval-cycle profile correlates with the tonal hierarchies are found to be those belonging to dominant harmonies, particularly the dominant seventh. The results of the analyses and the interval-cycle proximity hypothesis are discussed with respect to Browne's (1981) rare-interval hypothesis and Brower's (2000) musical pattern mapping schema.

Dealing with Uncertainty in Lexical Annotation

We present ALA, a tool for the automatic lexical annotation (i.e. annotation w.r.t. a thesaurus/lexical resource) of structured and semi-structured data sources and the discovery of probabilistic lexical relationships in a data integration environment. ALA performs automatic lexical annotation through the use of probabilistic annotations, i.e. an annotation is associated to a probability value. By performing probabilistic lexical annotation, we discover probabilistic inter-sources lexical relationships among schema elements. ALA extends the lexical annotation module of the MOMIS data integration system. However, it may be applied in general in the context of schema mapping discovery, ontology merging and data integration system and it is particularly suitable for performing “on-the-fly” data integration or probabilistic ontology matching.

RDFP rov: A relational RDF store for querying and managing scientific workflow provenance

Provenance metadata has become increasingly important to support scientific discovery reproducibility, result interpretation, and problem diagnosis in scientific workflow environments. The provenance management problem concerns the efficiency and effectiveness of the modeling, recording, representation, integration, storage, and querying of provenance metadata. Our approach to provenance management seamlessly integrates the interoperability, extensibility, and inference advantages of Semantic Web technologies with the storage and querying power of an RDBMS to meet the emerging requirements of scientific workflow provenance management. In this paper, we elaborate on the design of a relational RDF store, called RDFP rov, which is optimized for scientific workflow provenance querying and management. Specifically, we propose: i) two schema mapping algorithms to map an OWL provenance ontology to a relational database schema that is optimized for common provenance queries; ii) three efficient data mapping algorithms to map provenance RDF metadata to relational data according to the generated relational database schema, and iii) a schema-independent SPARQL-to-SQL translation algorithm that is optimized on-the-fly by using the type information of an instance available from the input provenance ontology and the statistics of the sizes of the tables in the database. Experimental results are presented to show that our algorithms are efficient and scalable. The comparison with two popular relational RDF stores, Jena and Sesame, and two commercial native RDF stores, AllegroGraph and BigOWLIM, showed that our optimizations result in improved performance and scalability for provenance metadata management. Finally, our case study for provenance management in a real-life biological simulation workflow showed the production quality and capability of the RDFP rov system. Although presented in the context of scientific workflow provenance management, many of our proposed techniques apply to general RDF data management as well.

Research on relational-algebra-based schema mapping of data integration

Integrated mapping is one of the basic issues in data integration,while it still has some deficiencies such as low repetition reducing rate,lack of accuracy,etc. Based on the research of Global-As-View (GAV) an d Local-As-View (LAV),two fundamental mapping modes of data integration,and the mode matching,in connection with the questions in integrated mapping like repetition reducing and the transformation between boolean element value and semantic value,the article extended the existing theory of relational algebra. A mapping program of source-objective mode was designed. In such a program,the object congregation and relation congregation of source mode were expanded,and then objective mode was to be obtained through processing the expanded mode with expanded relational algebra,and the specific derivation process during the operation was represented. The feasibility of the program is verified through illustrating examples.

An integrated translation of design data of a nuclear power plant from a specification-driven plant design system to neutral model data

Abstract How to efficiently integrate and manage lifecycle data of a nuclear power plant has gradually become an important object of study. Because plants usually have a very long period of operation and maintenance, the plant design data need to be presented in a computer-interpretable form and to be independent of any commercial systems. The conversion of plant design data from various design systems into neutral model data is therefore an important technology for the effective operation and maintenance of plants. In this study, a neutral model for the efficient integration of plant design data is chosen from among the currently available options and extended in order to cover the information model requirements of nuclear power plants in Korea. After the mapping of the neutral model and the data model of a specification-driven plant design system, a plant data translator is also implemented in accordance with the schema mapping results.