Schema Mapping Research Articles

OmicsViz: Cytoscape plug-in for visualizing omics data across species

Motivation: OmicsViz is a Cytoscape plug-in for mapping and visualizing large-scale omics datasets across species, including those with many-to-many mappings between homologs. This allows users to map their data onto pathways of related model organisms. Mapping schemas across species or different experimental protocols allow users to comparatively analyze the omics data. The data can also be visualized in parallel-coordinate plots.Availability: The latest version of OmicsViz with documentation, tutorial and jar files can be downloaded from http://metnet.vrac.iastate.edu/MetNet_fcmodeler.htmContact: julied@iastate.edu

Guest editorial: special issue on metadata management

In this special issue on metadata management, we present a new work on creating, gathering, managing, and understanding metadata. The work in this issue highlights the reality that the lack of metadata and effective techniques for managing them is currently one of the biggest challenges to meaningful use and sharing of the wealth (or should we say glut) of data available today. Our first paper, “Model-Independent SchemaTranslation” by Paolo Atzeni, Paolo Cappellari, Riccardo Torlone, Philip A. Bernstein, and Giorgio Gianforme, considers the schema translation problem—a problem as old as the data management field itself.Model IndependentData andSchemaTranslation (MIDST) is a framework for translating schemas from one data model to another. The explicit creation of metadata representing the relationship between the schema and its translation is central to the approach. The schema mapping is represented in a variant of Datalog with Skolem functions. Such mappings are in the spirit of source-to-target tuplegenerating-dependencies (commonly used in data integration and data exchange) but with special constructs for reasoning about data model specific concepts. These mappings play a crucial role in permitting the customization of the schema translation rules for different applications or domains. They also provide the mechanism through which MIDST can be extended to new data models. Our second paper, “PicShark: Mitigating Metadata Scarcity through Large-Scale P2P Collaboration” by Philippe

Learning to create data-integrating queries

The number of potentially-related data resources available for querying --- databases, data warehouses, virtual integrated schemas --- continues to grow rapidly. Perhaps no area has seen this problem as acutely as the life sciences, where hundreds of large, complex, interlinked data resources are available on fields like proteomics, genomics, disease studies, and pharmacology. The schemas of individual databases are often large on their own, but users also need to pose queries across multiple sources, exploiting foreign keys and schema mappings. Since the users are not experts, they typically rely on the existence of pre-defined Web forms and associated query templates, developed by programmers to meet the particular scientists' needs. Unfortunately, such forms are scarce commodities, often limited to a single database, and mismatched with biologists' information needs that are often context-sensitive and span multiple databases. We present a system with which a non-expert user can author new query templates and Web forms, to be reused by anyone with related information needs. The user poses keyword queries that are matched against source relations and their attributes; the system uses sequences of associations (e.g., foreign keys, links, schema mappings, synonyms, and taxonomies) to create multiple ranked queries linking the matches to keywords; the set of queries is attached to a Web query form. Now the user and his or her associates may pose specific queries by filling in parameters in the form. Importantly, the answers to this query are ranked and annotated with data provenance, and the user provides feedback on the utility of the answers, from which the system ultimately learns to assign costs to sources and associations according to the user's specific information need, as a result changing the ranking of the queries used to generate results. We evaluate the effectiveness of our method against "gold standard" costs from domain experts and demonstrate the method's scalability.

Comparing and evaluating mapping systems with STBenchmark

Schema mappings are fundamental building blocks in many information integration applications. Designing mappings is a time-consuming process and for that reason many mapping systems have been developed to assist in the task of designing mappings. However, to the best of our knowledge, a benchmark for comparing and evaluating these systems has not yet been developed. We demonstrate STBenchmark, a benchmark that we have developed for evaluating mapping systems. Our demonstration will showcase the different aspects of mapping systems that STBenchmark evaluates, highlight the results of our comparison and evaluation of four mapping systems, as well as make a case for the need for a standard specification input mechanism to mapping systems in order to make progress towards the development of a uniform testbed or repository for schema mappings and data exchange tasks.

Interactive source registration in community-oriented information integration

Modern Internet communities need to integrate and query structured information. Employing current information integration infrastructure, data integration is still a very costly effort, since source registration is performed by a central authority which becomes a bottleneck. We propose the community-based integration paradigm which pushes the source registration task to the independent community members. This creates new challenges caused by each community member's lack of a global overview on how her data interacts with the application queries of the community and the data from other sources. How can the source owner maximize the visibility of her data to existing applications, while minimizing the clean-up and reformatting cost associated with publishing? Does her data contradict (or could it contradict in the future) the data of other sources? We introduce RIDE, a visual registration tool that extends schema mapping interfaces like that of MS Biz Talk Server and IBM's Clio with a suggestion component that guides the source owner in the autonomous registration, assisting her in answering these questions. RIDE's implementation features efficient procedures for deciding various levels of self-reliance of a GLAV-style source registration for contributing answers to an application query and checking potential and definite inconsistency across sources.

PicShark: mitigating metadata scarcity through large-scale P2P collaboration

With the commoditization of digital devices, personal information and media sharing is becoming a key application on the pervasive Web. In such a context, data annotation rather than data production is the main bottleneck. Metadata scarcity represents a major obstacle preventing efficient information processing in large and heterogeneous communities. However, social communities also open the door to new possibilities for addressing local metadata scarcity by taking advantage of global collections of resources. We propose to tackle the lack of metadata in large-scale distributed systems through a collaborative process leveraging on both content and metadata. We develop a community-based and self-organizing system called PicShark in which information entropy--in terms of missing metadata--is gradually alleviated through decentralized instance and schema matching. Our approach focuses on semi-structured metadata and confines computationally expensive operations to the edge of the network, while keeping distributed operations as simple as possible to ensure scalability. PicShark builds on structured Peer-to-Peer networks for distributed look-up operations, but extends the application of self-organization principles to the propagation of metadata and the creation of schema mappings. We demonstrate the practical applicability of our method in an image sharing scenario and provide experimental evidences illustrating the validity of our approach.

Towards optimal workload-aware XML to relational schema mapping

Storing XML documents in relational databases has drawn much attention in recent years because it can leverage existing investments in relational database technologies. Different algorithms have been proposed to map XML DTD/Schema to relational schema in order to store XML data in relational databases. However, most work defines mapping rules based on heuristics without considering application characteristics, hence fails to produce efficient relational schema for various applications. In this paper, we propose a workload-aware approach to generate relational schema from XML data and user specified workload. Our approach adopts the genetic algorithm to find optimal mappings. An elegant encoding method and related operations are proposed to manipulate mappings using bit strings. Various techniques for optimization can be applied to the XML to relational mapping problem based on this representation. We implemented the proposed algorithm and our experiment results showed that our algorithm was more robust and produced better mappings than existing work.

Quasi-inverses of schema mappings

Schema mappings are high-level specifications that describe the relationship between two database schemas. Two operators on schema mappings, namely the composition operator and the inverse operator, are regarded as especially important. Progress on the study of the inverse operator was not made until very recently, as even finding the exact semantics of this operator turned out to be a fairly delicate task. Furthermore, this notion is rather restrictive, since it is rare that a schema mapping possesses an inverse. In this article, we introduce and study the notion of a quasi-inverse of a schema mapping. This notion is a principled relaxation of the notion of an inverse of a schema mapping; intuitively, it is obtained from the notion of an inverse by not differentiating between instances that are equivalent for data-exchange purposes. For schema mappings specified by source-to-target tuple-generating dependencies (s-t tgds), we give a necessary and sufficient combinatorial condition for the existence of a quasi-inverse, and then use this condition to obtain both positive and negative results about the existence of quasi-inverses. In particular, we show that every LAV (local-as-view) schema mapping has a quasi-inverse, but that there are schema mappings specified by full s-t tgds that have no quasi-inverse. After this, we study the language needed to express quasi-inverses of schema mappings specified by s-t tgds, and we obtain a complete characterization. We also characterize the language needed to express inverses of schema mappings, and thereby solve a problem left open in the earlier study of the inverse operator. Finally, we show that quasi-inverses can be used in many cases to recover the data that was exported by the original schema mapping when performing data exchange.

Validation of mappings between schemas

Mappings between schemas are key elements in several contexts such as data exchange, data integration and peer data management systems. In all these contexts, the mapping design process requires the participation of a mapping designer, who needs to be able to validate the mapping that is being defined, i.e., check whether the mapping is in fact what the designer intended. However, to date very little work has directly focused on the effective validation of schema mappings. In this paper, we present a new approach for validating schema mappings that allows the mapping designer to ask whether they have certain desirable properties. We consider four properties of mappings: mapping satisfiability, mapping inference, query answerability and mapping losslessness. We reformulate these properties in terms of the problem of checking whether a query is lively over a database schema. A query is lively if there is a consistent instance of the database for which the query gives a non-empty answer. To show the feasibility of our approach, we use an implementation of the CQC method and provide some experimental results.

Schema Mappings and Agents' Actions in P2P Data Integration System

Schema Mappings and Agents' Actions in P2P Data Integration System

A research agenda for query processing in large-scale peer data management systems

Peer Data Management Systems (P dms) are a novel, useful, but challenging paradigm for distributed data management and query processing. Conventional integrated information systems have a hierarchical structure with an integration component that manages a global schema and distributes queries against this schema to the underlying data sources. P dms are a natural extension to this architecture by allowing each participating system (peer) to act both as a data source and as an integrator. Peers are interconnected by schema mappings, which guide the rewriting of queries between the heterogeneous schemas, and thus form a P2P (peer-to-peer)-like network. Despite several years of research, the development of efficient P dms still holds many challenges. In this article we first survey the state of the art on peer data management: We classify P dms by characteristics concerning their system model, their semantics, their query planning schemes, and their maintenance. Then we systematically examine open research directions in each of those areas. In particular, we observe that research results from both the domain of P2P systems and of conventional distributed data management can have an impact on the development of P dms.

Inverting 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). Although the notion of an inverse of a schema mapping is important, the exact definition of an inverse mapping is somewhat elusive. This is because a schema mapping may associate many target instances with each source instance, and many source instances with each target instance. Based on the notion that the composition of a mapping and its inverse is the identity, we give a formal definition for what it means for a schema mapping M′ to be an inverse of a schema mapping M for a class S of source instances. We call such an inverse an S- inverse . A particular case of interest arises when S is the class of all source instances, in which case an S-inverse is a global inverse. We focus on the important and practical case of schema mappings specified by source-to-target tuple-generating dependencies, and uncover a rich theory. When S is specified by a set of dependencies with a finite chase, we show how to construct an S-inverse when one exists. In particular, we show how to construct a global inverse when one exists. Given M and M′, we show how to define the largest class S such that M′ is an S-inverse of M.

GridVine: An Infrastructure for Peer Information Management

GridVine is a semantic overlay infrastructure based on a peer-to-peer (P2P) access structure. Built following the principle of data independence, it separates a logical layer - in which data, schemas, and schema mappings are managed - from a physical layer consisting of a structured P2P network supporting decentralized indexing, key load-balancing, and efficient routing. The system is decentralized, yet fosters semantic interoperability through pair-wise schema mappings and query reformulation. GridVine's heterogeneous but semantically related information sources can be queried transparently using iterative query reformulation. The authors discuss a reference implementation of the system and several mechanisms for resolving queries collaboratively.

Rank Aggregation for Automatic Schema Matching

Schema matching is a basic operation of data integration, and several tools for automating it have been proposed and evaluated in the database community. Research in this area reveals that there is no single schema matcher that is guaranteed to succeed in finding a good mapping for all possible domains and, thus, an ensemble of schema matchers should be considered. In this paper, we introduce schema metamatching, a general framework for composing an arbitrary ensemble of schema matchers and generating a list of best ranked schema mappings. Informally, schema metamatching stands for computing a "consensus" ranking of alternative mappings between two schemata, given the "individual" graded rankings provided by several schema matchers. We introduce several algorithms for this problem, varying from adaptations of some standard techniques for general quantitative rank aggregation to novel techniques specific to the problem of schema matching, and to combinations of both. We provide a formal analysis of the applicability and relative performance of these algorithms and evaluate them empirically on a set of real-world schemata

A database ontology for signal transduction pathways

Ontologies are one of the key technologies for data integration and meta-databases, by connecting databases at a semantical level. Still, the database has to be connected to the ontology and vice versa. To achieve this, we propose a two step process. First, we automatically generate an ontology directly from the database schema. Next, we annotate this with concepts from a domain specific ontology. We demonstrate this for signal transduction pathways, which describe how cells can react to external stimuli. In this paper we present our mapping of database schemas to ontologies and describe how these ontologies can be enriched by semantical information.

Macor: A Maintainable XQuery Model for Representing Nested Schema Mappings

This paper proposes a model called mapping correlation (Macor) to represent nested schema mappings. With Macor, a full mapping is modeled as a number of simple atomic ones that are correlated with correlations. The expressive power of Macor by a fragment of XQuery called CoXQ is studied and the issues in implementing Macor are addressed. Preliminary experimental results show that with Macor a full mapping can be modeled incrementally in a piecemeal fashion, and in refining or maintaining the mappings, Macor makes it possible to locate modifications to few atomic mappings and correlations, and reuse other parts of the full mapping.

Information retrieval and machine learning for probabilistic schema matching

Schema matching is the problem of finding correspondences (mapping rules, e.g. logical formulae) between heterogeneous schemas e.g. in the data exchange domain, or for distributed IR in federated digital libraries. This paper introduces a probabilistic framework, called sPLMap, for automatically learning schema mapping rules, based on given instances of both schemas. Different techniques, mostly from the IR and machine learning fields, are combined for finding suitable mapping candidates. Our approach gives a probabilistic interpretation of the prediction weights of the candidates, selects the rule set with highest matching probability, and outputs probabilistic rules which are capable to deal with the intrinsic uncertainty of the mapping process. Our approach with different variants has been evaluated on several test sets.

2DCMA: An Effective Maintenance Algorithm of Materialized Views in Peer Data Management Systems

Update management is very important for data integration systems. So update management in peer data management systems (PDMSs) is a hot research area. This paper researches on view maintenance in PDMSs. First, the definition of view is extended and the peer view, local view and global view are proposed according to the requirements of applications. There are two main factors to influence materialized views in PDMSs. One is that schema mappings between peers are changed, and the other is that peers update their data. Based on the requirements, this paper proposes an algorithm called 2DCMA, which includes two sub-algorithms: data and definition consistency maintenance algorithms, to effectively maintain views. For data consistency maintenance, Mork’s rules are extended for governing the use of updategrams and boosters. The new rule system can be used to optimize the execution plan. And are extended for the data consistency maintenance algorithm is based on the new rule system. Furthermore, an ECA rule is adopted for definition consistency maintenance. Finally, extensive simulation experiments are conducted in SPDMS. The simulation results show that the 2DCMA algorithm has better performance than that of Mork’s when maintaining data consistency. And the 2DCMA algorithm has better performance than that of centralized view maintenance algorithm when maintaining definition consistency.

From bioinformatic web portals to semantically integrated Data Grid networks

We propose a semi-automated method for redeploying bioinformatic databases indexed in a Web portal as a decentralized, semantically integrated and service-oriented Data Grid. We generate peer-to-peer schema mappings leveraging on cross-referenced instances and instance-based schema matching algorithms. Analyzing real-world data extracted from an existing portal, we show how a rather trivial combination of lexicographical measures with set distance measures yields surprisingly good results in practice. Finally, we propose data models for redeploying all instances, schemas and schema mappings in the Data Grid, relying on standard Semantic Web technologies.

Efficient schema-based XML-to-Relational data mapping

Storing and querying XML documents using a RDBMS is a challenging problem since one needs to resolve the conflict between the hierarchical, ordered nature of the XML data model and the flat, unordered nature of the relational data model. This conflict can be resolved by the following XML-to-Relational mappings: schema mapping, data mapping and query mapping. In this paper, we propose: (i) a lossless schema mapping algorithm to generate a database schema from a DTD, which makes several improvements over existing algorithms, (ii) two linear data mapping algorithms based on DOM and SAX, respectively, to map ordered XML data to relational data. To our best knowledge, there is no published linear schema-based data mapping algorithm for mapping ordered XML data to relational data. Experimental results are presented to show that our algorithms are efficient and scalable.