Target Schema Research Articles

Querying Data Exchange Settings Beyond Positive Queries

Abstract Data exchange, the problem of transferring data from a source schema to a target schema, has been studied for several years. The semantics of answering positive queries over the target schema has been defined in early work, but little attention has been paid to more general queries. A few proposals of semantics for more general queries exist but they either do not properly extend the standard semantics under positive queries, giving rise to counterintuitive answers, or they make query answering undecidable even for the most important data exchange settings, for example, with weakly-acyclic dependencies. The goal of this paper is to provide a new semantics for data exchange that is able to deal with general queries. At the same time, we want our semantics to coincide with the classical one when focusing on positive queries, and to not trade-off too much in terms of complexity of query answering. We show that query answering is undecidable in general under the new semantics, but it is $\text{co}\text{NP}\text{-complete}$ when the dependencies are weakly-acyclic. Moreover, in the latter case, we show that exact answers under our semantics can be computed by means of logic programs with choice, thus exploiting existing efficient systems. For more efficient computations, we also show that our semantics allows for the construction of a representative target instance, similar in spirit to a universal solution, that can be exploited for computing approximate answers in polynomial time.

Integration of Building Information Modeling Interoperability into Nonlinear Finite Element Analysis of Bridge Substructures

Acceptance of building information modeling (BIM) throughout the bridge industry is widely recognized as inevitable. However, while adoption of BIM technologies has proliferated throughout the building industry, advances pertaining to BIM for bridges and other structures remain underway. The present study aims to increase the potential for integrating BIM processes into software packages that possess capabilities for nonlinear finite element analysis (FEA) of bridge substructures. Moreover, the present study is intended to galvanize stakeholders within the area of soil–structure interaction (SSI) modeling and analysis to more effectively participate in interoperability workflows as part of bridge design. A proof-of-concept exporter tool was formulated in a modular manner to robustly export finite element model data stored in a selected SSI analysis package into the Industry Foundation Classes (IFC) standard, with the IFC standard chosen as the target schema owing to its widespread usage in the construction industry. The ability of the exporter tool to—with fidelity—convert a model from the selected analysis software to the IFC standard is showcased through an illustrative case study. In carrying out the case study, collections of geotechnical modeling parameters were identified that, while being common to numerous nonlinear SSI analysis packages, are not present in the existing IFC schema. Inclusion of these parameters in the IFC schema is proposed, serving to: 1) lower the barrier for entry of SSI modeling and analysis stakeholders to take part in interoperability processes and 2) encourage adoption of novel BIM processes in the bridge industry.

Schema mapping generation in the wild

Schema mappings enable declarative and executable specification of transformations between different schematic representations of application concepts. Most work on mapping generation has assumed that the source and target schemas are well defined, e.g., with declared keys and foreign keys, and that the mapping generation processes exist to support the data engineer in the labour-intensive process of producing a high-quality integration. However, organizations increasingly have access to numerous independently produced datasets, e.g., in a data lake, with a requirement to produce rapid, best-effort integrations, without extensive manual effort. As a result, there is a need to generate mappings in settings without declared relationships, and thus on the basis of inferred profiling data, and over large numbers of sources. Our contributions include a dynamic programming algorithm for exploring the space of potential mappings, and techniques for propagating profiling data through mappings, so that the fitness of candidate mappings can be estimated. The paper also describes how the resulting mappings can be used to populate single and multi-relation target schemas. Experimental results show the effectiveness and scalability of the approach in a variety of synthetic and real-world scenarios.

Active Learning for Knowledge Graph Schema Expansion

Both entity typing and relation extraction from text corpora are widely used to identify the semantic types of an entity and a relation in a knowledge graph (KG). Most existing approaches rely on a pre-defined set of entity types and relation types in a KG. They thus cannot map entity mentions (relation mentions) to unseen entity types (relation types). To fundamentally overcome the limitations, we should add new semantic types of entities and relations to a KG schema. However, schema expansion traditionally requires manual conceptualization through a user’s observation on the text corpus while assuming the existence of suitable target KG schemas. In this work, we propose an <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ctive learning framework for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</b> nowledge graph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> chema <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> xpansion ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AKSE</b> ), which can generate a new semantic type for KG schemas, without depending on a set of target schemas and human users’ observation. Specifically, a granularity based active learning algorithm determines whether a KG schema requires new semantic types or not. We also introduce a KG schema attention-based neural method which assigns semantic types to the entities and relationships extracted. To the best of our knowledge, our work is the first study to expand a KG schema with active learning.

Glean

Extracting structured information from templatic documents is an important problem with the potential to automate many real-world business workflows such as payment, procurement, and payroll. The core challenge is that such documents can be laid out in virtually infinitely different ways. A good solution to this problem is one that generalizes well not only to known templates such as invoices from a known vendor, but also to unseen ones. We developed a system called Glean to tackle this problem. Given a target schema for a document type and some labeled documents of that type, Glean uses machine learning to automatically extract structured information from other documents of that type. In this paper, we describe the overall architecture of Glean, and discuss three key data management challenges : 1) managing the quality of ground truth data, 2) generating training data for the machine learning model using labeled documents, and 3) building tools that help a developer rapidly build and improve a model for a given document type. Through empirical studies on a real-world dataset, we show that these data management techniques allow us to train a model that is over 5 F1 points better than the exact same model architecture without the techniques we describe. We argue that for such information-extraction problems, designing abstractions that carefully manage the training data is at least as important as choosing a good model architecture.

Interoperable Visualization Framework Towards Enhancing Mapping and Integration of Official Statistics

The aim of this research is to introduce a new interoperable visual analytics framework Towards Enhancing Presentation of Official Statistics. This paper aims to investigate how data integration and information visualization could be used to increase readability and interoperability of statistical data. Statistical data has gained many interests from policy makers, city planners, researchers and ordinary citizens as well. from an official statistics’ point of view, data integration is of major interest as a means of using available information more efficiently and improving the quality of a statistical agency’s products, we implemented and proposed statistical indicators schema and mapping algorithm which is conceptually simple and is based on hamming distance and edit (Levenshtein) distance mapping methods in addition to the ontology. Also we build GUI to import the indicators with data values from different sources. The performance and accuracy of this algorithm was measured by experiment, we started to import the data and indicators from different sources to our target schema which contains the indicators, Units and Subgroups. during the data import using our algorithm, the exact matched indicators, units and subgroups will be mapped automatically to the indicators, units, and subgroups in the schema, in case that we import not exact matched indicator, units or subgroups the algorithm will calculate the edit distance (minimum operations needed) for mapping the imported indicator with the nearest indicator in the schema, the same thing will happen for units or subgroups, the results showed that the accuracy of the algorithm increased by adding ontology, ontology matching is a solution to the semantic heterogeneity problem.

SMAT: An attention-based deep learning solution to the automation of schema matching.

Schema matching aims to identify the correspondences among attributes of database schemas. It is frequently considered as the most challenging and decisive stage existing in many contemporary web semantics and database systems. Low-quality algorithmic matchers fail to provide improvement while manually annotation consumes extensive human efforts. Further complications arise from data privacy in certain domains such as healthcare, where only schema-level matching should be used to prevent data leakage. For this problem, we propose SMAT, a new deep learning model based on state-of-the-art natural language processing techniques to obtain semantic mappings between source and target schemas using only the attribute name and description. SMAT avoids directly encoding domain knowledge about the source and target systems, which allows it to be more easily deployed across different sites. We also introduce a new benchmark dataset, OMAP, based on real-world schema-level mappings from the healthcare domain. Our extensive evaluation of various benchmark datasets demonstrates the potential of SMAT to help automate schema-level matching tasks.

A Study on Information-Preserving Schema Transformations

The problem of determining the relative information capacity between two knowledge bases or schemas, of the same or different models, is inherent when implementing schema transformations. When restructuring one schema into another, one expects that the schema transformation supports the complete and correct mapping of all the information contents from the source schema to the target schema. Such a characteristic is commonly referred to as information capacity preservation or schema dominance. This paper presents a formal and constructive approach to measure the relative information capacity, in the restricted case of first-order schemas related by first-order mappings. It complements the existing definitions of information capacity preservation from the perspective of model theory, showing the exact relationships among the constraints of the involved schemas, the mappings between the components of these schemas, and the database states which the schemas admit. Since satisfying some sort of schema equivalence property is essential in areas such as database conceptual design and database reverse engineering, our approach allows us to characterize the notion of normalization in database design. We review the current literature concerning database normal forms and decompositions. We also review the process of reverse engineering a database schema. In addition, we provide deeper insight into database reverse engineering methodologies, suggesting horizontal decompositions as a useful tool for facilitating the discovery of more specific objects and relationships in the conceptualization phase of the process. With the aid of simple examples, we show the essence behind our reasoning. We discuss the need for an unambiguous means through which objects in the output schema can be identified. Ultimately, the knowledge this paper ensues will be beneficial to database engineers in performing a correct schema transformation.

Temporal data exchange

Data exchange is the problem of transforming data that is structured under a source schema into data structured under another schema, called the target schema, so that both the source and target data satisfy the relationship between the schemas. Many applications such as planning, scheduling, medical and fraud detection systems, require data exchange in the context of temporal data. Even though the formal framework of data exchange for relational database systems is well-established, it does not immediately carry over to the settings of temporal data, which necessitates reasoning over unbounded periods of time.In this work, we study data exchange for temporal data. We first motivate the need for two views of temporal data: the concrete view, which depicts how temporal data is compactly represented and on which the implementations are based, and the abstract view, which defines the semantics of temporal data as a sequence of snapshots. We first extend the chase procedure for the abstract view to have a conceptual basis for the data exchange for temporal databases. Considering non-temporal source-to-target tuple generating dependencies and equality generating dependencies, the chase algorithm can be applied on each snapshot independently. Then we define a chase procedure (called c-chase) on concrete instances and show the result of c-chase on a concrete instance is semantically aligned with the result of chase on the corresponding abstract instance. In order to interpret intervals as constants while checking if a dependency or a query is satisfied by a concrete database, we will normalize the instance with respect to the dependency or the query. To obtain the semantic alignment, the nulls (which are introduced by data exchange and model incompleteness) in the concrete view are annotated with temporal information. Furthermore, we show that the result of the concrete chase provides a foundation for query answering. We define naïve evaluation on the result of the c-chase and show it produces certain answers.

Incorporating Data Context to Cost-Effectively Automate End-to-End Data Wrangling

The process of preparing potentially large and complex data sets for further analysis or manual examination is often called data wrangling. In classical warehousing environments, the steps in such a process are carried out using Extract-Transform-Load platforms, with significant manual involvement in specifying, configuring or tuning many of them. In typical big data applications, we need to ensure that all wrangling steps, including web extraction, selection, integration and cleaning, benefit from automation wherever possible. Towards this goal, in the paper we: (i) introduce a notion of data context, which associates portions of a target schema with extensional data of types that are commonly available; (ii) define a scalable methodology to bootstrap an end-to-end data wrangling process based on data profiling; (iii) describe how data context is used to inform automation in several steps within wrangling, specifically, matching, value format transformation, data repair, and mapping generation and selection to optimise the accuracy, consistency and relevance of the result; and (iv) we evaluate the approach with real estate data and financial data, showing substantial improvements in the results of automated wrangling.

CDI: Configurable Data Integration Using Property Precedence Relations

Generally, data integration is performed through schema mapping representing high level correlation between heterogeneous data sources. Such mappings are generated using direct correspondences between data elements of source and target schemas, while other semantic relations are neglected. In this paper, we first use hierarchical relationships among properties (property precedence) as fundamental semantic relations within source and target schemas to semantically enhance schema mappings. Then, we use global property precedence relations between source and target elements to achieve Configurable Data Integration (CDI). This configurable setting allows trade-off between accuracy and completeness in query answering. Experiments using a working prototype of CDI show the potential of using this approach in various data integration scenarios.

Approximate top-K answering under uncertain schema mappings

Data integration techniques provide a communication bridge between isolated sources and offer a platform for information exchange. When the schemas of heterogeneous data sources map to the centralized schema in a mediated data integration system or a source schema maps to a target schema in a peer-to-peer system, multiple schema mappings may exist due to the ambiguities in the attribute matching. The obscure schema mappings lead to the uncertainty in query answering, and frequently people are only interested in retrieving the best k answers (top-k) with the biggest probabilities. Retrieving the top-k answers efficiently has become a research issue. For uncertain queries, two semantics, by-table and by-tuple, have been developed to capture top-k answers based on the schema mapping probabilities. However, although the existing algorithms support certain features to capture the accurate top-k answers and avoid accessing all data from sources, they cannot effectively reduce the number of processed tuples in most cases. In this paper, new algorithms based on the histogram approximation and heuristic are proposed to efficiently identify the top-k answers for the data integration systems under uncertain schema mappings. In the experiments, the Histogram algorithm in the by-table semantics and the expected approach in the by-tuple semantics are shown to significantly reduce the number of processed tuples while maintaining high accuracy with the estimated probabilistic confidence.

DATA INTEGRATION MODEL DESIGN FOR SUPPORTING DATA CENTER PATIENT SERVICES DISTRIBUTED INSURANCE PURCHASE WITH VIEW BASED DATA INTEGRATION

Data integration is an important step in integrating information from multiple sources. The problem is how to find and combine data from scattered data sources that are heterogeneous and have semantically informant interconnections optimally. The heterogeneity of data sources is the result of a number of factors, including storing databases in different formats, using different software and hardware for database storage systems, designing in different data semantic models (Katsis &amp; Papakonstantiou, 2009, Ziegler &amp; Dittrich , 2004). Nowadays there are two approaches in doing data integration that is Global as View (GAV) and Local as View (LAV), but both have different advantages and limitations so that proper analysis is needed in its application. Some of the major factors to be considered in making efficient and effective data integration of heterogeneous data sources are the understanding of the type and structure of the source data (source schema). Another factor to consider is also the view type of integration result (target schema). The results of the integration can be displayed into one type of global view or a variety of other views. So in integrating data whose source is structured the approach will be different from the integration of the data if the data source is not structured or semi-structured. Scheme mapping is a specific declaration that describes the relationship between the source scheme and the target scheme. In the scheme mapping is expressed in in some logical formulas that can help applications in data interoperability, data exchange and data integration. In this paper, in the case of establishing a patient referral center data center, it requires integration of data whose source is derived from a number of different health facilities, it is necessary to design a schema mapping system (to support optimization). Data Center as the target orientation schema (target schema) from various reference service units as a source schema (source schema) has the characterization and nature of data that is structured and independence. So that the source of data can be integrated tersetruktur of the data source into an integrated view (as a data center) with an equivalent query rewriting (equivalent). The data center as a global schema serves as a schema target requires a "mediator" that serves "guides" to maintain global schemes and map (mapping) between global and local schemes. Data center as from Global As View (GAV) here tends to be single and unified view so to be effective in its integration process with various sources of schema which is needed integration facilities "integration". The "Pemadu" facility is a declarative mapping language that allows to specifically link each of the various schema sources to the data center. So that type of query rewriting equivalent is suitable to be applied in the context of query optimization and maintenance of physical data independence.Keywords: Global as View (GAV), Local as View (LAV), source schema ,mapping schema

SEDEX: Scalable Entity Preserving Data Exchange

Data exchange is the process of generating an instance of a target schema from an instance of a source schema such that source data is reflected in the target. Generally, data exchnge is performed using schema mapping, representing high level relations between source and target schemas. In this paper, we argue that data exchange solely based on schema level information limits the ability to express semantics in data exchange. We show such schema level mappings not only may result in entity fragmentation, they are unable to resolve some ambiguous data exchange scenarios. To address this problem, we propose Scalable Entity Preserving Data Exchange (SEDEX), a hybrid method based on data and schema mapping that employs similarities between relation trees of source and target relations to find the best relations that can host source instances. Our experiments show SEDEX outperforms other methods in terms of quality and scalability of data exchange.

A Probabilistic Approach to Knowledge Translation

In this paper, we focus on a novel knowledge reuse scenario where the knowledge in the source schema needs to be translated to a semantically heterogeneous target schema. We refer to this task as “knowledge translation” (KT). Unlike data translation and transfer learning, KT does not require any data from the source or target schema. We adopt a probabilistic approach to KT by representing the knowledge in the source schema, the mapping between the source and target schemas, and the resulting knowledge in the target schema all as probability distributions, specially using Markov random fields and Markov logic networks. Given the source knowledge and mappings, we use standard learning and inference algorithms for probabilistic graphical models to find an explicit probability distribution in the target schema that minimizes the Kullback-Leibler divergence from the implicit distribution. This gives us a compact probabilistic model that represents knowledge from the source schema as well as possible, respecting the uncertainty in both the source knowledge and the mapping. In experiments on both propositional and relational domains, we find that the knowledge obtained by KT is comparable to other approaches that require data, demonstrating that knowledge can be reused without data.

Fuzzy data exchange

Data exchange is the problem of transforming data that conforms to one source schema into data that conforms to a target schema that reflects the source data as accurately as possible and in a way that is consistent with various dependencies. In a landmark paper, Fagin et al. have proposed a declarative, purely logical approach to this task. Since then, data exchange has been intensively studied in the database research community.In this paper we extend the classical data exchange approach of Fagin et al. to fuzzy data exchange as a framework concerned with the transformation of fuzzy data. We show that the most important properties of data exchange are preserved in this new setting. Moreover, the relation of fuzzy data exchange to the classical setting, probabilistic data exchange and fuzzy logic is studied.

Translating Relational Database Schemas into Object-based Schemas: University Case Study

In this paper, we present a solution for translating an existing relational database (RDB) schema with recent patents into relatively newer database (i.e., object-oriented and object-relational database) schemas, based on available standards. The solution is more beneficial compared to the existing approaches as it generates two different and equivalent target schemas based on the user choice. The main aim of the proposed method is to enrich the source RDB semantically with metadata information, which may have not been obviously represented in it. An enriched common data model is then produced, which preserves all necessary characteristics of target models to be mapped into any of the target schemas. A prototype has been designed in order to translate RDBs into target schemas realizing the concepts and algorithms of the approach. An experiment has been conducted, results of which have been very encouraging, and validating the feasibility, efficiency and correctness of the proposed solution.

Ontology-based mappings

Data translation consists of the task of moving data from a source database to a target database. This task is usually performed by developing mappings, i.e. executable transformations from the source to the target schema. However, a richer description of the target database semantics may be available in the form of an ontology. This is typically defined as a set of views over the base tables that provides a unified conceptual view of the underlying data. We investigate how the mapping process changes when such a rich conceptualization of the target database is available. We develop a translation algorithm that automatically rewrites a mapping from the source schema to the target ontology into an equivalent mapping from the source to the target databases. Then, we show how to handle this problem when an ontology is available also for the source. Differently from previous approaches, the language we use in view definitions has the full power of non-recursive Datalog with negation. In the paper, we study the implications of adopting such an expressive language. Experiments are conducted to illustrate the trade-off between expressibility of the view language and efficiency of the chase engine used to perform the data exchange.

Toward a view-oriented approach for aligning RDF-based biomedical repositories.

This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". The need for complementary access to multiple RDF databases has fostered new lines of research, but also entailed new challenges due to data representation disparities. While several approaches for RDF-based database integration have been proposed, those focused on schema alignment have become the most widely adopted. All state-of-the-art solutions for aligning RDF-based sources resort to a simple technique inherited from legacy relational database integration methods. This technique - known as element-to-element (e2e) mappings - is based on establishing 1:1 mappings between single primitive elements - e.g. concepts, attributes, relationships, etc. - belonging to the source and target schemas. However, due to the intrinsic nature of RDF - a representation language based on defining tuples < subject, predicate, object > -, one may find RDF elements whose semantics vary dramatically when combined into a view involving other RDF elements - i.e. they depend on their context. The latter cannot be adequately represented in the target schema by resorting to the traditional e2e approach. These approaches fail to properly address this issue without explicitly modifying the target ontology, thus lacking the required expressiveness for properly reflecting the intended semantics in the alignment information. To enhance existing RDF schema alignment techniques by providing a mechanism to properly represent elements with context-dependent semantics, thus enabling users to perform more expressive alignments, including scenarios that cannot be adequately addressed by the existing approaches. Instead of establishing 1:1 correspondences between single primitive elements of the schemas, we propose adopting a view-based approach. The latter is targeted at establishing mapping relationships between RDF subgraphs - that can be regarded as the equivalent of views in traditional databases -, rather than between single schema elements. This approach enables users to represent scenarios defined by context-dependent RDF elements that cannot be properly represented when adopting the currently existing approaches. We developed a software tool implementing our view-based strategy. Our tool is currently being used in the context of the European Commission funded p-medicine project, targeted at creating a technological framework to integrate clinical and genomic data to facilitate the development of personalized drugs and therapies for cancer, based on the genetic profile of the patient. We used our tool to integrate different RDF-based databases - including different repositories of clinical trials and DICOM images - using the Health Data Ontology Trunk (HDOT) ontology as the target schema. The importance of database integration methods and tools in the context of biomedical research has been widely recognized. Modern research in this area - e.g. identification of disease biomarkers, or design of personalized therapies - heavily relies on the availability of a technical framework to enable researchers to uniformly access disparate repositories. We present a method and a tool that implement a novel alignment method specifically designed to support and enhance the integration of RDF-based data sources at schema (metadata) level. This approach provides an increased level of expressiveness compared to other existing solutions, and allows solving heterogeneity scenarios that cannot be properly represented using other state-of-the-art techniques.

Optimizing the chase

We are interested in scalable data integration and data exchange under constraints/dependencies. In data exchange the problem is how to materialize a target database instance, satisfying the source-to-target and target dependencies, that provides the certain answers. In data integration, the problem is how to rewrite a query over the target schema into a query over the source schemas that provides the certain answers. In both these problems we make use of the chase algorithm, the main tool to reason with dependencies. Our first contribution is to introduce the frugal chase, which produces smaller universal solutions than the standard chase, still remaining polynomial in data complexity. Our second contribution is to use the frugal chase to scale up query answering using views under LAV weakly acyclic target constraints, a useful language capturing RDF/S. The latter problem can be reduced to query rewriting using views without constraints by chasing the source-to-target mappings with the target constraints. We construct a compact graph-based representation of the mappings and the constraints and develop an efficient algorithm to run the frugal chase on this representation. We show experimentally that our approach scales to large problems, speeding up the compilation of the dependencies into the mappings by close to 2 and 3 orders of magnitude, compared to the standard and the core chase, respectively. Compared to the standard chase, we improve online query rewriting time by a factor of 3, while producing equivalent, but smaller, rewritings of the original query.