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Global viewing of heterogeneous data sources

IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2001年第2期13卷 277-297页

作者： Castano, S De Antonellis, V di Vimercati, SD Univ Milan Dipartimento Sci Informaz I-20135 Milan Italy Univ Brescia Dipartimento Elettron Automaz I-25123 Brescia Italy

The problem of defining global views of heterogeneous data sources to support querying and cooperation activities is becoming more and more important due to the availability of multiple data sources within complex organizations and in global information systems. Global Views are defined to provide a unified representation of the information in the different sources by analyzing conceptual schemas associated with them and resolving possible semantic heterogeneity. In the paper. we propose an affinity-based unification method for global view construction. In the method: 1) The concept of affinity is introduced to assess the level of semantic relationship between elements in different schemas by taking into account semantic heterogeneity;2) Schema elements are classified by affinity levels using clustering procedures so that their different representations can be analyzed for unification;3) Global Views are constructed starting from selected clusters by unifying representations of their elements. Experiences of applying the proposed unification method and the associated tool environment ARTEMIS on databases of the Italian Public Administration information systems are described.

关键词： heterogeneous data sources affinity clustering global view schema analysis and unification

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Early Integration Testing for Entity Reconciliation in the Context of heterogeneous data sources

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IEEE TRANSACTIONS ON RELIABILITY 2018年第2期67卷 538-556页

作者： Blanco, Raquel Enriquez, Jose G. Dominguez-Mayo, Francisco J. Escalona, M. J. Tuya, Javier Univ Oviedo Dept Comp Sci Oviedo 33003 Spain Univ Seville Dept Comp & Language Syst Seville 41004 Spain

Entity reconciliation (ER) aims to combine data from different sources for a unified vision. The management of large volumes of data has given rise to significant challenges to the ER problem due to facts such as data becoming more unstructured, unclean, and incomplete or the existence of many datasets that store information about the same topic. Testing the applications that implement the ER problem is crucial to ensure both the correctness of the reconciliation process and the quality of the reconciled data. This paper presents an approach based on model-driven engineering that allows the creation of test models for the early integration testing of ER applications, contributing in three main aspects: the description of the elements of the proposed framework, the definition of the testing model, and the validation of the proposal through two real-world case studies. This validation verifies that the early integration testing of the ER application is capable of detecting a series of deficiencies, which a priori are not known and that will help to improve the final result that the ER application offers.

关键词： Early testing entity reconciliation heterogeneous data sources model-driven engineering software testing specification-based testing

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An approach for semantic integration of heterogeneous data sources

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PEERJ COMPUTER SCIENCE 2020年第3期6卷 e254页

作者： Fusco, Giuseppe Aversano, Lerina Univ Sannio Dept Engn Benevento BN Italy

Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semistructured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view.

关键词： data integration heterogeneous data sources Semantic integration Ontologies

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Observability quantification of public transportation systems with heterogeneous data sources: An information-space projection approach based on discretized space-time network flow models

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2019年第Oct.期128卷 302-323页

作者： Liu, Jiangtao Zhou, Xuesong Arizona State Univ Sch Sustainable Engn & Built Environm Tempe AZ 85281 USA

Focusing on how to quantify system observability in terms of different interested states, this paper proposes a modeling framework to systemically account for the multi-source sensor information in public transportation systems. By developing a system of linear equations and inequalities, an information space is generated based on the available data from heterogeneous sensor sources. Then, a number of projection functions are introduced to match the relation between the unique information space and different system states of interest, such as, the passenger flow/density on the platform or in the vehicle at specific time intervals, the path flow of each origin-destination pair, the earning collected from the tickets to different operation companies etc., in urban rail transit systems as our study object. Their corresponding observability represented by state estimate uncertainties is further quantified by calculating its maximum feasible state range in proposed space-time network flow models. All of proposed models are solved as linear programming models by Dantzig-Wolfe decomposition, and a k-shortest-path-based approximation approach is also proposed to solve our models in large-scale networks. Finally, numerical experiments are conducted to demonstrate our proposed methodology and algorithms. (C) 2019 Published by Elsevier Ltd.

关键词： System observability quantification Information space heterogeneous data sources Public transportation system Dantzig-Wolfe decomposition

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Revealing causality between heterogeneous data sources with deep restricted Boltzmann machines

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INFORMATION FUSION 2019年 50卷 139-147页

作者： Sokolovska, Nataliya Clement, Karine Zucker, Jean-Daniel Sorbonne Univ NutriOm Team Paris France Hop La Pitie Salpetriere AP HP Dept Nutr Paris France Res Inst Dev UMI 209 UMMISCO Bondy France

In a number of real life applications, scientists do not have access to temporal data, since budget for data acquisition is always limited. Here we challenge the problem of causal inference between groups of heterogeneous non-temporal observations obtained from multiple sources. We consider a family of probabilistic algorithms for causal inference based on an assumption that in case where X causes Y, P(X) and P(Y vertical bar X) are statistically independent. For a number of real world applications, deep learning methods were reported to achieve the most accurate empirical performance, what motivates us to use deep Boltzmann machines to approximate the marginal and conditional probabilities of heterogeneous observations as accurate as possible. We introduce a novel algorithm to infer causal relationships between blocks of variables. The proposed method was tested on a benchmark of multivariate cause-effect pairs. We show by our experiments that our method achieves the state-of-the-art empirical accuracy, and sometimes outperforms the state-of-the-art methods. An important part of our contribution is an application of the proposed algorithm to an original medical data set, where we explore relations between alimentary patters, human gut microbiome composition, and health status.

关键词： Probabilistic deep models Causal inference heterogeneous data sources Principal component analysis

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A Clinical Decision Support Framework for heterogeneous data sources

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2018年第6期22卷 1824-1833页

作者： Huang, Mengxing Han, Huirui Wang, Hao Li, Lefei Zhang, Yu Bhatti, Uzair Aslam Hainan Univ Coll Informat Sci & Technol State Key Lab Marine Resource Utilizat South Chin Haikou 570288 Hainan Peoples R China Norwegian Univ Sci & Technol Dept ICT & Nat Sci Big Data Lab N-6009 Alesund Norway Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China

To keep pace with the developments in medical informatics, health medical data is being collected continually. But, owing to the diversity of its categories and sources, medical data has become so complicated in many hospitals that it now needs a clinical decision support (CDS) system for its management. To effectively utilize the accumulating health data, we propose a CDS framework that can integrate heterogeneous health data from different sources such as laboratory test results, basic information of patients, and health records into a consolidated representation of features of all patients. Using the electronic health medical data so created, multilabel classification was employed to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients' health issues more efficiently. Once the physician diagnoses the disease of a patient, the next step is to consider the likely complications of that disease, which can lead to more diseases. Previous studies reveal that correlations do exist among some diseases. Considering these correlations, a k-nearest neighbors algorithm is improved for multilabel learning by using correlations among labels (CML-kNN). The CML-kNN algorithm first exploits the dependence between every two labels to update the origin label matrix and then performs multilabel learning to estimate the probabilities of labels by using the integrated features. Finally, it recommends the top N diseases to the physicians. Experimental results on real health medical data establish the effectiveness and practicability of the proposed CDS framework.

关键词： Diagnosis recommender systems clinical decision support system heterogeneous data sources multi-label classification

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Integrated data Management A Case Study in heterogeneous data sources in Brazilian Government

Integrated Data Management <i>A Case Study in Heterogeneous ...

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15th International Conference on Enterprise Information Systems (ICEIS)

作者： Rodrigues, Sergio Assis Alves, Marlon Girao, Allan Freitas Silva, Ricardo Chaves, Miriam de Souza, Jano Moreira Univ Fed Rio de Janeiro COPPE Dept Comp Sci BR-21945 Rio De Janeiro Brazil Minist Planning Budget & Management Brasilia DF Brazil

ISBN: (纸本)9789898565594

Typically, governments deal with huge masses of data generated by a variety of agencies at different hierarchical levels. Generally, this information is organized into different data sources resulting from legacy and heterogeneous systems. This lack of integration leads to management difficulties, since without a set of consolidated information decisions arc made lacking the proper grounding. Therefore;this work presents two initiatives of integrating data based on service oriented architecture, from heterogeneous sources in the Brazilian Government aiming at better management and information usage.

关键词： data Integration heterogeneous data sources Web Services Government Management

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Information Retrieval from heterogeneous data sources: An Application for Managing Medical Records

Information Retrieval from Heterogeneous Data Sources: An Ap...

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International Conference on Enterprise Information Systems

作者： Rosa-Paz, Darien Perez-Vazquez, Ramiro Fernandez-Luna, Juan M. Huete, Juan F. Univ Cent Marta Abreu de Las Villas Dept Ciencias Computac Carretera Camajuani Km 5-5 Santa Clara Villa Clara Cuba Univ Granada Dept Cienc Computac Inteligencia Artificial Granada 18071 Spain

ISBN: (纸本)9783642243516

Creating digital medical records and retrieve relevant information from them is a complex task. This complexity is given by the mixture of structured and unstructured information found in traditional medical records and that such information could be distributed in different data sources. The main contribution of this paper is the design of an architecture for an Information Retrieval (IR) system that integrates heterogeneous data sources, with a query interface in natural language. This system uses Natural Language Processing (NLP) techniques in order to transform the user query in subqueries to be executed on different data sources, profiting the query capabilities of database (DB) and IR technologies. The system is connected to the MedlinePlus Medical Encyclopedia to extend the results obtained from a query showing relevant articles to it. In addition, a general application of this design is proposed to address the medical records management problem in a Cuban hospital.

关键词： Information Retrieval databases heterogeneous data sources Integration Natural Language Query Interface

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Methodology of Big data Integration from A Priori Unknown heterogeneous data sources 18

Methodology of Big Data Integration from A Priori Unknown He...

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2nd International Conference on Computer Science and Artificial Intelligence (CSAI) / 10th International Conference on Information and Multimedia Technology (ICIMT)

作者： Samoylov, Alexey Sergeev, Nikolay Kucherova, Margarita Denisov, Boris Southern Fed Univ Chekhova St 2 Taganrog Russia Green Oasis Sch Tianmian 4030 Shennan Middle Rd Shenzhen 518026 Guangdong Peoples R China

ISBN: (纸本)9781450366069

The success of data preparation for Big data analytics directly depends on the quality of data integration from heterogeneous data sources. Extract, Transform and Load (ETL) systems have proved to be an efficient solution for this task. But to the moment, in the stages of data selection, definition of extraction rules and transformation, the decision is usually made exclusively by a data specialist. This, in turn, causes such problems as redundancy and inconsistency of imported data, narrow specialization of rules (up to uniqueness) with a limited number of analytical models and known requirements for the data mart. This paper presents the concept of solving the problem by providing methodological support for Big data preparation procedure to efficiently collect data from a priory unknown heterogeneous data sources.

关键词： Big data data integration knowledge extraction ETL heterogeneous data sources modeling semantics

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HDSAnalytics: A data Analytics Framework for heterogeneous data sources 18

HDSAnalytics: A Data Analytics Framework for Heterogeneous D...

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5th ACM India Joint International Conference on data Science and Management of data (CoDS) / International Conference on Management of data (COMAD)

作者： Jaybal, Yogalakshmi Ramanathan, Chandrashekar Rajagopalan, S. Int Inst Informat Technol Bangalore Karnataka India

ISBN: (纸本)9781450363419

This paper presents HDSAnalytics: A data analytics framework for heterogeneous data sources. This framework utilizes data from a variety of data sources differing in formats and volume. These data sources can contain data in structured, semi-structured or unstructured form. The integration of data from these different data sources into a single unified data source may result in some loss of information due to semantic, syntactic and schematic differences that arise among data sources. Semantic heterogeneity arises because of the presence of similar data in different forms in different data sources. Schematic and Syntactic heterogeneity arises due to the difference in formats/schema in which the data is stored and the way in which the data is accessed and retrieved respectively. Hence, the need to access, retrieve and utilize the information from different data sources possess challenges like 1. Mapping similar attributes among different data sources, 2. Retrieving specific attributes from different data sources that are relevant with respect to a users query, 3. Retrieving data from different data sources in different formats as requested by different components in the system. The proposed HDSAnalytics framework design aides analytic models in using heterogeneous data sources "As -Is" without integrating into a single data source, thereby overcoming all the above mentioned challenges. Our prototype of the framework, experimented using data from Bangalore Metropolitan Transport Corporation (BMTC), India, demonstrates how bus fleet operations can be smoothly analyzed, diagnosed and explored for improving bus fleet schedules and reducing the operations costs. It provides detailed insight on bus fleet operations. Our prototype scales and works efficiently well with increasing number of heterogeneous data sources.

关键词： heterogeneous data sources analytics query processing

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