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A systematic overview of data federation systems

SEMANTIC WEB 2024年第1期15卷 107-165页

作者： Gu, Zhenzhen Corcoglioniti, Francesco Lanti, Davide Mosca, Alessandro Xiao, Guohui Xiong, Jing Calvanese, Diego Free Univ Bozen Bolzano Fac Comp Sci KRDB Res Ctr Bolzano Italy Univ Bergen Dept Informat Sci & Media Studies Bergen Norway Univ Oslo Dept Informat Oslo Norway Ontopic Srl Milan Italy Umea Univ Dept Comp Sci Umea Sweden

data federation addresses the problem of uniformly accessing multiple, possibly heterogeneous data sources, by mapping them into a unified schema, such as an RDF(S)/OWL ontology or a relational schema, and by supporting the execution of queries, like SPARQL or SQL queries, over that unified schema. data explosion in volume and variety has made data federation increasingly popular in many application domains. Hence, many data federation systems have been developed in industry and academia, and it has become challenging for users to select suitable systems to achieve their objectives. In order to systematically analyze and compare these systems, we propose an evaluation framework comprising four dimensions: (i) federation capabilities, i.e., query language, data source, and federation techniques;(ii) data security, i.e., authentication, authorization, auditing, encryption, and data masking;(iii) interface, i.e., graphical interface, command line interface, and application programming interface;and (iv) development, i.e., main development language, deployment, commercial support, open source, and release. Using this framework, we thoroughly studied 51 data federation systems from the Semantic Web and database communities. This paper shares the results of our investigation and aims to provide reference material and insights for users, developers and researchers selecting or further developing data federation systems.

关键词： data federation systems federated query answering data virtualization heterogeneous data integration system evaluation framework

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A survey on machine learning based analysis of heterogeneous data in industrial automation

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COMPUTERS IN INDUSTRY 2023年第1期149卷

作者： Kamm, Simon Veekati, Sushma Sri Mueller, Timo Jazdi, Nasser Weyrich, Michael Univ Stuttgart Inst Ind Automat & Software Engn Pfaffenwaldring 47 D-70550 Stuttgart Germany

In many application domains data from different sources are increasingly available to thoroughly monitor and describe a system or device. Especially within the industrial automation domain, heterogeneous data and its analysis gain a lot of attention from research and industry, since it has the potential to improve or enable tasks like diagnostics, predictive maintenance, and condition monitoring. For data analysis, machine learning based approaches are mostly used in recent literature, as these algorithms allow us to learn complex correlations within the data. To analyze even heterogeneous data and gain benefits from it in an application, data from different sources need to be integrated, stored, and managed to apply machine learning algorithms. In a setting with heterogeneous data sources, the analysis algorithms should also be able to handle data source failures or newly added data sources. In addition, existing knowledge should be used to improve the machine learning based analysis or its training process. To find existing approaches for the machine learning based analysis of heterogeneous data in the industrial automation domain, this paper presents the result of a systematic literature review. The publications were reviewed, evaluated, and discussed concerning five requirements that are derived in this paper. We identified promising solutions and approaches and outlined open research challenges, which are not yet covered sufficiently in the literature.

关键词： Machine learning Multi -modal machine learning Adaptive machine learning (Physics-) informed machine learning heterogeneous data integration heterogeneous data management

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Distributed Learning Framework for Multi-timeframe data Modelling in Financial Multi-step Forecasting

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EMERGING MARKETS FINANCE AND TRADE 2025年第9期61卷 2877-2894页

作者： Mehrizi, Ali Yazdi, Hadi Sadoghi Ferdowsi Univ Mashhad Dept Comp Engn Mashhad Iran

Financial time series are inherently challenged by issues such as non-linearity and non-stationarity, which become particularly problematic when designing models for multi-step forecasting. To tackle the challenges of long-term forecasting, market traders often analyze data across multiple time frames and combine the results, providing them with insights into short-term, medium-term, and long-term trends. Inspired by this approach, we propose a model for multi-step forecasting of financial time series that effectively handles non-linearity and provides long-term forecasts without complex modeling. Our proposed model is a distributed probabilistic adaptive learning method that utilizes diverse data in each node. However, integrating diverse data in distributed models is challenging because improper data combination can disrupt the network's stability, and the data must be sufficiently aligned to ensure effective learning. To address this, we employ scale-space kernels, which are mathematically grounded and preserve the essential characteristics of the data. Moreover, the integration of these kernels into the distributed probabilistic adaptive learning network ensures the stability and convergence of the network under certain conditions. Experiments on Standard & Poor's 500 (S&P 500) and Tehran Stock Exchange (TSE) data demonstrate the model's superior accuracy and robustness, especially in extended forecasting horizons.

关键词： Financial time series forecasting scale-space diffusion information probabilistic distributed learning non-linear time series long-term forecasting heterogeneous data integration C22 C53 G17

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Graph integration of structured, semistructured and unstructured data for data journalism

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INFORMATION SYSTEMS 2022年 104卷 101846-101846页

作者： Anadiotis, Angelos Christos Balalau, Oana Conceicao, Catarina Galhardas, Helena Haddad, Mhd Yamen Manolescu, Ioana Merabti, Tayeb You, Jingmao Inst Polytech Paris Ecole Polytech Paris France Ecole Polytech Fed Lausanne Lausanne Switzerland Inst Polytech Paris INRIA Paris France Univ Lisbon INESC ID Lisbon Portugal Univ Lisbon IST Lisbon Portugal

Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmental-organizations, or small public administrations) need to be able to make sense of such heterogeneous corpora, even if they lack the ability to define and deploy custom extract-transform-load workflows, especially for dynamically varying sets of data sources. We describe a complete approach for integrating dynamic sets of heterogeneous datasets along the lines described above: the challenges we faced to make such graphs useful, allow their integration to scale, and the solutions we proposed for these problems. Our approach is implemented within the ConnectionLens system;we validate it through a set of experiments. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： data journalism heterogeneous data integration Information extraction

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An ensemble approach for healthcare application and diagnosis using natural language processing

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COGNITIVE NEURODYNAMICS 2022年第5期16卷 1203-1220页

作者： Alekhya, Badi Sasikumar, R. RMD Engn Coll Chennai Tamil Nadu India

integration of healthcare records into a single application is still a challenging process There are additional issues when data becomes heterogeneous, and its application based on users does not appear to be the same. Hence, we propose an application called MEDSHARE which is a web-based application that integrates the data from various sources and helps the patient to access all their health records in a single point of source. Apart just from the collection of data, this portal enables the process of diagnosis using Natural language processing. The process is carried out by fuzzy logic ruleset which is generated by using NLP packages. The resulted information is given to the SVM classifier which helps in the prediction of diseases resulting in 89% of accuracy and standing the best compared to other classifiers. Finally, the observations resulted are sent to the front end application and the concerned user mobile through text message in their own native language for which translation package is been used.

关键词： heterogeneous data integration MEDSHARE Natural language processing SVM Fuzzy logic

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Geospatial Enrichment of Urban data for Advanced City Planning: a Pilot Study

Geospatial Enrichment of Urban Data for Advanced City Planni...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Krasteva, Iva Petrova-Antonova, Dessislava Paoli, Flavio De Hristov, Emil Borukova, Milena Ciavotta, Michele Avogadro, Roberto Sofia University St.Kliment Ohridski Gate Institute Sofia Bulgaria Università di Milano-Bicocca Sistemistica e Comunicazione Dipartimento di Informatica Milan Italy

ISBN: (纸本)9798350324457

data enrichment facilitates the creation of rich, expressive, and high-quality datasets, enabling valuable analytics and enhanced decision-making. The accurate geolocation of residential addresses and travel routes is crucial for determining the most appropriate locations of critical social infrastructure, such as educational and medical centres. This paper introduces an interactive semantic enrichment approach that enhances urban data by integrating high-quality geospatial information. The approach is supported by a modular and extensible data enrichment framework, which leverages existing geolocation services, enabling seamless data integration. Human-in-the-loop revision is employed to enhance the quality of geocoding results. A real-world pilot study conducted in Sofia, Bulgaria, was used to validate this approach, demonstrating its promising potential in addressing pressing issues in parametric urban planning. © 2023 IEEE.

关键词： address geocoding heterogeneous data integration interactive data enrichment semantic data enrichment

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heterogeneous data integration Model Based on Virtual View

Heterogeneous Data Integration Model Based on Virtual View

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7th International Conference on Computer Science and Education

作者： Wu, Jinqun Loudi Vocat & Tech Coll Dept Elect & Elect Engn Loudi Peoples R China

ISBN: (纸本)9781467302395

Consider the implementation of heterogeneous data integration, this paper employs virtual view to design and implement data integration model based on middleware integration. The model that supported by XML technology, refer to the SQL language, its access interface is the Web Service, it effectively resolve the heterogeneous data exchange and sharing.

关键词： XML SQL virtual view Web Service heterogeneous data integration

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Tumor Subtype Identification with Weighted Sparse Non-negative Matrix Factorization for Multiple heterogeneous data integration

Tumor Subtype Identification with Weighted Sparse Non-negati...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Hyunsoo Kim Jeff Chuang Markus Bredel The Jackson Laboratory for Genomic Medicine Farmington CT USA Dept. of Radiation Oncology The University of Alabama at Birmingham Birmingham AL USA

ISBN: (纸本)9781479913114

Tumor subtype identification is an important topic for personalized medicine to design better treatment for each subcategory. It has been shown that non-negative matrix factorization (NMF) performs well for many practical problems including tumor subtype identification. However, input genes can also affect its performance. In this paper, we review a variation of sparse NMF (sNMF), and introduce a novel algorithm of the weighted sparse NMF (wsNMF) to incorporate known biological knowledge by integrating multiple heterogeneous data (e.g., gene expression, mutations, protein-protein interaction network, and transcription factor target network). wsNMF is applied to the identification of tumor subtypes of uterine corpus endometrial carcinoma.

关键词： component cancer survival subtype systems biology individualized medicine Precision Medicine matrix decomposition systems biology Subtypes heterogeneous data integration NMF protocol Cancer data integration Nonnegative Tumours

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Multimodal data Fusion in High-Dimensional heterogeneous datasets Via Generative Models

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 5175-5188页

作者： Yilmaz, Yasin Aktukmak, Mehmet Hero, Alfred Univ S Florida Elect Engn Dept Tampa FL 33620 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

The commonly used latent space embedding techniques, such as Principal Component Analysis, Factor Analysis, and manifold learning techniques, are typically used for learning effective representations of homogeneous data. However, they do not readily extend to heterogeneous data that are a combination of numerical and categorical variables, e.g., arising from linked GPS and text data. In this paper, we are interested in learning probabilistic generative models from high-dimensional heterogeneous data in an unsupervised fashion. The learned generative model provides latent unified representations that capture the factors common to the multiple dimensions of the data, and thus enable fusing multimodal data for various machine learning tasks. Following a Bayesian approach, we propose a general framework that combines disparate data types through the natural parameterization of the exponential family of distributions. To scale the model inference to millions of instances with thousands of features, we use the Laplace-Bernstein approximation for posterior computations involving nonlinear link functions. The proposed algorithm is presented in detail for the commonly encountered heterogeneous datasets with real-valued (Gaussian) and categorical (multinomial) features. Experiments on two high-dimensional and heterogeneous datasets (NYC Taxi and MovieLens-10 M) demonstrate the scalability and competitive performance of the proposed algorithm on different machine learning tasks such as anomaly detection, data imputation, and recommender systems.

关键词： data models Analytical models Load modeling Computational modeling Principal component analysis Correlation Loading heterogeneous data integration latent variable models variational inference factor analysis exponential family of distributions

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Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches

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BRIEFINGS IN BIOINFORMATICS 2021年第1期22卷 346-359页

作者： Paltun, Betul Guvenc Mamitsuka, Hiroshi Kaski, Samuel Aalto Univ Helsinki Finland Kyoto Univ Inst Chem Res Bioinformat Ctr Kyoto Japan

Predicting the response of cancer cell lines to specific drugs is one of the central problems in personalized medicine, where the cell lines show diverse characteristics. Researchers have developed a variety of computational methods to discover associations between drugs and cell lines, and improved drug sensitivity analyses by integrating heterogeneous biological data. However, choosing informative data sources and methods that can incorporate multiple sources efficiently is the challenging part of successful analysis in personalized medicine. The reason is that finding decisive factors of cancer and developing methods that can overcome the problems of integrating data, such as differences in data structures and data complexities, are difficult. In this review, we summarize recent advances in data integration-based machine learning for drug response prediction, by categorizing methods as matrix factorization-based, kernel-based and network-based methods. We also present a short description of relevant databases used as a benchmark in drug response prediction analyses, followed by providing a brief discussion of challenges faced in integrating and interpreting data from multiple sources. Finally, we address the advantages of combining multiple heterogeneous data sources on drug sensitivity analysis by showing an experimental comparison.

关键词： personalized medicine machine learning drug response prediction bioinformatics heterogeneous data integration

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