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Articoli di riviste sul tema "Data integration"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

Arputhamary, B., and L. Arockiam. "Data Integration in Big Data Environment." Bonfring International Journal of Data Mining 5, no. 1 (2015): 01–05. http://dx.doi.org/10.9756/bijdm.8001.

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Ahmed Salamkar, Muneer. "Data Integration: AI-Driven Approaches to Streamline Data Integration from Various Sources." International Journal of Science and Research (IJSR) 12, no. 3 (2023): 1855–63. https://doi.org/10.21275/sr230311115337.

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Samrat Medavarapu, Sachin. "XML-Based Data Integration." International Journal of Science and Research (IJSR) 13, no. 8 (2024): 1984–86. http://dx.doi.org/10.21275/sr24810074326.

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Abhijit, Joshi. "Scalable Data Integration Frameworks: Enhancing Data Cohesion in Complex Systems." Journal of Scientific and Engineering Research 9, no. 10 (2022): 83–94. https://doi.org/10.5281/zenodo.12772820.

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Data integration in large-scale environments is crucial for organizations to leverage diverse data sources for advanced analytics and decision-making. This paper delves into the latest frameworks and methodologies designed to enhance data cohesion in complex systems. We explore the challenges associated with integrating heterogeneous data sources and present scalable solutions to achieve seamless data integration. The study highlights advanced techniques and tools, including ETL processes, data lakes, and modern data integration platforms. Through detailed methodologies, pseudocode, and illust

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Abhijit, Joshi. "Scalable Data Integration Frameworks: Enhancing Data Cohesion in Complex Systems." Journal of Scientific and Engineering Research 9, no. 10 (2022): 83–94. https://doi.org/10.5281/zenodo.13337884.

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<strong>Abstract </strong>Data integration in large-scale environments is crucial for organizations to leverage diverse data sources for advanced analytics and decision-making. This paper delves into the latest frameworks and methodologies designed to enhance data cohesion in complex systems. We explore the challenges associated with integrating heterogeneous data sources and present scalable solutions to achieve seamless data integration. The study highlights advanced techniques and tools, including ETL processes, data lakes, and modern data integration platforms. Through detailed methodologi

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Sherin, Andrew, and Mary Kennedy. "Future pathways for sharing and integrating data: 04 Symposium: Advances in data accessibility and data management for marine species occurrence data: Discussion Panel 2." Biodiversity Information Science and Standards 1 (August 18, 2017): e20406. https://doi.org/10.3897/tdwgproceedings.1.20406.

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Future Pathways for sharing and integrating data is a discussion panel following the second session of the symposium Advances in data accessibility and data management for marine species occurrence data. Panelists will include presenters from the session and invited guest panelists. Questions for discussion will be: Questions for discussion: In your view, how advanced is the marine science research community in data discovery and accessibility? What tools exist to facilitate integration of species occurrence information, associated measurements and environmental data? What tools exist to facil

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T., Aditya Sai Srinivas, Sravanthi Y., Vinod Kumar Y., and Dwaraka Srihith I.V. "Data Standardization: Key to Effective Data Integration." Advanced Innovations in Computer Programming Languages 6, no. 1 (2023): 1–4. https://doi.org/10.5281/zenodo.10060920.

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<i>Data standardization is a critical step in data preprocessing and analysis. This process involves transforming data to have a consistent scale, enabling meaningful comparisons and effective modeling. In this digital age, where data fuels decision-making across industries, understanding and implementing data standardization techniques is essential. This abstract introduces the concept of data standardization, emphasizing its importance in enhancing data quality, supporting data integration efforts, and facilitating data-driven decision-making. We explore various methods and tools for standar

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Fasihuddin, Mirza. "Integrating with Various Data Sources and Formats, Including Structured, Semi-Structured, and Unstructured Data." Journal of Scientific and Engineering Research 8, no. 2 (2021): 263–68. https://doi.org/10.5281/zenodo.11216190.

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The increasing availability and importance of data in various formats have led to the necessity for efficient integration methods to extract meaningful insights. This academic journal explores the challenges and solutions associated with integrating data from multiple sources, including structured, semi-structured, and unstructured data. The study aims to provide an overview of the techniques and tools available to businesses and researchers for effectively integrating diverse data types, enabling better decision-making and improving overall data-driven processes.

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9

Olmsted, Aspen. "Heterogeneous system integration data integration guarantees." Journal of Computational Methods in Sciences and Engineering 17 (January 19, 2017): S85—S94. http://dx.doi.org/10.3233/jcm-160682.

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10

Kumar Reddy Thumburu, Sai. "Data Integration Strategies in Hybrid Cloud Environments." International Journal of Science and Research (IJSR) 11, no. 3 (2022): 1642–49. http://dx.doi.org/10.21275/sr22032114643.

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Vaishnawi, Chittamuru, and Dr Bhuvana J. "Renewable Energy Integration in Cloud Data Centers." International Journal of Research Publication and Reviews 5, no. 3 (2024): 2346–54. http://dx.doi.org/10.55248/gengpi.5.0324.0737.

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12

CALVANESE, DIEGO, GIUSEPPE DE GIACOMO, MAURIZIO LENZERINI, DANIELE NARDI, and RICCARDO ROSATI. "DATA INTEGRATION IN DATA WAREHOUSING." International Journal of Cooperative Information Systems 10, no. 03 (2001): 237–71. http://dx.doi.org/10.1142/s0218843001000345.

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Information integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration in Data Warehousing. Our approach is based on a conceptual representation of the Data Warehouse application domain, and follows the so-called local-as-view paradigm: both source and Data Warehou

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13

NASSIRI, Hassana. "Data Model Integration." International Journal of New Computer Architectures and their Applications 7, no. 2 (2017): 45–49. http://dx.doi.org/10.17781/p002327.

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14

Miller, Renée J. "Open data integration." Proceedings of the VLDB Endowment 11, no. 12 (2018): 2130–39. http://dx.doi.org/10.14778/3229863.3240491.

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15

Dong, Xin Luna, and Divesh Srivastava. "Big data integration." Proceedings of the VLDB Endowment 6, no. 11 (2013): 1188–89. http://dx.doi.org/10.14778/2536222.2536253.

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Dong, Xin Luna, and Divesh Srivastava. "Big Data Integration." Synthesis Lectures on Data Management 7, no. 1 (2015): 1–198. http://dx.doi.org/10.2200/s00578ed1v01y201404dtm040.

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Vargas-Vera, Maria. "Data Integration Framework." International Journal of Knowledge Society Research 7, no. 1 (2016): 99–112. http://dx.doi.org/10.4018/ijksr.2016010107.

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This paper presents a proposal for a data integration framework. The purpose of the framework is to locate automatically records of participants from the ALSPAC database (Avon Longitudinal Study of Parents and Children) within its counterpart GPRD database (General Practice Research Database). The ALSPAC database is a collection of data from children and parents from before birth to late puberty. This collection contains several variables of interest for clinical researchers but we concentrate in asthma as a golden standard for evaluation of asthma has been made by a clinical researcher. The m

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18

Tang, Lin. "Genomics data integration." Nature Methods 20, no. 1 (2023): 34. http://dx.doi.org/10.1038/s41592-022-01736-4.

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Slater, Ted, Christopher Bouton, and Enoch S. Huang. "Beyond data integration." Drug Discovery Today 13, no. 13-14 (2008): 584–89. http://dx.doi.org/10.1016/j.drudis.2008.01.008.

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Youngmann, Brit, Michael Cafarella, Babak Salimi, and Anna Zeng. "Causal Data Integration." Proceedings of the VLDB Endowment 16, no. 10 (2023): 2659–65. http://dx.doi.org/10.14778/3603581.3603602.

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Causal inference is fundamental to empirical scientific discoveries in natural and social sciences; however, in the process of conducting causal inference, data management problems can lead to false discoveries. Two such problems are (i) not having all attributes required for analysis, and (ii) misidentifying which attributes are to be included in the analysis. Analysts often only have access to partial data, and they critically rely on (often unavailable or incomplete) domain knowledge to identify attributes to include for analysis, which is often given in the form of a causal DAG. We argue t

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Bakshi, Waseem Jeelani, Rana Hashmy, Majid Zaman, and Muheet Ahmed Butt. "Logical Data Integration Model for the Integration of Data Repositories." International Journal of Database Theory and Application 11, no. 1 (2018): 21–28. http://dx.doi.org/10.14257/ijdta.2018.11.1.03.

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22

Huybrechts, Pieter. "Big Data for Beginners." Biodiversity Information Science and Standards 7 (August 18, 2023): e111301. https://doi.org/10.3897/biss.7.111301.

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With the increasing amount of datasets being published and made available through global aggregators, such as the Global Biodiversity Information Facility (GBIF), new opportunities have opened to answer research questions that previously could not be considered. Techniques for large scale data integration offer benefits for the biodiversity research community (Heberling et al. 2021, Kays et al. 2020), profiting from the great and continuing efforts in data mobilisation and standardisation (such as Darwin Core, Wieczorek et al. 2012). These benefits include integrating several large data source

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Saloni Kumari. "Data integration: “Seamless data harmony: The art and science of effective data integration”." International Journal of Engineering & Technology 12, no. 2 (2023): 26–30. http://dx.doi.org/10.14419/ijet.v12i2.32335.

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The idea of data integration has evolved as a key strategy in today's data-driven environment, as data is supplied from various and heterogeneous sources. This article explores the relevance, methodology, difficulties, and transformative possibilities of data integration, delving into its multidimensional world. Data integration serves as the cornerstone for well-informed decision-making by connecting heterogeneous datasets and fostering unified insights. This article gives readers a sneak preview of the in-depth investigation into data integration, illuminating its technical complexities and

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Merieme, El Abassi, Amnai Mohamed, Choukri Ali, Fakhri Youssef, and Gherabi Noreddine. "Matching data detection for the integration system." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 1008–14. https://doi.org/10.11591/ijece.v13i1.pp1008-1014.

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The purpose of data integration is to integrate the multiple sources of heterogeneous data available on the internet, such as text, image, and video. After this stage, the data becomes large. Therefore, it is necessary to analyze the data that can be used for the efficient execution of the query. However, we have problems with solving entities, so it is necessary to use different techniques to analyze and verify the data quality in order to obtain good data management. Then, when we have a single database, we call this mechanism deduplication. To solve the problems above, we propose in this ar

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Chromiak, Michal, and Marcin Grabowiecki. "Heterogeneous Data Integration Architecture-Challenging Integration Issues." Annales Universitatis Mariae Curie-Sklodowska, sectio AI – Informatica 15, no. 1 (2015): 7. http://dx.doi.org/10.17951/ai.2015.15.1.7-11.

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As of today, most of the data processing systems have to deal with a large amount of data originated from numerous sources. Data sources almost always differ regarding its purpose of existence. Thus model, data processing engine and technology differ intensely. Due to current trend for systems fusion there is a growing demand for data to be present in a common way regardless of its legacy. Many systems have been devised as a response to such integration needs. However, the present data integration systems mostly are dedicated solutions that bring constraints and issues when considered in gener

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26

Todorova, Violeta, Veska Gancheva, and Valeri Mladenov. "COVID-19 Medical Data Integration Approach." MOLECULAR SCIENCES AND APPLICATIONS 2 (July 18, 2022): 102–6. http://dx.doi.org/10.37394/232023.2022.2.11.

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The need to create automated methods for extracting knowledge from data arises from the accumulation of a large amount of data. This paper presents a conceptual model for integrating and processing medical data in three layers, comprising a total of six phases: a model for integrating, filtering, sorting and aggregating Covid-19 data. A medical data integration workflow was designed, including steps of data integration, filtering and sorting. The workflow for Covid-19 medical data from clinical records of 20400 potential patients was employed.

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Colleoni Couto, Julia, Olimar Teixeira Borges, and Duncan Dubugras Ruiz. "Data integration in a Hadoop-based data lake: A bioinformatics case." International Journal of Data Mining & Knowledge Management Process 12, no. 4 (2022): 1–24. http://dx.doi.org/10.5121/ijdkp.2022.12401.

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When we work in a data lake, data integration is not easy, mainly because the data is usually stored in raw format. Manually performing data integration is a time-consuming task that requires the supervision of a specialist, which can make mistakes or not be able to see the optimal point for data integration among two or more datasets. This paper presents a model to perform heterogeneous in-memory data integration in a Hadoop-based data lake based on a top-k set similarity approach. Our main contribution is the process of ingesting, storing, processing, integrating, and visualizing the data in

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Chinta, Umababu, Akshun Chhapola, and Shalu Jain. "Integration of Salesforce with External Systems: Best Practices for Seamless Data Flow." Journal of Quantum Science and Technology 1, no. 3 (2024): 25–41. http://dx.doi.org/10.36676/jqst.v1.i3.25.

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The integration of Salesforce with external systems is a critical aspect of modern enterprise architecture, enabling seamless data flow and ensuring that businesses can leverage the full potential of their technology ecosystems. As organizations increasingly rely on diverse platforms and applications, the need for efficient and reliable integration strategies becomes paramount. This paper explores best practices for integrating Salesforce with external systems, focusing on achieving seamless data flow while addressing the complexities and challenges associated with such integrations.To begin w

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Ruíz-Ceniceros, Juan Antonio, José Alfonso Aguilar-Calderón, Carolina Tripp-Barba, and Aníbal Zaldívar-Colado. "Dynamic Canonical Data Model: An Architecture Proposal for the External and Data Loose Coupling for the Integration of Software Units." Applied Sciences 13, no. 19 (2023): 11040. http://dx.doi.org/10.3390/app131911040.

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Integrating third-party and legacy systems has become a critical necessity for companies, driven by the need to exchange information with various entities such as banks, suppliers, customers, and partners. Ensuring data integrity, keeping integrations up-to-date, reducing transaction risks, and preventing data loss are all vital aspects of this complex task. Achieving success in this endeavor, which involves both technological and business challenges, necessitates the implementation of a well-suited architecture. This article introduces an architecture known as the Dynamic Canonical Data Model

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Kasyanova, Nataliia, Serhii Koverha та Vladyslav Okhrimenko. "УПРАВЛІННЯ ТА ІНТЕГРАЦІЯ ДАНИХ В УМОВАХ ЦИФРОВІЗАЦІЇ ЕКОНОМІЧНИХ ПРОЦЕСІВ: ВИКЛИКИ ТА ПЕРСПЕКТИВИ". Economical 1, № 27 (2023): 71–87. http://dx.doi.org/10.31474/1680-0044-2023-1(27)-71-87.

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Objective. The purpose of the article is to clarify the theoretical and methodological aspects, analyze data management methods in the context of digitalization of economic processes, and choose the priority method of integrating corporate information systems depending on the tasks to be solved in each case. Methods. The paper uses a set of data integration methods: application integration method (EAI), method of extracting data from external sources, transforming them in the appropriate structure and forming data warehouses (ETL); method of real-time integration of incomparable data types fro

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Hammock, Jennifer, and Katja Schulz. "Trait Data Integration from the Perspective of a Data Aggregator." Biodiversity Information Science and Standards 3 (August 20, 2019): e38411. https://doi.org/10.3897/biss.3.38411.

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The Encyclopedia of Life currently hosts ~8M attribute records for ~400k taxa (March 2019, not including geographic categories, Fig. 1). Our aggregation priorities include Essential Biodiversity Variables (Kissling et al. 2018) and other global scale research data priorities. Our primary strategy remains partnership with specialist open data aggregators; we are also developing tools for the deployment of evolutionarily conserved attribute values that scale quickly for global taxonomic coverage, for instance: tissue mineralization type (aragonite, calcite, silica...); trophic guild in certain c

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32

Meenakshi, Kathayat. "Application of Data Mining Techniques for Improving Continuous Integration." International Journal of Engineering and Management Research 8, no. 5 (2018): 20–23. https://doi.org/10.31033/ijemr.8.5.4.

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Continuous integration is a software development process where members of a team frequently integrate the work done by them. Generally each person integrates at least daily - leading to multiple integrations per day. Integration done by each developer is verified by an automated build (including test) to detect integration errors as quickly as possible. Many teams find that this approach reduces integration problems and allows a team to develop cohesive software rapidly. Continuous Integration doesn’t remove bugs, but it does make them dramatically easier to find and remove. This paper p

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33

Bernasconi, Anna. "Data quality-aware genomic data integration." Computer Methods and Programs in Biomedicine Update 1 (2021): 100009. http://dx.doi.org/10.1016/j.cmpbup.2021.100009.

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Salinas, Sonia Ordonez, and Alba Consuelo Nieto Lemus. "Data Warehouse and Big Data Integration." International Journal of Computer Science and Information Technology 9, no. 2 (2017): 01–17. http://dx.doi.org/10.5121/ijcsit.2017.9201.

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Mandala, Vishwanadham. "Data Integration and Data Engineering Techniques." International Journal of Scientific Research and Management (IJSRM) 5, no. 5 (2024): 5354–59. http://dx.doi.org/10.18535/ijsrm/v5i5.13.

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Data integration and data engineering techniques play a crucial role in the modern data landscape, facilitating the seamless amalgamation of diverse data sources to derive meaningful insights. As organizations increasingly rely on big data analytics, the need for efficient and robust data integration methodologies becomes paramount. This paper explores various techniques for data integration, including Extract, Transform, Load (ETL), data virtualization, and data federation, emphasizing their applicability across different domains. Additionally, we discuss data engineering practices that ensur

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Bernstein, Philip A. "Data Integration for Data-Intensive Science." OMICS: A Journal of Integrative Biology 15, no. 4 (2011): 241. http://dx.doi.org/10.1089/omi.2011.0020.

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Lu, James J. "A Data Model for Data Integration." Electronic Notes in Theoretical Computer Science 150, no. 2 (2006): 3–19. http://dx.doi.org/10.1016/j.entcs.2005.11.031.

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Nurhendratno, Slamet Sudaryanto, and Sudaryanto Sudaryanto. "DATA INTEGRATION MODEL DESIGN FOR SUPPORTING DATA CENTER PATIENT SERVICES DISTRIBUTED INSURANCE PURCHASE WITH VIEW BASED DATA INTEGRATION." Computer Engineering, Science and System Journal 3, no. 2 (2018): 162. http://dx.doi.org/10.24114/cess.v3i2.8895.

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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 & Papakonstantiou, 2009, Ziegler & Dittrich , 2004). Nowadays there are two approaches in doing data integr

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Chirag, Amrutlal Pethad. "Integrating IT Service Management and Customer Relationship Management Systems." Journal of Scientific and Engineering Research 8, no. 1 (2021): 230–40. https://doi.org/10.5281/zenodo.14050042.

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Integrating ServiceNow with Salesforce enhances operational efficiency and data consistency through various methods, including Integration Hub, MuleSoft, and custom API solutions. Key steps involve enabling Integration Hub, setting up connections, creating integration actions, and testing the integration. Security measures, such as OAuth authentication and data encryption, are crucial. Comprehensive testing, monitoring, and maintenance ensure the integration's robustness. Best practices include clear objectives, documentation, modular design, and stakeholder collaboration. This integration fac

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Curcin, V., A. Barton, M. M. McGilchrist, et al. "Clinical Data Integration Model." Methods of Information in Medicine 54, no. 01 (2015): 16–23. http://dx.doi.org/10.3414/me13-02-0024.

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SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Background: Primary care data is the single richest source of routine health care data. However its use, both in research and clinical work, often requires data from multiple clinical sites, clinical trials databases and registries. Data integration and interoperability are therefore of utmost importance.Objectives: TRANSFoRm’s general approach relies on a unified interoperability framework, described in a previous paper. We develope

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Neang, Andrew B., Will Sutherland, Michael W. Beach, and Charlotte P. Lee. "Data Integration as Coordination." Proceedings of the ACM on Human-Computer Interaction 4, CSCW3 (2021): 1–25. http://dx.doi.org/10.1145/3432955.

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Bertino, E., and E. Ferrari. "XML and data integration." IEEE Internet Computing 5, no. 6 (2001): 75–76. http://dx.doi.org/10.1109/4236.968835.

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Di Lorenzo, Giusy, Hakim Hacid, Hye-young Paik, and Boualem Benatallah. "Data integration in mashups." ACM SIGMOD Record 38, no. 1 (2009): 59–66. http://dx.doi.org/10.1145/1558334.1558343.

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Pineda, Silvia, Daniel G. Bunis, Idit Kosti, and Marina Sirota. "Data Integration for Immunology." Annual Review of Biomedical Data Science 3, no. 1 (2020): 113–36. http://dx.doi.org/10.1146/annurev-biodatasci-012420-122454.

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Over the last several years, next-generation sequencing and its recent push toward single-cell resolution have transformed the landscape of immunology research by revealing novel complexities about all components of the immune system. With the vast amounts of diverse data currently being generated, and with the methods of analyzing and combining diverse data improving as well, integrative systems approaches are becoming more powerful. Previous integrative approaches have combined multiple data types and revealed ways that the immune system, both as a whole and as individual parts, is affected

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Kaufman, G. "Pragmatic ECAD Data Integration." ACM SIGDA Newsletter 20, no. 1 (1990): 60–81. http://dx.doi.org/10.1145/378886.1062259.

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Svensson, A., and J. Holst. "Integration of Navigation Data." Journal of Navigation 48, no. 1 (1995): 114–35. http://dx.doi.org/10.1017/s0373463300012558.

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This article treats integration of navigation data from a variety of sensors in a submarine using extended Kalman filtering in order to improve the accuracy of position, velocity and heading estimates. The problem has been restricted to planar motion. The measurement system consists of an inertial navigation system, a gyro compass, a passive log, an active log and a satellite navigation system. These subsystems are briefly described and models for the measurement errors are given.Four different extended Kalman filters have been tested by computer simulations. The simulations distinctly show th

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Brazhnik, Olga, and John F. Jones. "Anatomy of data integration." Journal of Biomedical Informatics 40, no. 3 (2007): 252–69. http://dx.doi.org/10.1016/j.jbi.2006.09.001.

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Powell, V. J. H., and A. Acharya. "Disease Prevention: Data Integration." Science 338, no. 6112 (2012): 1285–86. http://dx.doi.org/10.1126/science.338.6112.1285-b.

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Riedemann, Catharina, and Christian Timm. "Services for data integration." Data Science Journal 2 (2003): 90–99. http://dx.doi.org/10.2481/dsj.2.90.

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Kezunovic, M. "Integration of Substation Data." IFAC Proceedings Volumes 44, no. 1 (2011): 12861–66. http://dx.doi.org/10.3182/20110828-6-it-1002.02654.

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