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Journal articles on the topic 'Data integration'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 July 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 (February 10, 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 (March 27, 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 (August 5, 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 (October 31, 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 illustrative graphs, we provide a comprehensive guide for data engineers to implement robust data integration frameworks.

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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 (October 31, 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 methodologies, pseudocode, and illustrative graphs, we provide a comprehensive guide for data engineers to implement robust data integration frameworks.

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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 facilitate visualization of geospatial layers? What are the two things you would recommend be addressed to advance discovery, accessibility, integration and visualization in the next five years?

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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 (November 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 standardizing data in Python, a widely used programming language for data analysis and machine learning. By mastering data standardization, organizations can unlock the full potential of their data, ensuring accuracy, reliability, and compatibility in an increasingly data-driven world.</i> 

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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 (February 28, 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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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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Kumar Reddy Thumburu, Sai. "Data Integration Strategies in Hybrid Cloud Environments." International Journal of Science and Research (IJSR) 11, no. 3 (March 5, 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 (March 9, 2024): 2346–54. http://dx.doi.org/10.55248/gengpi.5.0324.0737.

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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 (September 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 Warehouse relations are defined as views over the conceptual model. We propose a technique for declaratively specifying suitable reconciliation correspondences to be used in order to solve conflicts among data in different sources. The main goal of the method is to support the design of mediators that materialize the data in the Data Warehouse relations. Starting from the specification of one such relation as a query over the conceptual model, a rewriting algorithm reformulates the query in terms of both the source relations and the reconciliation correspondences, thus obtaining a correct specification of how to load the data in the materialized view.

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

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Dong, Xin Luna, and Divesh Srivastava. "Big data integration." Proceedings of the VLDB Endowment 6, no. 11 (August 27, 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 (February 15, 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 (January 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 main component of the framework is a module called Mapper which locates similar records and performs record linkage. The mapper contains a library of similarity measures such Jaccard, Jaro-Winkler, Monge-Elkan, MatchScore, Levenstein and TFIDF similarity. Finally, the author evaluates the approach on quality of the mappings.

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Tang, Lin. "Genomics data integration." Nature Methods 20, no. 1 (January 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 (July 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 (June 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 that data management techniques can surmount both of these challenges. In this work, we introduce the Causal Data Integration (CDI) problem, in which unobserved attributes are mined from external sources and a corresponding causal DAG is automatically built. We identify key challenges and research opportunities in designing a CDI system, and present a system architecture for solving the CDI problem. Our preliminary experimental results demonstrate that solving CDI is achievable and pave the way for future research.

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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 (March 31, 2018): 21–28. http://dx.doi.org/10.14257/ijdta.2018.11.1.03.

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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 sources or enriching existing occurrence data with other information. Several commonly encountered barriers to large-scale use of biodiversity occurrence data exist. These include the lack of facilities for local storage of large and rapidly changing datasets, the computational power required for processing, unfamiliarity with existing toolsets, and insufficient resources to maintain big data infrastructure. These challenges are well documented in the context of high-throughput genomics (Marx 2013), and more recently in occurrence-based biodiversity research (for example Thessen et al. 2018).However, while these hurdles and bottlenecks are very real, several of them have low cost of entry solutions. The aim of this presentation is to encourage the community to explore ambitious queries, to combine and examine all available data in its totality and to break down specific technical barriers, by providing a practical overview for researchers to maximise the power of large-scale data processing in their work.While big data processing may seem daunting, tools accessible to users without a background in big data are available for both local workstations and cloud computing services that allow for scalable data processing at low cost, for instance Databricks Community Edition or Apache Arrow. Using these resources, researchers can incorporate larger datasets into existing protocols, and by doing so, uncover patterns and insights that would be otherwise impossible to acquire using smaller subsets of the ever-expanding complex set that biodiversity occurrence data presents.

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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 (October 4, 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 strategic ramifications for companies and organizations looking to maximize the value of their data as-sets.

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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 (February 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 article a method to calculate the similarity between the potential duplicate data. This solution is based on graphics technology to narrow the search field for similar features. Then, a composite mechanism is used to locate the most similar records in our database to improve the quality of the data to make good decisions from heterogeneous sources.

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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 (January 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 general. In this paper we will focus on the present solutions for data integration, their flaws originating from their architecture or design concepts and present an abstract and general approach that could be introduced as an response to existing issues. The system integration is considered out of scope for this paper, we will focus particularly on efficient data integration.

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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 (July 31, 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 integration points. The algorithm for data integration is based on the Overlap coefficient since it presented better results when compared with the set similarity metrics Jaccard, Sørensen-Dice, and the Tversky index. We tested our model applying it on eight bioinformatics-domain datasets. Our model presents better results when compared to an analysis of a specialist, and we expect our model can be reused for other domains of datasets.

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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 (August 29, 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 with, the importance of understanding the unique requirements and constraints of both Salesforce and the external systems is emphasized. Integration strategies must be tailored to the specific use cases, whether they involve real-time data synchronization, batch processing, or event-driven architectures. A thorough analysis of the data types, formats, and structures is essential to ensure compatibility and to avoid data loss or corruption during the integration process.One of the key best practices highlighted in this paper is the use of middleware and integration platforms as a service (iPaaS) solutions. These tools provide a robust framework for managing data flows between Salesforce and external systems, offering features like data transformation, error handling, and process automation. The paper discusses the advantages of using middleware, such as reducing the complexity of integration projects, improving scalability, and enhancing the flexibility to adapt to changing business requirements.Another critical aspect covered is the importance of data governance and security in Salesforce integrations. As data moves between systems, ensuring its integrity, confidentiality, and compliance with regulatory requirements is vital. The paper explores strategies for implementing robust data governance policies, including the use of encryption, access controls, and audit trails to protect sensitive information. Additionally, the role of Salesforce's native security features, such as Shield and Event Monitoring, in safeguarding data during integration processes is discussed.The paper also delves into the challenges of integrating Salesforce with legacy systems, which often require custom solutions due to their outdated technologies and lack of standard integration capabilities. Strategies for overcoming these challenges, such as leveraging APIs, custom connectors, and data mapping tools, are examined. The importance of rigorous testing and validation processes to ensure that integrations meet performance and reliability standards is underscored.Furthermore, the paper emphasizes the need for continuous monitoring and maintenance of Salesforce integrations. As business needs evolve and systems are updated, integration workflows must be regularly reviewed and optimized to prevent disruptions and ensure ongoing efficiency. The use of monitoring tools and automated alerts is recommended to quickly identify and address any issues that arise.Finally, the paper presents several real-world case studies demonstrating successful Salesforce integrations with various external systems, including ERP platforms, marketing automation tools, and e-commerce solutions. These case studies provide practical insights into the application of best practices and highlight the benefits of seamless data flow, such as improved customer experiences, enhanced decision-making capabilities, and increased operational efficiency.In conclusion, integrating Salesforce with external systems requires a strategic approach that considers the unique characteristics of the systems involved, the importance of data governance and security, and the need for continuous monitoring and adaptation. By following the best practices outlined in this paper, organizations can achieve seamless data flow, enabling them to fully harness the power of Salesforce and their broader technology ecosystem.

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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 (October 7, 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 through Agnostic Messages. The proposal addresses the integration of loosely coupled software units, mainly when dealing with internal and external data integration. To illustrate the architecture’s components, a case study from the Mexican Logistics Company Paquetexpress is presented. This organization manages integrations across several platforms, including SalesForce and Oracle ERP, with clients like Amazon, Mercado Libre, Grainger, and Afull. Each of these incurs costs ranging from USD 30,000 to USD 36,000, with consultants from firms such as Quanam, K&F, TSOL, and TekSi playing a crucial role in their execution. This consumes much time, making maintenance costs considerably high when clients request data transmission or type changes, particularly when utilizing tools like Oracle Integration Cloud (OIC) or Oracle Service Bus (OSB). The article provides insights into the architecture’s design and implementation in a real-world scenario within the delivery company. The proposed architecture significantly reduces integration and maintenance times and costs while maximizing scalability and encouraging the reuse of components. The source code for this implementation has been registered in the National Registry of Copyrights in Mexico.

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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 from different sources (EI). Results. The paper proves that data management includes the formation and analysis of data architecture, integration of the database management system; data security, identification, segregation and storage of data sources. Data integration refers to the process of combining data from different sources into a single, holistic system and aims to provide access to a complete, updated and easy-to-analyze data set. Data integration is especially important in the areas of e-commerce, logistics and supply chains, where it is necessary to combine data from different sources to optimize processes, in the field of business intelligence, where processing large amounts of data and combining them allows you to identify useful information and certain patterns. Integration of enterprise information systems is the process of combining several IS and individual applications into a single, holistic system that works together to achieve a common goal, aimed at increasing the efficiency of the company, reducing duplication of efforts and streamlining processes. The main functional components of a corporate information system are identified: Business Process Automation IS, Financial Management IS, Customer Relationship Management IS, Supply Chain Management IS, Human Resources Management IS, Business Intelligence IS, Communication IS, and Data Security and Protection IS. Within a corporate information system, several narrowly focused software products operate simultaneously, capable of successfully solving a certain range of tasks. At the same time, some of them may not involve interaction with other information systems. The main approaches to data integration include universal access to data and data warehouses. Universal access technologies allow for equal access to data from different information systems, including on the basis of the concept of data warehouses - a database containing data collected from databases of different information subsystems for further analysis and use. It is proved that the most holistic approach to the integration of information systems is integration at the level of business processes. As part of the integration of business processes, there is an integration of applications, data integration, and integration of people involved in this business process. The article substantiates the feasibility of using three methods of big data management and integration: integration of corporate applications, integration of corporate information, and software for obtaining, transforming, and downloading data. As a result of comparing integration methods and building a generalized scheme for integrating heterogeneous IS, a number of situations have been identified in which the use of a specific integration method is preferable or the only possible one. The scientific novelty of the study is to identify the problems of integrating big data and corporate information systems. Approaches to choosing a method for integrating data and applications based on a generalized scheme for integrating heterogeneous information systems are proposed. Practical significance. The results of the analysis allow optimizing the methods of data integration within a corporate information system. The principles of integration inherent in the considered methods are used to solve a wide range of tasks: from real-time integration to batch integration and application integration. Implementation of the proposed methods of big data integration will make information more transparent; obtain additional detailed information about the efficiency of production and technological equipment, which stimulates innovation and improves the quality of the final product; use more efficient, accurate analytics to minimize risks and identify problems in advance before catastrophic consequences; more effectively manage supply chains, forecast demand, carry out comprehensive business planning, organize cooperation

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Meenakshi, Kathayat. "Application of Data Mining Techniques for Improving Continuous Integration." International Journal of Engineering and Management Research 8, no. 5 (October 30, 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 provides an overview of various issues regarding Continuous Integration and how various data mining techniques can be applied in continuous integration data for extracting useful knowledge and solving continuous integration problems.

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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 clades; sensory modalities. To support the aggregation and integration of trait information, data sets should be well structured, properly annotated and free of licensing or contractual restrictions so that they are 'findable, accessible, interoperable, and reusable' for both humans and machines (FAIR principles; Wilkinson et al. 2016). To this end, we are improving the documentation of protocols for the transformation, curation, and analysis of EOL data, and associated scripts and software are made available to ensure reproducibility. Proper acknowledgement of contributors and tracking of credit through derived data products promote both open data sharing and the use of aggregated resources. By exposing unique identifiers for data products, people, and institutions, data providers and aggregators can stimulate the development of automated solutions for the creation of contribution metrics. Since different aspects of provenance will be significant depending on the intended data use, better standardization of contributor roles (e.g., author, compiler, publisher, funder) is needed, as well as more detailed attribution guidance for data users. Global scale biodiversity data resources should resolve into a graph, linking taxa, specimens, occurrences, attributes, localities, and ecological interactions, as well as human agents, publications and institutions. Two key data categories for ensuring rich connectivity in the graph will be taxonomic and trait data. This graph can be supported by existing data hubs, if they share identifiers and/or create mappings between them, using standards and sharing practices developed by the biodiversity data community. Versioned archives of the combined graph could be published at intervals to appropriate open data repositories, and open source tools and training provided for researchers to access the combined graph of biodiversity knowledge from all sources. To achieve this, good communication among data hubs will be needed. We will need to share information about preferred vocabularies and identifier management practices, and collaborate on identifier mappings.

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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 (April 30, 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 (July 13, 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 ensure the quality, scalability, and accessibility of integrated data, such as data modeling, pipeline architecture, and real-time data processing. By examining case studies and emerging trends, this work highlights the significance of these techniques in enabling organizations to harness the full potential of their data, ultimately driving informed decision-making and fostering innovation in an increasingly data-driven world.

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Bernstein, Philip A. "Data Integration for Data-Intensive Science." OMICS: A Journal of Integrative Biology 15, no. 4 (April 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 (March 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 (August 1, 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 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

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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 (January 31, 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 facilitates seamless data flow and automation, improving both internal operations and customer interactions.

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Alpesh, Kanubhai Patel. "Seamless Integration: Connecting Salesforce with Third-Party Applications." Journal of Scientific and Engineering Research 8, no. 10 (October 31, 2021): 212–15. https://doi.org/10.5281/zenodo.13903083.

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In the modern business landscape, integrating Salesforce with third-party applications has become essential for optimizing operations and enhancing customer experiences. Salesforce, a leading CRM platform, provides robust tools for managing customer data and automating processes. However, its full potential is realized when combined with external systems, creating a unified ecosystem that streamlines data, automates workflows, and extends system capabilities. This article explores the numerous benefits and challenges associated with integrating Salesforce with various third-party applications. It provides insights into common integration scenarios, such as marketing automation, ERP and accounting systems, customer support platforms, and e-commerce solutions. The discussion includes best practices for successful integration, such as defining clear objectives, choosing appropriate integration methods, ensuring data security, and maintaining ongoing monitoring. Real-world case studies illustrate the practical impact of integrations on enhancing customer experiences and achieving operational efficiency. By adhering to these best practices, businesses can maximize the value of their technology investments and drive strategic success.

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Curcin, V., A. Barton, M. M. McGilchrist, H. Bastiaens, A. Andreasson, J. Rossiter, L. Zhao, 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 developed a core ontology for an interoperability framework based on data mediation. This article presents how such an ontology, the Clinical Data Integration Model (CDIM), can be designed to support, in conjunction with appropriate terminologies, biomedical data federation within TRANSFoRm, an EU FP7 project that aims to develop the digital infrastructure for a learning healthcare system in European Primary Care.Methods: TRANSFoRm utilizes a unified structural / terminological interoperability frame work, based on the local-as-view mediation paradigm. Such an approach mandates the global information model to describe the domain of interest independently of the data sources to be explored. Following a requirement analysis process, no ontology focusing on primary care research was identified and, thus we designed a realist ontology based on Basic Formal Ontology to support our framework in collaboration with various terminologies used in primary care.Results: The resulting ontology has 549 classes and 82 object properties and is used to support data integration for TRANSFoRm’s use cases. Concepts identified by researchers were successfully expressed in queries using CDIM and pertinent terminologies. As an example, we illustrate how, in TRANSFoRm, the Query Formulation Workbench can capture eligibility criteria in a computable representation, which is based on CDIM.Conclusion: A unified mediation approach to semantic interoperability provides a flexible and extensible framework for all types of interaction between health record systems and research systems. CDIM, as core ontology of such an approach, enables simplicity and consistency of design across the heterogeneous software landscape and can support the specific needs of EHR-driven phenotyping research using primary care data.

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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 (January 5, 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 (June 24, 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 (July 20, 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 by genetics, the microbiome, and other factors. In this review, we explore the data types that are available for studying immunology with an integrative systems approach, as well as the current strategies and challenges for conducting such analyses.

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Kaufman, G. "Pragmatic ECAD Data Integration." ACM SIGDA Newsletter 20, no. 1 (June 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 (January 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 that the passive subsystems alone are insufficient to improve the estimate of the position obtained from the inertial navigation system. A log measuring the velocity relative to the ground or a position determining system are needed. The improvement depends on the accuracy of the measuring instruments, the extent of time the instrument can be used and which filter is being used. The most complex filter, which contains fourteen states, eight to describe the motion of the submarine and six to describe the measurement system, including a model of the inertial navigation system, works very well.

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Brazhnik, Olga, and John F. Jones. "Anatomy of data integration." Journal of Biomedical Informatics 40, no. 3 (June 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 (December 6, 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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