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Influence of AI, BC and IoT for Healthcare – II
Published in Naveen Chilamkurti, T. Poongodi, Balamurugan Balusamy, Blockchain, Internet of Things, and Artificial Intelligence, 2021
T. Subha, R. Ranjana, T. Sheela
Patientry (POTY): It is a medical chain data-based project. It uses blockchain by which physicians, patients, and hospitals can manage their data securely. Data management involves the process of storing, accessing, and transferring data. The main use is to provide a data-secured environment for healthcare industry patients and doctors. This project aims to change the method by which electronic data in the healthcare industry is handled by using blockchain technology. Using PTOY, patients can purchase additional storage facilities from the hospital system. This token has three main purposes:Maintain good standards for the healthcare industryEffectively manage paid transactionsBalance the storage space of the network equally between doctors and patient platforms.
Introduction
Published in Walter R. Paczkowski, Deep Data Analytics for New Product Development, 2020
Data are often maintained or housed in different settings as simple as personal computer files and as complex as data stores, data warehouses, data lakes, and data marts. Since they are maintained in various types of settings, a method must be devised to manage them so that everyone involved in the NPD process can access and use them to extract information. So one important component of Deep Data Analytics is Data Management. Data Management involves the organization, cleansing, and distribution of data throughout the enterprise.
Instigation and Development of Data Science
Published in Pallavi Vijay Chavan, Parikshit N Mahalle, Ramchandra Mangrulkar, Idongesit Williams, Data Science, 2022
Priyali Sakhare, Pallavi Vijay Chavan, Pournima Kulkarni, Ashwini Sarode
Data integration is the process of merging various data from different sources into a single source for efficient data management. The data comes from several places and are integrated into one source. Data may vary in structure, size, and type ranging from various excel files and databases to text documents. We can create views and can also set operators in it.
Data Governance Model To Enhance Data Quality In Financial Institutions
Published in Information Systems Management, 2023
In the current research, data governance is often confused with data management (Al-Ruithe et al., 2018). According to the DAMA (2017), data governance is considered as a core capability for data management. Data governance represents the framework itself and data management is a practical implementation of data-related activities on the operational level (Engels, 2019; Yulfitri, 2016). Data governance should assure that all operational functions of data management contribute to the achievement of business vision, mission, and strategy (Brous et al., 2016). Data governance governs data management activities to ensure consistency between data management areas and their functions, goals, activities, and responsibilities (Abraham et al., 2019). Data management ensures that relevant stakeholders can have available high-quality data to deliver business value (Alansari et al., 2018). The right data at the right time enhance the decision-making process (Potančok, 2019) and helps reveal opportunities to monetize data.
A review of the state of the art in business intelligence software
Published in Enterprise Information Systems, 2022
Gautam Srivastava, Muneeswari S, Revathi Venkataraman, Kavitha V, Parthiban N
Figure 2 represents various components of BI and their respective characteristics. The initial stage is the MetaData stage. This stage consists of a layer that contains the technical and business process to store the data in a Metadata Layer Repository. The second stage, Data Source consists of both internal and external sources. Data management is the third stage and it is used to identify the organisation data. Data management is the process of handling data to maximise the potential for an enterprise as a valuable resource. Successful data processing includes a data plan and efficient methods for accessing, incorporating, organising, governing, maintaining, and planning data for analytics. In the fourth stage, data can be stored in DB while in the fifth stage, data analysis is sent to the end-user to analyse the data. In the final stage, BI tools are integrated with analytical performance to analyse the data from BI features and achieve the desired results like reporting, creating graphs, dashboards Homocianu (2006), and other ad-hoc analysis. A brief explanation of each stage is described in the sections below Berthold et al. (2010); Zheng (2019).
Critical review of data-driven decision-making in bridge operation and maintenance
Published in Structure and Infrastructure Engineering, 2021
Chengke Wu, Peng Wu, Jun Wang, Rui Jiang, Mengcheng Chen, Xiangyu Wang
485 journal articles were reviewed. A general picture of the articles is given by illustrating distributions of articles by time, region, and journal. Then, this paper reviews mainstream data types, data management, and applications of data-driven bridge O&M decision-making. Both raw and meaningful data are covered, along with the methods to covert raw data into meaningful data. For O&M applications, the current research focus is on generating structure related knowledge, evaluating failure probability and structure conditions, and making decisions about IMR&R tasks and tools. Mainstream algorithms and techniques adopted in these applications are summarised. Key data management components that support data-driven O&M process are reviewed, including data needs, quality, formats, schemas and sharing. State-of-art practices mentioned in both articles and practical documents are also introduced. Based on the review, four challenges related to data management are identified, i.e. poor definition of data needs, lack of methods to assess data quality, lack of data integration, and inadequate consideration of operational issues. Then, future research opportunities to address these challenges are discussed.