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Data Lakes: A Panacea for Big Data Problems, Cyber Safety Issues, and Enterprise Security
Published in Mohiuddin Ahmed, Nour Moustafa, Abu Barkat, Paul Haskell-Dowland, Next-Generation Enterprise Security and Governance, 2022
A. N. M. Bazlur Rashid, Mohiuddin Ahmed, Abu Barkat Ullah
Data integration is a challenging task of integrating raw data from a data lake during query time. On-demand data integration requires finding datasets containing relevant data and integrating them in a meaningful way. Relevant data can a modeled data that augments known entities with new properties or attributes. Relevant data can also be a schema described by keyword queries expressed over attribute names or other metadata. The information exchange between datasets with different schemas can be performed by schema mapping. In a sample-driven schema mapping, users can describe the schema using a set of records. Similarly, in multi-resolution schema mapping, users can describe schemas using incomplete records, data types, and value ranges. Apart from these, schema mapping, query-driven discovery can find tables to join or union with a query table.
Data-to-decision framework for monitoring railroad bridges
Published in Khaled M. Mahmoud, Asset Management of Bridges, 2017
S. Alampalli, S. Alampalli, M. Ettouney, J.P. Lynch
In this project, given the heterogeneity of data, many alternative designs were considered to store information with the key requirements for the system being scalability, consistency, and usability. Finally, Microsoft SQL Server—a relational database—has been used as a reliable data repository. The relational database links structural analytical models to physical bridge components; sensors to physical bridge components; and results to related components. The created database schema is structured and normalized to ensure that data are stored efficiently and optimized for highest performance. The design provides the capability to disclose knowledge and reliable hidden patterns; crosscheck various datasets; and validate and uncover relationships within data. For example, visual inspection constitutes a major basis for decisions regarding the performance of bridges. So, the visual inspection data is linked with sensor data in order to provide information regarding limit states of bridges below failure through different ratings.
Databases
Published in Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, Big Data and Social Science, 2020
We have shown that a relational database comprises a set of tables. The task of specifying the structure of the data to be stored in a database is called logical design. This task may be performed by a database administrator, in the case of a database to be shared by many people, or directly by users, if they are creating databases themselves. More specifically, the logical design process involves defining a schema. A schema comprises a set of tables (including, for each table, its columns and their types), their relationships, and integrity constraints. See, for example, Figure 4.4.
Are NoSQL Databases Affected by Schema?
Published in IETE Journal of Research, 2023
Neha Bansal, Shelly Sachdeva, Lalit K. Awasthi
In traditional relational databases, the data is fitted into a predefined schema, which is decided after normalisation, giving us the most effective schema for any dataset. Predefined schema provides data independence and user abstraction from the logical to the physical level. Still, the limitation is a) we may have to wrestle with logical schema changes while changing the database schema that becomes too complex after application development. b) with Big Data’s rise, this predefined schema provides limited support for various (semi-structured and unstructured) data. NoSQL databases alleviate the burden of formal schema definition by allowing schema flexibility and promoting redundancy. In NoSQL databases, similar information can be stored with many schemas, which supports data evolution due to the lack of restrictions imposed on the schema [7–9]. The question arises whether the schema matters in NoSQL databases because these databases do not require explicit schema declaration before data storage. And if it matters, what is its potential impact on application performance? Therefore, this paper seeks to answer why someone should take the time to define the schema by taking advantage of the schema flexibility feature rather than immediately storing the data. This paper has taken the insights of the work [10], where authors have analysed the impact of schema alternatives on document stores only, ignoring other types of NoSQL databases.
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
Schemas describe how data are structured and related in databases. Many articles adopt traditional relational schema due to the low technical requirements and compatibility with structured data, e.g. sensor readings (Bae, Lee, & Park, 2016; Kurata et al., 2013). In addition, mark-up language schema, e.g. eXtensible mark-up language (XML), is commonly used. XML can encode both structured and non-structured data in a standard and interoperable way and can be extended to suit specific tasks (Jeong, Hou, Lynch, Sohn, & Law, 2019; Zhu et al., 2020). For instance, Jeong, Hou, Lynch, Sohn, and Law (2017) applied the openBrIM schema to describe bridge drawings and inspection reports, and Jeong et al. (2016) used the sensorML schema to improve the organisation of sensor-based data.
Applying transfer learning to achieve precision marketing in an omni-channel system – a case study of a sharing kitchen platform
Published in International Journal of Production Research, 2021
Ming-Chuan Chiu, Kai-Hsiang Chuang
A table schema represents the logical configuration of all or part of a relational database. It can exist both as a visual representation and as a set of processes known as constraints that govern a database. These processes are illustrated in a data definition language. Typically, a database designer creates a database schema to help programmers whose software will interact with the database (Figure 4).