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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
Classical data query languages, such as SQL for relational databases, XQuery for XML databases, JSONiq for MongoDB, or SPARSQL for RDF, can be used for data access in a data lake. However, because of storing heterogeneous data in a data lake, a simultaneous query cannot be performed in the heterogeneous databases using the above query languages. Query techniques, such as Spark SQL and SQL++ from multistore, can be used for querying relational databases and semi-structured data in JSON format. Scalable query rewriting engine (SQRE), CloudMdsQL, Apache Phoenix, and Apache Drill are other query languages that can be used for data access in the heterogeneous data lake. For business users, interactive and user-friendly tools, such as Microsoft Power B.I. and Tableau, are also used for data reporting and visualization tasks over data lakes.
Storage, System Security and Access Control for Big Data IoT
Published in Naveen Chilamkurti, T. Poongodi, Balamurugan Balusamy, Blockchain, Internet of Things, and Artificial Intelligence, 2021
T. Lucia Agnes Beena, T. Kokilavani, D. I. George Amalarethinam
JSONiq is another platform-independent language based on Xquery and supported by MongoDB databases. It follows a JSON-based data model. Fine-grained attribute-based access control (ABAC) mechanism is provided through JSONiq and SQL++ unifying query languages for NoSQL data stores [41]. For the data to be analyzed, the ABAC approach derives an in-memory authorized view. The original queries to be executed on the data to be analyzed are executed on the derived in-memory views for enforcing the context-aware access control policies [37].
Factory optima: a web-based system for composition and analysis of manufacturing service networks based on a reusable model repository
Published in International Journal of Computer Integrated Manufacturing, 2019
Alexander Brodsky, Mohamad Omar Nachawati, Mohan Krishnamoorthy, William Z. Bernstein, Daniel A. Menascé
Moving to the bottom-most layer of the diagram in Figure 12, the proposed system depends on a number of external and low-level tools to ultimately provide the bulk of its diverse range of capabilities. The categories of low-level tools currently used by the Service Network Analysis system include: (1) solvers for mathematical programming-based optimisation, including the IBM CPLEX Optimiser for MILP problems and the MINOS solver for NLP problems, (2) algebraic modeling languages and systems, specifically AMPL and the IBM Optimisation Programming Language (OPL) and (3) languages and tools for data manipulation and analysis, primarily the JSONiq language and the Zorba query processor to handle semi-structured JSON data.