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The requirement for data interchange, access, and retrieval from diverse databases. This requirement is getting more and more crucial. However, retrieving this data from a heterogeneous database necessitates juggling many kinds of query languages, data models, and structure. The goal of this project is to create a unified access control approach (UACA), which will be managed by service data object (SDO) technologies and will be based on an agent mechanism for application access to business databases. To create a unified data access control, applications must be submitted through a UACA. To prevent a direct link to the database and application security threats, unified access control isolates applications from databases. Application systems submit a request for identity authentication to the database access control system using the data access protocol of the access control system. Data are finally transmitted to the application system after identity authentication, data gathering, and data transmission. This investigation led to the creation of a client-middle layer, which is also in charge of transformation and optimization, to ensure uniform data access control and access to the heterogeneous database. It also manages system communication using the TCP/IP protocol to guarantee the accuracy and reliability of the connection. SDO data retrieval offers strong adaptability and system compatibility. [ABSTRACT FROM AUTHOR]

Apache Sedona (formerly GeoSpark) is a new in-memory cluster computing system for processing large-scale spatial data, which extends the core of Apache Spark to support spatial datatypes, partitioning techniques, spatial indexes, and spatial operations (e.g. spatial range, nearest neighbor, and spatial join queries). Distance-based Join Queries (DJQs), like nearest neighbor join (kNNJQ) or closest pairs queries (kCPQ), are not supported by it. Therefore, in this paper, we investigate how to design and implement efficient DJQ distributed algorithms in Apache Sedona, using the most appropriate spatial partitioning and other optimization techniques. The results of an extensive set of experiments with real-world datasets are presented, demonstrating that the proposed kNNJQ and kCPQ distributed algorithms are efficient, scalable, and robust in Apache Sedona. Finally, Sedona is also compared to other similar cluster computing systems, showing the best performance for kCPQ and competitive results for kNNJQ. [ABSTRACT FROM AUTHOR]