In general, “multi-objects” can refer to multiple instances of an object. This could be anything such as multiple instances of a class in Object-Oriented Programming (OOP), multiple objects in a graphical user interface, etc.
When using it in the context of file data formats, “multi-objects” could refer to multiple data objects stored sequentially or in an array-like structure in a file. Especially in your case of a newline delimited objects file format (NDOF), it could mean that multiple such objects exist in the data file, each separated by a newline.
However, the actual meaning can vary based on context and the specific implementation, so it’s always a good idea to define such terms within your project or documentation.
| [Multiobjects to Ease Schema Evolution in an OODBMS | SpringerLink](https://link.springer.com/chapter/10.1007/978-3-540-49524-6_25) |
The multiobject mechanism is a pertinent way to implement specialization in an object database and differs from the classical mechanism used in most object-oriented database systems. It supports multiple instantiation, automatic classification and object migration. Consequently it is well suited to take into account schema evolution. It makes schema changes more pertinent, easier to implement, and less expensive than with the classical implementation of specialization indeed. The multiobject mechanism is implemented in the F2 database system which supports schema evolution.
| [Multibuffers Multiobjects Optimal Matching Scheme for Edge Devices in IIoT | IEEE Journals & Magazine | IEEE Xplore](https://ieeexplore.ieee.org/document/9328866) |
Environments built from the edge-based Industrial Internet of Things (IIoT) are maelstroms of information that continuously flows between heterogeneous data objects, such as sensors and devices, and edge nodes. However, the explosive growth in the number of IIoT data objects connected to edge nodes generally results in significant computation and storage requirements, which exceed those of resource-constrained edge nodes. The problem mentioned above causes a major concern related to efficient memory usage and faster communication processing in data acquisition. Therefore, in this article, we propose a multibuffers multiobjects (MBMOs) architecture to support the parallel delivery of multiple data objects to multiple variable-length buffer blocks. Furthermore, a mathematical model is established, aiming to find the optimum buffer according to the size of each communication data packet. For the aforementioned matching problem, a spatiotemporal resource allocation algorithm is designed to maximize memory usage while minimizing communication processing time. We implement our MBMO in a monitoring system in which embedded programmable logic controllers (ePLCs) serve as the edge nodes. The analyses illustrate that memory usage and time efficiency are improved greatly with the utilization of MBMO.