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Distributed data processing. Distributed data processing [1] (DDP) [2] was the term that IBM used for the IBM 3790 (1975) and its successor, the IBM 8100 (1979). Datamation described the 3790 in March 1979 as "less than successful." [3] [4] Distributed data processing was used by IBM to refer to two environments: IMS DB/DC; CICS/DL/I [5] [6]
Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets.
Data locality is a specific type of temporal locality common in signal and media processing applications where data is produced once, read once or twice later in the application, and never read again. Intermediate streams passed between kernels as well as intermediate data within kernel functions can capture this locality directly using the ...
Distributed Data Management Architecture (DDM) is IBM's open, published software architecture for creating, managing and accessing data on a remote computer. DDM was initially designed to support record-oriented files; it was extended to support hierarchical directories, stream-oriented files, queues, and system command processing; it was further extended to be the base of IBM's Distributed ...
The RM-ODP view model, which provides five generic and complementary viewpoints on the system and its environment.. Reference Model of Open Distributed Processing (RM-ODP) is a reference model in computer science, which provides a co-ordinating framework for the standardization of open distributed processing (ODP).
Google, one of the biggest internet companies, has created its own distributed file system, named Google File System (GFS), to meet the rapidly growing demands of Google's data processing needs, and it is used for all cloud services. GFS is a scalable distributed file system for data-intensive applications.
Distributed programming typically falls into one of several basic architectures: client–server, three-tier, n-tier, or peer-to-peer; or categories: loose coupling, or tight coupling. [34] Client–server: architectures where smart clients contact the server for data then format and display it to the users. Input at the client is committed ...
CADP [1] (Construction and Analysis of Distributed Processes) is a toolbox for the design of communication protocols and distributed systems. CADP is developed by the CONVECS team (formerly by the VASY team) at INRIA Rhone-Alpes and connected to various complementary tools.