Abstract: Disclosed are an electronic device and a non-transitory computer-readable storage medium. Conventionally, most of institutions, companies, etc. have defined and used their own data standards for the business purposes. Most of the data is stored in relational database in a structured format. Now as convergence between industries, new business and new data increases, an active data management system is becoming necessary. The electronic devices according to the disclosure can provide data hub device providing automatically extendable data management function interworking with an open data reference model management device (an active metadata management). Accordingly, the data hubs can exchange standardized data maps among them so as to quickly adapt to such changes in the open industry environment.

Abstract: Disclosed are an electronic device and a non-transitory computer-readable storage medium. Conventionally, most of institutions, companies, etc. have defined and used their own data standards for the business purposes. Most of the data is stored in relational database in a structured format. Now as convergence between industries, new business and new data increases, an active data management system is becoming necessary. The electronic devices according to the disclosure can provide data hub device providing automatically extendable data management function interworking with an open data reference model management device (an active metadata management). Accordingly, the data hubs can exchange standardized data maps among them so as to quickly adapt to such changes in the open industry environment.

Abstract: Provided is a data management system including a master group including a data manager configured to manage data, a process manager configured to manage a process of analyzing the data, and a meta data storage unit configured to store meta data information for managing the data; and at least one work group including a work manager configured to manage resources, the data, and a data process on the basis of a scope of groups, and at least one node manager configured to manage the resources, the data, and the data process on the basis of a scope of nodes. The master group and the at least one work group are dispersed from each other.

Abstract: Disclosed are a method of modeling a workflow used in the field of big data and a method and apparatus for executing a workflow model. The method of executing the workflow model according to an embodiment of the present invention comprises receiving a Unified Modeling Language (UML)-based workflow model needing at least one data storage engine and at least one data processing engine, parsing the received workflow model to generate structured information, verifying the validity of the workflow model using the structured information, and transmitting jobs included in the workflow model to data processing engines corresponding to the jobs when it is determined that the workflow model is valid. Thus, developers can be allowed to easily acquire a workflow runnable on various data storage engines and various data processing engines.

Abstract: Provided is a data management system including a master group including a data manager configured to manage data, a process manager configured to manage a process of analyzing the data, and a meta data storage unit configured to store meta data information for managing the data; and at least one work group including a work manager configured to manage resources, the data, and a data process on the basis of a scope of groups, and at least one node manager configured to manage the resources, the data, and the data process on the basis of a scope of nodes. The master group and the at least one work group are dispersed from each other.

Abstract: Disclosed are a method of modeling a workflow used in the field of big data and a method and apparatus for executing a workflow model. The method of executing the workflow model according to an embodiment of the present invention comprises receiving a Unified Modeling Language (UML)-based workflow model needing at least one data storage engine and at least one data processing engine, parsing the received workflow model to generate structured information, verifying the validity of the workflow model using the structured information, and transmitting jobs included in the workflow model to data processing engines corresponding to the jobs when it is determined that the workflow model is valid. Thus, developers can be allowed to easily acquire a workflow runnable on various data storage engines and various data processing engines.