Federative Factory Data Management An Approach Based Upon Service Oriented Architecture (Soa)

2007 ◽  
pp. 67-74 ◽  
Author(s):  
Reiner Anderl ◽  
Majid Rezaei
Author(s):  
Katarina Grolinger ◽  
Emna Mezghani ◽  
Miriam A. M. Capretz ◽  
Ernesto Exposito

Decision-making in disaster management requires information gathering, sharing, and integration by means of collaboration on a global scale and across governments, industries, and communities. Large volume of heterogeneous data is available; however, current data management solutions offer few or no integration capabilities and limited potential for collaboration. Moreover, recent advances in NoSQL, cloud computing, and Big Data open the door for new solutions in disaster data management. This chapter presents a Knowledge as a Service (KaaS) framework for disaster cloud data management (Disaster-CDM), with the objectives of facilitating information gathering and sharing; storing large amounts of disaster-related data; and facilitating search and supporting interoperability and integration. In the Disaster-CDM approach NoSQL data stores provide storage reliability and scalability while service-oriented architecture achieves flexibility and extensibility. The contribution of Disaster-CDM is demonstrated by integration capabilities, on examples of full-text search and querying services.


Big Data ◽  
2016 ◽  
pp. 588-614 ◽  
Author(s):  
Katarina Grolinger ◽  
Emna Mezghani ◽  
Miriam A. M. Capretz ◽  
Ernesto Exposito

Decision-making in disaster management requires information gathering, sharing, and integration by means of collaboration on a global scale and across governments, industries, and communities. Large volume of heterogeneous data is available; however, current data management solutions offer few or no integration capabilities and limited potential for collaboration. Moreover, recent advances in NoSQL, cloud computing, and Big Data open the door for new solutions in disaster data management. This chapter presents a Knowledge as a Service (KaaS) framework for disaster cloud data management (Disaster-CDM), with the objectives of facilitating information gathering and sharing; storing large amounts of disaster-related data; and facilitating search and supporting interoperability and integration. In the Disaster-CDM approach NoSQL data stores provide storage reliability and scalability while service-oriented architecture achieves flexibility and extensibility. The contribution of Disaster-CDM is demonstrated by integration capabilities, on examples of full-text search and querying services.


Author(s):  
Arcot Rajasekar ◽  
Mike Wan ◽  
Reagan Moore ◽  
Wayne Schroeder

Service-oriented architectures (SOA) enable orchestration of loosely-coupled and interoperable functional software units to develop and execute complex but agile applications. Data management on a distributed data grid can be viewed as a set of operations that are performed across all stages in the life-cycle of a data object. The set of such operations depends on the type of objects, based on their physical and discipline-centric characteristics. In this chapter, the authors define server-side functions, called micro-services, which are orchestrated into conditional workflows for achieving large-scale data management specific to collections of data. Micro-services communicate with each other using parameter exchange, in memory data structures, a database-based persistent information store, and a network messaging system that uses a serialization protocol for communicating with remote micro-services. The orchestration of the workflow is done by a distributed rule engine that chains and executes the workflows and maintains transactional properties through recovery micro-services. They discuss the micro-service oriented architecture, compare the micro-service approach with traditional SOA, and describe the use of micro-services for implementing policy-based data management systems.


2019 ◽  
Vol 8 (2) ◽  
pp. 25 ◽  
Author(s):  
Saqlain ◽  
Piao ◽  
Shim ◽  
Lee

The Internet of Things (IoT) is the global network of interrelated physical devices such as sensors, actuators, smart applications, objects, computing devices, mechanical machines, and people that are becoming an essential part of the internet. In an industrial environment, these devices are the source of data which provide abundant information in manufacturing processes. Nevertheless, the massive, heterogeneous, and time-sensitive nature of the data brings substantial challenges to the real-time collection, processing, and decision making. Therefore, this paper presents a framework of an IoT-based Industrial Data Management System (IDMS) which can manage the huge industrial data, support online monitoring, and control smart manufacturing. The framework contains five basic layers such as physical, network, middleware, database, and application layers to provide a service-oriented architecture for the end users. Experimental results from a smart factory case study demonstrate that the framework can manage the regular data and urgent events generated from various factory devices in the distributed industrial environment through state-of-the-art communication protocols. The collected data is converted into useful information which improves productivity and the prognosis of production lines.


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