Abstract:
A workflow engine framework for creating a single-domain adaptive and a cross-domain adaptive workflow performing platform is disclosed. The workflow engine framework includes: a resource management unit configured to manage resources including engine components and workflow property specification components; a system configuration unit configured to create an engine by assembling the property specification components; and a system control unit configured to drive and execute one or more engines. Further the workflow engine framework is allocated to each of two or more different signal domains and forms a cross-domain adaptive workflow engine framework.
Abstract:
A method of filtering content including privacy information in an Internet of things (IoT) terminal includes generating, by the processor, content management data prescribing a mapping relationship between pieces of content, a kind of a network, and a plurality of applications and storing the content management data in a content management data storage unit, based on a user input, the content management data prescribing a security policy associated with external transmission of the pieces of content, and when an external transmission request message corresponding to specific content of the pieces of content is received from the specific application, determining, by the processor, whether to allow external transmission of the specific content in response to the external transmission request message, based on the security policy prescribed in the content management data.
Abstract:
A workflow engine framework for creating a single-domain adaptive and a cross-domain adaptive workflow performing platform is disclosed. The workflow engine framework includes: a resource management unit configured to manage resources including engine components and workflow property specification components; a system configuration unit configured to create an engine by assembling the property specification components; and a system control unit configured to drive and execute one or more engines. Further the workflow engine framework is allocated to each of two or more different signal domains and forms a cross-domain adaptive workflow engine framework.
Abstract:
An automatic configuration device, for an Internet-of-Things (IoT) device, which operates a data analysis-based automation system includes when various types of IoT devices are registered in a system, a data analysis-based automation system for supporting both use of functions of the IoT devices and an effective analysis of collected data.
Abstract:
An apparatus and method for processing a path management packet is provided, the method including determining whether a router for processing a received path management packet is a storing node, and when the router is determined to be a storing node, controlling the path management packet to be stored in a routing table, or when the router is determined to be a non-storing node, failing to store the path management packet in the routing table and controlling the path management packet to be transmitted to a subsequent node.
Abstract:
Provided are an apparatus and method for detecting an anomaly in a plant pipe using multiple meta-learning. When a multi-sensor data stream about a plant pipe is received, each of a plurality of meta-learning modules for processing different packet section ranges, extracts one or more preset types of features from sensor data of packet section ranges set according to trend from an arbitrary reception time point, generates 2D image features of the features according to multi-sensor-specific times, generates 3D volume features by accumulating the 2D image features in a depth direction according to multiple sensors, and learns the 3D volume features in parallel through multi-sensor-specific learning modules. Results of the learning of the meta-learning modules are aggregated, and it is determined whether there is an anomaly in a plant pipe according to a learning result selected based on an optimal combination of multiple features, multiple sensors, and multiple packet sections.
Abstract:
Provided are an apparatus and method for detecting an anomaly in a plant pipe using multiple meta-learning. When a multi-sensor data stream about a plant pipe is received, each of a plurality of meta-learning modules for processing different packet section ranges, extracts one or more preset types of features from sensor data of packet section ranges set according to trend from an arbitrary reception time point, generates 2D image features of the features according to multi-sensor-specific times, generates 3D volume features by accumulating the 2D image features in a depth direction according to multiple sensors, and learns the 3D volume features in parallel through multi-sensor-specific learning modules. Results of the learning of the meta-learning modules are aggregated, and it is determined whether there is an anomaly in a plant pipe according to a learning result selected based on an optimal combination of multiple features, multiple sensors, and multiple packet sections.
Abstract:
Provided is a device and a method for smart device and sensor node integrated application update, including an application program updating device including a generator to generate a package in a storage space by installing a sensing application, an extractor to extract an installation file and a profile of an application program interworking with the sensing application from the package, a recognizer to recognize a sensor node by referring to the profile, an installer to install the application program by transmitting the installation file to the sensor node, and an executor to execute the application program and the sensing application when the application program is installed in the sensor node.
Abstract:
Provided are a data processing apparatus and method for merging and processing deterministic knowledge and non-deterministic knowledge. The data processing apparatus and method may efficiently process various real-time and large-scale data to convert the data into knowledge by merging and processing non-deterministic knowledge and also deterministic knowledge perceived by an expert. Thus, it is possible to adaptively operate in accordance with a dynamically changing application service environment by converting a conversion rule for converting collected data generated from an application service system into semantic data, a context awareness rule for perceiving context information from given information, and a user query for searching for knowledge information into knowledge and gradually augmenting the knowledge information in accordance with an application service environment.
Abstract:
Provided are a self-learning system and method for automatically performing machine learning (ML). The self-learning system includes a memory configured to store an ML knowledge database (DB) in which ML knowledge is stored and a program for automatically performing ML based on request information of a user, and a processor configured to execute the program stored in the memory. Here, when executing the program, the processor creates or recommends at least one workflow corresponding to the request information of the user based on the ML knowledge stored in the ML knowledge DB and generates an execution code for performing the created or recommended workflow.