Abstract: Disclosed herein are a method and an apparatus for storing blockchain data based on error correction code. The method for storing blockchain data based on error correction code includes dividing block data to be stored into multiple subblock datasets, generating parity datasets corresponding to the block data, and storing the subblock datasets and the parity datasets in proportion to storage capacities of the blockchain data storage nodes.

Abstract: A semiconductor device includes a first semiconductor layer having first and second regions, a plurality of first channel layers spaced apart from each other in a vertical direction on the first region of the first semiconductor layer, a first gate electrode surrounding the plurality of first channel layers, a plurality of second channel layers spaced apart from one another in the vertical direction on the second region of the first semiconductor layer, and a second gate electrode surrounding the plurality of second channel layers, wherein each of the plurality of first channel layers has a first crystallographic orientation, and each of the plurality of second channel layers has a second crystallographic orientation different from the first crystallographic orientation, and wherein a thickness of each of the plurality of first channel layers is different from a thickness of each of the plurality of second channel layers.

Abstract: Disclosed herein is a method for storing blockchain transaction data. The method includes selecting at least one block file storing blockchain transactions as an encoding block file target, generating encoded chunks including parity chunks using the encoding block file target, and making at least one of the encoded chunks correspond to at least one of blockchain nodes to store at least one of the encoded chunks.

Abstract: A database system based on just-in-time (JIT) compilation performs a branch optimization and a loop optimization on the stored procedure. An interpreted query processor generating a structured query language (SQL) execution plan regarding an input online transaction processing (OLTP) query, an online analytical processing (OLAP) analysis, and a stored procedure. A JIT-based query processor receives the SQL execution plan, optimizes a branch and a loop in the received SQL execution plan, and then generates an optimized JIT execution plan. The JIT-based query processor generates a query processing executor performing query processing based on the JIT execution plan and provides a result of the query processing performed by the query processing executor.

Abstract: A database system based on just-in-time (JIT) compilation performs a branch optimization and a loop optimization on the stored procedure. An interpreted query processor generating a structured query language (SQL) execution plan regarding an input online transaction processing (OLTP) query, an online analytical processing (OLAP) analysis, and a stored procedure. A JIT-based query processor receives the SQL execution plan, optimizes a branch and a loop in the received SQL execution plan, and then generates an optimized JIT execution plan. The JIT-based query processor generates a query processing executor performing query processing based on the JIT execution plan and provides a result of the query processing performed by the query processing executor.

Abstract: Provided are a method and apparatus for processing a data stream capable of providing a data flow processing language to support real-time processing of an exploding data stream and providing an environment for executing the data flow processing language in a cluster system. The data flow-based exploding data stream processing method includes receiving a big data real-time processing service described in a real-time data flow language, interpreting the big data real-time processing service to generate a distributed stream processing service, and distributively deploying the distributed stream processing service in a cluster system including multiple nodes and configuring an execution environment for executing the distributed stream processing service in each node.

Abstract: A speaker device and an electronic device including the speaker device. The speaker device includes a speaker body, which includes a front surface, a rear surface, and a side surface provided between the front surface and the rear surface, and a sealing member provided to enclose at least the side surface of the speaker body, in which the sealing member isolates sound output through the front surface of the speaker body and sound output through the rear surface of the speaker body from each other. The speaker device and the electronic device including the same may be implemented variously according to an embodiment.

Abstract: The following description relates to a method and an apparatus for merging distributed stream processing services. A method for merging distributed stream processing services according to an example includes generating a global service tree based on one distributed stream processing service; when a new distributed stream processing service different from the one distributed stream processing service is inputted, generating an individual service tree for the new distributed stream processing service; and updating the global service tree by merging at least one node commonly existing in both the global service tree and the individual service tree. Common resources may be allocated to tasks (or instances) existing commonly in distributed stream processing services.

Abstract: An apparatus and method for managing stream processing tasks are disclosed. The apparatus includes a task management unit and a task execution unit. The task management unit controls and manages the execution of assigned tasks. The task execution unit executes the tasks in response to a request from the task management unit, collects a memory load state and task execution frequency characteristics based on the execution of the tasks, detects low-frequency tasks based on the execution frequency characteristics if it is determined that a shortage of memory has occurred based on the memory load state, assigns rearrangement priorities to the low-frequency tasks, and rearranges the tasks based on the assigned rearrangement priorities.

Abstract: Provided are a method and apparatus for processing a data stream capable of providing a data flow processing language to support real-time processing of an exploding data stream and providing an environment for executing the data flow processing language in a cluster system. The data flow-based exploding data stream processing method includes receiving a big data real-time processing service described in a real-time data flow language, interpreting the big data real-time processing service to generate a distributed stream processing service, and distributively deploying the distributed stream processing service in a cluster system including multiple nodes and configuring an execution environment for executing the distributed stream processing service in each node.

Abstract: Disclosed is a system for distributed processing of stream data, including: a service management device which selects an operation device optimal to perform an operation constituting a service and assigns the operation in a node including the selected operation device; and a task execution device which performs one or more tasks included in the operation through the selected operation device when the assigned operation is an operation registered in a preregistered performance acceleration operation library.

Abstract: A speaker device and an electronic device including the speaker device. The speaker device includes a speaker body, which includes a front surface, a rear surface, and a side surface provided between the front surface and the rear surface, and a sealing member provided to enclose at least the side surface of the speaker body, in which the sealing member isolates sound output through the front surface of the speaker body and sound output through the rear surface of the speaker body from each other. The speaker device and the electronic device including the same may be implemented variously according to an embodiment.

Abstract: Disclosed herein are an apparatus and method for managing a data stream distributed parallel processing service. The apparatus includes a service management unit, a Quality of Service (QoS) monitoring unit, and a scheduling unit. The service management unit registers a plurality of tasks constituting the data stream distributed parallel processing service. The QoS monitoring unit gathers information about the load of the plurality of tasks and information about the load of a plurality of nodes constituting a cluster which provides the data stream distributed parallel processing service. The scheduling unit arranges the plurality of tasks by distributing the plurality of tasks among the plurality of nodes based on the information about the load of the plurality of tasks and the information about the load of the plurality of nodes.

Abstract: An apparatus and method for managing stream processing tasks are disclosed. The apparatus includes a task management unit and a task execution unit. The task management unit controls and manages the execution of assigned tasks. The task execution unit executes the tasks in response to a request from the task management unit, collects a memory load state and task execution frequency characteristics based on the execution of the tasks, detects low-frequency tasks based on the execution frequency characteristics if it is determined that a shortage of memory has occurred based on the memory load state, assigns rearrangement priorities to the low-frequency tasks, and rearranges the tasks based on the assigned rearrangement priorities.

Abstract: Disclosed herein are an apparatus and method for managing a data stream distributed parallel processing service. The apparatus includes a service management unit, a Quality of Service (QoS) monitoring unit, and a scheduling unit. The service management unit registers a plurality of tasks constituting the data stream distributed parallel processing service. The QoS monitoring unit gathers information about the load of the plurality of tasks and information about the load of a plurality of nodes constituting a cluster which provides the data stream distributed parallel processing service. The scheduling unit arranges the plurality of tasks by distributing the plurality of tasks among the plurality of nodes based on the information about the load of the plurality of tasks and the information about the load of the plurality of nodes.

Abstract: Disclosed are service providing method and device, including: collecting execution state information about a plurality of tasks that constitute at least one service, and are dynamically distributed and arranged over a plurality of nodes; and performing scheduling based on the collected execution state information about the plurality of tasks, wherein each of the plurality of tasks has at least one input source and output source, and a unit of data to be processed for each input source and a data processing operation are defined by a user, and the scheduling is to delete at least a portion of data input into at least one task or to process the at least a portion of input data in at least one duplicate task by referring to the defined unit of data. In particular, the present invention may effectively provide a service of analyzing and processing large stream data in semi-real time.

Abstract: Disclosed herein is a system for processing large-capacity data in a distributed parallel processing manner based on MapReduce using a plurality of computing nodes. The distributed parallel processing system is configured to provide an incremental MapReduce-based distributed parallel processing function for large-capacity stream data which is being continuously collected even during the performance of the distributed parallel processing, as well as for large-capacity stored data which has been previously collected.

Abstract: Provided are a system and method for processing continuous integrated queries on both data stream and stored data using user-defined shared trigger. The system includes a data stream manager for managing data stream inputted from outside; a continuous integrated queries manager for managing the continuous integrated queries inputted from an external application; a trigger manager for managing the user-defined shared trigger inputted from the external application and registering the user-defined shared trigger in an external database; a trigger result manager for forming and managing a trigger result set from a performance result of the user-defined shared trigger registered in the cooperation database; and a continuous integrated queries performer for processing the continuous integrated queries referring to the transmitted data stream and trigger result set.

Abstract: Disclosed herein is an apparatus for producing 3D sound. The apparatus includes a determination unit, a mono sound spreading unit, a stereo sound spreading unit, a selection unit, and a 3D sound accelerator. The determination unit receives a source sound file and determines whether the source sound file is mono or stereo. The mono sound spreading unit converts the source sound into pseudo-stereo sound and performs sound spreading on the pseudo-stereo sound, if the source sound is determined to be mono. The stereo sound spreading unit performs sound spreading on the source sound, if the source sound is determined to be stereo. The selection unit receives the output of the mono sound spreading unit or stereo sound spreading unit, and transfers the output to headphones if the headphone reproduction has been selected.

Abstract: Disclosed herein is an apparatus for providing 3D sound through plural-channel speakers.