Summary Data preparation is the most effortful task in the process of statistical learning. Many studies related to data mining are performed without data preparation by assuming that qualified datasets are already prepared. It may hide useful patterns of data, which can result in poor performance and incorrect learning. Automation of data preparation can solve these problems. For automation of data preparation, a few issues should be considered, such as flexible expression of requirements according to the purpose of the learning model, accessibility to data sources, and performance degradation due to automation. In this paper, we propose a dataset description model that can express the requirements for data processing and dataset retrieval system based on automated data preparation. The proposed system makes it possible to provide good quality datasets for statistical learning applications using data preparation methods such as data acquisition, refinement, and organization. In the experiment, we demonstrate that the proposed system doesn't have performance loss as compared to the existing manual systems. Moreover, the quality of the datasets are also improved by using the proposed system.

Abstract Context‐awareness techniques generally deal with steps for processing information that can be gathered from perspectives of objects in real‐world scenes in accordance with resolving heterogeneous devices and providing meaningful context information in recent computing domains such as Internet of Things, cloud computing, edge computing, and big data analysis. However, analysis applications are necessary to be improved in a way of cooperative approaches because they are independently executed far away from context‐aware systems. This means that the stages are applied to process the data or the derived results, are isolated. That leads difficulties of management and integration for other software modules. As a result, a computing paradigm called edge computing has been suggested to compromise such isolations. For processing context information, it is necessary to establish knowledge parts with validation processes against the isolations. In this paper, we suggest a method for processing datasets in a view of building knowledge parts. The suggested method uses an extensible data enrichment scheme, which represents the relations for the object. In the experiment section, applications of the proposed method are shown in one of the healthcare community centers, and the data abstraction with the analysis jobs can be processed for each real‐world object.

Summary Context awareness is a necessary technique for providing optimized services to users by recognizing their surrounding environment at a particular time. To provide context‐aware services, a context‐aware middleware is required to detect the changes surrounding the user as well as the processing procedure. However, when the context‐aware middleware is applied to wearable computing, the processing time increases in proportion to the increase in the number of context information due to the lack of processing capacity from the terminal devices. To access the terminal devices, a specific context representation with Resource Description Framework–based triplet is used. Since the triplet consists of three keyword elements that refer to the status of a given situation around the user, retrieving these elements takes O(n3) time complexity with a linear search. To overcome this problem, a hash‐based comparison method is suggested to minimize the response time. The suggested comparison method gives a better performance without searching every single keyword element among the triplet sets. In the experiment, we applied the suggested method to the context‐aware workflow middleware and demonstrated that the proposed method improves the processing time by at least 30% compared with the linear search by enhancing the comparison module in the middleware.

Summary In the smart agricultural environment such as greenhouse or vertical farm, the automation process is performed using the environment sensors to maintain the growth of the crops. Currently, the system rely on the defined rule to perform automation, but the situation can turn catastrophic. With the interruption in the communication, data forging, or eavesdropping, the crops will be rotten and destroyed. Therefore, to maintain the automation without failure, the connection needs to be secure and tampering has to be avoided. In this paper, we discuss the security solution for the wireless sensor networks in the automated agricultural environment without any human intervention. The sink node that collects the sensor values organizes the data and transfer the data to the server, along with the XML encryption and private key mechanism for the communication. The secure protocol is used to make connection using the private key, XML ecryption prevents the system from eavesdropping, and forging. Some of the events discussed in the papers are EVNData, SENSORError, DDoSAttack, and ConnThreat. With the help of the event and the tag information, the data security and confidentiality is increased, for the better automation and crop growth.