IoT-generated data are characterized by its continuous generation, large amount, and unstructured format. Existing relational database technologies are inadequate to handle such IoT-generated data due to the limited processing speed and the significant storage-expansion cost. Thus, big data processing technologies, which are normally based on distributed file systems, distributed database management, and parallel processing technologies, have arisen as a core technology to implement IoTgenerated data repositories. In this study, we propose a sensorintegrated RFID data repository-implementation model using MongoDB, the most popular big data-savvy document-oriented database system now. Firstly, we devise a data repository schema that can effectively integrate and store the heterogeneous IoT data sources such as RFID, sensor, and GPS, by extending the event data types in Electronic Product Code Information Services (EPCIS) standard, a de facto standard for the information exchange services for RFID-based traceability. Secondly, we propose an effective shard key to maximize query speed and uniform data distribution over data servers. Lastly, through a series of experiments measuring query speed and the level of data distribution, we show that the proposed design strategy, which is based on horizontal data partitioning and a compound shard key, is effective and efficient for the IoT-generated RFID/sensor big data.

In the future, with the advent of the smart factory era, manufacturing and logistics processes will become more complex, and the complexity and criticality of traceability will further increase. This research aims at developing a performance assessment method to verify scalability when implementing traceability systems based on key technologies for smart factories, such as Internet of Things (IoT) and BigData. To this end, based on existing research, we analyzed traceability requirements and an event schema for storing traceability data in MongoDB, a document-based Not Only SQL (NoSQL) database. Next, we analyzed the algorithm of the most representative traceability query and defined a query-level performance model, which is composed of response times for the components of the traceability query algorithm. Next, this performance model was solidified as a linear regression model because the response times increase linearly by a benchmark test. Finally, for a case analysis, we applied the performance model to a virtual automobile parts logistics. As a result of the case study, we verified the scalability of a MongoDB-based traceability system and predicted the point when data node servers should be expanded in this case. The traceability system performance assessment method proposed in this research can be used as a decision-making tool for hardware capacity planning during the initial stage of construction of traceability systems and during their operational phase.

This study proposed a MapReduce design for a passage-based distributed process discovery algorithm, which can discover a process model by analyzing stored event logs depending on the progress of the process. This study can be used for the diagnosis, variation measurement, and improvement of the process by discovering the process model in a smart factory environment with an autonomously changing process.