{'fraction_non_alphanumeric': 0.02384393063583815, 'fraction_numerical': 0.0, 'mean_word_length': 5.690821256038648, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analytics of IIoT Data Using a NoSQL Datastore

Many business and mission-critical decisions of the Industrial Internet of Things (IIoT) depend on efficient data management of sensor streams. Contemporary distributed IIoT applications consist of large numbers of sensors, producing massive volumes of heterogeneous sensor streams at high rates. The combination of these features of IIoT applications pose substantial challenges for existing Database Management Systems (DBMSs) in providing scalable data analytics. For example, Relational-DBMSs (RDBMSs) exhibit scalability issues, single point of failure, and difficulty in managing heterogeneity due to it’s rigid schemas. In contrast to RDBMSs, distributed NoSQL datastores could provide scalability of heterogeneous data. However, the simple query processing capabilities of NoSQL datastores limit advanced analytics. In this paper, we first compare both approaches, having an RDBMS and NoSQL backend for providing data-management solutions for distributed IIoT applications. Then, we utilize query processing in an in-memory database to integrate edge computing with the NoSQL datastore. By utilizing high-volume streams from a real-world IIoT application of Bosch Rexroth - Hägglund, we show that the proposed approach can potentially overcome the limitations of both RDBMS and NoSQL databases for performing advanced analytics.