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- Mahmood, Khalid, et al.
(författare)
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Analytics of IIoT Data Using a NoSQL Datastore
- 2021
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Ingår i: 2021 IEEE International Conference on Smart Computing (SMARTCOMP). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665412520 - 9781665429498 ; , s. 97-104
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 2. |
- Mahmood, Khalid
(författare)
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Scalable Data Management for Internet of Things
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Internet of Things (IoT) often involve considerable numbers of sensors that produce large volumes of data. In this context, efficient management of data could potentially enable automatic decision making based on analytics of sensors on equipment. However, these sensors are often geographically distributed and generate diverse formats of data in form of sensor streams at a high rate. The combination of these properties of IoT pose significant challenges for the existing database management systems (DBMSs) to provide scalable data storage and analytics.The problem of providing efficient data management of distributed IoT applications using DBMS technologies is addressed in this thesis. Initially, we developed a prototype system, Fused LOg database Query Processor (FLOQ), which enables general query processingover collections of relational databases that are deployed locally on distributed sites to store sensor measurement logs. Although FLOQ provides efficient query execution when scaling the number of distributed databases, it exhibits complexity and scalability issues for large IoT applications having heterogeneous data. The limitations of FLOQ are primarily inherent to its use of relational database backends for storage of sensor logs.When a relational database is used to store large-scale IoT data, it exhibits several challenges. The loading of massive logs produced at high rates is not fast enough due to its strong consistency mechanisms. Furthermore, it could demonstrate a single point of failure that limits the availability, and the inflexible schemas make it difficult to manage heterogeneity. In contrast to relational databases, distributed NoSQL data stores could provide scalable storage of heterogeneous data through data partitioning, replication, and high availability by sacrificing strong consistency. To understand the suitability of NoSQL databases, this thesis also investigates to what degree NoSQL DBMSs provide scalable storage and analytics of IoT applications by comparing a variety of state-of-the-art relational and NoSQL databases for real-world industrial IoT data. The experimental evaluations reveal that the scalability can be provided by the distributed NoSQL data stores; however, the support of advanced data analytics is difficult due to their limited query processing capabilities. Furthermore, data management of distributed IoT applications often requires seamless integration between a real-time edge analytics platform, a distributed storage manager, effective data integration, and query processing techniques for handling heterogeneity. Therefore, in order to provide a holistic data management solution, this thesis developed the Extended Query Processing (EQP) system, which enables advanced analytics for supporting both edge and offline analytics for large-scale IoT applications.These contributions enable efficient data management of large-scale heterogeneous IoT applications and supports advanced analytics.
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| 3. |
- Mahmood, Khalid, et al.
(författare)
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Scalable Real-Time Analytics for IoT Applications
- 2021
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Ingår i: 2021 IEEE International Conference on Smart Computing (SMARTCOMP). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665412520 - 9781665429498 ; , s. 404-406
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Konferensbidrag (refereegranskat)abstract
- Large-scale industrial internet of things (IIoT) applications usually access distributed equipment where high-volume sensor streams are processed. The building of scalable analytic queries and models over such streams could potentially enhance various industrial processes management tasks, e.g., distribution, delivery, and predictive online maintenance. To enable real-time and historical analytics over distributed IIoT applications, we have combined an edge data stream management system (EDSMS), sa.engine, with the highly distributed NoSQL database MongoDB. For supporting advanced analytics and high-volume stream injection into MongoDB, we integrated an extended query processing (EQP) system with sa.engine and MongoDB. This work demonstrates how EQP provides a holistic data management solution for IIoT based on a use case for sound anomaly detection of distributed equipment.
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