| 1. |
- Xu, Cheng, et al.
(författare)
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Scalable Validation of Industrial Equipment using a Functional DSMS
- 2017
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Ingår i: Journal of Intelligent Information Systems. - : Springer. - 0925-9902 .- 1573-7675. ; 48:3, s. 553-577
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Tidskriftsartikel (refereegranskat)abstract
- A stream validation system called SVALI is developed in order to continuouslyvalidate data streams from industrial equipment. The functional data model of SVALI allows the user to dene meta-data and queries about the equipment in terms of types and functions. The two system functions model-andvalidate and learn-and-validate provide such validation functionality. The experiments show that parallel stream processing enables SVALI to scale very well with respect to response time and system throughput. The paper is based on a real world application for wheel loader slippage detection at Volvo Construction Equipment.
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| 2. |
- Badiozamany, Sobhan, 1983-
(författare)
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Real-time data stream clustering over sliding windows
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In many applications, e.g. urban traffic monitoring, stock trading, and industrial sensor data monitoring, clustering algorithms are applied on data streams in real-time to find current patterns. Here, sliding windows are commonly used as they capture concept drift.Real-time clustering over sliding windows is early detection of continuously evolving clusters as soon as they occur in the stream, which requires efficient maintenance of cluster memberships that change as windows slide.Data stream management systems (DSMSs) provide high-level query languages for searching and analyzing streaming data. In this thesis we extend a DSMS with a real-time data stream clustering framework called Generic 2-phase Continuous Summarization framework (G2CS). G2CS modularizes data stream clustering by taking as input clustering algorithms which are expressed in terms of a number of functions and indexing structures. G2CS supports real-time clustering by efficient window sliding mechanism and algorithm transparent indexing. A particular challenge for real-time detection of a high number of rapidly evolving clusters is efficiency of window slides for clustering algorithms where deletion of expired data is not supported, e.g. BIRCH. To that end, G2CS includes a novel window maintenance mechanism called Sliding Binary Merge (SBM). To further improve real-time sliding performance, G2CS uses generation-based multi-dimensional indexing where indexing structures suitable for the clustering algorithms can be plugged-in.
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| 3. |
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| 4. |
- Johanson, Mathias, et al.
(författare)
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Relaying Controller Area Network Frames over Wireless Internetworks for Automotive Testing Applications
- 2009
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Ingår i: Proc. 4th International Conference on Systems and Networks Communications. - Piscataway, NJ : IEEE. - 9781424447725 ; , s. 1-5
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Konferensbidrag (refereegranskat)abstract
- In this paper we describe how Controller Area Network (CAN) frames can be relayed over a wireless Internet connection, enabling remote access to the CAN buses of vehicles for applications in automotive testing. This opens up many new possibilities for automotive diagnostics, monitoring, testing, analysis and verification, which we believe can significantly reduce the time required for the testing and verification phases of automotive development. A CAN-over-IP tunneling protocol is described and the design and implementation of a generic system for remote access to the CAN bus of vehicles is presented. Examples of applications of the technology are given and implications in terms of new possibilities and challenges in automotive engineering are discussed.
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| 5. |
- 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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| 6. |
- Mahmood, Khalid, et al.
(författare)
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Comparison of NoSQL Datastores for Large Scale Data Stream Log Analytics
- 2019
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Ingår i: 2019 IEEE International Conference on Smart Computing (SMARTCOMP). - : IEEE. - 9781728116891 ; , s. 478-480
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Konferensbidrag (refereegranskat)abstract
- With the advent of cyber-physical systems, industrial internet of things (IIoT) and industrial analytics numerous application scenarios have emerged where business and mission-critical decisions depend upon large scale analysis of data in form of sensor streams. However, large volumes of sensor stream data generated at high frequency pose substantial challenges for existing scalable data analysis techniques requiring the use of high-performance distributed datastores. This work covers in-depth performance comparison of three principal categories of distributed state-of-the-art NoSQL datastores by evaluating their applicability and efficiency for large-scale analysis of sensor logs from real-world hydraulic power systems. One central datastore is selected from each of the three principal categories of NoSQL datastores: MongoDB from the document store, Cassandra from the column store and Redis from the distributed main memory key-value store to be included in the performance evaluation. Understanding the differences and behavior of this type of systems are crucial for optimizing application performance. Key insights from this work can serve as a basis for an improved understanding of the applicability of NoSQL datastores in systems for large scale data stream analysis. This will be important for supporting data analytics in IIoT applications as found in monitoring and control of Power plants, Smart Cities, Transportation systems, Environmental and Health monitoring, etc.
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| 7. |
- 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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| 8. |
- 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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| 9. |
- Mahmood, Khalid, et al.
(författare)
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Wrapping a NoSQL Datastore for Stream Analytics
- 2020
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Ingår i: 2020 IEEE 21st International Conference On Information Reuse And Integration For Data Science (IRI 2020). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 301-305
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Konferensbidrag (refereegranskat)abstract
- With the advent of the Industrial Internet of Things (IIoT) and Industrial Analytics, numerous application scenarios emerge, where business and mission-critical decisions depend upon large scale analytics of sensor streams. However, very large volumes of data from data streams generated at a high rate pose substantial challenges in providing scalable analytics from existing Database Management Systems (DBMS). While scalability can be provided by high-performance distributed datastores, due to the simple query operations, access to high-level query-based data analytics is usually limited. This work combines high-level query-based data analytics capabilities with high-performance distributed scalability by applying a wrapper-mediator approach. The Amos II extensible main-memory DBMS provides online query processing data analytics engine in front of the MongoDB distributed NoSQL datastore to support large-scale distributed data analytics over persisted data streams. Thus, the implemented system enables query-based online data stream analytics over persisted data streams stored/logged in distributed NoSQL datastores.
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| 10. |
- Nyström, Mattias, et al.
(författare)
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Engineering information integration using object-oriented mediator technology
- 2006
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Ingår i: Software, practice & experience. - : Wiley. - 0038-0644 .- 1097-024X. ; 34:10, s. 949-975
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Tidskriftsartikel (refereegranskat)abstract
- The mechanical product development process uses many different software systems to virtually simulate the behaviour of a design. The present work deals with flexible and efficient integration using object-oriented mediator technology that provides transparent access to distributed engineering systems. The use of mediator technology is investigated for semi-automatically integrating engineering information resident in computer aided design systems with a Common Object Request Broker Architecture based application programming interface. The purpose is to provide engineering analysis applications access to computer aided design system information and computational methods through a declarative query language. We conclude that the use of a declarative query language for developing engineering applications shows great potential in terms of flexibility, development productivity, performance, and ease of use, compared with using a procedural programming language. The work also shows new use of mediator technology, declarative queries, and active rules within engineering information integration that traditionally is accomplished using procedural programming.
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