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Risch, Tore
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Publications (10 of 150) Show all publications
Mahmood, K., Risch, T. & Orsborn, K. (2021). Analytics of IIoT Data Using a NoSQL Datastore. In: 2021 IEEE International Conference on Smart Computing (SMARTCOMP): . Paper presented at 7th IEEE International Conference on Smart Computing (SMARTCOMP), AUG 23-27, 2021, Online (pp. 97-104). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Analytics of IIoT Data Using a NoSQL Datastore
2021 (English)In: 2021 IEEE International Conference on Smart Computing (SMARTCOMP), Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 97-104Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021
Series
International Conference on Smart Computing (SMARTCOMP), E-ISSN 2693-8340
Keywords
IoT, Edge Computing, NoSQL, Data Streams
National Category
Computer Sciences Computer Systems
Identifiers
urn:nbn:se:uu:diva-458413 (URN)10.1109/SMARTCOMP52413.2021.00034 (DOI)000853326000014 ()978-1-6654-1252-0 (ISBN)978-1-6654-2949-8 (ISBN)
Conference
7th IEEE International Conference on Smart Computing (SMARTCOMP), AUG 23-27, 2021, Online
Funder
eSSENCE - An eScience CollaborationEU, FP7, Seventh Framework Programme
Available from: 2021-11-09 Created: 2021-11-09 Last updated: 2022-10-24Bibliographically approved
Andrejev, A., Orsborn, K. & Risch, T. (2020). Strategies for array data retrieval from a relational back-end based on access patterns. Computing, 102(5), 1139-1158
Open this publication in new window or tab >>Strategies for array data retrieval from a relational back-end based on access patterns
2020 (English)In: Computing, ISSN 0010-485X, E-ISSN 1436-5057, Vol. 102, no 5, p. 1139-1158Article in journal (Refereed) Published
Abstract [en]

Multidimensional numeric arrays are often serialized to binary formats for efficient storage and processing. These representations can be stored as binary objects in existing relational database management systems. To minimize data transfer overhead when arrays are large and only parts of arrays are accessed, it is favorable to split these arrays into separately stored chunks. We process queries expressed in an extended graph query language SPARQL, treating arrays as node values and having syntax for specifying array projection, element and range selection operations as part of a query. When a query selects parts of one or more arrays, only the relevant chunks of each array should be retrieved from the relational database. The retrieval is made by automatically generated SQL queries. We evaluate different strategies for partitioning the array content, and for generating the SQL queries that retrieve it on demand. For this purpose, we present a mini-benchmark, featuring a number of typical array access patterns. We draw some actionable conclusions from the performance numbers.

Keywords
Arrays, Arrays queries, Array storage, Access patterns, Pattern discovery
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-423841 (URN)10.1007/s00607-020-00804-x (DOI)000522594300001 ()
Funder
Swedish Foundation for Strategic Research , RIT08-0041eSSENCE - An eScience Collaboration
Available from: 2020-11-03 Created: 2020-11-03 Last updated: 2020-11-03Bibliographically approved
Mahmood, K., Orsborn, K. & Risch, T. (2020). Wrapping a NoSQL Datastore for Stream Analytics. In: 2020 IEEE 21st International Conference On Information Reuse And Integration For Data Science (IRI 2020): . Paper presented at 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), Las Vegas, USA, 11-13 Aug. 2020 (pp. 301-305). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Wrapping a NoSQL Datastore for Stream Analytics
2020 (English)In: 2020 IEEE 21st International Conference On Information Reuse And Integration For Data Science (IRI 2020), Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 301-305Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020
Keywords
NoSQL Datastores, MongoDB, IIoT, Data Streams
National Category
Computer Systems
Identifiers
urn:nbn:se:uu:diva-442569 (URN)10.1109/IRI49571.2020.00050 (DOI)000635425100042 ()
Conference
2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), Las Vegas, USA, 11-13 Aug. 2020
Funder
EU, FP7, Seventh Framework Programme
Available from: 2021-05-18 Created: 2021-05-18 Last updated: 2021-11-22Bibliographically approved
Mahmood, K., Orsborn, K. & Risch, T. (2019). Comparison of NoSQL Datastores for Large Scale Data Stream Log Analytics. In: 2019 IEEE International Conference on Smart Computing (SMARTCOMP): . Paper presented at 5th IEEE International Conference on Smart Computing (SMARTCOMP), JUN 12-14, 2019, Washington, DC (pp. 478-480). IEEE
Open this publication in new window or tab >>Comparison of NoSQL Datastores for Large Scale Data Stream Log Analytics
2019 (English)In: 2019 IEEE International Conference on Smart Computing (SMARTCOMP), IEEE , 2019, p. 478-480Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
NoSQL Datastores, IoT, Smart Computing
National Category
Computer Systems Computer Sciences
Identifiers
urn:nbn:se:uu:diva-402954 (URN)10.1109/SMARTCOMP.2019.00093 (DOI)000502836700073 ()978-1-7281-1689-1 (ISBN)
Conference
5th IEEE International Conference on Smart Computing (SMARTCOMP), JUN 12-14, 2019, Washington, DC
Funder
eSSENCE - An eScience Collaboration
Available from: 2020-01-22 Created: 2020-01-22 Last updated: 2021-11-22Bibliographically approved
Xu, C., Källström, E., Risch, T., Lindström, J., Håkansson, L. & Larsson, J. (2017). Scalable validation of industrial equipment using a functional DSMS. Journal of Intelligent Information Systems, 48(3), 553-577
Open this publication in new window or tab >>Scalable validation of industrial equipment using a functional DSMS
Show others...
2017 (English)In: Journal of Intelligent Information Systems, ISSN 0925-9902, E-ISSN 1573-7675, Vol. 48, no 3, p. 553-577Article in journal (Refereed) Published
Abstract [en]

A stream validation system called SVALI is developed in order to continuously validate correct behavior of industrial equipment. A functional data model allows the user to define meta-data, analyses, and queries about the monitored equipment in terms of types and functions. Two different approaches to validate that sensor readings in a data stream indicate correct equipment behavior are supported: with the model-and-validate approach anomalies are detected based on a physical model, while with learn-and-validate anomalies are detected by comparing streaming data with a model of normal behavior learnt during a training period. Both models are expressed on a high level using the functional data model and query language. The experiments show that parallel stream processing enables SVALI to scale very well with respect to system throughput and response time. The paper is based on a real world application for wheel loader slippage detection at Volvo Construction Equipment implemented in SVALI.

Keywords
Data stream management, Distributed stream systems, Data stream validation, Parallelization, Anomaly detection
National Category
Computer Sciences
Research subject
Computer Science with specialization in Database Technology
Identifiers
urn:nbn:se:uu:diva-292767 (URN)10.1007/s10844-016-0427-2 (DOI)000401468300004 ()
Funder
EU, FP7, Seventh Framework ProgrammeSwedish Foundation for Strategic Research , RIT08-0041
Available from: 2016-08-18 Created: 2016-05-09 Last updated: 2018-01-10Bibliographically approved
Melander, L., Orsborn, K., Risch, T. & Wedlund, D. (2017). VisDM-A Data Stream Visualization Platform. In: Candan, S Chen, L Pedersen, TB Chang, L Hua, W (Ed.), Database Systems For Advanced Applications (DASFAA 2017), Proceedings Pt II: . Paper presented at 22nd International Conference on Database Systems for Advanced Applications (DASFAA), MAR 27-30, 2017, Suzhou, PEOPLES R CHINA (pp. 677-680). SPRINGER INTERNATIONAL PUBLISHING AG
Open this publication in new window or tab >>VisDM-A Data Stream Visualization Platform
2017 (English)In: Database Systems For Advanced Applications (DASFAA 2017), Proceedings Pt II / [ed] Candan, S Chen, L Pedersen, TB Chang, L Hua, W, SPRINGER INTERNATIONAL PUBLISHING AG , 2017, p. 677-680Conference paper, Published paper (Refereed)
Abstract [en]

Visual Data stream Monitor (VisDM) is a new approach to integrate a visual programming language with a data stream management system (DSMS) to support the construction, configuration, and visualization of data stream applications through a set of building blocks, Visual Data Flow Components (VDFCs). This functionality is provided by extending the LabVIEW visual programming platform to support easy declarative specification of continuous visualizations of continuous query results. With actor-based data flows, visualization of data stream output becomes more accessible.

Place, publisher, year, edition, pages
SPRINGER INTERNATIONAL PUBLISHING AG, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10178
Keywords
Data stream management, Data stream visualization, Visual data flow programming
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-346844 (URN)10.1007/978-3-319-55699-4_45 (DOI)000412832600045 ()978-3-319-55698-7 (ISBN)978-3-319-55699-4 (ISBN)
Conference
22nd International Conference on Database Systems for Advanced Applications (DASFAA), MAR 27-30, 2017, Suzhou, PEOPLES R CHINA
Available from: 2018-03-27 Created: 2018-03-27 Last updated: 2018-03-27Bibliographically approved
Stefanova, S. & Risch, T. (2016). Scalable long-term preservation of relational data through SPARQL queries. Semantic Web, 7(2), 117-137
Open this publication in new window or tab >>Scalable long-term preservation of relational data through SPARQL queries
2016 (English)In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 7, no 2, p. 117-137Article in journal (Refereed) Published
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-199570 (URN)10.3233/SW-150173 (DOI)000373208100002 ()
Projects
eSSENCE
Available from: 2016-02-12 Created: 2013-05-07 Last updated: 2018-01-11Bibliographically approved
Mahmood, K., Truong, T. & Risch, T. (2015). NoSQL approach to large scale analysis of persisted streams. In: Data Science: . Paper presented at BICOD 2015, July 6–8, Edinburgh, UK (pp. 152-156). Springer
Open this publication in new window or tab >>NoSQL approach to large scale analysis of persisted streams
2015 (English)In: Data Science, Springer, 2015, p. 152-156Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9147
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-274783 (URN)10.1007/978-3-319-20424-6_15 (DOI)000364104600015 ()978-3-319-20423-9 (ISBN)
Conference
BICOD 2015, July 6–8, Edinburgh, UK
Available from: 2015-06-11 Created: 2016-01-26 Last updated: 2021-11-22Bibliographically approved
Zhu, M., Mahmood, K. & Risch, T. (2015). Scalable queries over log database collections. In: Data Science: . Paper presented at BICOD 2015, July 6–8, Edinburgh, UK (pp. 173-185). Springer
Open this publication in new window or tab >>Scalable queries over log database collections
2015 (English)In: Data Science, Springer, 2015, p. 173-185Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9147
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-274784 (URN)10.1007/978-3-319-20424-6_17 (DOI)000364104600017 ()978-3-319-20423-9 (ISBN)
Conference
BICOD 2015, July 6–8, Edinburgh, UK
Available from: 2015-06-11 Created: 2016-01-26 Last updated: 2021-11-22Bibliographically approved
Truong, T. & Risch, T. (2015). Transparent inclusion, utilization, and validation of main memory domain indexes. In: Proc. 27th International Conference on Scientific and Statistical Database Management: . Paper presented at SSDBM 2015, June 29–July 1, San Diego, CA. New York: ACM Press
Open this publication in new window or tab >>Transparent inclusion, utilization, and validation of main memory domain indexes
2015 (English)In: Proc. 27th International Conference on Scientific and Statistical Database Management, New York: ACM Press, 2015Conference paper, Published paper (Refereed)
Abstract [en]

Main-memory database systems (MMDBs) are viable solutions for many scientific applications. Scientific and engineering data often require special indexing methods, and there is a large number of domain specific main memory indexing implementations developed. However, adding an index structure into a database system can be challenging. Mexima (Main memory External Index Manager) provides an MMDB where new main-memory index structures can be plugged-in without modifying the index implementations. This has allowed to plug into Mexima complex and highly optimized index structures implemented in C/C++ without code changes. To utilize new user defined indexes in queries transparently, Mexima automatically transforms query fragments into index operations based on index properly tables containing index meta-data. For scalable processing of complex numerical query expressions, Mexima includes an algebraic query transformation mechanism that reasons on numerical expressions to expose potential utilization of indexes. The index property tables furthermore enable validating the correctness of an index implementation by executing automatically generated test queries based on index meta-data. Experiments show that the performance penalty of using an index plugged into Mexima is low compared to using the corresponding stand-alone C/C++ implementation. Substantial performance gains are shown by the index exposing rewrite mechanisms.

Place, publisher, year, edition, pages
New York: ACM Press, 2015
Keywords
Domain Indexing; Extensible Databases; Query Processing; Automatic Testing
National Category
Computer Sciences
Research subject
Computer Science with specialization in Database Technology
Identifiers
urn:nbn:se:uu:diva-280368 (URN)10.1145/2791347.2791375 (DOI)000382164600019 ()978-1-4503-3709-0 (ISBN)
Conference
SSDBM 2015, June 29–July 1, San Diego, CA
Available from: 2015-06-29 Created: 2016-03-09 Last updated: 2021-06-10Bibliographically approved
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