Dusan Ramijak
ABSTRACT
The increasing proliferation of the Internet of Things (IoT) devices and systems result in large amounts of highly heterogeneous data to be collected. Although at least some of the collected sensor data is often consumed by the real-time decision making and control of the IoT system, that is not the only use of such data. Invariably, the collected data is stored, perhaps in some filtered or downselected fashion, so that it can be used for a variety of lower-frequency operations. It is expected that in a smart city environment with numerous IoT deployments, the volume of such data can become enormous. Therefore, mechanisms for lossy data compression that provide a trade-off between compression ratio and data usefulness for offline statistical analysis becomes necessary. In this paper, we discuss several simple pattern mining based compression strategies for multi-attribute IoT data streams. For each method, we evaluate the compressibility of the method vs. the level of similarity between original and compressed time series in the context of the home energy management system.
- Eleanor Ainy et al. 2015. Approximated Summarization of Data Provenance. In ACM CIKM. Google Scholar
Digital Library
- Muhammad Naveed Aman et al. 2017. Secure Data Provenance for the Internet of Things. In ACM IoTPTS. 11--14. Google ScholarDigital Library
- Khaled Bachour et al. 2015. Provenance for the People: An HCI Perspective on the W3C PROV Standard Through an Online Game. In ACM CHI. 2437--2446. Google ScholarDigital Library
- Sean Barker et al. 2012. Smart*: An open data set and tools for enabling research in sustainable homes. (2012).Google Scholar
- Sabine Bauer et al. 2013. Data provenance in the Internet of things. In Conference Seminar SS.Google Scholar
- Kaushik Chakrabarti et al. 2002. Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. ACM Trans. Database Syst. 27, 2 (2002), 188--228. Google ScholarDigital Library
- Christos Faloutsos et al. 1994. Fast Subsequence Matching in Time-series Databases. In ACM SIGMOD. 419--429. Google ScholarDigital Library
- Sorabh Gandhi et al. 2010. Space-efficient online approximation of time series data: Streams, amnesia, and out-of-order. In IEEE ICDE. 924--935.Google Scholar
- Jayavardhana Gubbi et al. 2013. Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems 29, 7 (2013), 1645--1660. Google ScholarDigital Library
- Luc Moreau et al. 2008. The Open Provenance Model: An Overview. 323--326.Google Scholar
- Themistoklis Palpanas et al. 2004. Online Amnesic Approximation of Streaming Time Series. In IEEE ICDE. 339--349. Google ScholarDigital Library
- Ivan Popivanov et al. 2002. Similarity Search Over Time-Series Data Using Wavelets. In IEEE ICDE. 212--221. Google ScholarDigital Library
- Karen Rose et al. 2015. The internet of things: An overview. The Internet Society (2015), 1--50.Google Scholar
- Joan Serra et al. 2014. An empirical evaluation of similarity measures for time series classification. Knowledge-Based Systems 67 (2014), 305--314. Google ScholarDigital Library
- Salmin Sultana et al. 2015. A distributed system for the management of fine-grained provenance. Journal of Database Management 26, 2 (2015), 32--47. Google ScholarDigital Library
- Nikolaj Tatti et al. 2012. The long and the short of it: summarising event sequences with serial episodes. In ACM KDD. 462--470. Google ScholarDigital Library
- Rob van der Meulen. 2017. Gartner Says 8.4 Billion Connected âĂŸThingsâĂŹ Will Be in Use in 2017, Up 31 Percent From 2016. (2017).Google Scholar
- Yulai Xie et al. 2011. Compressing Provenance Graphs.. In TaPP.Google Scholar
- Yulai Xie et al. 2013. Evaluation of a hybrid approach for efficient provenance storage. ACM Transactions on Storage 9, 4 (2013), 14. Google ScholarDigital Library
- Byoung-Kee Yi et al. 2000. Fast Time Sequence Indexing for Arbitrary Lp Norms. In VLDB. 385--394. Google ScholarDigital Library
Index Terms
- Pattern mining based compression of IoT data
Recommendations
A secure and extensible blockchain-based data provenance framework for the Internet of Things
AbstractAs data collected and provided by Internet of Things (IoT) devices power an ever-growing number of applications and services, it is crucial that this data can be trusted. Data provenance solutions combined with blockchain technology are one way to ...
Blockchain-based Data Provenance for the Internet of Things
IoT '19: Proceedings of the 9th International Conference on the Internet of ThingsAs more and more applications and services depend on data collected and provided by Internet of Things (IoT) devices, it is of importance that such data can be trusted. Data provenance solutions together with blockchain technology are one way to make ...
Lossless compression of VLSI layout image data
We present a novel lossless compression algorithm called Context Copy Combinatorial Code (C4), which integrates the advantages of two very disparate compression techniques: context-based modeling and Lempel-Ziv (LZ) style copying. While the algorithm ...
Comments