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+tags:: [[readingnotes]]
+title:: @Efficient Data Streaming Analytic Designs for Parallel and Distributed Processing
+item-type:: [[journalArticle]]
+original-title:: Efficient Data Streaming Analytic Designs for Parallel and Distributed Processing
+language:: en
+authors:: [[Hannaneh Najdataei]]
+library-catalog:: Zotero
+links:: [Local library](zotero://select/library/items/I82KJNLV), [Web library](https://www.zotero.org/users/1039502/items/I82KJNLV)
+
+- [[Abstract]]
+	- Today, ubiquitously sensing technologies enable inter-connection of physical objects, as part of Internet of Things (IoT), and provide massive amounts of data streams. In such scenarios, the demand for timely analysis has resulted in a shift of data processing paradigms towards continuous, parallel, and multitier computing. However, these paradigms are followed by several challenges especially regarding analysis speed, precision, costs, and deterministic execution. This thesis studies a number of such challenges to enable efficient continuous processing of streams of data in a decentralized and timely manner. In the first part of the thesis, we investigate techniques aiming at speeding up the processing without a loss in precision. The focus is on continuous machine learning/data mining types of problems, appearing commonly in IoT applications, and in particular continuous clustering and monitoring, for which we present novel algorithms; (i) Lisco, a sequential algorithm to cluster data points collected by LiDAR (a distance sensor that creates a 3D mapping of the environment), (ii) p-Lisco, the parallel version of Lisco to enhance pipeline- and data-parallelism of the latter, (iii) pi-Lisco, the parallel and incremental version to reuse the information and prevent redundant computations, (iv) g-Lisco, a generalized version of Lisco to cluster any data with spatio-temporal locality by leveraging the implicit ordering of the data, and (v) Amble, a continuous monitoring solution in an industrial process.
+- [[Attachments]]
+	- [Najdataei - Efficient Data Streaming Analytic Designs for Para.pdf](https://research.chalmers.se/publication/531736/file/531736_Fulltext.pdf) {{zotero-imported-file THKTVIBT, "Najdataei - Efficient Data Streaming Analytic Designs for Para.pdf"}}
+- [[Highlights]]
+	- ((63b98c01-8b4d-4f37-9346-f1d2c0fdcde3))
+	  id:: 63b98746-230b-4f4a-976d-650d4fce5e7d
+	- ((63b98c4b-d05a-443e-879d-7a7d4628e438))
+	  id:: 63b98818-0835-4dfb-97ac-14d06beef911
+	- ((63b98c76-74e4-4db5-b675-73032e781ef1))
+	- ((63b9963c-7fa3-49c5-aefd-0edf30f50aea))
+		- How would also add **how data mining/analysis tasks are specified/developed**
+		- #+BEGIN_IMPORTANT
+		  ((63b99873-26f0-4e2d-ad9a-f5b9addda8f9))
+		  
+		  ((63b9987c-3d40-46ae-8eff-e9aee9ef0481))
+		  #+END_IMPORTANT
+	- Example
+		- ((63b998ec-5b05-4ea1-b75a-8d870292da65))
+		- ((63b998db-4004-4323-9087-af18f15fafd9))
+		-