Wavelet-based Algorithms for Approximate Processing in the Big Data Era

Μυτιλήνης, Ιωάννης; Mytilinis, Ioannis

dc.contributor.author	Μυτιλήνης, Ιωάννης	el
dc.contributor.author	Mytilinis, Ioannis	en
dc.date.accessioned	2021-12-15T11:14:20Z
dc.date.available	2021-12-15T11:14:20Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/54180
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.21878
dc.rights	Default License
dc.subject	wavelets, approximate query processing	en
dc.title	Wavelet-based Algorithms for Approximate Processing in the Big Data Era	en
dc.contributor.department	Computer Science - Computing Systems Laboratory	el
heal.type	doctoralThesis
heal.generalDescription	Έχω ήδη παραδώσει το cd στην κεντρική βιβλιοθήκη	el
heal.classification	Computer Science, Databases	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2019-11-20
heal.abstract	Modern analytics involve computations over enormous numbers of data records, which often ar- rive in the form of high-throughput streams. The need for real-time processing of huge amounts of data places increasing emphasis on the efficiency of approximate query processing (AQP). A common practice for enabling AQP is to construct a lossy, compressed representation of a dataset and execute user queries against these synopses instead of the original data. A major challenge over the past years has been the construction of synopses that provide deterministic quality guarantees, often expressed in terms of maximum error metrics. Deterministic guaran- tees are strong and easier for the user to understand and interpret. As samples and sketches usually provide statistical guarantees, deterministic schemes are mainly supported by space- partitioning techniques such as histograms and wavelets. By approximating sharp discontinu- ities, wavelet decomposition has proven to be a very effective tool for data reduction. However, existing wavelet thresholding schemes that minimize maximum error metrics are constrained with impractical complexities for large datasets. Furthermore, they cannot efficiently handle the multidimensional version of the problem. In order to provide a practical solution, the first part of this dissertation proposes parallel algorithms that take advantage of key-properties of the wavelet decomposition and efficiently construct synopses that minimize non-Euclidean errors. The experimental evaluation over the Hadoop distributed processing framework showed linear scalability with both the data and cluster size; when the whole execution fits in the cluster and all workers can run fully in parallel, a synopsis construction speedup of 20× is witnessed. The second part of the thesis targets the problem in an IoT streaming environment. The IoT era has brought forth a computing paradigm shift from traditional high-end servers to “edge” devices of limited processing and memory capabilities. Thus, the designed algorithms for such architec- tures should be “cheap” in time complexity and have a minimal memory footprint. Moreover, in many streaming scenarios, fresh data tend to be prioritized. A sliding-window model is an important case of stream processing, where only the most recent elements remain active and the rest are discarded. As in IoT scenarios the available memory is typically much less than the window size, queries are answered from compact synopses that are maintained in an online fashion. For the efficient construction of such synopses, wavelet-based algorithms are presented. The proposed algorithms provide deterministic guarantees and near exact results for a variety of data distributions and query workloads.	en
heal.advisorName	Koziris, Nectarios
heal.committeeMemberName	Koziris, Nectarios
heal.committeeMemberName	Tsoumakos, Dimitrios
heal.committeeMemberName	Tsanakas, Panayiotis
heal.committeeMemberName	Deligiannakis, Antonios
heal.committeeMemberName	Papavassiliou, Symeon
heal.committeeMemberName	Pagourtzis, Aris
heal.committeeMemberName	Kotidis, Yannis
heal.academicPublisher	Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.fullTextAvailability	false