dc.contributor.author | Costa, Constantinos | en |
dc.contributor.author | Konstantinidis, Andreas | en |
dc.contributor.author | Charalampous, Andreas | en |
dc.contributor.author | Zeinalipour-Yazti, Demetrios | en |
dc.contributor.author | Mokbel, Mohamed F. | en |
dc.creator | Costa, Constantinos | en |
dc.creator | Konstantinidis, Andreas | en |
dc.creator | Charalampous, Andreas | en |
dc.creator | Zeinalipour-Yazti, Demetrios | en |
dc.creator | Mokbel, Mohamed F. | en |
dc.date.accessioned | 2021-01-22T10:47:52Z | |
dc.date.available | 2021-01-22T10:47:52Z | |
dc.date.issued | 2019 | |
dc.identifier.issn | 1384-6175 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/62478 | |
dc.description.abstract | In this paper, we present two novel decaying operators for Telco Big Data (TBD), coined TBD-DP and CTBD-DP that are founded on the notion of Data Postdiction. Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which does not exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space | en |
dc.description.abstract | (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. Additionally, we provide three decaying focus methods that can be plugged into the operators we propose, namely: (i) FIFO-amnesia, which is based on the time that the tuple was created | en |
dc.description.abstract | (ii) SPATIAL-amnesia, which is based on the cellular tower’s location related with the tuple | en |
dc.description.abstract | and (iii) UNIFORM-amnesia, which picks randomly the tuples to be decayed. Similarly, CTBD-DP enables the decaying of streaming data utilizing the TBD-DP tree to extend and update the stored models. In our experimental setup, we measure the efficiency of the proposed operator using a ∼10GB anonymized real telco network trace. Our experimental results in Tensorflow over HDFS are extremely encouraging as they show that TBD-DP saves an order of magnitude storage space while maintaining a high accuracy on the recovered data. Our experiments also show that CTBD-DP improves the accuracy over streaming data. | en |
dc.language.iso | English (US) | en |
dc.source | GeoInformatica | en |
dc.source.uri | https://experts.umn.edu/en/publications/continuous-decaying-of-telco-big-data-with-data-postdiction | |
dc.title | Continuous decaying of telco big data with data postdiction | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1007/s10707-019-00364-z | |
dc.description.volume | 23 | |
dc.description.issue | 4 | |
dc.description.startingpage | 533 | |
dc.description.endingpage | 557 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.contributor.orcid | Zeinalipour-Yazti, Demetrios [0000-0002-7239-2387] | |
dc.contributor.orcid | Konstantinidis, Andreas [0000-0001-5370-8692] | |
dc.gnosis.orcid | 0000-0002-7239-2387 | |
dc.gnosis.orcid | 0000-0001-5370-8692 | |