dc.contributor.author | Costa, Constantinos | en |
dc.contributor.author | Charalampous, Andreas | en |
dc.contributor.author | Konstantinidis, Andreas | en |
dc.contributor.author | Zeinalipour-Yazti, Demetrios | en |
dc.contributor.author | Mokbel, Mohamed F. | en |
dc.creator | Costa, Constantinos | en |
dc.creator | Charalampous, Andreas | en |
dc.creator | Konstantinidis, Andreas | en |
dc.creator | Zeinalipour-Yazti, Demetrios | en |
dc.creator | Mokbel, Mohamed F. | en |
dc.date.accessioned | 2021-01-22T10:47:34Z | |
dc.date.available | 2021-01-22T10:47:34Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/62338 | |
dc.description.abstract | In this paper, we present a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (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. In our experimental setup, we measure the efficiency of the proposed operator using a 10GB anonymized real telco network trace and 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. | en |
dc.source | 2018 19th IEEE International Conference on Mobile Data Management (MDM) | en |
dc.title | Decaying Telco Big Data with Data Postdiction | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1109/MDM.2018.00027 | |
dc.description.startingpage | 106 | |
dc.description.endingpage | 115 | |
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 | Conference Object | 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 | |