TY - JOUR
T1 - Continuous decaying of telco big data with data postdiction
AU - Costa, Constantinos
AU - Konstantinidis, Andreas
AU - Charalampous, Andreas
AU - Zeinalipour-Yazti, Demetrios
AU - Mokbel, Mohamed F.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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; (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; (ii) SPATIAL-amnesia, which is based on the cellular tower’s location related with the tuple; 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.
AB - 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; (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; (ii) SPATIAL-amnesia, which is based on the cellular tower’s location related with the tuple; 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.
KW - Data decaying
KW - Data reduction
KW - Machine learning
KW - Spatio-temporal analytics
KW - Telco big data
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U2 - 10.1007/s10707-019-00364-z
DO - 10.1007/s10707-019-00364-z
M3 - Article
AN - SCOPUS:85068127112
SN - 1384-6175
VL - 23
SP - 533
EP - 557
JO - GeoInformatica
JF - GeoInformatica
IS - 4
ER -