TY - GEN
T1 - Decaying telco big data with data postdiction
AU - Costa, Constantinos
AU - Charalampous, Andreas
AU - Konstantinidis, Andreas
AU - Zeinalipour-Yazti, Demetrios
AU - Mokbel, Mohamed F.
PY - 2018/7/13
Y1 - 2018/7/13
N2 - 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; (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.
AB - 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; (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.
KW - big data
KW - data decaying
KW - data reduction
KW - machine learning
KW - spatio-temporal analytics
KW - telco
UR - http://www.scopus.com/inward/record.url?scp=85050810761&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050810761&partnerID=8YFLogxK
U2 - 10.1109/MDM.2018.00027
DO - 10.1109/MDM.2018.00027
M3 - Conference contribution
AN - SCOPUS:85050810761
T3 - Proceedings - IEEE International Conference on Mobile Data Management
SP - 106
EP - 115
BT - Proceedings - 2018 IEEE 19th International Conference on Mobile Data Management, MDM 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Mobile Data Management, MDM 2018
Y2 - 26 June 2018 through 28 June 2018
ER -