TY - GEN
T1 - TBD-DP
T2 - 19th IEEE International Conference on Mobile Data Management, MDM 2018
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 demonstration paper, we present the TBD-DP operator, which relies on existing Machine Learning (ML) algorithms to abstract Telco Big Data (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. Our framework also includes visual and declarative interfaces for a variety of telco-specific data exploration tasks. We demonstrate the efficiency of the proposed operator using SPATE, which is a novel TBD visual analytic architecture we have developed. Our demo will enable attendees to interactively explore synthetic antenna signal traces, we will provide, in both visual and SQL mode. In both cases, the performance of the propositions will be quantitatively conveyed to the attendees through dedicated dashboards.
AB - In this demonstration paper, we present the TBD-DP operator, which relies on existing Machine Learning (ML) algorithms to abstract Telco Big Data (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. Our framework also includes visual and declarative interfaces for a variety of telco-specific data exploration tasks. We demonstrate the efficiency of the proposed operator using SPATE, which is a novel TBD visual analytic architecture we have developed. Our demo will enable attendees to interactively explore synthetic antenna signal traces, we will provide, in both visual and SQL mode. In both cases, the performance of the propositions will be quantitatively conveyed to the attendees through dedicated dashboards.
KW - big data
KW - data reduction
KW - visual analytics
UR - http://www.scopus.com/inward/record.url?scp=85050797655&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050797655&partnerID=8YFLogxK
U2 - 10.1109/MDM.2018.00050
DO - 10.1109/MDM.2018.00050
M3 - Conference contribution
AN - SCOPUS:85050797655
T3 - Proceedings - IEEE International Conference on Mobile Data Management
SP - 280
EP - 281
BT - Proceedings - 2018 IEEE 19th International Conference on Mobile Data Management, MDM 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 June 2018 through 28 June 2018
ER -