TBD-DP: Telco Big Data Visual Analytics with Data Postdiction
Ημερομηνία
2018Συγγραφέας
Costa, ConstantinosCharalampous, Andreas
Konstantinidis, Andreas
Zeinalipour-Yazti, Demetrios
Mokbel, Mohamed F.
Source
2018 19th IEEE International Conference on Mobile Data Management (MDM)Pages
280-281Google Scholar check
Metadata
Εμφάνιση πλήρους εγγραφήςΕπιτομή
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.