Mobile Big Data Analytics: Research, Practice and Opportunities

The rapid expansion of broadband mobile networks by Telecom Operators, has introduced a versatile global infrastructure that internally generates vast amounts of spatio-temporal network-level data (e.g., user id, location, device type, etc.) At the same time, mobile app vendors have nowadays at their fingertips massive amounts of app-level data collected through implicit or explicit crowdsourcing schemes with multi-sensing smartphones that have become a commodity. Mobile big data analytics refers to the discovery of previously unknown meaningful patterns and knowledge from a few dozen terabytes to many petabytes of data collected from mobile users at the network-level or the app-level. Example analytics range from high-level metrics and summaries (e.g., through clustering, classification and association rule mining) useful to executive managers to alert-based analytics (e.g., anomaly detection) useful to front-line engineers and users. This panel will explore how the academia and industry are tackling mobile big data analytic challenges. It will also identify and debate the key challenges and opportunities, in terms of applications, queries, architectures, to which the mobile data management and mobile data mining communities should contribute to.