  An increasing amount of analytics is performed on data that is procured in a
real-time fashion to make real-time decisions. Such tasks include simple
reporting on streams to sophisticated model building. However, the practicality
of such analyses are impeded in several domains because they are faced with a
fundamental trade-off between data collection latency and analysis accuracy.
  In this paper, we study this trade-off in the context of a specific domain,
Cellular Radio Access Networks (RAN). Our choice of this domain is influenced
by its commonalities with several other domains that produce real-time data,
our access to a large live dataset, and their real-time nature and
dimensionality which makes it a natural fit for a popular analysis technique,
machine learning (ML). We find that the latency accuracy trade-off can be
resolved using two broad, general techniques: intelligent data grouping and
task formulations that leverage domain characteristics. Based on this, we
present CellScope, a system that addresses this challenge by applying a domain
specific formulation and application of Multi-task Learning (MTL) to RAN
performance analysis. It achieves this goal using three techniques: feature
engineering to transform raw data into effective features, a PCA inspired
similarity metric to group data from geographically nearby base stations
sharing performance commonalities, and a hybrid online-offline model for
efficient model updates. Our evaluation of CellScope shows that its accuracy
improvements over direct application of ML range from 2.5x to 4.4x while
reducing the model update overhead by up to 4.8x. We have also used CellScope
to analyze a live LTE consisting of over 2 million subscribers for a period of
over 10 months, where it uncovered several problems and insights, some of them
previously unknown.
