  Large organizations have seamlessly incorporated data-driven decision making
in their operations. However, as data volumes increase, expensive big data
infrastructures are called to rescue. In this setting, analytics tasks become
very costly in terms of query response time, resource consumption, and money in
cloud deployments, especially when base data are stored across geographically
distributed data centers. Therefore, we introduce an adaptive Machine Learning
mechanism which is light-weight, stored client-side, can estimate the answers
of a variety of aggregate queries and can avoid the big data backend. The
estimations are performed in milliseconds are inexpensive and accurate as the
mechanism learns from past analytical-query patterns. However, as analytic
queries are ad-hoc and analysts' interests change over time we develop
solutions that can swiftly and accurately detect such changes and adapt to new
query patterns. The capabilities of our approach are demonstrated using
extensive evaluation with real and synthetic datasets.
