Periodicity detection is a crucial step in time series tasks, including monitoring and
forecasting of metrics in many areas, such as IoT applications and self-driving database …

In recent years, there has been tremendous interest in research that applies machine
learning to database systems. Being one of the most complex components of a DBMS, query …

Modern analytical workloads are highly heterogeneous and massively complex, making
generic out of the box query optimizers untenable for many customers and scenarios. As a …

Autoscaling is a critical component for efficient resource utilization with satisfactory quality of
service (QoS) in cloud computing. This paper investigates proactive autoscaling for widely …

Inter-job dependencies pervade shared data analytics infrastructures (so-called``data
lakes''), as jobs read output files written by previous jobs, yet are often invisible to current …

Microsoft's internal big-data infrastructure is one of the largest in the world---with over 300k
machines running billions of tasks from over 0.6 M daily jobs. Operating this infrastructure is …

Big data systems have become increasingly complex making the job of a query optimizer
incredibly difficult. This is due to more complicated decision making, more complex query …

Right-sizing resource allocation for big-data queries, particularly in serverless environments,
is critical for improving infrastructure operational efficiency, capacity availability, query …

Many database systems offer index tuning tools that help automatically select appropriate
indexes for improving the performance of an input workload. Index tuning is a resource …

Optimizing resource allocation for analytical workloads is vital for reducing costs of cloud-
data services. At the same time, it is incredibly hard for users to allocate resources per query …