3 papers
cs.DB2020
ML-AQP: Query-Driven Approximate Query Processing based on Machine Learning
Fotis Savva, Christos Anagnostopoulos, Peter Triantafillou
As more and more organizations rely on data-driven decision making, large-scale analytics become increasingly important. However, an analyst is often stuck waiting for an exact res…
cs.DB2019
Adaptive Learning of Aggregate Analytics under Dynamic Workloads
Fotis Savva, Christos Anagnostopoulos, Peter Triantafillou
Large organizations have seamlessly incorporated data-driven decision making in their operations. However, as data volumes increase, expensive big data infrastructures are called t…
cs.DB2018
Explaining Aggregates for Exploratory Analytics
Fotis Savva, Christos Anagnostopoulos, Peter Triantafillou
Analysts wishing to explore multivariate data spaces, typically pose queries involving selection operators, i.e., range or radius queries, which define data subspaces of possible i…