16 citations · 44 across the 9 of their papers we have counts for
9 papers
Towards Unbounded Machine Unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes +1
Deep machine unlearning is the problem of `removing' from a trained neural network a subset of its training set. This problem is very timely and has many applications, including th…
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data
Meghdad Kurmanji, Peter Triantafillou
Machine Learning (ML) is changing DBs as many DB components are being replaced by ML models. One open problem in this setting is how to update such ML models in the presence of dat…
Graphical Join: A New Physical Join Algorithm for RDBMSs
Ali Mohammadi Shanghooshabad, Peter Triantafillou
Join operations (especially n-way, many-to-many joins) are known to be time- and resource-consuming. At large scales, with respect to table and join-result sizes, current state of…
Model Joins: Enabling Analytics Over Joins of Absent Big Tables
Ali Mohammadi Shanghooshabad, Peter Triantafillou
This work is motivated by two key facts. First, it is highly desirable to be able to learn and perform knowledge discovery and analytics (LKD) tasks without the need to access raw-…
Weighted Random Sampling over Joins
Michael Shekelyan, Graham Cormode, Peter Triantafillou +2
Joining records with all other records that meet a linkage condition can result in an astronomically large number of combinations due to many-to-many relationships. For such challe…
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…
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…
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…
ART : Sub-Logarithmic Decentralized Range Query Processing with Probabilistic Guarantees
Spyros Sioutas, Peter Triantafillou, George Papaloukopoulos +3
We focus on range query processing on large-scale, typically distributed infrastructures, such as clouds of thousands of nodes of shared-datacenters, of p2p distributed overlays, e…