3 citations · 3 across the 4 of their papers we have counts for
7 papers · 1 filter
F-IVM: Analytics over Relational Databases under Updates
Ahmet Kara, Milos Nikolic, Dan Olteanu +1
This article describes F-IVM, a unified approach for maintaining analytics over changing relational data. We exemplify its versatility in four disciplines: processing queries with…
Machine Learning over Static and Dynamic Relational Data
Ahmet Kara, Milos Nikolic, Dan Olteanu +1
This tutorial overviews principles behind recent works on training and maintaining machine learning models over relational data, with an emphasis on the exploitation of the relatio…
Scalable Querying of Nested Data
Jaclyn Smith, Michael Benedikt, Milos Nikolic +1
While large-scale distributed data processing platforms have become an attractive target for query processing, these systems are problematic for applications that deal with nested…
F-IVM: Learning over Fast-Evolving Relational Data
Milos Nikolic, Haozhe Zhang, Ahmet Kara +1
F-IVM is a system for real-time analytics such as machine learning applications over training datasets defined by queries over fast-evolving relational databases. We will demonstra…
Maintaining Triangle Queries under Updates
Ahmet Kara, Milos Nikolic, Hung Q. Ngo +2
We consider the problem of incrementally maintaining the triangle queries with arbitrary free variables under single-tuple updates to the input relations. We introduce an approach…
Counting Triangles under Updates in Worst-Case Optimal Time
Ahmet Kara, Hung Q. Ngo, Milos Nikolic +2
We consider the problem of incrementally maintaining the triangle count query under single-tuple updates to the input relations. We introduce an approach that exhibits a space-time…