39 citations · 84 across the 12 of their papers we have counts for
14 papers
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…
Conjunctive Queries with Free Access Patterns under Updates
Ahmet Kara, Milos Nikolic, Dan Olteanu +1
We study the problem of answering conjunctive queries with free access patterns (CQAPs) under updates. A free access pattern is a partition of the free variables of the query into…
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…
Trade-offs in Static and Dynamic Evaluation of Hierarchical Queries
Ahmet Kara, Milos Nikolic, Dan Olteanu +1
We investigate trade-offs in static and dynamic evaluation of hierarchical queries with arbitrary free variables. In the static setting, the trade-off is between the time to partia…
Laconic Deep Learning Computing
Sayeh Sharify, Mostafa Mahmoud, Alberto Delmas Lascorz +2
We motivate a method for transparently identifying ineffectual computations in unmodified Deep Learning models and without affecting accuracy. Specifically, we show that if we deco…
DPRed: Making Typical Activation and Weight Values Matter In Deep Learning Computing
Alberto Delmas, Sayeh Sharify, Patrick Judd +3
We show that selecting a single data type (precision) for all values in Deep Neural Networks, even if that data type is different per layer, amounts to worst case design. Much shor…
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…
Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How
Alberto Delmas, Patrick Judd, Dylan Malone Stuart +5
We show that, during inference with Convolutional Neural Networks (CNNs), more than 2x to $8x ineffectual work can be exposed if instead of targeting those weights and activations…
Incremental View Maintenance with Triple Lock Factorization Benefits
Milos Nikolic, Dan Olteanu
We introduce F-IVM, a unified incremental view maintenance (IVM) approach for a variety of tasks, including gradient computation for learning linear regression models over joins, m…
LINVIEW: Incremental View Maintenance for Complex Analytical Queries
Milos Nikolic, Mohammed ElSeidy, Christoph Koch
Many analytics tasks and machine learning problems can be naturally expressed by iterative linear algebra programs. In this paper, we study the incremental view maintenance problem…
DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views
Yanif Ahmad, Oliver Kennedy, Christoph Koch +1
Applications ranging from algorithmic trading to scientific data analysis require realtime analytics based on views over databases that change at very high rates. Such views have t…