5 citations · 19 across the 12 of their papers we have counts for
9 papers · 1 filter
QPOPSS: Query and Parallelism Optimized Space-Saving for Finding Frequent Stream Elements
Victor Jarlow, Charalampos Stylianopoulos, Marina Papatriantafilou
The frequent elements problem, a key component in demanding stream-data analytics, involves selecting elements whose occurrence exceeds a user-specified threshold. Fast, memory-eff…
On the Semantic Overlap of Operators in Stream Processing Engines
Vincenzo Gulisano, Alessandro Margara, Marina Papatriantafilou
Stream processing is extensively used in the IoT-to-Cloud spectrum to distill information from continuous streams of data. Streaming applications usually run in dedicated Stream Pr…
STRETCH: Virtual Shared-Nothing Parallelism for Scalable and Elastic Stream Processing
Vincenzo Gulisano, Hannaneh Najdataei, Yiannis Nikolakopoulos +3
Stream processing applications extract value from raw data through Directed Acyclic Graphs of data analysis tasks. Shared-nothing (SN) parallelism is the de-facto standard to scale…
Consistent Lock-free Parallel Stochastic Gradient Descent for Fast and Stable Convergence
Karl Bäckström, Ivan Walulya, Marina Papatriantafilou +1
Stochastic gradient descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous parallel shared-memory SGD (AsyncSGD), including synchronization-free al…
Performance Modeling and Vertical Autoscaling of Stream Joins
Hannaneh Najdataei, Vincenzo Gulisano, Alessandro V. Papadopoulos +3
Streaming analysis is widely used in cloud as well as edge infrastructures. In these contexts, fine-grained application performance can be based on accurate modeling of streaming o…
MindTheStep-AsyncPSGD: Adaptive Asynchronous Parallel Stochastic Gradient Descent
Karl Bäckström, Marina Papatriantafilou, Philippas Tsigas
Stochastic Gradient Descent (SGD) is very useful in optimization problems with high-dimensional non-convex target functions, and hence constitutes an important component of several…
Piecewise Linear Approximation in Data Streaming: Algorithmic Implementations and Experimental Analysis
Romaric Duvignau, Vincenzo Gulisano, Marina Papatriantafilou +1
Piecewise Linear Approximation (PLA) is a well-established tool to reduce the size of the representation of time series by approximating the series by a sequence of line segments w…
Shared-object System Equilibria: Delay and Throughput Analysis
Iosif Salem, Elad M. Schiller, Marina Papatriantafilou +1
We consider shared-object systems that require their threads to fulfill the system jobs by first acquiring sequentially the objects needed for the jobs and then holding on to them…
Lock-free Concurrent Data Structures
Daniel Cederman, Anders Gidenstam, Phuong Ha +3
Concurrent data structures are the data sharing side of parallel programming. Data structures give the means to the program to store data, but also provide operations to the progra…