5 citations · 19 across the 12 of their papers we have counts for
16 papers
LMQ-Sketch: Lagom Multi-Query Sketch for High-Rate Online Analytics
Martin Hilgendorf, Marina Papatriantafilou
Data sketches balance resource efficiency with controllable approximations for extracting features in high-volume, high-rate data. Two important points of interest are highlighted…
Cuckoo Heavy Keeper and the balancing act of maintaining heavy hitters in stream processing
Vinh Quang Ngo, Marina Papatriantafilou
Finding heavy hitters in databases and data streams is a fundamental problem with applications ranging from network monitoring to database query optimization, machine learning, and…
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…
Geographical Peer Matching for P2P Energy Sharing
Romaric Duvignau, Vincenzo Gulisano, Marina Papatriantafilou +1
Significant cost reductions attract ever more households to invest in small-scale renewable electricity generation and storage. Such distributed resources are not used in the most…
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…
Lisco: A Continuous Approach in LiDAR Point-cloud Clustering
Hannaneh Najdataei, Yiannis Nikolakopoulos, Vincenzo Gulisano +1
The light detection and ranging (LiDAR) technology allows to sense surrounding objects with fine-grained resolution in a large areas. Their data (aka point clouds), generated conti…
Aiding Autonomous Vehicles with Fault-tolerant V2V Communication
Vladimir Savic, Elad M. Schiller, Marina Papatriantafilou
Vehicle-to-vehicle (V2V) communication is a key component of the future autonomous driving systems. V2V can provide an improved awareness of the surrounding environment, and the kn…
Distributed Algorithm for Collision Avoidance at Road Intersections in the Presence of Communication Failures
Vladimir Savic, Elad M. Schiller, Marina Papatriantafilou
Vehicle-to-vehicle (V2V) communication is a crucial component of the future autonomous driving systems since it enables improved awareness of the surrounding environment, even with…
Efficient data streaming multiway aggregation through concurrent algorithmic designs and new abstract data types
Vincenzo Gulisano, Yiannis Nikolakopoulos, Daniel Cederman +2
Data streaming relies on continuous queries to process unbounded streams of data in a real-time fashion. It is commonly demanding in computation capacity, given that the relevant a…
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…