most citedQuality-Driven Disorder Handling for M-way Sliding Window Stream Joins

12 citations · 24 across the 4 of their papers we have counts for

collaborators

5 papers

cs.DC201711 cited

Sieve: Actionable Insights from Monitored Metrics in Microservices

Jörg Thalheim, Antonio Rodrigues, Istemi Ekin Akkus +5

Major cloud computing operators provide powerful monitoring tools to understand the current (and prior) state of the distributed systems deployed in their infrastructure. While suc…

cs.DC2017

Approximate Stream Analytics in Apache Flink and Apache Spark Streaming

Do Le Quoc, Ruichuan Chen, Pramod Bhatotia +3

Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to c…

cs.DC20171 cited

Elastic and Secure Energy Forecasting in Cloud Environments

André Martin, Andrey Britoy, Christof Fetzer

Although cloud computing offers many advantages with regards to adaption of resources, we witness either a strong resistance or a very slow adoption to those new offerings. One rea…

cs.DB201712 cited

Quality-Driven Disorder Handling for M-way Sliding Window Stream Joins

Yuanzhen Ji, Jun Sun, Anisoara Nica +3

Sliding window join is one of the most important operators for stream applications. To produce high quality join results, a stream processing system must deal with the ubiquitous d…

cs.DC2016

Inspector: A Data Provenance Library for Multithreaded Programs

Jörg Thalheim, Pramod Bhatotia, Christof Fetzer

Data provenance strives for explaining how the computation was performed by recording a trace of the execution. The provenance trace is useful across a wide-range of workflows to i…