Latency or throughput is often critical performance metrics in stream processing. Applications’ can fluctuate depending on the input stream. This unpredictability due to variety data arrival frequency and size, complexity, other factors. Researchers are constantly investigating new ways mitigate impact of these variations with self-adaptive techniques involving elasticity micro-batching. Howeve...

This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated, non-overlapping cameras. Unlike other approaches this technique uses an incremental learning method to create the spatio-temporal links between cameras, and thus model the posterior probability distribution of these links. This can then be used with an appearance model of the obj...

this paper addresses a production and outbound distribution scheduling problem in which a set of jobs have to be process on a single machine for delivery to customers or to other machines for further processing. we assume that there is a sufficient number of vehicles and the delivery costs is independent of batch size but it is dependent on each trip. in this paper, we present an artificial imm...

Apache Flink1 is an open-source system for processing streaming and batch data. Flink is built on the philosophy that many classes of data processing applications, including real-time analytics, continuous data pipelines, historic data processing (batch), and iterative algorithms (machine learning, graph analysis) can be expressed and executed as pipelined fault-tolerant dataflows. In this pape...

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that realtime query processing and in-stream processing is the immediate need in many practical applications. In recent years, this idea got a lot of traction and a whole bunch of solutions like Twitter’s Storm, Yahoo’s S4, Cloudera’s Impala, A...

While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics for being a unified framework for both, batch and stream data processing. However, recent studies on micro-architectural characterization of in-memory data analytics are limited to only batch processing workloads. We ...