In this paper, we address the problem of compliant geo-distributed query processing. In particular, we focus on dataflow policies that impose restrictions on movement of data across geographical or institutional borders. Traditional ways to distributed ...

Advances in real-world applications require high-throughput processing over large data streams. Micro-batching has been proposed to support the needs of these applications. In micro-batching, the processing and batching of the data are interleaved, ...

When designing a data processing and analytics pipeline for data streams, it is important to provide the data load and be able to successfully balance it over the available resources. This can be achieved more easily if small processing modules, which ...

A growing number of domains (finance, seismology, internet-of-things, etc.) collect massive time series. When the number of series grow to the hundreds of millions or even billions, similarity queries become intractable on a single machine. Further, ...

Distributed dataflow systems such as Apache Spark and Apache Flink are used to derive new insights from large datasets. While they efficiently execute concrete data processing workflows, expressed as dataflow graphs, they lack generic support for ...

Intelligent Transport System (ITS) has seen growing interest in collecting vehicle location data in order to build up real-time traffic monitoring and analytic systems. However handling these data creates challenges, as they are massive in volume and ...

The broad success of Hadoop has led to a fast-evolving and diverse ecosystem of application engines that are building upon the YARN resource management layer. The open-source implementation of MapReduce is being slowly replaced by a collection of ...

Graph has been a ubiquitous and essential data representation to model real world objects and their relationships. Today, large amounts of graph data have been generated by various applications. Graph summarization techniques are crucial in uncovering ...

For cloud systems a set of stochastic models taking account of system context actualization is proposed. These models are based on queueing theory and extend its applications by studying the multichannel systems with warm-up and hyperexponential phase-...

With the advance of mobile devices, cloud computing has enabled people to access data and computing resources without spatiotemporal constraints. A common assumption is that mobile devices are well connected to remote data centers and the data centers ...

Deployment of pattern recognition applications for large-scale data sets is an open issue that needs to be addressed. In this paper, an attempt is made to explore new methods of partitioning and distributing data, that is, resource virtualization in the ...

To accelerate and simplify human activity recognition research, we have been developing a data processing tool named "HASC Tool." As the activity corpus becomes huge, it is not simple to handle the large number of files because it takes a lot of time to ...

Recent developments in wireless sensing technology lead to implement advanced algorithms for distributed data processing in various applications; intelligent transportation system is one of the main applications of the advanced networked sensing ...

Approximation algorithms can sometimes provide efficient solutions when no efficient exact computation is known. In particular, approximations are often useful in a distributed setting where the inputs are held by different parties and may be extremely ...

This paper describes an architecture model for data acquisition systems based on Compact PCI platforms. The aim is to increase real time data processing capabilities in experimental environments such as nuclear fusion devices (e.g. ITER). The model has ...

This paper reports the results of a field study conducted to obtain managers perceptions of their Management Information Systems (MIS). The basic study involved responses from over 100 MIS/DP managers, users, and other business professionals. The ...

"Distributed Data Processing" (DDP) is examined from a technological, organizational, and economic perspective. DDP projections are made for the 1980's as are recommendations for capitalizing on and minimizing the risks of this emerging technology.

The demand for new or expanded computer-based information systems far exceeds the capacity of present DP organizations to meet this demand. Assuming that a massive expansion of DP personnel is not feasible, one solution is to make existing computer ...