T-Hoarder: A framework to process Twitter data streams

Congosto, Mariluz; Val, Pablo Basanta; Sánchez-Fernandez, Luis

doi:10.1016/j.jnca.2017.01.029

Cited by 55 publications

(41 citation statements)

References 20 publications

(11 reference statements)

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“…Most of the applications described in Section 3 require a multitude of data to be enabled, data that are later processed according to big data principles, with specific engines such as those described in [48][49][50]. In order to collect these different types of unstructured data coming from such heterogeneous sources, the MCS paradigm can be used.…”

Section: Using Mcs Inmentioning

confidence: 99%

How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

Pilloni

2018

Future Internet

110

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We are living in the era of the fourth industrial revolution, namely Industry 4.0. This paper presents the main aspects related to Industry 4.0, the technologies that will enable this revolution, and the main application domains that will be affected by it. The effects that the introduction of Internet of Things (IoT), Cyber-Physical Systems (CPS), crowdsensing, crowdsourcing, cloud computing and big data will have on industrial processes will be discussed. The main objectives will be represented by improvements in: production efficiency, quality and cost-effectiveness; workplace health and safety, as well as quality of working conditions; products' quality and availability, according to mass customisation requirements. The paper will further discuss the common denominator of these enhancements, i.e., data collection and analysis. As data and information will be crucial for Industry 4.0, crowdsensing and crowdsourcing will introduce new advantages and challenges, which will make most of the industrial processes easier with respect to traditional technologies.

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Section: Using Mcs Inmentioning

confidence: 99%

How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

Pilloni

2018

Future Internet

110

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“…Every mapper processes its chunk of data and generates key, value pairs. [28]. Hadoop is widely used MapReduce based model in bioinformatics in recent few years [29].…”

Section: Mapreduce and Spark Streamingmentioning

confidence: 99%

StreamAligner: a streaming based sequence aligner on Apache Spark

Rathee

Kashyap

2018

J Big Data

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“…A JCL cluster can also be composed of single‐board computers compatible with the Oracle JVM. In this way, similar to Congosto et al solution, JCL can run on Raspberry Pi, Galileo, Cubieboard, and many other small devices.…”

Section: Jcl Architecturementioning

confidence: 99%

A general‐purpose distributed computing Java middleware

Almeida

Cimino

Resende

et al. 2018

Concurrency and Computation

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Summary The middleware solutions for General‐Purpose Distributed Computing (GPDC) have distinct requirements, such as task scheduling, processing/storage fault tolerance, code portability for parallel or distributed environments, simple deployment (including over grid or multi‐cluster environments), collaborative development, low code refactoring, native support for distributed data structures, asynchronous task execution, and support for distributed global variables. These solutions do not integrate these requirements into a single deployment with a unique API exposing most of these requirements to users. The consequence is the utilization of several solutions with their particularities, thus requiring different user skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce this integration gap, in this paper, we present Java Cá&Lá (JCL), a distributed‐shared‐memory and task‐oriented lightweight middleware for the Java community that separates business logic from distribution issues during the development process and incorporates several requirements that were presented separately in the GPDC middleware literature over the last few decades. JCL allows building distributed or parallel applications with only a few portable API calls, thus reducing the integration problems. Finally, it also runs on different platforms, including small single‐board computers. This work compares and contrasts JCL with other Java middleware systems and reports experimental evaluations of JCL applications in several distinct scenarios.

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T-Hoarder: A framework to process Twitter data streams

Cited by 55 publications

References 20 publications

How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

StreamAligner: a streaming based sequence aligner on Apache Spark

A general‐purpose distributed computing Java middleware

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