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Computers, Volume 3, Issue 4 (December 2014) – 2 articles , Pages 117-173

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Open AccessReview
A Survey on M2M Service Networks
Computers 2014, 3(4), 130-173; https://doi.org/10.3390/computers3040130 - 20 Nov 2014
Cited by 10 | Viewed by 5128
Abstract
The number of industrial applications relying on the Machine to Machine (M2M) services exposed from physical world has been increasing in recent years. Such M2M services enable communication of devices with the core processes of companies. However, there is a big challenge related [...] Read more.
The number of industrial applications relying on the Machine to Machine (M2M) services exposed from physical world has been increasing in recent years. Such M2M services enable communication of devices with the core processes of companies. However, there is a big challenge related to complexity and to application-specific M2M systems called “vertical silos”. This paper focuses on reviewing the technologies of M2M service networks and discussing approaches from the perspectives of M2M information and services, M2M communication and M2M security. Finally, a discussion on technologies and approaches potentially enabling future autonomic M2M service networks are provided. According to our conclusions, it is seen that clear definition of the architectural principles is needed to solve the “vertical silo” problem and then, proceeding towards enabling autonomic capabilities for solving complexity problem appears feasible. Several areas of future research have been identified, e.g., autonomic information based services, optimization of communications with limited capability devices, real-time messaging, creation of trust and end to end security, adaptability, reliability, performance, interoperability, and maintenance. Full article
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Open AccessReview
Beyond Batch Processing: Towards Real-Time and Streaming Big Data
Computers 2014, 3(4), 117-129; https://doi.org/10.3390/computers3040117 - 17 Oct 2014
Cited by 48 | Viewed by 7411
Abstract
Today, big data are generated from many sources, and there is a huge demand for storing, managing, processing, and querying on big data. The MapReduce model and its counterpart open source implementation Hadoop, has proven itself as the de facto solution to big [...] Read more.
Today, big data are generated from many sources, and there is a huge demand for storing, managing, processing, and querying on big data. The MapReduce model and its counterpart open source implementation Hadoop, has proven itself as the de facto solution to big data processing, and is inherently designed for batch and high throughput processing jobs. Although Hadoop is very suitable for batch jobs, there is an increasing demand for non-batch requirements like: interactive jobs, real-time queries, and big data streams. Since Hadoop is not suitable for these non-batch workloads, new solutions are proposed to these new challenges. In this article, we discussed two categories of these solutions: real-time processing, and stream processing of big data. For each category, we discussed paradigms, strengths and differences to Hadoop. We also introduced some practical systems and frameworks for each category. Finally, some simple experiments were performed to approve effectiveness of new solutions compared to available Hadoop-based solutions. Full article
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