Big data analytics for intelligent manufacturing systems: A review
With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the
amount of data from manufacturing systems has been increasing rapidly. With massive …
amount of data from manufacturing systems has been increasing rapidly. With massive …
State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing
Abstract The Internet of Things (IoT) is a paradigm based on the Internet that comprises
many interconnected technologies like RFID (Radio Frequency IDentification) and WSAN …
many interconnected technologies like RFID (Radio Frequency IDentification) and WSAN …
Apache spark: a unified engine for big data processing
Apache Spark: a unified engine for big data processing Page 1 56 COMMUNICATIONS OF THE
ACM | NOVEMBER 2016 | VOL. 59 | NO. 11 contributed articles DOI:10.1145/2934664 This …
ACM | NOVEMBER 2016 | VOL. 59 | NO. 11 contributed articles DOI:10.1145/2934664 This …
Mllib: Machine learning in apache spark
On-line portfolio selection is a practical financial engineering problem, which aims to
sequentially allocate capital among a set of assets in order to maximize long-term return. In …
sequentially allocate capital among a set of assets in order to maximize long-term return. In …
Resource management in fog/edge computing: a survey on architectures, infrastructure, and algorithms
Contrary to using distant and centralized cloud data center resources, employing
decentralized resources at the edge of a network for processing data closer to user devices …
decentralized resources at the edge of a network for processing data closer to user devices …
Naiad: a timely dataflow system
Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers
the high throughput of batch processors, the low latency of stream processors, and the ability …
the high throughput of batch processors, the low latency of stream processors, and the ability …
A survey on ensemble learning for data stream classification
Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …
classification. Their popularity is attributable to their good performance in comparison to …
The dataflow model: a practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing
T Akidau, R Bradshaw, C Chambers… - Proceedings of the …, 2015 - dl.acm.org
Unbounded, unordered, global-scale datasets are increasingly common in day-to-day
business (eg Web logs, mobile usage statistics, and sensor networks). At the same time …
business (eg Web logs, mobile usage statistics, and sensor networks). At the same time …
Live video analytics at scale with approximation and {Delay-Tolerance}
Video cameras are pervasively deployed for security and smart city scenarios, with millions
of them in large cities worldwide. Achieving the potential of these cameras requires …
of them in large cities worldwide. Achieving the potential of these cameras requires …
A survey of open source tools for machine learning with big data in the Hadoop ecosystem
With an ever-increasing amount of options, the task of selecting machine learning tools for
big data can be difficult. The available tools have advantages and drawbacks, and many …
big data can be difficult. The available tools have advantages and drawbacks, and many …