Smart mobile devices and wireless networks are changing the way people execute applications and access information. In the meantime, more and more personal data are thus spread around different data silos in the Inter...
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Smart mobile devices and wireless networks are changing the way people execute applications and access information. In the meantime, more and more personal data are thus spread around different data silos in the Internet. One day, you will find that you may want to access your own data, but the service providers may not allow or the vendors do not provide any application for your need. It means your personal data are locked in by the service vendors. While the advances in the hardware/software technology of the smart mobile devices enable more complicated application than the user can image a couple of years ago, those devices still fall short of big storage, powerful computing and more battery capacity for better user experience. The user may expect a device with secure and unlimited storage for his personal data and run his application as he wishes without incurring more energy consumption. So far, no simple solutions have been proposed to enable compute cloud for code offloading Android application and trusted storage cloud for personal data. The work described in this paper enhances current Android application framework to address the aforementioned issues. We introduce the MobileFBP framework to augment the compute part for the Android device and propose to leverage the central storage part with personal data store, that is, PDS, for trusted usage. In addition to the design and implementation of the enhanced framework, our preliminary experimental results are illustrated as well. Copyright (c) 2014 John Wiley & Sons, Ltd.
This paper proposes a cooperative multi-agent deep reinforcement learning (MADRL) algorithm for energy trading among multiple unmanned aerial vehicles (UAVs) in order to perform big-data processing in a distributed ma...
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ISBN:
(纸本)9781728191010
This paper proposes a cooperative multi-agent deep reinforcement learning (MADRL) algorithm for energy trading among multiple unmanned aerial vehicles (UAVs) in order to perform big-data processing in a distributed manner. In order to realize UAV-based aerial surveillance or mobile cellular services, seamless and robust wireless charging mechanisms are required for delivering energy sources from charging infrastructure (i.e., charging towers) to UAVs for the consistent operations of the UAVs in the sky. For actively and intelligently managing the charging towers, MADRL-based energy management system (EMS) is proposed and designed for energy trading among the energy storage systems those are equipped with charging towers. If the required energy for charging UAVs is not enough, the purchasing energy from utility company is desired which takes high consts. The main purpose of MADRL-based EMS learning is for minimizing purchasing energy from outside utility company for minimizing operational costs. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance.
Analyses with data mining and knowledge discovery techniques are not always successful as they occasionally yield no actionable results. This is especially true in the big-data context where we routinely deal with com...
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ISBN:
(数字)9783030197599
ISBN:
(纸本)9783030197599;9783030197582
Analyses with data mining and knowledge discovery techniques are not always successful as they occasionally yield no actionable results. This is especially true in the big-data context where we routinely deal with complex, heterogeneous, diverse and rapidly changing data. In this context, visual analytics play a key role in helping both experts and users to readily comprehend and better manage analyses carried on data stored in Infrastructure as a Service (IaaS) cloud services. To this end, humans should play a critical role in continually ascertaining the value of the processed information and are invariably deemed to be the instigators of actionable tasks. The latter is facilitated with the assistance of sophisticated tools that let humans interface with the data through vision and interaction. When working with big-data problems, both scale and nature of data undoubtedly present a barrier in implementing responsive applications. In this paper, we propose a software architecture that seeks to empower big-data analysts with visual analytics tools atop large-scale data stored in and processed by IaaS. Our key goal is to not only yield on-line analytic processing but also provide the facilities for the users to effectively interact with the underlying IaaS machinery. Although we focus on hierarchical and spatiotemporal datasets here, our proposed architecture is general and can be used to a wide number of application domains. The core design principles of our approach are: (a) On-line processing on cloud with Apache Spark. (b) Integration of interactive programming following the notebook paradigm through Apache Zeppelin. (c) Offering robust operation when data and/or schema change on the fly. Through experimentation with a prototype of our suggested architecture, we demonstrate not only the viability of our approach but also we show its value in a use-case involving publicly available crime data from United Kingdom.
With growing data volumes and the scaling of data center clusters, communication resources often become a bottleneck in service provisioning for many MapReduce applications (e.g., training machine learning models). Th...
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With growing data volumes and the scaling of data center clusters, communication resources often become a bottleneck in service provisioning for many MapReduce applications (e.g., training machine learning models). Therefore, data placements that bring data blocks closer to data consumers (e.g., MapReduce applications) are seen as a promising solution. In this article, we propose an efficient data-placement technique that considers network traffic reduction as well as QoS guarantees for the data blocks to optimize the communication resources. We first formulate the joint optimization of the data-placement problem, propose a generic model for minimizing communication costs, and show that the joint data-placement problem is NP-hard. To solve this problem, we propose a heuristic algorithm considering traffic flows in the network topology of data centers by first seeking optimal QoS-aware data placement based on golden division on a Zipflike replica distribution, then transforming the joint data-placement problem into a block-dependence tree (BDT) construction problem, and finally reducing the BDT construction to a graph-partitioning problem. The experimental results demonstrate that our data-placement approach could effectively improve the performance of MapReduce jobs with lower communication costs and less job execution time for big-data processing.
On account of the extreme expansion of the scientific research paper databases, the usage of searching and recommender systems in this area increased, as they can help researchers find appropriate papers by searching ...
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ISBN:
(纸本)9789897586477
On account of the extreme expansion of the scientific research paper databases, the usage of searching and recommender systems in this area increased, as they can help researchers find appropriate papers by searching in enormous indexed datasets. Depending on where the papers are published, there might be stricter policies that force the author to also add the needed metadata, but still there are other for which these metadata are not complete. As a result, many of the current solutions for searching and recommending papers are usually biased to a certain database. This paper proposes a retrieval system that can overcome these problems by aggregating data from different databases in a dynamic and efficient way. Extracting data from different sources dynamically and not only statically, based on a certain database, is important for assuring a complete interrogation, but in the same time incur complex operations that may affect the performance of the system. The performance could be maintained by using carefully designed architecture that relies on tools that allow high level of parallelization. The main original characteristic of the system is represented by the hybrid interrogation of static data (stored in databases) and dynamic data (obtained through real-time web interrogations).
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