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parallel data processing approaches for effective intensive care units with internet of things

INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2019年第4期19卷 474-482页

作者： Manikandan, Narayanan Subha, Srinivasan VIT Univ Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India

Computerisation in healthcare is more general and monitoring intensive care units (ICUs) is more significant and life critical. Accurate monitoring in ICU is essential. Failing to take the right decision at the right time may prove fatal. Similarly, a timely decision can save people's lives in various critical situations. In order to increase the accuracy and timeliness in ICU monitoring, two major technologies can be used, namely parallel processing through vectorisation of ICU data and data communication through internet of things (IoT). With our approach, we can improve efficiency and accuracy in data processing. This paper proposes a parallel decision tree algorithm in ICU data to take faster and accurate decisions on data selection. The uses of parallelised algorithms optimise the process of collecting a large set of patient information. Decision tree algorithm is used for examining and extracting knowledge-based data from large databases. Finalised information will be transferred to concerned medical experts in case of medical emergency using IoT. parallel implementation of decision tree algorithm is implemented with threads and output data is stored in local IoT table for further processing.

关键词： medical data processing internet of things IoT ICU data vectorisation multicore architecture parallel data processing

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parallel data processing With Magnonic Holographic Co-Processor

Parallel Data Processing With Magnonic Holographic Co-Proces...

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IEEE International Conference on Rebooting Computing (ICRC)

作者： Balynsky, M. Gutierrez, D. Chiang, H. Khitun, A. Kozhevnikov, A. Khivintsev, Y. Dudko, G. Filimonov, Y. Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA Russian Acad Sci Kotelnikov Inst Radioengn & Elect Saratov 410019 Russia

ISBN: (纸本)9781509013708

In this work, we present experimental data demonstrating the capabilities of Magnonic Holographic Co-Processor for parallel data processing. It is a type of magnetic logic device, which utilizes spin waves for data transfer and processing. Its operation is based on the correlation between the phases and the amplitudes of the input spin waves and the output inductive voltages. We present experimental data obtained for 8-terminal prototype based on Y3Fe2(FeO4)(3) structure. The input of the device is provided by the phased array of spin wave generating elements allowing us to produce input phase patterns of an arbitrary form. The obtained data demonstrate the capabilities of Magnonic Holographic Co-Processor for parallel data processing by using spin wave superposition. Potentially, magnonic holographic devices can be implemented as complementary logic units to digital processors. Physical limitations and technological constrains are also discussed.

关键词： spin waves holography parallel data processing

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Improved Task Graph-based parallel data processing for Dynamic Resource Allocation in Cloud

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Procedia Engineering 2012年 38卷 2172-2178页

作者： A. Ajitha D. Ramesh M. E. Software Engineering Anna University of Technology Tiruchirappalli-620024 India Assistant Professor CSE Department Anna University of Technology Tiruchirappalli-620024 India

In recent years large-set parallel data processing has gained quantum as one of the predominant applications of Infrastructure-as-a-Service (IaaS) clouds. data processing frameworks like Google's MapReduce and its open source implementation Hadoop, Microsoft's Dryad and so on are currently in use for parallel data processing in cloud-based companies. But the problem with them is that they are designed for homogeneous environments like clusters and disregard the dynamic and heterogeneous nature of a cloud. As a result, allocation and de-allocation of compute nodes at runtime is ineffective thereby increasing processing time and cost. In this paper we present our approach towards parallel data processing exploiting dynamic resource allocation in IaaS clouds. Our architecture ensures parallel data processing using Directed Acyclic task graph. To reduce the latency and to improve throughput, load balancing is introduced in the architecture. Incoming jobs are divided into tasks that are assigned to different types of virtual machines that are dynamically instantiated and terminated during job execution.

关键词： parallel data processing IaaS Cloud Task graph Heterogeneous environment Load balancing

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The parallel data processing of the Network

The Parallel Data Processing of the Network

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International Conference on Experimental Mechanics 2008 (ICEM 2008)

作者： Jingyan Wang,Wei Heng National Mobile Communications Research Laboratory,Southeast University,Nanjing,Jiangsu, China

We all know that it is hard and expensive to increase the speed of data processing by improving the capability of the *** paper puts forward a method of image transmission and reception between several computers in th... 详细信息

关键词： transmitter receiver parallel data processing speed network

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parallel Massive data Monitoring and processing Using Sensor Networks 14

Parallel Massive Data Monitoring and Processing Using Sensor...

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14th IEEE International Conference on Intelligence and Security Informatics - Cybersecurity and Big data (IEEE ISI)

作者： Naji, Hamid Reza Rezaee, Najmeh Grad Univ Adv Technol Coll Elect & Comp Engn Kerman Iran IAU Dept Comp Engn Kerman Iran

ISBN: (纸本)9781509038657

Sensor Networks have great potential for many applications. In time-critical and security applications, it is important to have on time delivery, minimum access time and maximum data rate. Most of sensor networks have a large number of sensor nodes which considering the memory and processor of each of them all together they provide a huge data storage space and also high processing capability. In this paper we propose a method to implement the parallel data monitoring and processing on a large number of sensor nodes in a sensor network that can be used is a security environment. We can use the sensor, memory and processing elements of each sensor node store and process a part of large database or computation. The data which is entered to the database can come from the sensors of sensor nodes. By this method not only we can use the memory of each sensor node to store apart of our data but also we can do parallel processing on the stored data of all sensor nodes. The results of our simulations show this method provides a huge storage space to store a considerable amount of data and also speed up the computations.

关键词： parallel data Monitoring parallel data processing Sensor networks Security applications

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Platform for parallel processing of intense experimental data flow on remote supercomputers 4th

Platform for parallel processing of intense experimental dat...

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4th International Young Scientists Conference on Computational Science (YSC)

作者： Shchapov, Vladislav Masich, Grigoriy Masich, Alexey Perm Natl Res Polytech Univ Perm Russia Inst Continuous Media Mech UB RAS Perm Russia

Modern experimental facilities generate large amounts of data that should be processed, saved to hard disk and presented to the user as fast as possible. However, in-situ data analysis requires technical resources which are often not available. The existence of accessible high-speed networks allows to forward data processing and storage to a remote supercomputer centers and datacenters. These capabilities can be realized through the development of architectural solutions for effective data transmission trough a long-distance high-speed networks, data input/ output and data distribution over computers and data storage systems. In this paper, we describe the results of investigations into the development of a software platform for parallel processing of intense experimental data-streams on ICMM UB RAS (Perm) and IMM UB RAS (Yekaterinburg) supercomputers, interconnected by a high-speed network. The reported studies was partially supported by RFBR, research project No. 1407- 96001-r_ural_a and by Program of UD RAS, project No 15-7-1-25.

关键词： Long fat network Supercomputer parallel data processing Middleware Distributed system

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A Workflow Application for parallel processing of Big data from an Internet Portal

A Workflow Application for Parallel Processing of Big Data f...

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14th Annual International Conference on Computational Science

作者： Czarnul, Pawel Gdansk Univ Technol Fac Elect Telecommun & Informat Gdansk Poland

The paper presents a workflow application for efficient parallel processing of data downloaded from an Internet portal. The workflow partitions input files into subdirectories which are further split for parallel processing by services installed on distinct computer nodes. This way, analysis of the first ready subdirectories can start fast and is handled by services implemented as parallel multithreaded applications using multiple cores of modern CPUs. The goal is to assess achievable speed-ups and determine which factors influence scalability and to what degree. data processing services were implemented for assessment of context (positive or negative) in which the given keyword appears in a document. The testbed application used these services to determine how a particular brand was recognized by either authors of articles or readers in comments in a specific Internet portal focused on new technologies. Obtained execution times as well as speed-ups are presented for data sets of various sizes along with discussion on how factors such as load imbalance and memory/disk bottlenecks limit performance.

关键词： parallel data processing parallel performance Internet parallel workflow application

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Platform for parallel processing of Intense Experimental data Flow on Remote Supercomputers

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Procedia Computer Science 2015年 66卷 515-524页

作者： Vladislav Shchapov Grigoriy Masich Alexey Masich Perm National Research Polytechnic University Perm Russia Institute of Continuous Media Mechanics UB RAS Perm Russia

Modern experimental facilities generate large amounts of data that should be processed, saved to hard disk and presented to the user as fast as possible. However, in-situ data analysis requires technical resources which are often not available. The existence of accessible high-speed networks allows to forward data processing and storage to a remote supercomputer centers and datacenters. These capabilities can be realized through the development of architectural solutions for effective data transmission trough a long-distance high-speed networks, data input/output and data distribution over computers and data storage systems. In this paper, we describe the results of investigations into the development of a software platform for parallel processing of intense experimental data-streams on ICMM UB RAS (Perm) and IMM UB RAS (Yekaterinburg) supercomputers, interconnected by a high-speed network. The reported studies was partially supported by RFBR, research project No. 14-07-96001-r_ural_a and by Program of UD RAS, project No 15-7-1-25.

关键词： Long fat network Supercomputer parallel data processing Middleware Distributed system

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A Workflow Application for parallel processing of Big data from an Internet Portal

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Procedia Computer Science 2014年 29卷 499-508页

作者： Paweł Czarnul Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Poland

The paper presents a workflow application for efficient parallel processing of data downloaded from an Internet portal. The workflow partitions input files into subdirectories which are further split for parallel processing by services installed on distinct computer nodes. This way, analysis of the first ready sub-directories can start fast and is handled by services implemented as parallel multithreaded applications using multiple cores of modern CPUs. The goal is to assess achievable speed-ups and determine which factors influence scalability and to what degree. data processing services were implemented for assessment of context (positive or negative) in which the given keyword appears in a document. The testbed application used these services to determine how a particular brand was recognized by either authors of articles or readers in comments in a specific Internet portal focused on new technologies. Obtained execution times as well as speed-ups are presented for data sets of various sizes along with discussion on how factors such as load imbalance and memory/disk bottlenecks limit performance.

关键词： parallel data processing parallel performance Internet parallel workflow application

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A Locality-aware Cooperative Distributed Memory Caching for parallel data Analytic Applications 36

A Locality-aware Cooperative Distributed Memory Caching for ...

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36th IEEE International parallel and Distributed processing Symposium (IEEE IPDPS)

作者： Hung, Chia-Ting Chou, Jerry Chen, Ming-Hung Chung, I-Hsin Natl Tsing Hua Univ Comp Sci Dept Hsinchu Taiwan IBM TJ Watson Res Ctr New York NY USA

ISBN: (纸本)9781665497473

Memory caching has long been used to fill up the performance gap between processor and disk for reducing the data access time of data-intensive computations. Previous studies on caching mostly focus on optimizing the hit rate of a single machine. But in this paper, we argue that the caching decision of a distributed memory system should be performed in a cooperative manner for the parallel data analytic applications, which are commonly used by emerging technologies, such as Big data and AI (Artificial Intelligence), to perform data mining and sophisticated analytics on larger data volume in a shorter time. A parallel data analytic job consists of multiple parallel tasks. Hence, the completion time of a job is bounded by its slowest task, meaning that the job cannot benefit from caching until all inputs of its tasks are cached. To address the problem, we proposed a cooperative caching design that periodically rearranges the cache placement among nodes according to the data access pattern while taking the task dependency and network locality into account. Our approach is evaluated by a trace-driven simulator using both synthetic workload and real-world traces. The results show that we can reduce the average completion times up to 33% compared to a non-collaborative caching polices and 25% compared to other start-of-the-art collaborative caching policies.

关键词： parallel data processing Caching Algorithm Performance Distributed Systems

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