Development of an information system for distributed processing of streaming data

Track guidance vehicle (RGV) is widely used in logistics warehousing and intelligent workshop, and its scheduling effectiveness will directly affect the production and operation efficiency of enterprises. In practical operation, central information system often lacks flexibility and timeliness. By contrast, mobile computing can balance the central information system and the distributed processing system, so that useful, accurate, and timely information can be provided to RGV. In order to optimize the RGV scheduling problem in uncertain environment, a genetic algorithm scheduling rule (GAM) using greedy algorithm as the genetic screening criterion is proposed in this paper. In the experiment, RGV scheduling of two-step processing in an intelligent workshop is selected as the research object. The experimental results show that the GAM model can carry out real-time dynamic programming, and the optimization efficiency is remarkable before a certain threshold.

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Advanced EHR Architectures–Promises or Reality

Methods of Information in Medicine ◽

10.1055/s-0038-1634044 ◽

2006 ◽

Vol 45 (01) ◽

pp. 95-101 ◽

Cited By ~ 10

Author(s):

B. G. M. E. Blobel

Keyword(s):

Information System ◽

Health Information ◽

Reference Model ◽

Distributed Processing ◽

Health Information System ◽

Generic Properties ◽

System Architectures ◽

Model Driven ◽

New Concepts ◽

New Generation

Summary Objectives: Forming the informational reflection of the patients and their care, the Electronic Health Record (EHR) is the core application of any complex health information system or health network. Such an ideally lifelong history file must be reliable, flexible, adaptable to new concepts and technologies, and robust, to allow for sharing knowledge over its lifetime. A sophisticated architecture must be chosen for meeting this challenge. Methods: An advanced EHR architecture for designing and implementing future-proof EHR systems must be a model of generic properties required for any Electronic Patient Record to provide communicable, comprehensive, useful, effective, and legally binding records that preserve their integrity over the time, independent of platforms and systems as well as of national special-ties. The resulting approach is based on the ISO Reference Model – Open Distributed Processing. Results: Based on advanced architectural principles introduced in the paper, a new generation of EHR systems has been designed and implemented for demonstrating the feasibility of the approach. This result is presented and evaluated regarding the achievements and problems using the component-based paradigm of model-driven health information system architectures. Conclusions: The future-proof EHR approach that has been established has been shortly evaluated. Advantages regarding flexibility, reliability, and portability of policy-driven, highly secure, role-dependent applications have to be considered in the light of performance as well as of the availability of network and application services.

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SLA-Based Adaptation Schemes in Distributed Stream Processing Engines

Applied Sciences ◽

10.3390/app9061045 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1045 ◽

Cited By ~ 2

Author(s):

Muhammad Hanif ◽

Eunsam Kim ◽

Sumi Helal ◽

Choonhwa Lee

Keyword(s):

Distributed Processing ◽

Service Level Agreement ◽

Stream Processing ◽

Big Data Analytics ◽

Service Level ◽

Streaming Data ◽

Adaptive Watermarking ◽

The Status ◽

Distributed Stream Processing ◽

Cloud Applications

With the upswing in the volume of data, information online, and magnanimous cloud applications, big data analytics becomes mainstream in the research communities in the industry as well as in the scholarly world. This prompted the emergence and development of real-time distributed stream processing frameworks, such as Flink, Storm, Spark, and Samza. These frameworks endorse complex queries on streaming data to be distributed across multiple worker nodes in a cluster. Few of these stream processing frameworks provides fundamental support for controlling the latency and throughput of the system as well as the correctness of the results. However, none has the ability to handle them on the fly at runtime. We present a well-informed and efficient adaptive watermarking and dynamic buffering timeout mechanism for the distributed streaming frameworks. It is designed to increase the overall throughput of the system by making the watermarks adaptive towards the stream of incoming workload, and scale the buffering timeout dynamically for each task tracker on the fly while maintaining the Service Level Agreement (SLA)-based end-to-end latency of the system. This work focuses on tuning the parameters of the system (such as window correctness, buffering timeout, and so on) based on the prediction of incoming workloads and assesses whether a given workload will breach an SLA using output metrics including latency, throughput, and correctness of both intermediate and final results. We used Apache Flink as our testbed distributed processing engine for this work. However, the proposed mechanism can be applied to other streaming frameworks as well. Our results on the testbed model indicate that the proposed system outperforms the status quo of stream processing. With the inclusion of learning models like naïve Bayes, multilayer perceptron (MLP), and sequential minimal optimization (SMO)., the system shows more progress in terms of keeping the SLA intact as well as quality of service (QoS).

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