scholarly journals Decision Support System in a Memristor-Based Mobile CIM Architecture Applied on Big Data Computation and Storage

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wei Li ◽  
Zhao Deng
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Computer Architecture ◽  
Support System ◽  
Integrated Circuit ◽  
Data Communication ◽  
Oxide Semiconductor ◽  
And Storage

Data computation and storage are essential parts of developing big data applications. The memristor device technology could remove the speed and energy efficiency bottleneck in the existing data processing. The present experimental work investigates the decision support system in a new architecture, computation-in-memory (CIM) architecture, which can be utilized to store and process big data in the same physical location at a faster rate. The decision support system is used for data computation and storage, with the aims of helping memory units read, write, and erase data and supporting their decisions under big data communication ambiguities. Data communication is realized within the crossbar by the support of peripheral controller blocks. The feasibility of the CIM architecture, adaptive read, write, and erase methods, and memory accuracy were investigated. The integrated circuit emphasis (SPICE) simulation results show that the proposed CIM architecture has the potential of improving the computing efficiency, energy consumption, and performance area by at least two orders of magnitude. CIM architecture may be used to mitigate big data processing limits caused by the conventional computer architecture and complementary metal-oxide-semiconductor (CMOS) transistor process technologies.

A Proactive Decision Support System for Online Event Streams

2018 ◽  
Vol 17 (06) ◽  
pp. 1891-1913 ◽  
Author(s):  
Yongheng Wang ◽  
Xiaozan Zhang ◽  
Zengwang Wang
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Bayesian Network ◽  
Support System ◽  
Sequential Decision ◽  
Big Data Processing ◽  
Future System ◽  
System States

In-stream big data processing is an important part of big data processing. Proactive decision support systems can predict future system states and execute some actions to avoid unwanted states. In this paper, we propose a proactive decision support system for online event streams. Based on Complex Event Processing (CEP) technology, this method uses structure varying dynamic Bayesian network to predict future events and system states. Different Bayesian network structures are learned and used according to different event context. A networked distributed Markov decision processes model with predicting states is proposed as sequential decision making model. A Q-learning method is investigated for this model to find optimal joint policy. The experimental evaluations show that this method works well for congestion control in transportation system.

scholarly journals A decision support system for cost determination in grain storage facility operations

2011 ◽  
Vol 31 (4) ◽  
pp. 735-744 ◽  
Author(s):  
Domingos S. M. Valente ◽  
Daniel M. de Queiroz ◽  
Paulo C. Corrêa ◽  
Luis C. da Silva ◽  
Sônia M. L. R. do Vale
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Turnover Rate ◽  
Grain Storage ◽  
Storage Facility ◽  
Factors Associated ◽  
Processing And Storage ◽  
Cost Determination ◽  
And Storage

Many research works have being carried out on analyzing grain storage facility costs; however a few of them had taken into account the analysis of factors associated to all pre-processing and storage steps. The objective of this work was to develop a decision support system for determining the grain storage facility costs and utilization fees in grain storage facilities. The data of a CONAB storage facility located in Ponta Grossa - PR, Brazil, was used as input of the system developed to analyze its specific characteristics, such as amount of product received and stored throughout the year, hourly capacity of drying, cleaning, and receiving, and dispatch. By applying the decision support system, it was observed that the reception and expedition costs were exponentially reduced as the turnover rate of the storage increased. The cleaning and drying costs increased linearly with grain initial moisture. The storage cost increased exponentially as the occupancy rate of the storage facility decreased.

Sustainable Infrastructure Project Planning

2011 ◽  
pp. 95-110
Author(s):  
Omar Mohd Faizal ◽  
Bambang Trigunarsyah ◽  
Johnny Wong
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Support Systems ◽  
Data Communication ◽  
Team Members ◽  
Infrastructure Project ◽  
Collaborative Platform

Most infrastructure project developments are complex in nature, particularly in the planning phase. During this stage, many vague alternatives are tabled - from the strategic to operational level. Human judgement and decision making are characterised by biases, errors and the use of heuristics. These factors are intangible and hard to measure because they are subjective and qualitative in nature. The problem with human judgement becomes more complex when a group of people are involved. The variety of different stakeholders may cause conflict due to differences in personal judgements. Hence, the available alternatives increase the complexities of the decision making process. Therefore, it is desirable to find ways of enhancing the efficiency of decision making to avoid misunderstandings and conflict within organisations. As a result, numerous attempts have been made to solve problems in this area by leveraging technologies such as decision support systems. However, most construction project management decision support systems only concentrate on model development and neglect fundamentals of computing such as requirement engineering, data communication, data management and human centred computing. Thus, decision support systems are complicated and are less efficient in supporting the decision making of project team members. It is desirable for decision support systems to be simpler, to provide a better collaborative platform, to allow for efficient data manipulation, and to adequately reflect user needs. In this chapter, a framework for a more desirable decision support system environment is presented. Some key issues related to decision support system implementation are also described.

Towards a Decision Support System for Big Data Projects

2010 ◽  
pp. 357-368
Author(s):  
Matthias Volk ◽  
◽  
Daniel Staegemann ◽  
Sascha Bosse ◽  
Abdulrahman Nahhas ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Support System

scholarly journals A novel decision support system for the interpretation of remote sensing big data

2017 ◽  
Vol 11 (1) ◽  
pp. 31-45 ◽  
Author(s):  
Wadii Boulila ◽  
Imed Riadh Farah ◽  
Amir Hussain
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Support System

scholarly journals A Predictive Model-Based Decision Support System for Diabetes Patient Empowerment

2021 ◽  
Author(s):  
Dietmar Glachs ◽  
Tuncay Namli ◽  
Felix Strohmeier ◽  
Gustavo Rodríguez Suárez ◽  
Michel Sluis ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Support System ◽  
Patient Empowerment ◽  
Personal Data ◽  
Diabetes Patient ◽  
Treatment Goals ◽  
Management Support ◽  
Periodic Review

The main objective of POWER2DM is to develop and validate a personalized self-management support system (SMSS) for T1 and T2 diabetes patients that combines and integrates i) a decision support system (DSS) based on leading European predictive personalized models for diabetes interlinked with predictive computer models, ii) automated e-coaching functionalities based on Behavioral Change Theories, and iii) real-time Personal Data processing and interpretation. The SMSS offers a guided workflow based on treatment goals and activities where a periodic review evaluates the patients progress and provides detailed feedback on how to improve towards a healthier, diabetes appropriate lifestyle.

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