Data-based Decision Support System Research Articles

Fertilizer application parameters for drip-irrigated peanut based on the fertilizer effect function established from a "3414" field trial.

Scientific fertilization is an important technical means of achieving high and stable peanut yields. Using soil testing and formula fertilization, the "3414" optimal regression design was used and included 14 nitrogen (N), phosphorus (P), and potassium (K) fertilization treatments. Ternary quadratic functions of the fertilizer effect were established according to three-season field experiments and the regression analysis of fertilizer-yield function was performed to explore the optimal fertilizer application mode and ratio for peanuts under mulched drip irrigation (MDI), and a suitable fertilizer application system was established. The ternary quadratic equation relating peanut yield (y) and the fertilizer application rates of N (N), P (P2O5), and K (K2O) was obtained after fitting, i.e., y = 2912.528 + 21.432N + 16.324P + 6.181K - 0.051N2 - 0.109P2 - 0.061K2 + 0.017NP + 0.023NK + 0.086PK, and significance analysis and typicality assessment were performed. The model R 2 was 0.9709, both values are extremely significant (p < 0.01), which indicates that the obtained ternary quadratic fertilizer effect function is typical and could be used for statistical purposes and fertilization recommendations. Three quadratic fertilizer effect functions were obtained. Among them, the equation for K is extremely significant, and the equations of N and P are significant. According to the assumption that the marginal yield is zero and the marginal profit is zero, the fertilizer application rate with the maximum yield, the fertilizer application rate with the best economic benefits, and the corresponding yields were obtained. The optimal fertilizer application rate predicted by the ternary quadratic fertilizer effect function was relatively high, so the three quadratic fertilizer effect functions were used for prediction. Under the test conditions, the recommended fertilizer application rates for peanuts under MDI are 256.6 kg N per ha, 164.2 kg P2O5 per ha, and 213.2 kg K2O per ha, the recommended fertilization ratio is 1:0.64:0.83, and the recommended ratio under formula fertilization is 23:15:19. The study has developed a data-based decision support system for Xinjiang drip-irrigated peanut, which assists farmers and agricultural managers in making more scientific and precise fertilization decisions based on the specific growth requirements of the crops and soil conditions. This evidence-based methodology enhances the precision of agricultural management, which is conducive to increasing crop yields while reducing resource wastage and environmental impact. However, multipoint and multiyear experiments are still needed to ensure that the findings are adaptable to the diverse soil conditions and fluctuating climate patterns that may be encountered in practice.

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches.

From last decade, Big data analytics and machine learning is a hotspot research area in the domain of agriculture. Agriculture analytics is a data intensive multidisciplinary problem. Big data analytics becomes a key technology to perform analysis of voluminous data. Irrigation water management is a challenging task for sustainable agriculture. It depends on various parameters related to climate, soil and weather conditions. For accurate estimation of requirement of water for a crop a strong modeling is required. This paper aims to review the application of big data based decision support system framework for sustainable water irrigation management using intelligent learning approaches. We examined how such developments can be leveraged to design and implement the next generation of data, models, analytics and decision support tools for agriculture irrigation water system. Moreover, water irrigation management need to rapidly adapt state-of-the-art using big data technologies and ICT information technologies with the focus of developing application based on analytical modeling approach. This study introduces the area of research, including a irrigation water management in smart agriculture, the crop water model requirement, and the methods of irrigation scheduling, decision support system, and research motivation.

Business process improvement by means of Big Data based Decision Support Systems: a case study on Call Centers

Big Data is a rapidly evolving and maturing field which places significant data storage and processing power at our disposal. To take advantage of this power, we need to create new means of collecting and processing large volumes of data at high speed. Meanwhile, as companies and organizations, such as health services, realize the importance and value of "joined-up thinking" across supply chains and healthcare pathways, for example, this creates a demand for a new type of approach to Business Activity Monitoring and Management. This new approach requires Big Data solutions to cope with the volume and speed of transactions across global supply chains. In this paper we describe a methodology and framework to leverage Big Data and Analytics to deliver a Decision Support framework to support Business Process Improvement, using near real-time process analytics in a decision-support environment. The system supports the capture and analysis of hierarchical process data, allowing analysis to take place at different organizational and process levels. Individual business units can perform their own process monitoring. An event-correlation mechanism is built into the system, allowing the monitoring of individual process instances or paths.

Strategi Lembaga Layanan Pendidikan Tinggi Wilayah I Sumatera Utara dalam Peningkatan Mutu Perguruan Tinggi Swasta

&lt;p&gt;&lt;em&gt;&lt;em&gt;This study aims to analyze internal and external factors and formulate strategies in improving the quality of higher education institutions in LLDIKTI Region I, North Sumatra. This research uses descriptive quantitative research methods. The analysis method used is a SWOT analysis using an analysis of internal factors and external factors. Strategies that can be carried out by LLDIKTI Region I North Sumatra are improvement and development of data-based information systems in coaching the quality of private universities, internal strengthening of LLDIKTI Region I North Sumatra in terms of more competent human resources, implementing and developing data-based decision support systems that can map quality PTS, guidance in implementing SPMI, provides training to increase the number of qualified auditors.&lt;/em&gt;&lt;/em&gt;&lt;/p&gt;

Incorporating big data within retail organizations: A case study approach

The rapid growth in consumer-generated Big Data that are mostly sourced from various types of mobile devices and sensor technologies has placed increasing competitive pressure on retailers to leverage such data within their location decision-making practices. This paper examines the incorporation of Big Data within retail organizations. Through the analysis of three in-depth case-studies of major retail-related organizations operating in Canada, this research addresses the following two inter-related objectives: (i) to identify the awareness, availability, and development of Big Data environments; and, (ii) to explore the opportunities and challenges associated with new Big Data-based decision support systems within retail organizations. The characteristics of an ideal data environment for Big Data adoption and development to take place are identified. The key findings reveal that while there was general awareness of the importance of Big Data, it was evident that the adoption and development of Big Data decision support was heavily reliant on a data environment that promotes transparency and a clear corporate data strategy.

A Middleware Platform for Intelligent Automation: An Industrial Prototype Implementation

The development of dynamic data-based Decision Support Systems (DSSs) along with the increasing availability of data in the industry, makes real-time data acquisition and management a challenge. Intelligent automation appears as a holistic combination of automation with analytics and decisions made by artificial intelligence, delivering smart manufacturing and mass customization while improving resource efficiency. However, challenges towards the development of intelligent automation architectures include the lack of interoperability between systems, complex data preparation steps, and the inability to deal with both high-frequency and high-volume data in a timely fashion. This paper contributes to industrial frameworks focused on the development of standardized system architectures for Industry 4.0, closing the gap between generic architectures and physical realizations. It proposes a platform for intelligent automation relying on a gateway or middleware between field devices, enterprise databases, and DSSs in real-time scenarios. This is achieved by providing the middleware interoperability, determinism, and automatic data structuring over an industrial communication infrastructure such as the OPC UA Standard over Time Sensitive Networks (TSN). Cloud services and database warehousing used to address some of the challenges are handled using fog computing and a multi-workload database. This paper presents an implementation of the platform in the pharmaceutical industry, providing interoperability and real-time reaction capability to changes to an industrial prototype using dynamic scheduling algorithms.

Improving weather dependent zone specific irrigation control scheme in IoT and big data enabled self driven precision agriculture mechanism

ABSTRACT Precision agriculture involves manipulation of variations in field productivity, maximization of income, scale backing of wastes, and minimizing of the impact on surroundings using automated machine-controlled information assortment and documentation. This work focuses on the efficient control of farm irrigation by exploiting the capabilities of Internet of Things (IoT) and Big Data-based Decision Support System (DSS) to generate adequate valve control commands. Three varieties of prediction techniques such as Deep Neural Network (DNN), Random Forest (RF) and Resilient Back-Propagation Neural Network model are tested to predict soil Moisture Content (MC) in one hour advance by considering 6 numbers of different sensors. The real-time data collection is performed using the proposed IoT node deployment strategy tested in the field. An integrated IoT-based DSS framework is proposed to accumulate 17 numbers of soil and environmental parameters to predict future variation of soil MC in 1 h advance. Further, Structural Similarity (SSIM) Index is used to visualize and maintain uniform MC all over the agriculture area during the entire cropping period. Site and zone specific irrigation control scheme is tested in the test site using fuzzy logic-based weather dependent model. The complete system architecture, deployment strategy and performance of the proposed IoT-based DSS mechanism is discussed in this article.

Towards a Process to Guide Big Data Based Decision Support Systems for Business Processes

Abstract Process performance improvement initiatives generally require the application of both knowledge management techniques and analysis tools to assist business users in decision making. Decision support systems (DSS) are a valuable asset to measure process performance; however, they require a vast amount of process performance data in order to support a valuable analysis with highest precision and accuracy. Moreover, this analysis needs to be attained in a timely manner in order to respond quickly to non-compliant situations. Existing process performance improvement initiatives lack of the appropriate methods and tools to give full support to business users. Contrarily to this, they are focused on a purely methodology perspective. We introduce a Business Process Improvement methodology for overcoming this limitation by integrating process improvement with big data based DSSs.

A Decision Support System for Determining Optimal Retention Stocks for Service Parts Inventories

Abstract This paper reports on the development and successful implementation of a decision support system (DSS) for analyzing service parts inventory retention stocks. The DSS was implemented in a Fortune 100 company with an initial $50,000,000 investment in service parts inventory. During the last two years of use, approximately $13,000,000 of service parts have been scrapped (disposed) with the help of the DSS. Very few of these parts have had to be repurchased from scrap dealers. This has resulted in approximately $6,000,000 in tax savings alone. The system continues 'to be used regularly by the company. The paper contributes to the service parts management literature in three ways. First, the paper proposes a new forecasting model for the retention stock problem. Second, the paper develops a new inventory model which captures a richer operating environment. Third, the paper suggests how these models may be integrated in an interactive, menu-driven, databased DSS. Although the forecasting model, invent...