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Zhou, Shou-Han, Gerard Loughnane, Redmond O'Connell, Mark A. Bellgrove, and Trevor T. J. Chong. "Distractors Selectively Modulate Electrophysiological Markers of Perceptual Decisions." Journal of Cognitive Neuroscience 33, no. 6 (May 1, 2021): 1020–31. http://dx.doi.org/10.1162/jocn_a_01703.
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Abstract Current models of perceptual decision-making assume that choices are made after evidence in favor of an alternative accumulates to a given threshold. This process has recently been revealed in human EEG recordings, but an unresolved issue is how these neural mechanisms are modulated by competing, yet task-irrelevant, stimuli. In this study, we tested 20 healthy participants on a motion direction discrimination task. Participants monitored two patches of random dot motion simultaneously presented on either side of fixation for periodic changes in an upward or downward motion, which could occur equiprobably in either patch. On a random 50% of trials, these periods of coherent vertical motion were accompanied by simultaneous task-irrelevant, horizontal motion in the contralateral patch. Our data showed that these distractors selectively increased the amplitude of early target selection responses over scalp sites contralateral to the distractor stimulus, without impacting on responses ipsilateral to the distractor. Importantly, this modulation mediated a decrement in the subsequent buildup rate of a neural signature of evidence accumulation and accounted for a slowing of RTs. These data offer new insights into the functional interactions between target selection and evidence accumulation signals, and their susceptibility to task-irrelevant distractors. More broadly, these data neurally inform future models of perceptual decision-making by highlighting the influence of early processing of competing stimuli on the accumulation of perceptual evidence.
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Suni, Eugenius Kau. "ANALISIS DAN PERANCANGAN DATA WAREHOUSE UNTUK MENDUKUNG KEPUTUSAN REDAKSI TELEVISI MENGGUNAKAN METODE NINE-STEP KIMBALL." JURNAL TEKNIK INFORMATIKA 11, no. 2 (November 28, 2018): 197–206. http://dx.doi.org/10.15408/jti.v11i2.8560.
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ABSTRAK Masalah yang sering dihadapi di redaksi televisi dalam rutinitas editorial atau pemberitaan adalah tidak adanya suatu sistem yang dapat menjadi instrumen pendukung keputusan cepat untuk merespon setiap perubahan informasi yang bergerak sangat cepat. Teknologi data warehouse yang dapat dimanfaatkan dalam bisnis maupun lembaga, organisasi, atau pun korporasi memiliki kemampuan dalam mengumpulkan data dalam kurun waktu tertentu dan dari berbagai sumber, dan informasi hasil pengolahan data warehouse dapat dimanfaatkan untuk mendukung pengambilan keputusan cepat. Aplikasi pusat data pada data warehouse dengan mengumpulkan berbagai data, informasi, dan pengetahuan dalam kurun waktu tertentu mampu memenuhi kebutuhan pencarian cepat atau query, pelaporan rutin dan berkala atau reporting, dan data mining. Dengan demikian, teknologi data warehouse dapat dimanfaatkan di redaksi televisi untuk mendukung pengambilan keputusan cepat, termasuk membantu memberikan informasi dan pengetahuan yang lebih akurat dalam membuat suatu keputusan strategis. Selain itu, kemampuan data warehouse juga dapat dimanfaatkan untuk pengukuran kinerja karyawan dan tim redaksi televisi untuk kepentingan pengembangan tim dan pengembangan karier. Sebagai tahap awal, diperlukan suatu analisis dan perancangan data warehouse untuk mendukung keputusan redaksi televisi. Masalahnya adalah bagaimana melakukan perancangan analisis dan perancangan data warehouse untuk mendukung keputusan redaksi televisi? Dengan mengkaji kasus redaksi Kompas TV Jakarta, pada riset ini, peneliti menganalisis dan merancang data warehouse untuk mendukung keputusan redaksi televisi menggunakan metode nine-step Kimball. Hasil analisis dan perancangan tersebut dapat diimplementasikan untuk mendukung keputusan cepat terkait pemilihan berita untuk ditayangkan, dan keputusan penjenjangan karier dan pemberian insentif bagi karyawan dengan kinerja bagus. ABSTRACT The problem that often occurs in the editorial editorial or reporting routine is that there is no system that can be an instrument that supports quick decisions to receive information that moves very quickly.Data warehouse technology that can be utilized in businesses and institutions, organizations, or even corporations has the ability to collect data within a certain period of time and from various sources, and information on the results of data warehouse processing can be used to support rapid decision making. Data center applications in the data warehouse by collecting various data, information, and knowledge within a certain period of time can meet the needs of quick search or query, routine and periodic reporting or reporting, and data mining. Thus, data warehouse technology can be utilized in television editors to support quick decision making, including helping to provide more accurate information and knowledge in making strategic decisions. In addition, the data warehouse capability can also be used to measure the performance of employees and the television editorial team for the benefit of team development and career development. As an initial stage, an analysis and design of a data warehouse is needed to support the decision of the television editor. The problem is how to design the analysis and design of a data warehouse to support the decision of the television editor? By reviewing the Kompas TV Jakarta editorial case, in this research, researchers analyzed and designed the data warehouse to support the decision of television editors using the nine-step Kimball method. The results of the analysis and design can be implemented to support quick decisions regarding the selection of news to be aired, and career lending decisions and providing incentives for employees with good performance.
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Rahardjo, Broto, Chriswardani Suryawati, and Farid Agushybana. "Pengaruh Kepemimpinan Demokratis Kepala Ruang Rawat Inap Terhadap Kepuasan Kerja Perawat di Rumah Sakit Umum Aro Pekalongan." Jurnal Manajemen Kesehatan Indonesia 7, no. 2 (August 31, 2019): 109–14. http://dx.doi.org/10.14710/jmki.7.2.2019.109-114.
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Leadership styles that are less suitable can reduce employee motivation, performance and job satisfaction. 4 out of 10 nurses in the inpatient room of Aro Pekalongan General Hospital felt dissatisfied with the leadership of the head of the inpatient room. Therefore a study was conducted to analyze the influence of the democratic leadership style of the inpatient head on the job satisfaction of nurses in Aro Pekalongan General Hospital.The study design was cross sectional with an observational quantitative approach. The research subjects were 32 nurses in the inpatient room of Aro General Hospital who were selected by the total sampling method. Data was collected by questionnaire. Processing and analysis of data using multiple logistic regression analysis.The results showed the variables of democratic leadership style related to nurse job satisfaction in Aro Pekalongan Hospital were delegation of responsibility variables (p = 0.005) and headroom decision-making variables (p = 0.034), while other variables not related to nurse job satisfaction were variable of headroom activity (p = 0.077) and empathy variable (p = 0.075). The variable delegation of tagging answers to the head of the room has a 19 times better effect on influencing nurse job satisfaction compared to the head of the room with poor delegation of responsibilities (p = 0.011 Exp (B) = 19.826). It is recommended for management for periodic monitoring and evaluation, the selection of the head of the room is based on the length of work, competence and experience of advice for the head of the room to improve effective communication and hold regular meetings with nurses in the inpatient room of Aro Pekalongan General Hospital.
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Kumar, Akshay, and T. V. Vijay Kumar. "A Multi-Objective Approach to Big Data View Materialization." International Journal of Knowledge and Systems Science 12, no. 2 (April 2021): 17–37. http://dx.doi.org/10.4018/ijkss.2021040102.
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Big data comprises voluminous and heterogeneous data that has a limited level of trustworthiness. This data is used to generate valuable information that can be used for decision making. However, decision making queries on Big data consume a lot of time for processing resulting in higher response times. For effective and efficient decision making, this response time needs to be reduced. View materialization has been used successfully to reduce the query response time in the context of a data warehouse. Selection of such views is a complex problem vis-à-vis Big data and is the focus of this paper. In this paper, the Big data view selection problem is formulated as a bi-objective optimization problem with the two objectives being the minimization of the query evaluation cost and the minimization of the update processing cost. Accordingly, a Big data view selection algorithm that selects Big data views for a given query workload, using the vector evaluated genetic algorithm, is proposed. The proposed algorithm aims to generate views that are able to reduce the response time of decision-making queries.
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Dogan, Onur, and Basar Öztaysi. "In-store behavioral analytics technology selection using fuzzy decision making." Journal of Enterprise Information Management 31, no. 4 (July 9, 2018): 612–30. http://dx.doi.org/10.1108/jeim-02-2018-0035.
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Purpose With the emerging technologies, collecting and processing data about the behaviors of customers or employees in a specific location has become possible. The purpose of this paper is to evaluate existing data collection technologies. Design/methodology/approach Technology evaluation problem is handled as a multi-criteria decision-making (MCDM) problem. In this manner, a decision model containing four criteria and eight sub-criteria and four alternatives are formed. The problem is solved using hesitant analytic hierarchy process (AHP) and trapezoidal fuzzy numbers (TrFN). Findings The results show that the most important sub-criteria are: accuracy, quantity, ıntrospective and cost. Decision makers’ evaluate for alternatives, namely wireless fidelity (WiFi), camera, radio-frequency identification and Bluetooth. The best alternative is found as Bluetooth which is followed by WiFi and Camera. Research limitations/implications Technology evaluation problem, just like many other MCDM problems are solved using expert evaluations. Thus, the generalizability of the findings is low. Originality/value In this paper, technology selection problem has been handled using hesitant AHP for the first time. In addition, the original methodology is extended by using TrFN to represent the expert evaluations in a better way.
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Tazzit, Siham, Niamat Ullah Ibne Hossain, Farjana Nur, Fatine Elakramine, Raed Jaradat, and Safae El Amrani. "Selecting a Biomass Pelleting Processing Depot Using a Data Driven Decision-Making Approach." Systems 9, no. 2 (May 6, 2021): 32. http://dx.doi.org/10.3390/systems9020032.
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Bioenergy is one of the potential solutions to satisfy the extensive demand for energy and reduce fossil fuel dependency. For biomass to be an efficient source of bioenergy, it must be converted to a usable form, one of which is pellets. This study compares three commonly used methods to produce pellets in a biomass depot and presents a framework to select the most effective and economic pelleting processes. The comparison is performed using a data driven decision-making method called the Preference Index Selection Method (PSI). We considered three main pelletization technologies and compared four of their most critical attributes. The three popular biomass pellet processing methods used for this study are the conventional pelleting process (CPP), the high moisture pelleting process (HMPP), and the ammonia fiber expansion (AFEX). These processes were evaluated from both economic and environmental perspectives. We used the state of Mississippi as a testing ground for our analyses. The results obtained through the PSI method were validated with the Grey relational analysis (GRA) method. The results revealed that of the three available pelleting processes, the conventional pelleting process and the high moisture pelleting process were the most economic and environmentally friendly.
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Rahmatillah, Syaukas, and M. Fadhli. "Sistem Informasi Pemilihan Karyawan Terbaik Menggunakan Metode Simple Additive Weighting (SAW) pada Balai Pendidikan dan Pelatihan Ilmu Pelayaran BP2IP Malahayati." Journal Innovations Computer Science 1, no. 2 (November 30, 2022): 50–66. http://dx.doi.org/10.56347/jics.v1i2.63.
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The purpose of this research is to assist the Balai Pendidikan dan Pelatihan Ilmu Pelayaran (BP2IP) Malahayati to process data on the selection of the best employees to computerize the current system so that it can analyze and assist database processing activities at an even better level of effectiveness and efficiency, so that it is expected to be able to ; designing the best web-based Employee Decision Making System (DSS) Application program, the best Employee Decision Making System (DSS) system made in the form of a questionnaire with several alternatives and criteria as basic ingredients in data processing using the Simple Additive Weighting (SAW) Method, and generate reports - more detailed ranking reports, criteria, alternatives, and employees of each required data. Based on the results of the research and decision-making system using the Simple Additive Weighting (SAW) method as the selection of the best employees at the Balai Pendidikan dan Pelatihan Ilmu Pelayaran (BP2IP) Malahayati which has been carried out by the author, several conclusions can be drawn, namely; This research succeeded in creating a decision support system for evaluating the best employee selection at the Balai Pendidikan dan Pelatihan Ilmu Pelayaran (BP2IP) Malahayati, and this research succeeded in conducting alternative rankings from the results of calculating the weight of employee scores using SAW (Simple Additive Weighting).
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Kumar, Akshay, and T. V. Vijay Kumar. "View Materialization Over Big Data." International Journal of Data Analytics 2, no. 1 (January 2021): 61–85. http://dx.doi.org/10.4018/ijda.2021010103.
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Advances in technology have resulted in the generation of a large volume of heterogeneous big data for large enterprises engaged in e-commerce, healthcare, education, etc. This is being created at a rapid rate but is low in its veracity. This big data includes large sets of semi-structured and unstructured data and is stored over a distributed file system (DFS). This data can be processed in a fault tolerant manner using several frameworks, tools, and advanced database technologies. Big data can provide important information, which can be used for business decision making. View materialization, which has been widely studied for structured databases or data warehouse, has been extended to big data to enhance efficiency of big data query processing. This paper focuses on the selection of big data views for materialization. The big data views can be identified by extracting a set of query attributes from the set of query workload of an enterprise. The query attributes are interrelated resulting in the creation of alternate access paths for query evaluation. The cost of query processing using big data views involves the integrity of different data types of heterogeneous big data, frequency of queries, change in the size of big data, selected sets of big data materialized views, and updates on big data and these sets of materialized views. The cost of query processing is computed using the stored size of big data views on the DFS system, which is a consistent processing framework of DFS. A big data view selection algorithm that is capable of selecting views from structured, semi-structured, and unstructured data has been proposed in this paper. The proposed algorithm would select big data views that would result in faster processing of most user queries resulting in efficient decision making.
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Kumar, Amit, and T. V. Vijay Kumar. "Improved Quality View Selection for Analytical Query Performance Enhancement Using Particle Swarm Optimization." International Journal of Reliability, Quality and Safety Engineering 24, no. 06 (November 17, 2017): 1740001. http://dx.doi.org/10.1142/s0218539317400010.
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A data warehouse, which is a central repository of the detailed historical data of an enterprise, is designed primarily for supporting high-volume analytical processing in order to support strategic decision-making. Queries for such decision-making are exploratory, long and intricate in nature and involve the summarization and aggregation of data. Furthermore, the rapidly growing volume of data warehouses makes the response times of queries substantially large. The query response times need to be reduced in order to reduce delays in decision-making. Materializing an appropriate subset of views has been found to be an effective alternative for achieving acceptable response times for analytical queries. This problem, being an NP-Complete problem, can be addressed using swarm intelligence techniques. One such technique, i.e., the similarity interaction operator-based particle swarm optimization (SIPSO), has been used to address this problem. Accordingly, a SIPSO-based view selection algorithm (SIPSOVSA), which selects the Top-[Formula: see text] views from a multidimensional lattice, has been proposed in this paper. Experimental comparison with the most fundamental view selection algorithm shows that the former is able to select relatively better quality Top-[Formula: see text] views for materialization. As a result, the views selected using SIPSOVSA improve the performance of analytical queries that lead to greater efficiency in decision-making.
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Indahingwati, Asmara, Muh Barid Nizarudin Wajdi, Dwi Ermayanti Susilo, Nuning Kurniasih, and Robbi Rahim. "Comparison Analysis of TOPSIS and Fuzzy Logic Methods On Fertilizer Selection." International Journal of Engineering & Technology 7, no. 2.3 (March 8, 2018): 109. http://dx.doi.org/10.14419/ijet.v7i2.3.12630.
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Decision Support System is an interactive system that supports decision in the decision-making process through alternatives derived from the processing of data, information and design of the models. Selection decision support system of chemical fertilizer in fruit plant is expected to help anyone who wants to cultivate fruit trees can determine the chemical fertilizer as expected based alternatives and criteria set by the user. In this research method used is TOPSIS Method and Method of Fuzzy Logic. TOPSIS method is one of multiple criteria decision making method that uses the principle that the alternatives selected must have the shortest distance. Fuzzy Logic is a methodology of control systems troubleshooting, the fuzzy logic stated that everything is a binary which means it is only two possibilities, "Yes or No", "True or False", "Good or Bad", and others. Therefore, all of these can have a membership value of 0 or 1.
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Juliana, Juliana, Jasmir Jasmir, and Pareza Alam Jusia. "Decision Support System for Supplier Selection using Analytical Hierarchy Process (AHP) Method." Scientific Journal of Informatics 4, no. 2 (November 10, 2017): 158–68. http://dx.doi.org/10.15294/sji.v4i2.12015.
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Technological growth is characterized by a variety of technological finds and computational methods used to facilitate human work in the era of globalization that is the method of Decision Support System (DSS) that helps decision makers to use data and models to solve problems that are not Structured. Toko Harapan Baru in making decisions in determining the best suppliers still use intuition, analysis, calculation and comparison of the manual in determining the supplier of goods to his shop takes a long time, the difficulty of searching data because there is no data processing cause is quite complicated without any particular method which gives inaccurate results. Then, Toko Harapan Baru requires a system that can be a solution to the problem that is being encountered. The Analytical Hierarchy Process (AHP) method is a functional hierarchy to help decision-makers better in making decisions on many objective issues.
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Lepora, Nathan F., and Kevin N. Gurney. "The Basal Ganglia Optimize Decision Making over General Perceptual Hypotheses." Neural Computation 24, no. 11 (November 2012): 2924–45. http://dx.doi.org/10.1162/neco_a_00360.
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The basal ganglia are a subcortical group of interconnected nuclei involved in mediating action selection within cortex. A recent proposal is that this selection leads to optimal decision making over multiple alternatives because the basal ganglia anatomy maps onto a network implementation of an optimal statistical method for hypothesis testing, assuming that cortical activity encodes evidence for constrained gaussian-distributed alternatives. This letter demonstrates that this model of the basal ganglia extends naturally to encompass general Bayesian sequential analysis over arbitrary probability distributions, which raises the proposal to a practically realizable theory over generic perceptual hypotheses. We also show that the evidence in this model can represent either log likelihoods, log-likelihood ratios, or log odds, all leading proposals for the cortical processing of sensory data. For these reasons, we claim that the basal ganglia optimize decision making over general perceptual hypotheses represented in cortex. The relation of this theory to cortical encoding, cortico-basal ganglia anatomy, and reinforcement learning is discussed.
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Kumar, Amit, and T. V. Vijay Kumar. "Materialized View Selection Using Set Based Particle Swarm Optimization." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 3 (July 2018): 18–39. http://dx.doi.org/10.4018/ijcini.2018070102.
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A data warehouse is a central repository of historical data designed primarily to support analytical processing. These analytical queries are exploratory, long and complex in nature. Further, the rapid and continuous growth in the size of data warehouse increases the response times of such queries. Query response times need to be reduced in order to speedup decision making. This problem, being an NP-Complete problem, can be appropriately dealt with by using swarm intelligence techniques. One such technique, i.e. the set-based particle swarm optimization (SPSO), has been proposed to address this problem. Accordingly, a SPSO based view selection algorithm (SPSOVSA), which selects the Top-K views from a multidimensional lattice, is proposed. Experimental based comparison of SPSOVSA with the most fundamental view selection algorithm shows that SPSOVSA is able to select comparatively better quality Top-K views for materialization. The materialization of these selected views would improve the performance of analytical queries and lead to efficient decision making.
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Wang, Chia-Nan, Van Thanh Nguyen, Hoang Tuyet Nhi Thai, Ngoc Nguyen Tran, and Thi Lan Anh Tran. "Sustainable Supplier Selection Process in Edible Oil Production by a Hybrid Fuzzy Analytical Hierarchy Process and Green Data Envelopment Analysis for the SMEs Food Processing Industry." Mathematics 6, no. 12 (December 4, 2018): 302. http://dx.doi.org/10.3390/math6120302.
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Today, business organizations are facing increasing pressure from a variety of sources to operate using sustainable processes. Thus, most companies need to focus on their supply chains to enhance sustainability to meet customer demands and comply with environmental legislation. To achieve these goals, companies must focus on criteria that include CO2 (carbon footprint) and toxic emissions, energy use and efficiency, wastage generations, and worker health and safety. As in other industries, the food processing industry requires large inputs of resources, which results in several negative environmental effects; thus, decision-makers have to evaluate qualitative and quantitative factors. This work identifies the best supplier for edible oil production in the small and medium enterprise (SME) food processing industry in Vietnam. This study also processes a hybrid multicriteria decision-making (MCDM) model using a fuzzy analytical hierarchy process (FAHP) and green data envelopment analysis (GDEA) model to identify the weight of all criteria of a supplier’s selection process based on opinions from company procurement experts. Subsequently, GDEA is applied to rank all potential supplier lists. The primary objective of this work is to present a novel approach which integrates FAHP and DEA for supplier selection and also consider the green issue in edible oil production in uncertain environments. The aim of this research is also to provide a useful guideline for supplier selection based on qualitative and quantitative factors to improve the efficiency of supplier selection in the food industry and other industries. The results reveal that Decision-Making Unit 1 (DMU 1), DMU 3, DMU 7, and DMU 9 are identified as extremely efficient for five DEA models, which are the optimal suppliers for edible oil production. The contributions of this research include a proposed MCDM model using a hybrid FAHP and GDEA model for supplier selection in the SME food processing industry under a fuzzy environment conditions in Vietnam. This research also is part of an evolution of a new hybrid model that is flexible and practical for decision-makers. In addition, the research also provides a useful guideline in supplier selection in the food processing industry and a guideline for supplier selection in other industries.
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., Faisal, Muhammad Ridwan, and Mardawati . "The Expert Choice Implementation in Selecting the Electronic Voting Software." International Journal of Engineering & Technology 7, no. 2.29 (May 22, 2018): 811. http://dx.doi.org/10.14419/ijet.v7i2.29.14262.
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The voting service process conducted today is still done manually so that it becomes a decision to utilize the electronic voting. The electronic voting greatly reduces human control and human direct influence on this voting process. Problems faced by end users in the selection are there are so many choices of electronic voting software. Decision making, essentially a form of election of the various alternatives of action or multi-criteria decision making that can be selected. Decision support system in this research is used to select the type of electronic voting software. The method used in this research is the multi criteria decision making and analytical hierarchy process using expert choice software. And aims to make decisions that can make certain parties to take the best decision in choosing the type of electronic voting software. From the data processing is concluded that the first sequence is online voting 58.3%, express vote 17.2%, simply voting 17% and ballot online 7.5%. Processing of data obtained from the respondent expert inconsistencies value ratio is less than 0.1, thus the combined geometric calculation result data is fairly consistent.
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Stott, Jeffrey J., and A. David Redish. "A functional difference in information processing between orbitofrontal cortex and ventral striatum during decision-making behaviour." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1655 (November 5, 2014): 20130472. http://dx.doi.org/10.1098/rstb.2013.0472.
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Both orbitofrontal cortex (OFC) and ventral striatum (vStr) have been identified as key structures that represent information about value in decision-making tasks. However, the dynamics of how this information is processed are not yet understood. We recorded ensembles of cells from OFC and vStr in rats engaged in the spatial adjusting delay-discounting task , a decision-making task that involves a trade-off between delay to and magnitude of reward. Ventral striatal neural activity signalled information about reward before the rat's decision, whereas such reward-related signals were absent in OFC until after the animal had committed to its decision. These data support models in which vStr is directly involved in action selection, but OFC processes decision-related information afterwards that can be used to compare the predicted and actual consequences of behaviour.
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Arun, Biri, and T. V. Vijay Kumar. "Materialized View Selection Using Bumble Bee Mating Optimization." International Journal of Decision Support System Technology 9, no. 3 (July 2017): 1–27. http://dx.doi.org/10.4018/ijdsst.2017070101.
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Decision support systems (DSS) constitute one of the most crucial components of almost every corporation's information system. Data warehouse provides the DSS with massive volumes of quality corporate data for analysis. On account of the volume of corporate data, its processing time of on-line analytical queries is huge (in hours and days). Materialized views have been used to substantially improve query performance. Nevertheless, selecting appropriate sets of materialized views is an NP-Complete problem. In this paper, a new discrete bumble bee mating inspired view selection algorithm (BBMVSA) that selects Top-K views from a multidimensional lattice has been proposed. Experimental results show that BBMVSA was able to select fairly good quality Top-K views incurring a lower TVEC. Materialization of the selected views would improve the overall data analysis of DSS and would facilitate the decision making process.
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Akperov, Gurru, Ilgar Alekperov, Anastasia Gorbacheva, Imran Magerramov, and Anatoly Bocharov. "Method of Fuzzy Parametric Selection for Making a Reasonable Decision by an Intelligent Training Module." Journal of Physics: Conference Series 2131, no. 3 (December 1, 2021): 032111. http://dx.doi.org/10.1088/1742-6596/2131/3/032111.
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Abstract Selection of an optimal route within the intelligent approach provides for the possibility of applying soft models and computing in estimation of the trainee presence in the knowledge space. Among numerous ways of representation and processing of information of this type, the special place is held by those able to adapt to the maximal number of NO factors, characterizing the actual training situations, their measurable data and actual methods and ways of their processing that have ambiguities, uncertainties and incompleteness of the respective models and methods. In this paper, we suggest to extend the certain well-proven best practices in data analysis and transformation in the training environment information space to solving the actual training management problems. In addition, the paper demonstrates approaches to the use of Pareto-optimal approaches for fuzzy and underdetermined situations in actual training processes. Formally, this problem is solved with a fuzzy systemic graph. Variants of calculating procedures, allowing the use of the available apparatus of soft models and computing with the purpose to eliminate uncertainties when forming grounded decisions, are given. Methods and criteria of route options selection with regard of vaguely defined functional specification requirements have been developed pursuant to the study.
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Prakash, Jay, and T. V. Vijay Kumar. "A Multi-Objective Approach for Materialized View Selection." International Journal of Operations Research and Information Systems 10, no. 2 (April 2019): 1–19. http://dx.doi.org/10.4018/ijoris.2019040101.
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In today's world, business transactional data has become the critical part of all business-related decisions. For this purpose, complex analytical queries have been run on transactional data to get the relevant information, from therein, for decision making. These complex queries consume a lot of time to execute as data is spread across multiple disparate locations. Materializing views in the data warehouse can be used to speed up processing of these complex analytical queries. Materializing all possible views is infeasible due to storage space constraint and view maintenance cost. Hence, a subset of relevant views needs to be selected for materialization that reduces the response time of analytical queries. Optimal selection of subset of views is shown to be an NP-Complete problem. In this article, a non-Pareto based genetic algorithm, is proposed, that selects Top-K views for materialization from a multidimensional lattice. An experiments-based comparison of the proposed algorithm with the most fundamental view selection algorithm, HRUA, shows that the former performs comparatively better than the latter. Thus, materializing views selected by using the proposed algorithm would improve the query response time of analytical queries and thereby facilitate in decision making.
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Hadi, Febri, and Dodi Guswandi. "Penentuan Penerimaan Mahasiswa Baru Pascasarjana Menggunakan Simple Additive Weighting (SAW)." Indonesian Journal of Computer Science 8, no. 2 (October 31, 2019): 121–29. http://dx.doi.org/10.33022/ijcs.v8i2.175.
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The decision-making system for the selection of new postgraduate student admissions which is carried out manually requires 7 days to submit the decision results. The selection is very important, so that the quality of input (input) of prospective students can be maintained in accordance with established standards. Therefore we need a system that can help in the decision making process quickly, precisely, and accurately. The purpose of this study is to help postgraduate master's study programs in conducting the selection of prospective graduate students in accordance with their abilities and disciplines. The method used in data processing using the Simple Additive Weighting (SAW) method, is a method of weighting the sum of the criteria values of each alternative. The results of the decision in the form of ranking the number of values, based on the passing grade value that has been set> 0.70 declared passed, or <0.70 declared not passed.
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Ruda, Aleš. "CARTOGRAPHIC VISUALIZATION OF OUTPUTS FOR SPATIAL DECISION-MAKING IN REGIONAL DEVELOPMENT." Geodesy and Cartography 41, no. 4 (December 17, 2015): 174–84. http://dx.doi.org/10.3846/20296991.2015.1120431.
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Regional development is full of planning and decision making. Having precise results for spatial decision making (SDM) is more than necessary. On one site, there are many approaches how to process input data, on the other hand thematic cartography also operates with many visualizing methods and techniques. Loss of accuracy of the results is more than expected because there are two phases (data processing during SDM and cartographic visualization) where the accuracy might be distorted. In both phases processing recommendations must be obeyed. Selection of spatial decision making method must follow considered aims as well as visualization techniques and setting their parameters (especially during reclassification, interpolation or generalization). Paper deals with the proposal of elementary scheme of SDM and related visualization during two case studies (CS). First CS represents composite indicators proposal followed by weighted sum method using heuristics approaches with the aim to identify the tourism influence on the landscape. Combined visualization techniques for quantitative and qualitative data are presented. Second CS uses ordered weighted average method for finding the best place for building of a new public logistics centre. Constraints and factors represent key indicators and following factor and order weights enable to propose the best accepted risk model. In this case grid maps describe derived values and chosen reclassification documents conversion into linguistic variables.
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Misut, Martin, and Pavol Jurik. "DATAFICATION AS A NECESSARY STEP IN THE PROCESSING OF BIG DATA IN DECISION-MAKING TASKS OF BUSINESS." Proceedings of CBU in Natural Sciences and ICT 2 (October 24, 2021): 75–80. http://dx.doi.org/10.12955/pns.v2.156.
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The digital transformation of business in the light of opportunities and focusing on the challenges posed by the introduction of Big Data in enterprises allows for a more accurate reflection of the internal and external environmental stimuli. Intuition ceases to be present in the decision-making process, and decision-making becomes strictly data-based. Thus, the precondition for data-based decision-making is relevant data in digital form, resulting from data processing. Datafication is the process by which subjects, objects and procedures are transformed into digital data. Only after data collection can other natural steps occur to acquire knowledge to improve the company's results if we move in the industry's functioning context. The task of finding a set of attributes (selecting attributes from a set of available attributes) so that a suitable alternative can be determined in its decision-making is analogous to the task of classification. Decision trees are suitable for solving such a task. We verified the proposed method in the case of logistics tasks. The analysis subject was tasks from logistics and 80 well-described quantitative methods used in logistics to solve them. The result of the analysis is a matrix (table), in which the rows contain the values of individual attributes defining a specific logistic task. The columns contain the values of the given attribute for different tasks. We used Incremental Wrapper Subset Selection IWSS package Weka 3.8.4 to select attributes. The resulting classification model is suitable for use in DSS. The analysis of logistics tasks and the subsequent design of a classification model made it possible to reveal the contours of the relationship between the characteristics of a logistics problem explicitly expressed through a set of attributes and the classes of methods used to solve them.
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Ponisio, Laura, Pascal van Eck, and Lourens Riemens. "Using Network Analysis to Improve Decision Making for Partner Selection in Inter-Organizational Networks." International Journal of Human Capital and Information Technology Professionals 4, no. 3 (July 2013): 26–39. http://dx.doi.org/10.4018/jhcitp.2013070103.
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Professionals in decision making roles are often faced with the problem of choosing partners for closer cooperation, for instance, to start new joint IT development projects or for harvesting best practices. The large amounts of information involved in these decision processes obscure possibilities, and therefore choices are made ad hoc. In this article, the authors present an approach that uses concrete data and network analysis to support decision makers in processing and understanding this information. Central in the authors’ approach are questionnaires capturing aspired and current development levels of the processes of the cooperating organizations and graphs generated using network analysis techniques. The advantage of the authors’ approach, which they validated via expert interviews, is that results are semi-automatically translated to visualizations; which in turn offer an overall view of the current and aspired situation in the network without losing the ability to pinpoint particular, individual processes of interest. This, in turn, enables IT professionals to make better decisions.
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Kumar, Akshay, and T. V. Vijay Kumar. "Multi-Objective Big Data View Materialization Using NSGA-III." International Journal of Decision Support System Technology 14, no. 1 (January 1, 2022): 1–28. http://dx.doi.org/10.4018/ijdsst.311066.
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Present day applications process large amount of data that is being produced at brisk rate and is heterogeneous with levels of trustworthiness. This Big data largely consists of semi-structured and unstructured data, which needs to be processed in admissible time so that timely decisions are taken that benefit the organization and society. Such real time processing would require Big data view materialization that would enable faster and timely processing of decision making queries. Several algorithms exist for Big data view materialization. These algorithms aim to select Big data views that minimize the total query processing cost for the query workload. In literature, this problem has been articulated as a bi-objective optimization problem, which minimizes the query evaluation cost along with the update processing cost. This paper proposes to adapt the reference point based non-dominated sorting genetic algorithm, to design an NSGA-III based Big data view selection algorithm (BDVSANSGA-III) to address this bi-objective Big data view selection problem. Experimental results revealed that the proposed BDVSANSGA-III was able to compute diverse non-dominated Big data views and performed better than the existing algorithms..
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Kusuma, Aniek Suryanti, Welda Welda, and I. Komang Juliana. "Penentuan Lokasi Fasilitas Kesehatan Strategis Menggunakan Metode Naive Bayes pada RSU Bintang." INFORMAL: Informatics Journal 6, no. 2 (August 30, 2021): 52. http://dx.doi.org/10.19184/isj.v6i2.23798.
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At present the selection of strategic health facility locations is not easy, to determine the right location and in accordance with the needs of patients must use the right calculation. Bintang General Hospital (RSU Bintang) has difficulties in determining the strategic location of new health facilities. The difficulty is due to the absence of data processing from the current system so that in determining the location of strategic health facilities is not based on data that has been analyzed. Based on the problems experienced by RSU Bintang and to assist in making a decision in establishing a strategic health facility location, a study was made to design a decision support system that can perform calculations to determine the location of the most strategic health facility with the title "Decision Support System. Determining the Location of Strategic Health Facilities Using the Naive Bayes Method at RSU Bintang”. Decision support system that is built will have several functions, such as processing patient register data, user data processing, alternative location data processing, criteria data processing, data processing rules, Naive Bayes calculations and managing several reports that can be used as decision support for the RSU Bintang. in determining the location of the most strategic health facilities. In this system, testing has been done by using blackbox testing which gets the test results in accordance with the system design.
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Saleem, Saba, Mehmood Ahmed, Luqman Shah, Ali Imran Jehangiri, Muhammad Naeem, Yousaf Saeed, Muhammad Junaid, and Fahad Ali Khan. "Features Selection for Supervised Learning Using Centrality Measures." Revista Gestão Inovação e Tecnologias 11, no. 4 (July 22, 2021): 2976–97. http://dx.doi.org/10.47059/revistageintec.v11i4.2333.
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The data mining methods have been extensively used in the process of decision making. The popularity of data mining methods is due to availability of high speed algorithms, processing and storage power of computers. The effective use of data mining methods help in mining datasets and taking better decisions. The data need to be preprocessed before applying data mining methods. Some datasets require little preparation like dealing with missing and redundant instances while some high-dimensional datasets require strong processing like dimensionality reduction. One of the techniques used for dimensionality reduction is feature selection. This study uses graph based centrality measure for feature selection. Graph based centrality measures are used for ranking features which is used for removing irrelevant attributes. After comparison of results with other approaches, it has been found that the proposed approach results in reduction of feature space without compromising accuracy. The results also shows that proposed approach performs better than some other feature selection approaches not only in terms of accuracy but also on the basis of larger reduction in feature space.
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Rye, Sara, and Emel Aktas. "A Multi-Attribute Decision Support System for Allocation of Humanitarian Cluster Resources Based on Decision Makers’ Perspective." Sustainability 14, no. 20 (October 18, 2022): 13423. http://dx.doi.org/10.3390/su142013423.
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The rush of the humanitarian suppliers into the disaster area proved to be counter-productive. To reduce this proliferation problem, the present research is designed to provide a technique for supplier ranking/selection in disaster response using the principles of utility theory. A resource allocation problem is solved using optimisation based on decision maker’s preferences. Due to the lack of real-time data in the first 72 h after the disaster strike, a Decision Support System (DSS) framework called EDIS is introduced to employ secondary historical data from disaster response in four humanitarian clusters (WASH: Water, Sanitation and Hygiene, Nutrition, Health, and Shelter) to estimate the demand of the affected population. A methodology based on multi-attribute decision-making (MADM), Analytical Hierarchy processing (AHP) and Multi-attribute utility theory (MAUT) provides the following results. First a need estimation technique is put forward to estimate minimum standard requirements for disaster response. Second, a method for optimization of the humanitarian partners selection is provided based on the resources they have available during the response phase. Third, an estimate of resource allocation is provided based on the preferences of the decision makers. This method does not require real-time data from the aftermath of the disasters and provides the need estimation, partner selection and resource allocation based on historical data before the MIRA report is released.
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Chen, Maowei, Tingting Zhao, Jungwook Lee, and Hyangsook Lee. "Developing a Decision-Making Process of Location Selection for Truck Public Parking Lots in Korea." Sustainability 15, no. 2 (January 12, 2023): 1467. http://dx.doi.org/10.3390/su15021467.
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As the number of truck registrations and the traffic volume have increased continuously in urban areas, the problem of truck illegal parking on the roadside has become more serious in Korea. Although the government presented the necessity and the construction plan of the truck public parking lots through the ‘4th Comprehensive Plan for Expansion of Truck Resting Facilities’, there is no detailed guideline for the local government of where to construct them. The previous studies on the location selection of the parking lots have mainly focused on passenger cars and buses, but not trucks, and the process for candidate location selection was not mentioned. To fill this gap, this study presents a decision-making process for the location selection of truck public parking lots in urban areas based on mixed-integer programming; it includes a candidate location selection by spatial analysis and an optimal location determination by the application of the competitive p-median algorithm. A case study of Incheon was conducted to validate the presented decision-making process. This study introduced a systematic decision-making process that includes standards establishment, data processing, and methodology application; it is universalistic enough to be utilized as a guideline for the government to efficiently construct truck public parking lots.
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Polshchykov, K., S. Lazarev, O. Polshchykova, and E. Igityan. "The Algorithm for Decision-Making Supporting on the Selection of Processing Means for Big Arrays of Natural Language Data." Lobachevskii Journal of Mathematics 40, no. 11 (November 2019): 1831–36. http://dx.doi.org/10.1134/s1995080219110222.
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Gui, Chengzhong, Weiwei Lin, Zuwei Huang, Guangtao Xin, Jun Xiao, and Liuxin Yang. "A Decision-Making Algorithm for Concrete-Filled Steel Tubular Arch Bridge Maintenance Based on Structural Health Monitoring." Materials 15, no. 19 (October 6, 2022): 6920. http://dx.doi.org/10.3390/ma15196920.
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This study focuses on establishing a novel heuristic algorithm for life-cycle performance evaluation. Special attention is given to decision-making algorithms for concrete-filled steel tubular (CFST) arch bridge maintenance. The main procedure is developed, including the ultimate loading-capacity modeling of CFST members, multi-parameter selection, ultimate thresholds presetting based on the finite element method, data processing, crucial parameters determination among sub-parameters, multi-parameter regression, ultimate state prediction, and system maintenance decision-making suggestions based on the multi-parameter performance evaluation. A degenerated ultimate loading-capacity model of CFST members is adopted in the finite element analysis and multi-parameter performance assessment. The multi-source heterogeneous data processing and temperature-effect elimination are performed for the data processing. The key sub-parameters were determined by the Principal Component Analysis method and the Entropy-weight method. The polynomial mathematical model is used in the multi-parameter regression, and the ±95% confidence bounds were verified. The system maintenance decision-making model combines the relative monitoring state, the relative ultimate state by the numerical analysis, and the relative residual life of degenerated members. The optimal system maintenance decision-making suggestions for the bridge maintenance system can be identified, including the most unfavorable maintenance time and parameter index. A case study on a CFST truss-arch bridge is conducted to the proposed algorithms. The obtained results demonstrated that the crack width deserves special attention in concrete bridge maintenance. Additionally, these technologies have enormous potential for the life-cycle performance assessment of the structural health monitoring system for existing concrete bridge structures.
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Nurjannah and Dito Putro Utomo. "Sistem Pendukung Keputusan Penyeleksian Colour Guard Pada Marching Band Ginada Dengan Menggunakan Metode Vikor Dan Borda." JUKI : Jurnal Komputer dan Informatika 2, no. 1 (May 27, 2020): 35–48. http://dx.doi.org/10.53842/juki.v2i1.27.
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Decision support system for selecting color guard with VIKOR and Borda methods. It has been made as a tool to select color guard at the Sei Rampah High School. The criteria used in the decision support system for color guard selection are: height, weight, agility, stamina, and body language. Color guard selection activities are a routine activity every year, so GINADA marching band coach Sei Rampah hereby selects to select permanent members in the marching band. Decision Support System in an organization can be seen as important in supporting the smooth running of activities and achieving an organizational goal. SPK can come in various forms, ranging from simple forms of data processing to complex application forms, and can also be used to accelerate and improve the quality of the decision-making process in the organization.
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Leiber, Michael J., and Kristan C. Fox. "Race and the Impact of Detention on Juvenile Justice Decision Making." Crime & Delinquency 51, no. 4 (October 2005): 470–97. http://dx.doi.org/10.1177/0011128705275976.
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In recent years, the growing number of minority youth disproportionately confined in secure detention facilities has led to a search for a better understanding of this occurrence. Explanations vary but tend to center on either differential offending or selection bias. The present study examines the extent both may explain decision making by specifically assessing the effect of race on detention and the degree that race and detention influence further court processing in one juvenile court jurisdiction in the state of Iowa. Multivariate analyses using juvenile court data (1980 through 2000) show that although legal factors account for some of the decision making and minority over representation, so too does race. Evidence is presented that, through detention, race has direct, interaction, and indirect effects that often work to the disadvantage of African American youth relative to White youth. Implications for future research and policy are discussed.
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Shobowale, Kafayat Oluwatoyin, Fakhruldin Mohd Hashim, and Hilmi bin Hussien. "Subsea Processing Equipment: A Strategy for Effective Assessment and Selection." Applied Mechanics and Materials 773-774 (July 2015): 1335–39. http://dx.doi.org/10.4028/www.scientific.net/amm.773-774.1335.
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Subsea processing equipment’s are deployed in Deepwater / subsea marginal field, fields having challenging reservoir characteristics (which includes: high viscosity, high GVF) in order to economically recover oil and gas. They includes: multiphase booster pump, subsea separation and compression equipment’s. These equipment’s faces a high level of uncertainty as regards well and reservoir conditions, putting the equipment in an unfavorable condition covering a wide and variable range of processes including transient Flow, variable oil flow, fluid pressures, temperature and gas compression effects. More so, knowledge engineers in different areas are assessing this domain in different ways making the performance parameters and relations to be defined differently when utilizing computer based tools for assessment and selection. A four step process is proposed which are: domain knowledge acquisition, failure data analysis, knowledge model and a knowledge base system will reveal the key components and parameters that are needed to make an optimum decision. The applicability of these four step process is demonstrated in the assessment and selection of subsea multiphase booster pumps.
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Moiseev, A. A. "Improved rank selection algorithm." Radio industry (Russia) 31, no. 1 (April 7, 2021): 37–44. http://dx.doi.org/10.21778/2413-9599-2021-31-1-37-44.
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Problem statement. A rank algorithm for selecting radio emission modes for operation in conditions of heterogeneity of sources and complex interference conditions, including the possible presence of mutual interference, is synthesized.Objective. The synthesis purpose is to ensure the independence of mode recognition from particular features of radio emission observation. Algorithm input is the primary signal processing result that includes such estimations as pulses durability, frequency and amplitude dynamics, and absolute variations. Primary decision statistics are formed using these values: observable signal base and relation variations of frequency and amplitude. Secondary statistics are formed based on primary ones using median and recursive or maximum and recursive smoothing. Each of the decision statistics in the multi-threshold procedure is transformed into a row of ranks, the size of which corresponds to the number of recognized modes. In aggregate, these lines form a ranking table (matrix) with colons representing recognized modes’ discrete descriptions. Fluent observation processing includes rank formation for used decision statistics. Mode recognition is performed either following a ranking table or using an additional voting procedure 2/3. An alternative approach consists of constructing the Manhattan mismatch metric of the current and reference ranks and making a decision on the criterion of the minimum mismatch metric.Results. Mode recognition performed on results of this comparison using unbalance metrics minimum criterion. Thresholds in frames of the ranking procedure are formed heuristically at ranking table formation. They are then used at fluent rank formation for observable modes. The performed numerical experiment shows that maximal and recursive filtration provides an errorless selection of all observable modes. This filtration represents the composition of maximum selection in sliding window and subsequent recursive first-order filtration. An additional advantage of this filtration is a simpler maximum selection in comparison with the median one. In perspective, it can provide increased operating speed.Practical implications. Performed consideration shows that rank selection is worthwhile at the observation of heterogeneous irradiation sources. Algorithm strength is decision simplicity in a complex situation. Additional algorithm advantage is the possibility of extending alternative irradiation modes and, hence, for more representative data sets.
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Tofigh, Mohammad Ali, and Nurhasliza Hashim. "Site Selection for an Offshore Wind Power Station Under a Fuzzy Environment." Journal of Intelligent Systems and Internet of Things 6, no. 2 (2022): 08–21. http://dx.doi.org/10.54216/jisiot.060201.
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The Malaysian government’s support for offshore wind power production has led to an increase in a few proposals. An important factor in the overall efficiency of any offshore wind farm is the site selection process, which is a multi-criteria decision-making (MCDM) task. However, classical MCDM techniques often fail to choose a suitable site because of three main challenges. First, compensation is regarded as a problem in the processing of information. Second, data usage and data leakage are often ignored in the decision-making process. Third, interaction difficulty in fuzzy environments is easily ignored. This study provides a framework for making site selection decisions for offshore wind farms while addressing the constraints. Fuzzy VIKOR is used in the second stage of the AHP process to analyze the site’s results with respect to evaluation criteria for offshore wind farms. A comprehensive index system, which incorporates the veto criteria and evaluation criteria for selecting offshore wind power station sites, is devised. Then, the system is used to transmit imprecise information to decision makers by using a triangular fuzzy set. Likelihood-based valued comparisons indicate that imprecise choice information can be correctly used, and issues of information loss can be logically avoided. A case study of Malaysia is used to demonstrate the validity and practicality of the site selection technique. This research offers a theoretical basis for accurate offshore wind power evaluation in Malaysia.
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Et. al., Asha Rani,. "Multi Criteria Decision Making (MCDM) based preference elicitation framework for life insurance recommendation system." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 1848–58. http://dx.doi.org/10.17762/turcomat.v12i2.1523.
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The global life insurance industry has shown a phenomenal growth in number of companies, insurance products and their users. The digital revolution has played a pivotal role in the field of insurance too. Increased numbers of companies and insurance plans have increased the complexities and time involved in selection of appropriate policies. At present, major share of policy selling goes to the agents which may be biased and time consuming. The web aggregators too have failed to provide customized and personalized suggestions. Major portion of population still finds the selection of best insurance plan unfriendly and tedious. This huge volume of data requires intelligent system to facilitate efficient and effective retrieval, processing and management of the data from multiple dimensions. This research paper proposes a framework to provide a personalized life insurance recommender system using TOPSIS method of multi-criteria decision making. Point allocation method along with TOPSIS provides preference elicitation and list of recommended policies ranked according to closeness coefficients. Sensitivity analysis in the paper shows the effect of changing the policy features’ preferences (criteria weights) on the final recommended products. The proposed framework helps in achieving computational excellence for efficient decision making with reduced complexity
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Dyduch, Janusz, and Mieczysław Kornaszewski. "Selection of exploitation data from railway traffic control devices for the purposes of gathering and analyzing data processes from railway transport objects." Transportation Overview - Przeglad Komunikacyjny 2019, no. 5 (May 1, 2019): 43–53. http://dx.doi.org/10.35117/a_eng_19_05_04.
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The evaluation of the technical systems reliability and safety requires collecting and processing of reliable data which characterizes the processes. The data from current exploitation is particularly important for decision-making processes. It can be used for creation of occurring exploitation phenomena models and allows to determine the expected object behaviour in the future. The railway traffic control devices often work in very difficult exploitation and environmental conditions. The information about their technical condition can be gathered and used for a proper prophylaxis as well as a predictive maintenance of railway traffic. It will allow to choose a maintenance strategy which will consist in optimal use of railway traffic control devices.
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Kim, Jong Hyen, and Byeong Seok Ahn. "The Hierarchical VIKOR Method with Incomplete Information: Supplier Selection Problem." Sustainability 12, no. 22 (November 18, 2020): 9602. http://dx.doi.org/10.3390/su12229602.
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To solve a multi-criteria decision-making problem, many attempts have been made to alleviate difficulties of obtaining precise preference information attributed to time pressure, lack of data and domain knowledge, limited attention and information processing capabilities, etc. Structuring any decision problem hierarchically is known to be an efficient way of dealing with complexity and identifying the major components of the problem. In this paper, we propose the hierarchical VIKOR method that uses incomplete alternatives’ values as well as incomplete criteria weights, extending previous works that consider mostly intervals or fuzzy under a flat structure of criteria. It ranks alternatives using the aggregated scores of group utility and individual regret scores which are computed from the linear programs. To show how to use our proposed method, we exemplified an international supplier selection problem that affects the organization’s sustainable growth.
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Wu, Shuang, Li He, Zhaolong Zhang, and Yu Du. "Forecast of Short-Term Electricity Price Based on Data Analysis." Mathematical Problems in Engineering 2021 (February 16, 2021): 1–14. http://dx.doi.org/10.1155/2021/6637183.
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The decision-making of power generation enterprises, power supply enterprises, and power consumers can be affected by forecasting the price of electricity. There are many irrelevant samples and features in big data, which often lead to low forecasting accuracy and high time-cost. Therefore, this paper proposes a forecasting framework based on big data processing, which selects a small quantity of data to achieve accurate forecasting while reducing the time-cost. First, the sample selection based on grey correlation analysis (GCA) is established to eliminate useless samples from the periodicity. Second, the feature selection based on GCA is established considering the feature classification and the temporal correlation features to further eliminate useless features. Third, principal component analysis is applied to reduce the noise among the data. Then, combined with a differential evolution algorithm (DE), a support-vector machine (SVM) is applied to forecast the price. Finally, the proposed framework is applied to the New England electricity market to forecast the short-term electricity price. The results show that, compared with DE-SVM without data processing, the forecasting accuracy is improved from 81.68% to 91.44%, and the time-cost is decreased from 35,074 s to 1,809 s which shows that the proposed method and model can provide a valuable tool for data processing and forecasting.
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Quan, Jing, Bo Zeng, and Dai Liu. "Green Supplier Selection for Process Industries Using Weighted Grey Incidence Decision Model." Complexity 2018 (October 8, 2018): 1–12. http://dx.doi.org/10.1155/2018/4631670.
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Proper supplier selection to meet production demand is a major aspect of all manufacturing and process industries. Green supplier selection has been one of the most critical factors for environmental protection on account of increasing consumption levels and for sustainable development as well. This paper aims at developing an applicable methodology for green supplier selection for the process industry. In this study, both economic and environmental criteria are considered and a comprehensive weighted grey incidence decision approach for green supplier evaluation and selection in a process industry is proposed. First, an overall green supplier selection index system for process industries is considered; then a weighted grey incidence decision-making model with improved grey incidence coefficients and weighted degree of grey incidence is provided. Improved grey incidence coefficients are defined using transformation sequences of the initial data. To eliminate the ill effects from the use of equal weights, the maximum entropy method is used to determine the weights of the improved grey incidence coefficients. An application example is proposed with the data collected for the chemical processing industry, which provides acceptable results in determining the better supplier. In the end appendix, some theory regarding the weights for grey incidence coefficients is proposed. The empirical results indicate that the model is of great practical value for green supplier selection in the process industry.
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Adila, Nia, and Andri Andri. "Desain Dan Implementasi Data Warehouse Pada Perpustakaan Daerah Provinsi Sumatera Selatan." Jurnal Nasional Ilmu Komputer 2, no. 1 (November 18, 2021): 33–50. http://dx.doi.org/10.47747/jurnalnik.v2i1.520.
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This research will focus on processing visitor data, member data, book borrowing data and book return data at the Regional Library of South Sumatra Province. The data is stored in the form of an excel file with a large amount of data, causing problems in the storage system such as data accumulation, data loss, no data analysis and delays in the reporting process. The process of data storage and data retrieval will be well integrated by building a data Warehouse at the Regional Library of South Sumatra Province. Data Warehouse is a system that contains several years of history and facilitates decision making. At the data Warehouse design stage using the Nine-Step method (Kimball, 2002), in this method there are nine steps in designing a data Warehouse, namely Process Selection, Grain Selection, Identification of dimensional adjustments, Fact Selection, Storage of initial calculations in the fact table, Reviewing the dimension table, selecting the database duration, tracking dimension changes, and prioritizing, querying the model and selecting the physical design. And the design and data processing process will use the Pentaho kettle and public Tableau applications, with the design and implementation of the data Warehouse, it is expected to help facilitate the reporting and analysis process for the Regional Library of South Sumatra Province.
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Arun, Biri, and T. V. Vijay Kumar. "Materialized View Selection using Artificial Bee Colony Optimization." International Journal of Intelligent Information Technologies 13, no. 1 (January 2017): 26–49. http://dx.doi.org/10.4018/ijiit.2017010102.
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Data warehouse is an essential component of almost every modern enterprise information system. It stores huge amount of subject-oriented, time-stamped, non-volatile and integrated data. It is highly required of the system to respond to complex online analytical queries posed against its data warehouse in seconds for efficient decision making. Optimization of online analytical query processing (OLAP) could substantially minimize delays in query response time. Materialized view is an efficient and effective OLAP query optimization technique to minimize query response time. Selecting a set of such appropriate views for materialization is referred to as view selection, which is a nontrivial task. In this regard, an Artificial Bee Colony (ABC) based view selection algorithm (ABCVSA), which has been adapted by incorporating N-point and GBFS based N-point random insertion operations, to select Top-K views from a multidimensional lattice is proposed. Experimental results show that ABCVSA performs better than the most fundamental view selection algorithm HRUA. Thus, the views selected using ABCVSA on materialization would reduce the query response time of OLAP queries and thereby aid analysts in arriving at strategic business decisions in an effective manner.
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Winatha, Komang Redy, I. Nyoman Tri Anindia Putra, and Naufal Akbar Ihsan Baedlawi. "The Implementation of the Weight Product (WP) Method on the Best Employee Selection." Ultimatics : Jurnal Teknik Informatika 13, no. 2 (January 23, 2022): 89–100. http://dx.doi.org/10.31937/ti.v13i2.2092.
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PT. Autogrill Services Indonesia is a private company engaged in the food and beverages selling. There are 8 outlets and 379 employees. To achieve maximum performance within the company environment, PT Autogrill Services Indonesia gives an appreciation to employees in the form of the best rewards every month and year calculated based on certain criteria. PT AutoGrill Services Indonesia needs to have a decision support system to simplify the decision-making process. To meet these needs, a web-based decision support system for selecting the best employees was designed using the weight product (WP) method at PT Autogrill Services Indonesia. The design stage includes needs analysis, context diagrams, data flow diagrams, and designing database tables. This system is web-based, using the programming language PHP and MySQL as database storage. The main features contained in this system include processing user data, outlets, employees, criteria, periods, alternatives, scores, and the calculation of monthly and annual winners. Based on the test results, all system functionality components can run well and by expectations.
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Ismail, M. Panji. "SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENERIMAAN MAHASISWA BARU JALUR BEASISWA DENGAN METODE TOPSIS (TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION)." JIKO (Jurnal Informatika dan Komputer) 3, no. 1 (February 22, 2018): 1. http://dx.doi.org/10.26798/jiko.2018.v3i1.79.
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The scholarship path is one of the paths used by the University of Technology Yogyakarta (UTY) in accepting new students. The track consists of seven criteria: school accreditation, family condition, report card score, achievement, written test, interview test. In order to process the criteria, it is necessary to have a Decision Support System (DSS) capable of conducting the scholarship selection process quickly, accurately and objectively. DSS is a system that can perform a decision-making process based on the human mindset. SPK can tolerate any kind of data whether it is certain or uncertain. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a Multi-Criteria Decision Making (MCDM) category which is a decision-making technique of some alternative options, especially Multi Attribute Decision Making (MADC). TOPSIS aims to determine the ideal ideal solution and the ideal negative solution of the scholarship criteria. A positive ideal solution maximizes benefit criteria and minimizes cost criteria, whereas a negative ideal solution maximizes cost criteria and minimizes benefit criteria. The research methodology used is SDLC (System Development Life Cycle) and system implementation built using Delphi 7 programming language and data processing using DBMS SQL Server 2012.. The results of the system show that the system provides accurate and valid results in accordance with the data owned by prospective students so that full scholarship can be given in the right target.
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Kelleher, John H., and Bruce G. Coury. "The Influence of Personality in the Processing of Display Formats." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 13 (October 1993): 910–14. http://dx.doi.org/10.1177/154193129303701301.
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The importance of personality characteristics in the processing of displays is examined. Forty-six people, screened for specific information processing and decision making preferences using the MBTI, learned to classify instances of system data into one of four state categories. System data were presented in one of three types of display formats: a polygon; a bargraph; and a digital display. The experiment involved two sessions: a training session where each person learned the classification task; and an extended practice session with no feedback. The ability of people to accurately classify instances was found to be influenced by both the type of display and their personality type. The importance of individual differences in display research is discussed, with emphasis placed on the implications for training, and selection of display formats.
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Yanti Saragih, Nova. "Sistem Pendukung Keputusan Pemilihan JSK di Ramayana Menerapkan Metode DEMATEL dan ARAS." Bulletin of Computer Science Research 2, no. 1 (December 27, 2021): 11–17. http://dx.doi.org/10.47065/bulletincsr.v2i1.123.
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JSK(Junior Supervisor cashier) is a person who has good experience, knowledge and skills to lead and manage finances every day. JSK also plays a role in regulating and coordinating cashiers on duty, starting from managing shifts, taking capital, and minimizing losses caused by cashiers. Decision Support System is a computer-based system consisting of several components, namely language system components, knowledge system components, and problem processing system components that are interrelated with one another, in making decisions through the use of data. -data and decision models in order to solve a problem. DEMATEL (Decision Making Trial And Evaluation Laboratory) aims to find direct (dependency) in a system of variables. The Additive Ratio Assessment (ARAS) method is a method used for ranking criteria, in carrying out the ranking process, the ARAS method has several steps that must be done to calculate and this method is very suitable for use in the selection of JSK candidates where the highest value is the best value of the final result
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Fauzi, Ahmad, Novita Indriyani, and Andika Bayu Hasta Yanto. "Selection of Coffee Shop Business Locations Using the Analytical Hierarchy Process Method." JURNAL TEKNOLOGI DAN OPEN SOURCE 4, no. 2 (December 20, 2021): 133–40. http://dx.doi.org/10.36378/jtos.v4i2.1771.
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The Coffee Shop business is one of the trending businesses in Indonesia, so competition in this business field can be said to be increasing rapidly. In order to get a strategic business location, the majority of decision makers are wrong in choosing the location of their business, this is due to the lack of analysis and to the data from the location survey and the lack of decision-making limitations in analyzing the survey data. For this reason, it is necessary to create a system, where this system uses the Analytical Hierarchy Process (AHP) method to assist in processing survey data. The application of the Analytical Hierarchy Process (AHP) method in determining the location of the Coffee Shop business is determined using 7 (criteria) criteria including rental prices, buildings, clean water, accessibility, distance to offices, internet connections and electricity sources. The final result of the process using the AHP method is ranking of several alternative locations that have been set, and several alternative locations get the highest global weight, it becomes recommendations for the development of this coffee shop business
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Gunawan, Heri, Ahir Yugo Nugroho, Ria Eka Sari, and Adnan Buyung Nst. "Increasing the Accuracy of Recipient Selection for the Smart Indonesia Program (PIP) Using the Moora Method." Formosa Journal of Applied Sciences 1, no. 7 (December 29, 2022): 1395–410. http://dx.doi.org/10.55927/fjas.v1i7.2022.
Full textAbstract:
The selection mechanism of the Smart Indonesia Program (PIP) is very complicated, and the targets or eligibility criteria for Smart Indonesia Program (PIP) participants do not match the accurate data or do not match the reality on the ground, there is still feeling of control in the process which is interpreted as a form of understanding what other people feel and also imagine yourself if you were in that person's position. For this reason, it is necessary to design a desktop-based system using the Microsoft Visual Studio 2010 programming language and created using the Microsoft SQL Server R2 2008 database as the data storage medium, so that if there is an update the system criteria data can be used without having to disassemble it, and data processing runs efficiently and effectively in the system. Decision making on the Moora method is based on criteria and sub-criteria data by considering the total weight of the benefit criteria attributes and the cost attributes of the alternative student data entered into the system, it is known that 6 students have been accepted as potential recipients of education funds through a decision support system , with a decision value.
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Deng, Ran, and Taile Ni. "Information Visualization Design of Web under the Background of Big Data." Mathematical Problems in Engineering 2022 (June 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/9578848.
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With the rapid development of the Internet, the information on the Internet presents an explosive growth. Cloud computing and big data analysis technology based on Internet information rise accordingly. However, all web pages contain not only important information but also the noise information irrelevant to the subject information. They seriously affect the accuracy of information extraction, so the research of web page information extraction technology arises at the historic moment and becomes the research hotspot. The quality of web page text information will directly affect the accuracy of later information processing and decision-making. If we can accurately evaluate the information of the web pages captured from the Internet and classify the extracted web pages according to the corresponding characteristics, we can not only improve the efficiency of information processing, but also improve the practical value of the information decision-making system. From the practical application requirements and user-friendly operation point of view, the information visualization of web design based on big data is studied in this paper. Specifically, the system designed in this paper improves the traditional template-based web information extraction method, establishes a web information extraction rule scheme combined with templates, and achieves the goal of web information extraction rule selection and template generation in the visual environment. Finally, the visualization algorithm based on T-SNE verifies the effectiveness of the web page information visualization algorithm designed in this paper.
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Kumar, Amit, and T. V. Vijay Kumar. "Materialized View Selection Using Self-Adaptive Perturbation Operator-Based Particle Swarm Optimization." International Journal of Applied Evolutionary Computation 11, no. 3 (July 2020): 50–67. http://dx.doi.org/10.4018/ijaec.2020070104.
Full textAbstract:
A data warehouse is a central repository of time-variant and non-volatile data integrated from disparate data sources with the purpose of transforming data to information to support data analysis. Decision support applications access data warehouses to derive information using online analytical processing. The response time of analytical queries against speedily growing size of the data warehouse is substantially large. View materialization is an effective approach to decrease the response time for analytical queries and expedite the decision-making process in relational implementations of data warehouses. Selecting a suitable subset of views that deceases the response time of analytical queries and also fit within available storage space for materialization is a crucial research concern in the context of a data warehouse design. This problem, referred to as view selection, is shown to be NP-Hard. Swarm intelligence have been widely and successfully used to solve such problems. In this paper, a discrete variant of particle swarm optimization algorithm, i.e. self-adaptive perturbation operator based particle swarm optimization (SPOPSO), has been adapted to solve the view selection problem. Accordingly, SPOPSO-based view selection algorithm (SPOPSOVSA) is proposed. SPOPSOVSA selects the Top-K views in a multidimensional lattice framework. Further, the proposed algorithm is shown to perform better than the view selection algorithm HRUA.