Academic literature on the topic 'Periodical selection Decision making Data processing'
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Journal articles on the topic "Periodical selection Decision making Data processing"
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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.
Dissertations / Theses on the topic "Periodical selection Decision making Data processing"
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陳頌富 and Chung-fu Leslie Chan. "Machining process selection and sequencing under conditions of uncertainty." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31214927.
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Shelton, Debra Kay. "A selection model for automated guided vehicles." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/101465.
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This research identifies the attributes to be considered in the selection of an automated guided vehicle (AGV). A distinction is made between automated guided vehicles (AGVs) and an automated guided vehicle system (AGVS). This research is concerned only with the selection of automated guided vehicles (AGVs).
A selection model is developed which forces the user to evaluate attributes. his requirements and preferences for AGV The first step of the model allows the user to enter his specifications for AGV attributes which are applicable to his production environment. The second step in the selection model is for the user to determine 8-15 attributes to use as selection criteria. In the third phase, the user inputs his preferences and priorities with respect to the attributes chosen as selection criteria in the second step. model ranks the Based on this information, the selection AGV models in the feasible set. A description of the model and a numerical example are included.
Steps 1 and 2, described above, are implemented using an R:BASE™ program. The BASIC computer language was used to perform the interrogation of the user with respect to his priorities and preferences among attributes in Step 3. The IBM PC™ is the hardware chosen for running the selection model.M.S.
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Amein, Hussein Aly Abbass. "Computational intelligence techniques for decision making : with applications to the dairy industry." Thesis, Queensland University of Technology, 2000. https://eprints.qut.edu.au/36867/1/36867_Digitised%20Thesis.pdf.
Full textBooks on the topic "Periodical selection Decision making Data processing"
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Koh, Irene S. Y. Human assessment and computer support: A decision support system for personnel selection. Tilburg, Netherlands: Tilburg University Press, 1994.
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K, Lee Jae, and Trippi Robert R, eds. Artificial intelligence in finance & investing: State-of-the-art technologies for securities selection and portfolio management. Chicago: Irwin Professional Publishing, 1996.
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Advances In Chance Discovery Extended Selection From International Workshops. Springer, 2012.
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Ohsawa, Yukio, and Akinori Abe. Advances in Chance Discovery: Extended Selection from International Workshops. Springer London, Limited, 2012.
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Ohsawa, Yukio, and Akinori Abe. Advances in Chance Discovery: Extended Selection from International Workshops. Springer, 2014.
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Palmeri, Thomas J., Jeffrey D. Schall, and Gordon D. Logan. Neurocognitive Modeling of Perceptual Decision Making. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.15.
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Mathematical psychology and systems neuroscience have converged on stochastic accumulator models to explain decision making. We examined saccade decisions in monkeys while neurophysiological recordings were made within their frontal eye field. Accumulator models were tested on how well they fit response probabilities and distributions of response times to make saccades. We connected these models with neurophysiology. To test the hypothesis that visually responsive neurons represented perceptual evidence driving accumulation, we replaced perceptual processing time and drift rate parameters with recorded neurophysiology from those neurons. To test the hypothesis that movement related neurons instantiated the accumulator, we compared measures of neural dynamics with predicted measures of accumulator dynamics. Thus, neurophysiology both provides a constraint on model assumptions and data for model selection. We highlight a gated accumulator model that accounts for saccade behavior during visual search, predicts neurophysiology during search, and provides insights into the locus of cognitive control over decisions.
Book chapters on the topic "Periodical selection Decision making Data processing"
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Post, Ruben, Iris Beerepoot, Xixi Lu, Stijn Kas, Sebastiaan Wiewel, Angelique Koopman, and Hajo Reijers. "Active Anomaly Detection for Key Item Selection in Process Auditing." In Lecture Notes in Business Information Processing, 167–79. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_13.
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AbstractProcess mining allows auditors to retrieve crucial information about transactions by analysing the process data of a client. We propose an approach that supports the identification of unusual or unexpected transactions, also referred to as exceptions. These exceptions can be selected by auditors as “key items”, meaning the auditors wants to look further into the underlying documentation of the transaction. The approach encodes the traces, assigns an anomaly score to each trace, and uses the domain knowledge of auditors to update the assigned anomaly scores through active anomaly detection. The approach is evaluated with three groups of auditors over three cycles. The results of the evaluation indicate that the approach has the potential to support the decision-making process of auditors. Although auditors still need to make a manual selection of key items, they are able to better substantiate this selection. As such, our research can be seen as a step forward with respect to the usage of anomaly detection and data analysis in process auditing.
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Pretzsch, Sebastian, Holger Drees, and Lutz Rittershaus. "Mobility Data Space." In Designing Data Spaces, 343–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93975-5_21.
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AbstractTo successfully support decision-making or even automatically make decisions of their own, intelligent transport and mobility systems require large amounts of data. Although multitudes of mobility data are already being collected today, the comprehensive processing and exploitation of this data have often been impossible due to technical, legal, or economic reasons. With Mobility Data Space, an open data space is now being created which offers access to real-time traffic data and sensitive mobility data beyond their secure exchange and which links existing data platforms to each other. In the future, it will thus be possible to provide comprehensive mobility data on a national level.Based on a decentralized system architecture developed by the International Data Spaces Association e. V., the Mobility Data Space offers an ecosystem in which data providers can specify and control the conditions under which their data can be used by third parties. This approach creates data sovereignty as well as trust, and data users can be sure about data origin and quality. By integrating data from the public and private sector via regional and national platforms, the Mobility Data Space will become a digital distribution channel for data-driven business models, providing entirely new options of data acquisition, linking, and exploitation.Whether data provider, user, developer, or end user—the Mobility Data Space takes all acting parties into consideration and offers: Data sovereignty and security along the value chain Standardized access to data from both public and private sources Space for the emergence of new business models, distribution channels and services, as well as a larger selection of innovative mobility services and applications
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Carnero, María Carmen, and Andrés Gómez. "Selection of Maintenance Strategies in an Operating Theatre." In Advanced Models and Tools for Effective Decision Making Under Uncertainty and Risk Contexts, 1–35. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3246-1.ch001.
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The aim of this chapter is to select the most suitable combination of maintenance policies in the different systems that make up an operating theatre: air conditioning, sterile water, power supply, medicinal gases, and operating theatre lighting. To do so, a multicriteria model will be developed using the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) approach considering multiple decision centres. The model uses functional, safety, and technical-economic criteria, amongst which is availability. Mean availability for repairable systems has been measured to assess this criterion, using Markov chains from the data obtained over three years from the subsystems of a hospital operating theatre. The alternatives considered are corrective maintenance; preventive maintenance together with corrective maintenance by means of daily, weekly, monthly, and yearly programmes; periodical predictive maintenance together with corrective maintenance; and corrective together with preventive and predictive maintenance.
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Prakash, Jay, and T. V. Vijay Kumar. "A Multi-Objective Approach for Materialized View Selection." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 512–33. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch026.
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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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Marimuthu, Ramalatha, Shivappriya S. N., and Saroja M. N. "Generation and Management of Data for Healthcare and Health Diagnostics." In Theory and Practice of Business Intelligence in Healthcare, 106–32. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2310-0.ch005.
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Healthcare Analytics deals with patient records, effective management of hospitals, and clinical care. But the big data available is still not enough for focused research as it is complicated to find insights from complex, noisy, heterogeneous, and voluminous data, which takes time and effort, while a small clinical data will be more effective for decision making. The health care data also varies in data collection methods and their processing methods. Data generated through patient records is structured, wearable technologies generate semi structured data, and X rays and images provide unstructured data. Storing and extracting information from the structured, semi-structured, and unstructured data is a challenging task. Different machine learning techniques can simplify the process. The chapter discusses the data characteristics, identifying critical attributes, various classification and optimization algorithms for decision making purposes. The purpose of the discussion is to create a basis for selection of algorithms based on size, temporal validity, and outcomes expected.
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Daneshpour, Negin, and Ahmad Abdollahzadeh Barfourosh. "Dynamic View Management System for Query Prediction to View Materialization." In Developments in Data Extraction, Management, and Analysis, 132–61. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2148-0.ch007.
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On-Line Analytical Processing (OLAP) systems based on data warehouses are the main systems for managerial decision making and must have a quick response time. Several algorithms have been presented to select the proper set of data and elicit suitable structured environments to handle the queries submitted to OLAP systems, which are called view selection algorithms to materialize. As users’ requirements may change during run time, materialization must be viewed dynamically. In this work, the authors propose and operate a dynamic view management system to select and materialize views with new and improved architecture, which predicts incoming queries through association rule mining and three probabilistic reasoning approaches: Conditional probability, Bayes’ rule, and Naïve Bayes’ rule. The proposed system is compared with DynaMat system and Hybrid system through two standard measures. Experimental results show that the proposed dynamic view selection system improves these measures. This system outperforms DynaMat and Hybrid for each type of query and each sequence of incoming queries.
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Abbassi, Kamel, and Tahar Ezzedine. "Queries Processing in Wireless Sensor Network." In Wireless Sensor Networks - Design, Deployment and Applications. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.94749.
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For the super-excellence applications used to control the water level in rivers, temperature handles a very large volume of information and does not stop constantly changing. These spatio-temporal data collected by a network of sensors form a set of thematic, integrated, non-volatile and historical data organized to help decision-making. Usually this process is performed with temporal, spatial and spatiotemporal queries. This in turn increases the execution time of the query load. In the literatures, several techniques have been identified such as materialized views (MV), indexes, fragmentation, scheduling, and buffer management. These techniques do not consider the update of the request load and the modification at the database level. In this chapter, we propose an optimal dynamic selection solution based on indexes and VMs. the solution is optimal when it meets the entire workload with a reasonable response time. The proposed approach supports modification at the database level and at the workload level to ensure the validity of the optimal solution for this the knapsack algorithm was used.
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Costa, Igor Pinheiro de Araújo, Marcio Pereira Basílio, Sérgio Mitihiro do Nascimento Maêda, Marcus Vinícius Gonçalves Rodrigues, Miguel Ângelo Lellis Moreira, Carlos Francisco Simões Gomes, and Marcos dos Santos. "Algorithm Selection for Machine Learning Classification: An Application of the MELCHIOR Multicriteria Method." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210243.
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This paper aims to select an algorithm for the Machine Learning (ML) classification task. For the proposed analysis, the Multi-criteria Decision Aid (MCDA) Méthode d’ELimination et de CHoix Includent les relations d’ORdre (MELCHIOR) method was applied. The experiment considered the following criteria as relevant: Accuracy, sensitivity, and processing time of the algorithms. The data used refers to the intention of buying on the Internet and the purpose is to predict whether the customer will finalize a particular purchase. Among various MCDA techniques available, MELCHIOR was chosen to support the decision-making process because this method provides the evaluation of alternatives without the need to elicit the weights of the criteria. As a result, the Gradient Boosting Decision Tree algorithm has been selected as the most suitable for the ML classification task.
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Mousa, Rania. "The Risk Assessment Enhancement Process at the Federal Deposit Insurance Corporation." In Encyclopedia of Organizational Knowledge, Administration, and Technology, 407–20. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3473-1.ch031.
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Supervisory banking institutions are often daunted by the volume and complexity of the data received on a regular basis from filer banks. Chief technology officers and data strategists face challenges as they strive to implement a technology that could facilitate the data collection and processing to produce secure, timely and reliable information for decision making purpose. Technology selection might be a concern for adopting government agencies, but the methodology of developing and implementing technologies could present a bigger challenge. This is due to the fact that government agencies may not adapt easily to new technologies in a timely fashion or accommodate further developments to their current systems while operating under strict budget. To strengthen its bank supervisory role and develop its bank examination applications, the FDIC decided to adopt The Rational Unified Process System Methodology, known as the RUP®. The chapter examines how the FDIC followed the RUP® to develop an existing bank examination tool application to support its risk assessment process.
Conference papers on the topic "Periodical selection Decision making Data processing"
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Lim, Serena, Kayvan Pazouki, Alan J. Murphy, and Ben Zhang. "Capturing and Analysing Real-Time Data From Tugs." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-78003.
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Holistic energy management in the shipping industry involves reliable data collection, systematic processing and smart analysis. The era of digitisation allows sensor technology to be used on-board vessels, converting different forms of signal into a digital format that can be exported conveniently for further processing. Appropriate sensor selection is important to ensure continuous data collection when vessels sail through harsh conditions. However, without proper processing, this leads to the collection of big data sets but without resulting useful intelligence that benefits the industry. The adoption of digital and computer technology, allows the next phase of fast data processing. This contributes to the growing area of big data analysis, which is now a problem for many technological sectors, including the maritime industry. Enormous databases are often stored without clear goals or suitable uses. Processing of data requires engineering knowledge to ensure suitable filters are applied to raw data. This systematic processing of data leads to transparency in real time data display and contributes to predictive analysis. In addition, the generation of series of raw data when coupled with other external data such as weather information provides a rich database that reflects the true scenario of the vessel. Subsequent processing will then provide improved decision making tools for optimal operations. These advances open the door for different market analyses and the generation of new knowledge. This paper highlights the crucial steps needed and the challenges of sensor installation to obtain accurate data, followed by pre and post processing of data to generate knowledge. With this, big data can now provide information and reveal hidden patterns and trends regarding vessel operations, machinery diagnostics and energy efficient fleet management. A case study was carried out on a tug boat that operates in the North Sea, firstly to demonstrate confidence in the raw data collected and secondly to demonstrate the systematic filtration, aggregation and display of useful information.
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Dong, Zexuan, Ilyana Folmar, Jay Chen, Ligang Lu, Qiushuo Su, Puneet Seth, Mohamed Sidahmed, Manoj Sarfare, and Ihab Akil. "Machine Learning in Automating Carbon Storage Site Assessment." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/210824-ms.
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Abstract To store CO2 in depleted oil and gas fields or saline aquifers, a detailed site assessment is typically done manually, which is time-consuming and costly, as there are large number of older wells with poor quality records. The study presented here will leverage cloud computing and artificial intelligence (AI) tools like Optical Character Recognition (OCR) and Natural Language Processing (NLP) to automate the legacy well assessment for efficient decision-making in storage site selection, thus reducing human effort. Results from our preliminary tests show that with this approach one can extract 80% of the desired information from various data sources including hand-written well reports and analyze information to accelerate CO2 storage risk level estimation.
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Alipour K, Mehdi, Bin Dai, Jimmy Price, Christopher Michaell Jones, Darren Gascooke, Anthony VanZuilekom, Hoda Tahani, et al. "AUTO-NAVIGATION ON PRESSURE AND SAMPLING LOCATION IN WIRELINE AND LWD: BIG DATA CHALLENGES AND SOLUTIONS." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0092.
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Measuring formation pressure and collecting representative samples are the essential tasks of formation testing operations. Where, when and how to measure pressure or collect samples are critical questions which must be addressed in order to complete any job successfully. Formation testing data has a crucial role in reserve estimation especially at the stage of field exploration and appraisal, but can be time consuming and expensive. Optimum location has a major impact on both the time spent performing and the success of pressure testing and sampling. Success and optimization of rig-time paradoxically requires careful and extensive but also quick pre-job planning. The current practice of finding optimum locations for testing heavily rely on expert knowledge. With nearly complete digitization of data collection, the oil industry is now dealing with massive data flow giving rise to the question of its application and the necessity to collect. Some data may be so called “dark data” of which a very tiny portion is used for decision making. For instance, a variety of petrophysical logs may be collected in a single well to provide measures of formation properties. The logs may include conventional gamma ray, neutron, density, caliper, resistivity or more advanced tools such as high-resolution image logs, acoustic, or NMR. These data can be integrated to help decide where to pressure test and sample, however, this effort is nearly exclusively driven by experts and is manpower intensive. In this paper we present a workflow to gather, process and analyze conventional log data in order to optimize formation testing operations. The data is from an enormous geographic distribution of wells. Tremendous effort has been performed to extract, transform and load (ETL) the data into a usable format. Stored files contains multi-million to multi-billions rows of data thereby creating technology challenges in terms of reading, processing and analyzing in a timely manner for pre-job planning. We address the technological challenges by deploying cutting-edge data technology to solve this problem. Upon completion of the workflow we have been able to build a scalable petrophysical interpretation log platform which can be easily utilized for machine learning and application deployment. This type of data base is invaluable asset especially in places where there is a need for knowledge of analogous wells. Exploratory data analysis on worldwide data on mobility and some key influencing features on pressure test and sampling quality, is performed and presented. We further show how this data is integrated and analyzed in order to automate selection of locations for which to formation test.
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Carpenter, Steven, Xinming Yu, Melih Altun, James Graham, J. Jim Zhu, and Janusz Starzyk. "Vision Guided Motion Control of a Biomimetic Quadruped Robot: RoboCat." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-63805.
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This paper presents the vision system and visual processing for a biomimetic elastic cable-driven quadruped robot—RoboCat. The paper is geared towards selection of appropriate visual servoing techniques for RoboCat such as vision algorithms, high-level cognition algorithms, software architecture and hardware implementation. The system uses two video cameras for stereo vision data acquisition and a SUMIT-ism form factor embedded computer for vision data processing. The vision system employs a color based target recognition algorithm, a neural network based shape recognition algorithm and a Color and Zernike moment based face detection algorithm. The paper presents the vision algorithms, vision guidance and motion tracking algorithms, rule-based decision making algorithms and the open architecture of the autonomous vision tracking system. Experimental testing results (including video clips) are also presented.
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Adeshina, Qozeem Adeniyi, and Baidya Nath Saha. "Using Machine Learning to Predict Distributed Denial-of-Service (DDoS) Attack." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.21.
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The IT space is growing in all aspects ranging from bandwidth, storage, processing speed, machine learning and data analysis. This growth has consequently led to more cyber threat and attacks which now requires innovative and predictive security approach that uses cutting-edge technologies in order to fight the menace. The patterns of the cyber threats will be observed so that proper analysis from different sets of data will be used to develop a model that will depend on the available data. Distributed Denial of Service is one of the most common threats and attacks that is ravaging computing devices on the internet. This research talks about the approaches and the development of machine learning classifiers to detect DDoS attacks before it eventually happen. The model is built with seven different selection techniques each using ten machine learning classifiers. The model learns to understand the normal network traffic so that it can detect an ICMP, TCP and UDP DDoS traffic when they arrive. The goal is to build a data-driven, intelligent and decision-making machine learning algorithm model that will use classifiers to categorize normal and DDoS traffic using KDD-99 dataset. Results have shown that some classifiers have very good predictions obtained within a very short time.
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Memmolo, Vittorio, Yevgeniya Lugovtsova, Massimiliano Olino, and Jens Prager. "Application of Temperature Compensation Strategies for Ultrasonic Guided Waves to Distributed Sensor Networks." In 2022 49th Annual Review of Progress in Quantitative Nondestructive Evaluation. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/qnde2022-98534.
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Abstract Temperature compensation strategies play a key role in the implementation of guided wave based structural health monitoring approaches. The varying temperature influences the performance of the inspection system inducing false alarms or missed detection, with a consequent reduction of reliability. This paper quantitatively assesses two temperature compensation methods, namely the optimal baseline selection (OBS) and the baseline signal stretch (BSS), with the aim to extend their use to the case of distributed sensor networks (DSN). The effect of temperature separation between baseline time-traces in OBS and BSS are investigated considering multiple couples of sensors employed in the DSN. A decision strategy that uses frequent value warning to define the optimal baseline or stretching parameter is found to be effective analyzing data from two several experiments, which use different frequency analysis with either predominantly A0 mode or S0 mode data or both. The focus is given on the fact that different paths are available in a sensor network and several possible combinations of results are available. Nonetheless, introducing a frequent value warning it is possible to increase the efficiency of the OBS and BSS approach making use of fewer signal processing algorithms. In addition, the effectiveness of those approach is quantified using damage indicators as metric, which confirms that the performance of OBS and BSS quantitatively agree with predictions and also demonstrate that the use of compensation strategies improve detectability of damage with a higher reliability of the system.
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Al-Aruri, Ahmad, Elrad Iskakov, and Han Young Park. "Development of Gas Injection Strategy Optimized Under Subsurface Uncertainties in Naturally Fractured Karachaganak Reservoir." In SPE Annual Caspian Technical Conference. SPE, 2022. http://dx.doi.org/10.2118/212061-ms.
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Abstract Optimization of injection strategy for the giant fractured gas-condensate Karachaganak field is the focus of this paper. Numerous development alternatives were assessed using DPDK simulation model coupled with surface network and further optimized by integration with streamline analysis. Advanced risk assessment enabled to mitigate risks associated with high uncertainties in fracture distributions and complex clinoforms within the development area. Optimization results are compared to alternative models from JV partners, considering geological, surface facilities and project uncertainties. Four main workflows have been discussed; firstly, constructing DPDK model through integration of seismic, dynamic and petrophysical data to properly characterize fracture properties and reproduce reservoir connectivity. This process has been guided by advanced Design of Experiment to manage numerous uncertainties in history match and model selection. Secondly, coupling subsurface model with the surface network simulator and addressing re-routing challenges to generate realistic forecast and efficient production system. Thirdly, devising risk management workflow to ease decision making for the placement of future gas injectors, their completion designs and gauging benefits of different conformance control options. Lastly, finalizing injection strategy through using streamline-assisted optimization workflow under geological/surface facilities/project startup uncertainties. Key observations are: Alignment of stratigraphy and enhanced permeability/fracture distribution in DPDK and SPSK models helped in achieving comparable outcomes. Adoption of advanced risk analysis and early agreement with multi-disciplinary stakeholders on subsurface and surface uncertainty parameters for multiple available models enabled generating high-quality risk assessments. Benchmarking outcomes from standalone vs. coupled models is essential step to ensure reliability of coupled models. Re-routing of wells between processing units improves recovery. Agreement with Partners on the surface simulator (ENS) integrated with different subsurface simulators allows uninterrupted analysis of information. Automating dual connection due to frequent change in boundary processing conditions accelerates delivery of results. Frequent and well-prepared engagements with stakeholders improves communication and provides better management of expectations that helped meeting project deadlines. Confining gas injection inside outboard Clinoforms farther away from fractures is the most rewarding and safest option by minimizing gas breakthroughs and improving recovery. This work was proposed by KPO, and conducted by Chevron Karachaganak support team, in part, on request of the Karachaganak Petroleum Operating Company. It was used by the Karachaganak Petroleum Operating Company and JV partnership along with alternative models to support decision-making for the next phase of phase development – the Karachaganak Expansion Project. Evolution of optimized gas injection strategy under subsurface and surface uncertainties is reported. Remedy to mitigate limitation of ENS tool in handling change of boundary processing conditions is described. The novelty of streamline tracing-assisted gas injection optimization method applied to the DPDK model for gas injection optimization is described as mean of improving the management of fractured reservoir.
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Song, Mengxin, Bingxin Xu, Mei Feng, and Xinxi Fu. "Optimization of Exploration Prospects Based on Ant Colony Algorithm and XGBoost Combined Optimization Model." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207721-ms.
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Abstract Traditional exploration prospect optimization is uncertain due to human factor, the primary reason of that problem is the complex nonlinear relationship between trap quality and related geological factors. Some researchers proposed use artificial neural network (ANN) to solve the problem of the comprehensive geological evaluation of traps, because ANN can describe the nonlinear relationship of multiple geological factors. Considering ANN has some drawbacks, such as it is need lots of parameters for training, and the learning process can not be observed. In this paper we proposed a combined optimization model to accomplish optimization of exploration prospects, and express the affinity order between the prospects and its related geological factors, also can provide the data support for exploration. Based on trap data of an oilfield in Africa, there are 12 geological factors related to trap quality, including trap coefficient, trap depth, trap scale, trap area, Reservoir coefficient, Preservation coefficient, hydrocarbon source coefficient, resources etc.. The ant colony algorithm is used for feature selection, and irrelevant and redundant features are eliminated through multiple iterations, making it suitable for model processing and improving training speed. Based on ant colony algorithm, we get the key parameters for XGBoost model training, namely trap area, reservoir coefficient, preservation coefficient, resource, and the key features are used in XGBoost model for training and prediction. Finally, we compared our prediction results with expert prediction, the error is 0. In this paper, we proposed a combined optimization model based on ant colony algorithm and XGBoost for exploration prospect optimization. We recognized the key geological factors and different characteristic rules for exploration prospect optimization, in the process of optimization, ant colony discards the bad features that interfere with classification and recognition, and retains the features that contribute greatly to classification. In comprehensive geological evaluate of trap, the proposed combined optimization model is suitable for complicated nonlinear geological relationship, and express the affinity order between the prospects, the proposed method can work as an auxiliary way in petroleum exploration, also the proposed method can provide decision support for exploration prospect optimization, and finally can fulfill cost decreasing and benefit increasing.