Bibliografías temáticas / Data mining. Computer Science

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Literatura académica sobre el tema "Data mining. Computer Science"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 1 de febrero de 2022

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Artículos de revistas sobre el tema "Data mining. Computer Science"

Musicant, David R. "A data mining course for computer science." ACM SIGCSE Bulletin 38, no. 1 (2006): 538–42. http://dx.doi.org/10.1145/1124706.1121508.

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Malkić, Jasmin, and Nermin Sarajlić. "INTERDISCIPLINARY APPLICATION OF ALGORITHMS FOR DATA MINING." Journal Human Research in Rehabilitation 3, no. 2 (2013): 6–9. http://dx.doi.org/10.21554/hrr.091303.

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Interdisciplinary application of data mining is linked with the ability to receive and process the large amounts of data. Although even the first computers could help in executing the tasks that required accuracy and reliability atypical to the human way of information processing, only increasing the speed of computer processors and advances in computer science have introduced the possibility that computers can play a more active role in decision making. Applications of these features are found in medicine, where data mining is used in clinical trials to determine the factors that influence health, and examine the effectiveness of medical treatments. With its ability to detect patterns and similarities within the data, data mining can help determine the statistical significance, pointing to the complex combinations of factors that cause certain effect. Such approach opens the opportunities of deeper analysis than it is the case with reliance solely on statistics.

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Radhakrishna, Vangipuram, Gunupudi Rajesh Kumar, Gali Suresh Reddy, and Dammavalam Srinivasa Rao. "Machine Learning for Data Mining, Data Science and Data Analytics." Recent Advances in Computer Science and Communications 14, no. 5 (2021): 1356–57. http://dx.doi.org/10.2174/266625581405210129161546.

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Bin, Liu, Zhang Hui, Liu Sifeng, and Dang Yaoguo. "Data mining techniques based on grey system theories for time sequence data." Computer Science and Information Systems 3, no. 2 (2006): 73–82. http://dx.doi.org/10.2298/csis0602073b.

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Data mining is an interesting focus in computer science field now This paper deals with data mining techniques based on Grey system theories for time sequence data. Firstly, thoughts of data mining with embedded knowledge are expatiated, and the status quo of Data mining techniques is presented briefly. Then, based on the above thoughts and the Grey system theories, data mining techniques based on Grey system theories for time sequence data are proposed for the first time, and the idiographic arithmetic with GM(1,1) as an example is introduced in this paper. Last, it forecasts the total homes in 2002~2005 connecting with Internet in Shang Hai City by the arithmetic.

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Wu, Xiang Min, Cheng Lin Zhao, and Pan Cao. "Research of Data Base and Data Mining in CRM." Applied Mechanics and Materials 543-547 (March 2014): 2988–91. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2988.

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Customer Relationship Management (CRM) is becoming the focus of enterprise and an active research field of computer science. The ariticle introduces some basic concepts about CRM and data mining, and some benefits brought by data mining in CRM. At the end it points out how to apply data mining applications in CRM.

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Velásquez-Pérez, T., J. A. Camargo-Pérez, and E. L. Quintero-Quintero. "Application of data mining as a tool in computer science." Journal of Physics: Conference Series 1587 (July 2020): 012018. http://dx.doi.org/10.1088/1742-6596/1587/1/012018.

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McCarthy, John. "Phenomenal data mining." Communications of the ACM 43, no. 8 (2000): 75–79. http://dx.doi.org/10.1145/345124.345152.

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Mikut, Ralf, and Markus Reischl. "Data mining tools." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, no. 5 (2011): 431–43. http://dx.doi.org/10.1002/widm.24.

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Leung, Carson Kai-Sang. "Mining uncertain data." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, no. 4 (2011): 316–29. http://dx.doi.org/10.1002/widm.31.

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Fayyad, Usama, David Haussler, and Paul Stolorz. "Mining scientific data." Communications of the ACM 39, no. 11 (1996): 51–57. http://dx.doi.org/10.1145/240455.240471.

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Tesis sobre el tema "Data mining. Computer Science"

Taylor, Phillip. "Data mining of vehicle telemetry data." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/77645/.

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Driving a safety critical task that requires a high level of attention and workload from the driver. Despite this, people often perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. As well as these distractions, the driver may also be overloaded for other reasons, such as dealing with an incident on the road or holding conversations in the car. One solution to this distraction problem is to limit the functionality of in-car devices while the driver is overloaded. This can take the form of withholding an incoming phone call or delaying the display of a non-urgent piece of information about the vehicle. In order to design and build these adaptions in the car, we must first have an understanding of the driver's current level of workload. Traditionally, driver workload has been monitored using physiological sensors or camera systems in the vehicle. However, physiological systems are often intrusive and camera systems can be expensive and are unreliable in poor light conditions. It is important, therefore, to use methods that are non-intrusive, inexpensive and robust, such as sensors already installed on the car and accessible via the Controller Area Network (CAN)-bus. This thesis presents a data mining methodology for this problem, as well as for others in domains with similar types of data, such as human activity monitoring. It focuses on the variable selection stage of the data mining process, where inputs are chosen for models to learn from and make inferences. Selecting inputs from vehicle telemetry data is challenging because there are many irrelevant variables with a high level of redundancy. Furthermore, data in this domain often contains biases because only relatively small amounts can be collected and processed, leading to some variables appearing more relevant to the classification task than they are really. Over the course of this thesis, a detailed variable selection framework that addresses these issues for telemetry data is developed. A novel blocked permutation method is developed and applied to mitigate biases when selecting variables from potentially biased temporal data. This approach is infeasible computationally when variable redundancies are also considered, and so a novel permutation redundancy measure with similar properties is proposed. Finally, a known redundancy structure between features in telemetry data is used to enhance the feature selection process in two ways. First the benefits of performing raw signal selection, feature extraction, and feature selection in different orders are investigated. Second, a two-stage variable selection framework is proposed and the two permutation based methods are combined. Throughout the thesis, it is shown through classification evaluations and inspection of the features that these permutation based selection methods are appropriate for use in selecting features from CAN-bus data.

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Ghoting, Amol. "Memory- and knowledge-conscious data mining." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1186979749.

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Wang, Grant J. (Grant Jenhorn) 1979. "Algorithms for data mining." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38315.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. Includes bibliographical references (p. 81-89). Data of massive size are now available in a wide variety of fields and come with great promise. In theory, these massive data sets allow data mining and exploration on a scale previously unimaginable. However, in practice, it can be difficult to apply classic data mining techniques to such massive data sets due to their sheer size. In this thesis, we study three algorithmic problems in data mining with consideration to the analysis of massive data sets. Our work is both theoretical and experimental - we design algorithms and prove guarantees for their performance and also give experimental results on real data sets. The three problems we study are: 1) finding a matrix of low rank that approximates a given matrix, 2) clustering high-dimensional points into subsets whose points lie in the same subspace, and 3) clustering objects by pairwise similarities/distances. by Grant J. Wang. Ph.D.

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Tziatzios, Achilleas. "Data mining of range-based classification rules for data characterization." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/65902/.

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Advances in data gathering have led to the creation of very large collections across different fields like industrial site sensor measurements or the account statuses of a financial institution's clients. The ability to learn classification rules, rules that associate specific attribute values with a specific class label, from this data is important and useful in a range of applications. While many methods to facilitate this task have been proposed, existing work has focused on categorical datasets and very few solutions that can derive classification rules of associated continuous ranges (numerical intervals) have been developed. Furthermore, these solutions have solely relied in classification performance as a means of evaluation and therefore focus on the mining of mutually exclusive classification rules and the correct prediction of the most dominant class values. As a result existing solutions demonstrate only limited utility when applied for data characterization tasks. This thesis proposes a method that derives range-based classification rules from numerical data inspired by classification association rule mining. The presented method searches for associated numerical ranges that have a class value as their consequent and meet a set of user defined criteria. A new interestingness measure is proposed for evaluating the density of range-based rules and four heuristic based approaches are presented for targeting different sets of rules. Extensive experiments demonstrate the effectiveness of the new algorithm for classification tasks when compared to existing solutions and its utility as a solution for data characterization.

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Liu, Tantan. "Data Mining over Hidden Data Sources." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343313341.

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Zhang, Nan. "Privacy-preserving data mining." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1080.

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Al-Hashemi, Idrees Yousef. "Applying data mining techniques over big data." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21119.

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Thesis (M.S.C.S.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. The rapid development of information technology in recent decades means that data appear in a wide variety of formats — sensor data, tweets, photographs, raw data, and unstructured data. Statistics show that there were 800,000 Petabytes stored in the world in 2000. Today’s internet has about 0.1 Zettabytes of data (ZB is about 1021 bytes), and this number will reach 35 ZB by 2020. With such an overwhelming flood of information, present data management systems are not able to scale to this huge amount of raw, unstructured data—in today’s parlance, Big Data. In the present study, we show the basic concepts and design of Big Data tools, algorithms, and techniques. We compare the classical data mining algorithms to the Big Data algorithms by using Hadoop/MapReduce as a core implementation of Big Data for scalable algorithms. We implemented the K-means algorithm and A-priori algorithm with Hadoop/MapReduce on a 5 nodes Hadoop cluster. We explore NoSQL databases for semi-structured, massively large-scaling of data by using MongoDB as an example. Finally, we show the performance between HDFS (Hadoop Distributed File System) and MongoDB data storage for these two algorithms.

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Gu, Zhuoer. "Mining previously unknown patterns in time series data." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/99207/.

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The emerging importance of distributed computing systems raises the needs of gaining a better understanding of system performance. As a major indicator of system performance, analysing CPU host load helps evaluate system performance in many ways. Discovering similar patterns in CPU host load is very useful since many applications rely on the pattern mined from the CPU host load, such as pattern-based prediction, classification and relative rule mining of CPU host load. Essentially, the problem of mining patterns in CPU host load is mining the time series data. Due to the complexity of the problem, many traditional mining techniques for time series data are not suitable anymore. Comparing to mining known patterns in time series, mining unknown patterns is a much more challenging task. In this thesis, we investigate the major difficulties of the problem and develop the techniques for mining unknown patterns by extending the traditional techniques of mining the known patterns. In this thesis, we develop two different CPU host load discovery methods: the segment-based method and the reduction-based method to optimize the pattern discovery process. The segment-based method works by extracting segment features while the reduction-based method works by reducing the size of raw data. The segment-based pattern discovery method maps the CPU host load segments to a 5-dimension space, then applies the DBSCAN clustering method to discover similar segments. The reduction-based method reduces the dimensionality and numerosity of the CPU host load to reduce the search space. A cascade method is proposed to support accurate pattern mining while maintaining efficiency. The investigations into the CPU host load data inspired us to further develop a pattern mining algorithm for general time series data. The method filters out the unlikely starting positions for reoccurring patterns at the early stage and then iteratively locates all best-matching patterns. The results obtained by our method do not contain any meaningless patterns, which has been a different problematic issue for a long time. Comparing to the state of art techniques, our method is more efficient and effective in most scenarios.

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Mao, Shihong. "Comparative Microarray Data Mining." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1198695415.

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Zou, Beibei 1974. "Data mining with relational database management systems." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82456.

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With the increasing demands of transforming raw data into information and knowledge, data mining becomes an important field to the discovery of useful information and hidden patterns in huge datasets. Both machine learning and database research have made major contributions to the field of data mining. However, there is still little effort made to improve the scalability of algorithms applied in data raining tasks. Scalability is crucial for data mining algorithms, since they have to handle large datasets quite often. In this thesis we take a step in this direction by extending a popular machine learning software, Weka3.4, to handle large datasets that can not fit into main memory by relying on relational database technology. Weka3.4-DB is implemented to store the data into and access the data from DB2 with a loose coupling approach in general. Additionally, a semi-tight coupling is applied to optimize the data manipulation methods by implementing core functionalities within the database. Based on the DB2 storage implementation, Weka3.4-DB achieves better scalability, but still provides a general interface for developers to implement new algorithms without the need of database or SQL knowledge.

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Libros sobre el tema "Data mining. Computer Science"

Schumaker, Robert P. Sports Data Mining. Springer Science+Business Media, LLC, 2010.

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ChengXiang, Zhai, and SpringerLink (Online service), eds. Mining Text Data. Springer US, 2012.

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Liu, Huan. Feature Selection for Knowledge Discovery and Data Mining. Springer US, 1998.

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Džeroski, Sašo. Relational Data Mining. Springer Berlin Heidelberg, 2001.

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Braha, Dan. Data Mining for Design and Manufacturing: Methods and Applications. Springer US, 2001.

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Managing and mining graph data. Springer, 2010.

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W, Mielke Paul, ed. Statistical Mining and Data Visualization in Atmospheric Sciences. Springer US, 2000.

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Runkler, Thomas A. Data analytics: Models and algorithms for intelligent data analysis. Springer Vieweg, 2012.

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service), SpringerLink (Online, ed. Data Analytics: Models and Algorithms for Intelligent Data Analysis. Vieweg+Teubner Verlag, 2012.

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Cordeiro, Robson L. F. Data Mining in Large Sets of Complex Data. Springer London, 2013.

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Capítulos de libros sobre el tema "Data mining. Computer Science"

Möhlmann, Kevin, and Jörn Syrbe. "Term Extraction from German Computer Science Textbooks." In Data Mining and Big Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_21.

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Voisin, Bruno. "Mining Astronomical Data." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44759-8_61.

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Revesz, Peter. "Prediction and Data Mining." In Texts in Computer Science. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84996-095-3_19.

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Mărginean, Flaviu Adrian. "Computational Science and Data Mining." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44863-2_63.

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HaCohen-Kerner, Yaakov, Avi Rosenfeld, Maor Tzidkani, and Daniel Nisim Cohen. "Classifying Papers from Different Computer Science Conferences." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53914-5_45.

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Burns, Alex, Andrew Kusiak, and Terry Letsche. "Mining Transformed Data Sets." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30132-5_25.

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Kumar, Vipin, Mahesh V. Joshi, Eui-Hong Han, Pang-Ning Tan, and Michael Steinbach. "High Performance Data Mining." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36569-9_8.

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Jensen, Richard. "Fuzzy-Rough Data Mining." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21881-1_7.

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Pio Alvarez, Sergio, Adriana Marotta, and Libertad Tansini. "Data Currency Assessment Through Data Mining." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25747-1_27.

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Chakraverty, Shampa, Ankuj Gupta, Akhil Goyal, and Ashish Singal. "Data Mining on Grids." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22606-9_36.

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Actas de conferencias sobre el tema "Data mining. Computer Science"

Ying Wah, The, Mustaffa Kamal Nor, Zaitun Abu Bakar, and Lee Sai Peck. "Data Mining in Computer Auditing." In 2002 Informing Science + IT Education Conference. Informing Science Institute, 2002. http://dx.doi.org/10.28945/2586.

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In this paper, we first introduce the readers about the main function of a computer auditor. This is followed by a description of auditing the usage of stationeries in the Faculty of Computer Science and Information Technology, University of Malaya. It is a very time consuming process to audit all stationeries. Therefore, we introduce the data mining techniques to help us find the relevant stationeries. We use this information to recommend purchasers to purchase relevant items together in order to achieve efficiently in purchasing stationeries process.

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Musicant, David R. "A data mining course for computer science." In the 37th SIGCSE technical symposium. ACM Press, 2006. http://dx.doi.org/10.1145/1121341.1121508.

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Gramoli, Vincent, Michael Charleston, Bryn Jeffries, et al. "Mining autograding data in computer science education." In ACSW '16: Australasian Computer Science Week. ACM, 2016. http://dx.doi.org/10.1145/2843043.2843070.

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Kumar M., Bharath, and Y. N. Srikant. "The Best Nurturers in Computer Science Research." In Proceedings of the 2005 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2005. http://dx.doi.org/10.1137/1.9781611972757.62.

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Xuan, Liu, and Liu Chang. "Analysis of Computer Science Based on Big-Data Mining." In 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2020. http://dx.doi.org/10.1109/icbase51474.2020.00028.

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Bird, Christian, Premkumar Devanbu, Earl Barr, Vladimir Filkov, Andre Nash, and Zhendong Su. "Structure and Dynamics of Research Collaboration in Computer Science." In Proceedings of the 2009 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2009. http://dx.doi.org/10.1137/1.9781611972795.71.

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Gomes, Daniela De Souza, Marcos Henrique Fonseca Ribeiro, Giovanni Ventorim Comarela, and Gabriel Philippe Pereira. "Failure Analysis in University and Computer Science Contexts With Data Mining." In Workshop sobre Educação em Computação. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wei.2020.11132.

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High failure rates are a worrying and relevant problem in Brazilian universities. From a data set of student transcripts, we performed a study case for both general and Computer Science contexts, in which Data Mining Techniques were used to find patterns concerning failures. The knowledge acquired can be used for better educational administration and also build intelligent systems to support students’ decision making.

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Goharian, N., D. Grossman, and N. Raju. "Extending the undergraduate computer science curriculum to include data mining." In International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. IEEE, 2004. http://dx.doi.org/10.1109/itcc.2004.1286461.

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López-Ostenero, Fernando, Laura Plaza, Juan Martinez-Romo, and Lourdes Araujo. "NATURAL LANGUAGE PROCESSING FOR DATA MINING IN COMPUTER SCIENCE EDUCATION." In 12th International Conference on Education and New Learning Technologies. IATED, 2020. http://dx.doi.org/10.21125/edulearn.2020.0731.

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Pérez-Quiñones, Manuel A. "How Can Computer Science Education Address Inequities." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2020. http://dx.doi.org/10.1145/3394486.3411063.

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Informes sobre el tema "Data mining. Computer Science"

Wachen, John, Steven McGee, Don Yanek, and Valerie Curry. Coaching Teachers of Exploring Computer Science: A Report on Four Years of Implementation. The Learning Partnership, 2021. http://dx.doi.org/10.51420/report.2021.1.

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In this technical report, we examine the implementation of a coaching model for teachers of the Exploring Computer Science course in Chicago Public Schools over a period of four academic years (from 2016-2017 to 2019-2020). We first provide a description of the coaching model and how it evolved over time. Next, we present findings from a descriptive analysis of data collected through logs of coaching interactions and surveys of ECS teacher coaches during the 2019-2020 school year. Coaching logs and survey data were also collected during the 2018-2019 school year and, where appropriate, we compare results across years. We then discuss the products that were produced by the coaching team to support the implementation of the model. Finally, we provide an overview of next steps for the coaching team in the 2020-2021 school year and beyond.

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Wachen, John, Mark Johnson, Steven McGee, Faythe Brannon, and Dennis Brylow. Computer Science Teachers as Change Agents for Broadening Participation: Exploring Perceptions of Equity. The Learning Partnership, 2021. http://dx.doi.org/10.51420/conf.2021.2.

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In this paper, the authors share findings from a qualitative analysis of computer science teachers’ perspectives about equity within the context of an equity-focused professional development program. Drawing upon a framework emphasizing educator belief systems in perpetuating inequities in computer science education and the importance of equity-focused teacher professional development, we explored how computer science teachers understand the issue of equity in the classroom. We analyzed survey data from a sample of participants in a computer science professional development program, which revealed that teachers have distinct ways of framing their perceptions of equity and also different perspectives about what types of strategies help to create equitable, inclusive classrooms reflective of student identity and voice.

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Johnson, Mark, John Wachen, and Steven McGee. Entrepreneurship, Federalism, and Chicago: Setting the Computer Science Agenda at the Local and National Levels. The Learning Partnership, 2020. http://dx.doi.org/10.51420/conf.2020.1.

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From 2012-13 to 2018-19, the number of Chicago Public Schools (CPS) high school students taking an introductory computer science course rose from three thousand per year to twelve thousand per year. Our analysis examines the policy entrepreneurship that helped drive the rapid expansion of computer science education in CPS, within the broader context of the development of computer science at the national level. We describe how actions at the national level (e.g., federal policy action and advocacy work by national organizations) created opportunities in Chicago and, likewise, how actions at the local level (e.g., district policy action and advocacy by local educators and stakeholders) influenced agenda setting at the national level. Data from interviews with prominent computer science advocates are used to document and explain the multidirectional (vertical and horizontal) flow of advocacy efforts and how these efforts influenced policy decisions in the area of computer science. These interviews with subsystem actors––which include district leaders, National Science Foundation program officers, academic researchers, and leaders from advocacy organizations––provide an insider’s perspective on the unfolding of events and highlight how advocates from various organizations worked to achieve their policy objectives.

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Tucker Blackmon, Angelicque. Formative External Evaluation and Data Analysis Report Year Three: Building Opportunities for STEM Success. Innovative Learning Center, LLC, 2020. http://dx.doi.org/10.52012/mlfk2041.

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Henrick, Erin, Steven McGee, Lucia Dettori, et al. Research-Practice Partnership Strategies to Conduct and Use Research to Inform Practice. The Learning Partnership, 2021. http://dx.doi.org/10.51420/conf.2021.3.

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This study examines the collaborative processes the Chicago Alliance for Equity in Computer Science (CAFÉCS) uses to conduct and use research. The CAFÉCS RPP is a partnership between Chicago Public Schools (CPS), Loyola University Chicago, The Learning Partnership, DePaul University, and University of Illinois at Chicago. Data used in this analysis comes from three years of evaluation data, and includes an analysis of team documents, meeting observations, and interviews with 25 members of the CAFÉCS RPP team. The analysis examines how three problems are being investigated by the partnership: 1) student failure rate in an introductory computer science course, 2) teachers’ limited use of discussion techniques in an introductory computer science class, and 3) computer science teacher retention. Results from the analysis indicate that the RPP engages in a formalized problem-solving cycle. The problem-solving cycle includes the following steps: First, the Office of Computer Science (OCS) identifies a problem. Next, the CAFÉCS team brainstorms and prioritizes hypotheses to test. Next, data analysis clarifies the problem and the research findings are shared and interpreted by the entire team. Finally, the findings are used to inform OCS improvement strategies and next steps for the CAFÉCS research agenda. There are slight variations in the problem-solving cycle, depending on the stage of understanding of the problem, which has implications for the mode of research (e.g hypothesis testing, research and design, continuous improvement, or evaluation).

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