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Relevant bibliographies by topics / Data structures and algorithms for data management

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Academic literature on the topic 'Data structures and algorithms for data management'

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Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Data structures and algorithms for data management"

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Nadkarni, Prakash M. "Management of Evolving Map Data: Data Structures and Algorithms Based on the Framework Map." Genomics 30, no. 3 (December 1995): 565–73. http://dx.doi.org/10.1006/geno.1995.1278.

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Satya Sai Kumar, Avula, S. Mohan, and R. Arunkumar. "A Survey on Security Models for Data Privacy in Big Data Analytics." Asian Journal of Computer Science and Technology 7, S1 (November 5, 2018): 87–89. http://dx.doi.org/10.51983/ajcst-2018.7.s1.1798.

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As emerging data world like Google and Wikipedia, volume of the data growing gradually for centralization and provide high availability. The storing and retrieval in large volume of data is specialized with the big data techniques. In addition to the data management, big data techniques should need more concentration on the security aspects and data privacy when the data deals with authorized and confidential. It is to provide secure encryption and access control in centralized data through Attribute Based Encryption (ABE) Algorithm. A set of most descriptive attributes is used as categorize to produce secret private key and performs access control. Several works proposed in existing based on the different access structures of ABE algorithms. Thus the algorithms and the proposed applications are literally surveyed and detailed explained and also discuss the functionalities and performance aspects comparison for desired ABE systems.

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Sapiecha, Krzysztof, and Grzegorz Lukawski. "Scalable Distributed Two-Layer Data Structures (SD2DS)." International Journal of Distributed Systems and Technologies 4, no. 2 (April 2013): 15–30. http://dx.doi.org/10.4018/jdst.2013040102.

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Scalability and fault tolerance are important features of modern applications designed for the distributed, loosely-coupled computer systems. In the paper, two-layer scalable structures for storing data in a distributed RAM of a multicomputer (SD2DS) are introduced. A data unit of SD2DS (a component) is split into a header and a body. The header identifies the body and contains its address in a network. The headers are stored in the first layer of SD2DS, called the component file, while the bodies are stored in the second layer, called the component storage. Both layers are managed independently. Details of the management algorithms are given, along with SD2DS variant suitable for storing plain records of data. The SD2DS is compared to similar distributed structures and frameworks. Comparison considerations together with test results are also given. The results proved superiority of SD2DS over similar structures.

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Chen, Chin Chun, Yuan Horng Lin, and Jeng Ming Yih. "Management of Abstract Algebra Concepts Based on Knowledge Structure." Applied Mechanics and Materials 284-287 (January 2013): 3537–42. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3537.

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Knowledge Management of Mathematics Concepts was essential in educational environment. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. Fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. A new improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance (FCM-NM) is proposed. Use the best performance of clustering Algorithm FCM-NM in data analysis and interpretation. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of Mathematics Concepts to construct the model of features in the pattern recognition completely. This procedure will also useful for cognition diagnosis. To sum up, this integrated algorithm could improve the assessment methodology of cognition diagnosis and manage the knowledge structures of Mathematics Concepts easily.

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Denoyelle, Nicolas, John Tramm, Kazutomo Yoshii, Swann Perarnau, and Pete Beckman. "NUMA-AWARE DATA MANAGEMENT FOR NEUTRON CROSS SECTION DATA IN CONTINUOUS ENERGY MONTE CARLO NEUTRON TRANSPORT SIMULATION." EPJ Web of Conferences 247 (2021): 04020. http://dx.doi.org/10.1051/epjconf/202124704020.

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The calculation of macroscopic neutron cross-sections is a fundamental part of the continuous-energy Monte Carlo (MC) neutron transport algorithm. MC simulations of full nuclear reactor cores are computationally expensive, making high-accuracy simulations impractical for most routine reactor analysis tasks because of their long time to solution. Thus, preparation of MC simulation algorithms for next generation supercomputers is extremely important as improvements in computational performance and efficiency will directly translate into improvements in achievable simulation accuracy. Due to the stochastic nature of the MC algorithm, cross-section data tables are accessed in a highly randomized manner, resulting in frequent cache misses and latency-bound memory accesses. Furthermore, contemporary and next generation non-uniform memory access (NUMA) computer architectures, featuring very high latencies and less cache space per core, will exacerbate this behaviour. The absence of a topology-aware allocation strategy in existing high-performance computing (HPC) programming models is a major source of performance problems in NUMA systems. Thus, to improve performance of the MC simulation algorithm, we propose a topology-aware data allocation strategies that allow full control over the location of data structures within a memory hierarchy. A new memory management library, known as AML, has recently been created to facilitate this mapping. To evaluate the usefulness of AML in the context of MC reactor simulations, we have converted two existing MC transport cross-section lookup “proxy-applications” (XSBench and RSBench) to utilize the AML allocation library. In this study, we use these proxy-applications to test several continuous-energy cross-section data lookup strategies (the nuclide grid, unionized grid, logarithmic hash grid, and multipole methods) with a number of AML allocation schemes on a variety of node architectures. We find that the AML library speeds up cross-section lookup performance up to 2x on current generation hardware (e.g., a dual-socket Skylake-based NUMA system) as compared with naive allocation. These exciting results also show a path forward for efficient performance on next-generation exascale supercomputer designs that feature even more complex NUMA memory hierarchies.

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Ciaburro, Giuseppe, and Gino Iannace. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review." Data 6, no. 6 (May 25, 2021): 55. http://dx.doi.org/10.3390/data6060055.

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To predict the future behavior of a system, we can exploit the information collected in the past, trying to identify recurring structures in what happened to predict what could happen, if the same structures repeat themselves in the future as well. A time series represents a time sequence of numerical values observed in the past at a measurable variable. The values are sampled at equidistant time intervals, according to an appropriate granular frequency, such as the day, week, or month, and measured according to physical units of measurement. In machine learning-based algorithms, the information underlying the knowledge is extracted from the data themselves, which are explored and analyzed in search of recurring patterns or to discover hidden causal associations or relationships. The prediction model extracts knowledge through an inductive process: the input is the data and, possibly, a first example of the expected output, the machine will then learn the algorithm to follow to obtain the same result. This paper reviews the most recent work that has used machine learning-based techniques to extract knowledge from time series data.

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Li, Zhenlong, Wenwu Tang, Qunying Huang, Eric Shook, and Qingfeng Guan. "Introduction to Big Data Computing for Geospatial Applications." ISPRS International Journal of Geo-Information 9, no. 8 (August 12, 2020): 487. http://dx.doi.org/10.3390/ijgi9080487.

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The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for geospatial applications. Crucial to the advancements highlighted here is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. This editorial first introduces the background and motivation of this special issue followed by an overview of the ten included articles. Conclusion and future research directions are provided in the last section.

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Kallinikos, Jannis, and Ioanna D. Constantiou. "Big Data Revisited: A Rejoinder." Journal of Information Technology 30, no. 1 (March 2015): 70–74. http://dx.doi.org/10.1057/jit.2014.36.

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We elaborate on key issues of our paper New games, new rules: big data and the changing context of strategy as a means of addressing some of the concerns raised by the paper's commentators. We initially deal with the issue of social data and the role it plays in the current data revolution. The massive involvement of lay publics as instrumented by social media breaks with the strong expert cultures that have underlain the production and use of data in modern organizations. It also sets apart the interactive and communicative processes by which social data is produced from sensor data and the technological recording of facts. We further discuss the significance of the very mechanisms by which big data is produced as distinct from the very attributes of big data, often discussed in the literature. In the final section of the paper, we qualify the alleged importance of algorithms and claim that the structures of data capture and the architectures in which data generation is embedded are fundamental to the phenomenon of big data.

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Livnat, Joshua, and Jyoti Singh. "Machine Learning Algorithms to Classify Future Returns Using Structured and Unstructured Data." Journal of Investing 30, no. 3 (February 12, 2021): 62–78. http://dx.doi.org/10.3905/joi.2021.1.169.

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Fayyadh, Moatasem Mohammed, and Hashim Abdul Razak. "DAMAGE IDENTIFICATION AND ASSESSMENT IN RC STRUCTURES USING VIBRATION DATA: A REVIEW." Journal of Civil Engineering and Management 19, no. 3 (June 14, 2013): 375–86. http://dx.doi.org/10.3846/13923730.2012.744773.

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Inspection of structural components for damage is essential to decision-making for the maintenance of such structures. There have been many studies to assess the reinforced concrete (RC) structural elements. However, the experimental approach is still based on the conventional static test, which is time-consuming, costly, has intensive equipment and labour requirements and causes major disruptions to the existing use. Modal testing provides an integrated approach, i.e. both local and global characteristics can be ascertained for structural assessment. Depending on the accessibility to damage elements, little or no disruption to the existing use is incurred during testing works. The approach towards structural assessment work provides not only a viable but also a robust, less expensive and powerful alternative to conventional techniques. This paper presents the background of the behaviour of the RC material at different loading and unloading conditions, in order to understand its effect on the modal parameters. The use of modal testing for support stiffness deterioration is highlighted and studies on the use of modal testing for classification of damage source are presented. Studies on the use of modal testing for detection of damage severity and location algorithms and procedures are also presented.

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Dissertations / Theses on the topic "Data structures and algorithms for data management"

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Karras, Panagiotis. "Data structures and algorithms for data representation in constrained environments." Thesis, Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38897647.

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蔡纓 and Ying Choi. "Improved data structures for two-dimensional library management and dictionary problems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31213029.

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Choi, Ying. "Improved data structures for two-dimensional library management and dictionary problems /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18037240.

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Upadhyay, Abhyudaya. "Big Vector: An External Memory Algorithm and Data Structure." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439279714.

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Johansen, Valdemar. "Object serialization vs relational data modelling in Apache Cassandra: a performance evaluation." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-10391.

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Context. In newer database solutions designed for large-scale, cloud-based services, database performance is of particular concern as these services face scalability challenges due to I/O bottlenecks. These issues can be alleviated through various data model optimizations that reduce I/O loads. Object serialization is one such approach. Objectives. This study investigates the performance of serialization using the Apache Avro library in the Cassandra database. Two different serialized data models are compared with a traditional relational database model. Methods. This study uses an experimental approach that compares read and write latency using Twitter data in JSON format. Results. Avro serialization is found to improve performance. However, the extent of the performance benefit is found to be highly dependent on the serialization granularity defined by the data model. Conclusions. The study concludes that developers seeking to improve database throughput in Cassandra through serialization should prioritize data model optimization as serialization by itself will not outperform relational modelling in all use cases. The study also recommends that further work is done to investigate additional use cases, as there are potential performance issues with serialization that are not covered in this study.

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Fischer, Johannes. "Data Structures for Efficient String Algorithms." Diss., lmu, 2007. http://nbn-resolving.de/urn:nbn:de:bvb:19-75053.

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Ochoa, Méndez Carlos Ernesto. "Synergistic (Analysis of) algorithms and data structures." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170961.

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Tesis para optar al grado de Doctor en Ciencias, Mención Computación
Los refinamientos actuales del análisis del peor caso sobre instancias con tamaño de entrada fijo consideran el orden de la entrada (por ejemplo, las subsecuencias ordenadas en una secuencia de números y las cadenas poligonales simples en las que puede dividirse una secuencia de puntos) o la estructura de la entrada (por ejemplo, la multiplicidad de los elementos en un multiconjunto y las posiciones relativas entre un conjunto de puntos en el plano), pero nunca, hasta donde sabemos, ambos al mismo tiempo. En esta tesis se proponen nuevas técnicas que combinan soluciones que se aprovechan del orden y la estructura de la entrada en una sola solución sinérgica para ordenar multiconjuntos, y para calcular la eficiencia de Pareto y la envoltura convexa de un conjunto de puntos en el plano. Estas soluciones sinérgicas se aprovechan del orden y la estructura de la entrada de tal forma que asintóticamente superan cualquier solución comparable que se aproveche solo de una de estas características. Como resultados intermedios, se describen y analizan varios algoritmos de mezcla: un algoritmo para mezclar secuencias ordenadas que es óptimo para cada instancia del problema; el primer algoritmo adaptativo para mezclar eficiencias de Pareto; y un algoritmo adaptativo para mezclar envolturas convexas en el plano. Estos tres algoritmos se basan en un paradigma donde las estructuras se dividen antes de ser mezcladas. Este paradigma es conveniente para extenderlo al contexto donde se responden consultas. Karp et al. (1998) describieron estructuras de datos diferidas como estructuras "perezosas" que procesan la entrada gradualmente a medida que responden consultas sobre los datos, trabajando la menor cantidad posible en el peor caso sobre instancias de tamaño fijo y número de consultas fijo. En esta tesis se desarrollan nuevas técnicas para refinar aún más estos resultados y aprovechar al mismo tiempo el orden y la estructura de la entrada y el orden y la estructura de la secuencia de consultas en tres problemas distintos: calcular el rango y la posici\'on de un elemento en un multiconjunto, determinar si un punto está dominado por la eficiencia de Pareto de un conjunto de puntos en el plano y determinar si un punto pertenece a la envoltura convexa de un conjunto de puntos en el plano. Las estructuras de datos diferidas que se obtienen superan todas las soluciones previas que solo se aprovechan de un subconjunto de estas características. Como una extensión natural a los resultados sinérgicos obtenidos en este trabajo para ordenar un multiconjunto, se describen estructuras de datos comprimidas que se aprovechan del orden y la estructura de la entrada para representar un multiconjunto, mientras se responden consultas del rango y la posición de elementos en el multiconjunto.
CONICYT-PCHA/Doctorado Nacional/2013-63130161, y los proyectos CONICYT Fondecyt/Regular nos 1120054 y 1170366

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Tsanakas, Panagiotis D. "Algorithms and data structures for hierarchical image processing." Ohio : Ohio University, 1985. http://www.ohiolink.edu/etd/view.cgi?ohiou1184075678.

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Allen, Sam D. "Algorithms and data structures for three-dimensional packing." Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/12779/.

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Cutting and packing problems are increasingly prevalent in industry. A well utilised freight vehicle will save a business money when delivering goods, as well as reducing the environmental impact, when compared to sending out two lesser-utilised freight vehicles. A cutting machine that generates less wasted material will have a similar effect. Industry reliance on automating these processes and improving productivity is increasing year-on-year. This thesis presents a number of methods for generating high quality solutions for these cutting and packing challenges. It does so in a number of ways. A fast, efficient framework for heuristically generating solutions to large problems is presented, and a method of incrementally improving these solutions over time is implemented and shown to produce even higher packing utilisations. The results from these findings provide the best known results for 28 out of 35 problems from the literature. This framework is analysed and its effectiveness shown over a number of datasets, along with a discussion of its theoretical suitability for higher-dimensional packing problems. A way of automatically generating new heuristics for this framework that can be problem specific, and therefore highly tuned to a given dataset, is then demonstrated and shown to perform well when compared to the expert-designed packing heuristics. Finally some mathematical models which can guarantee the optimality of packings for small datasets are given, and the (in)effectiveness of these techniques discussed. The models are then strengthened and a novel model presented which can handle much larger problems under certain conditions. The thesis finishes with a discussion about the applicability of the different approaches taken to the real-world problems that motivate them.

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Parry-Smith, David John. "Algorithms and data structures for protein sequence analysis." Thesis, University of Leeds, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277404.

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Books on the topic "Data structures and algorithms for data management"

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Gabrini, Philippe J. Data structures and algorithms with Modula-2. Boston: Jones and Bartlett Publishers, 1997.

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L, Kurtz Barry, ed. Data structures and algorithms, with Modula-2. Lexington, Mass: D.C. Heath, 1992.

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Efficient structures for geometric data management. Berlin: Springer-Verlag, 1988.

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Harbron, Thomas R. File systems: Structures and algorithms. London: Prentice-Hall International, 1988.

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Jacquemain, Karl Josef. Effiziente Datenstrukturen und Algorithmen für mehrdimensionale Suchprobleme. Heidelberg: A. Hüthig, 1988.

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Logunova, Oksana, Petr Romanov, and Elena Il'ina. Processing of experimental data on a computer. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1064882.

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The textbook provides information about the main methods and tools for automating computational processes used in data processing; methods for representing and generating models of experimental data; data models and classification of processing tasks; and the organization of the user interface in automated systems for processing experimental data. Contains structured chapters on the specifics of experimental research. The features of using software for processing experimental data are clearly and logically described. Theoretical material and basic algorithms for processing experimental data used in industrial statistics are presented. Examples of processing experimental data in the field of metallurgy and management in higher education are given. Meets the requirements of the Federal state educational standards of higher education of the latest generation. For students and postgraduates of higher educational institutions.

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V, Goldberg Andrew, and Zhou Yunhong, eds. Algorithmic aspects in information and management: 5th international conference, AAIM 2009, San Francisco, CA, USA, June 15-17, 2009 : proceedings. Berlin: Springer, 2009.

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WALCOM 2010 (2010 Dhaka, Bangladesh). WALCOM: Algorithms and computation : 4th international workshop, WALCOM 2010, Dhaka, Bangladesh, February 10-12, 2010 : proceedings. Berlin: Springer, 2010.

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Atallah, Mikhail. Frontiers in Algorithmics and Algorithmic Aspects in Information and Management: Joint International Conference, FAW-AAIM 2011, Jinhua, China, May 28-31, 2011. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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Lewis, Harry R. Data structures & their algorithms. New York, NY: HarperCollins Publishers, 1991.

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Book chapters on the topic "Data structures and algorithms for data management"

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Bae, Sammie. "JavaScript Memory Management." In JavaScript Data Structures and Algorithms, 89–97. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3988-9_7.

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Drew, John H., Diane L. Evans, Andrew G. Glen, and Lawrence M. Leemis. "Data Structures and Simple Algorithms." In International Series in Operations Research & Management Science, 33–45. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43323-3_3.

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Drew, John H., Diane L. Evans, Andrew G. Glen, and Lawrence M. Leemis. "Data Structures and Simple Algorithms." In International Series in Operations Research & Management Science, 89–109. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43323-3_7.

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Fischer, M., F. Meyer auf der Heide, and W. B. Strothmann. "Dynamic data structures for realtime management of large geometric scenes." In Algorithms — ESA '97, 157–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63397-9_13.

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Sioutas, Spyros, Gerasimos Vonitsanos, Nikolaos Zacharatos, and Christos Zaroliagis. "Scalable and Hierarchical Distributed Data Structures for Efficient Big Data Management." In Algorithmic Aspects of Cloud Computing, 122–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58628-7_8.

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Nothnagel, K., A. Paul, and G. Sachs. "Adaptive Data Structures and Algorithms for Efficient Visualization and Data Management at Runtime of Terrain and Feature Data." In Lecture Notes in Computational Science and Engineering, 297–304. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-55919-8_33.

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Wang, Qing, Hongzhi Wang, Hong Gao, and Jianzhong Li. "Compression Algorithms for Structural Query Results on XML Data." In Web-Age Information Management, 141–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16720-1_15.

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Shruthi, J., and Suma Swamy. "Simplified Framework of Natural Language Processing for Structure Management of Current-Age Data." In Intelligent Algorithms in Software Engineering, 35–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51965-0_3.

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Henrich, Andreas. "A Relaxed Algorithm for Similarity Queries Performed with High-Dimensional Access Structures." In XML-Based Data Management and Multimedia Engineering — EDBT 2002 Workshops, 376–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36128-6_22.

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Kågström, Bo. "Management of Deep Memory Hierarchies – Recursive Blocked Algorithms and Hybrid Data Structures for Dense Matrix Computations." In Applied Parallel Computing. State of the Art in Scientific Computing, 21–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11558958_3.

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Conference papers on the topic "Data structures and algorithms for data management"

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Yi, Ke. "Session details: Session 6: Algorithms and Data Structures." In SIGMOD/PODS'16: International Conference on Management of Data. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3252643.

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Geerts, Floris. "Session details: PODS Session 2: Algorithms, Data Structures, Benchmarking." In SIGMOD/PODS'17: International Conference on Management of Data. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3247751.

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Cohen, Nachshon, and Erez Petrank. "Efficient Memory Management for Lock-Free Data Structures with Optimistic Access." In SPAA '15: 27th ACM Symposium on Parallelism in Algorithms and Architectures. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2755573.2755579.

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Agarwal, Pankaj K., Kyle Fox, Kamesh Munagala, and Abhinandan Nath. "Parallel Algorithms for Constructing Range and Nearest-Neighbor Searching Data Structures." In SIGMOD/PODS'16: International Conference on Management of Data. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2902251.2902303.

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Kropf, Carsten. "Towards Efficient Reorganisation Algorithms of Hybrid Index Structures Supporting Multimedia Search Conditions." In 3rd International Conference on Data Management Technologies and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004996302310242.

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Guo, Demi. "Learning Algorithms for Automatic Data Structure Design." In SIGMOD/PODS '21: International Conference on Management of Data. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3448016.3450570.

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Frisch, Jerome, Ralf-Peter Mundani, and Ernst Rank. "Adaptive Distributed Data Structure Management for Parallel CFD Applications." In 2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2013. http://dx.doi.org/10.1109/synasc.2013.73.

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Fan, Ce. "A Versatile Data Structure Schema and Algorithms Based on Edge-Symmetry." In 2010 International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII). IEEE, 2010. http://dx.doi.org/10.1109/iciii.2010.289.

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Lei, Ningrong, and Seung Ki Moon. "Decision Support Systems Design for Data-Driven Management." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34871.

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This article discusses a design methodology for a Decision Support System (DSS) in the area of Data-Driven Management (DDM). We partition the DSS into an offline and an online system. Through rigorous testing, the offline system finds the best combination of Data Mining (DM) and Artificial Intelligence (AI) algorithms. Only the best algorithms are used in the online system to extract information from data and to make sense of this information by providing an objective second opinion on a decision result. To support the proposed design methodology, we construct a DSS that uses DM methods for market segmentation and AI methods for product positioning. As part of the offline system construction, we evaluate four intrinsic dimension estimation, three dimension reduction and four clustering algorithms. The performance is evaluated with statistical methods, silhouette mean and 10-fold stratified cross validated classification accuracy. We find that every DSS problem requires us to search a suitable algorithm structure, because different algorithms, for the same task, have different merits and shortcomings and it is impossible to know a priory which combination of algorithms gives the best results. Therefore, to select the best algorithms is empirical science where the possible combinations are tested. With this study, we deliver a blueprint on how to construct a DSS for product positioning. The proposed design methodology can be easily adopted to serve in a wide range of DDM problems.

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Feng, Guanghui, Rui Ma, Zhiping Zeng, and Yanchun Liang. "Construction and Applications on Open Online Course of Data Structure and Algorithms." In 2020 International Conference on Modern Education and Information Management (ICMEIM). IEEE, 2020. http://dx.doi.org/10.1109/icmeim51375.2020.00118.

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Reports on the topic "Data structures and algorithms for data management"

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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, May 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.

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Bajaj, Chandrajit L. Efficient Algorithms and Data Structures in Geometric Design. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada295445.

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Portier, Rebecca W., Richard D. Peacock, and Paule A. Reneke. Data structures for the fire data management system, FDMS 2.0. Gaithersburg, MD: National Institute of Standards and Technology, 1997. http://dx.doi.org/10.6028/nist.ir.6088.

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Gordon, Henry G. ADA, Object-Oriented Techniques, and Concurrency in Teaching Data Structures and File Management. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada275385.

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Gusfield, D., E. L. Lawler, K. Balasubramanian, W. I. Chang, A. Cobbs, J. Kececioglu, M. Kriguer, J. Lampe, and D. Naor. Efficient algorithms and data structures in support of DNA mapping and sequence analysis. Progress report, February 1991--February 1992. Office of Scientific and Technical Information (OSTI), April 1992. http://dx.doi.org/10.2172/10134015.

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Robert A. van Engelen. Creating a New Generation of Software Development Environments, Compilers, and Algorithms for High-Performance Computing, Networks, and Data Management. Office of Scientific and Technical Information (OSTI), January 2007. http://dx.doi.org/10.2172/907755.

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Heiser, G., K. Hinrichs, A. Meier, and J. Nievergelt. Proceedings of the Course on Algorithms and Data Structures for Geometric Computations Held at CISM (Centre International des Sciences Mecaniques) in Udine (Italy) on 8-12 July 1985,. Fort Belvoir, VA: Defense Technical Information Center, July 1985. http://dx.doi.org/10.21236/ada159513.

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Brinkerhoff, Derick W., Sarah Frazer, and Lisa McGregor-Mirghani. Adapting to Learn and Learning to Adapt: Practical Insights from International Development Projects. RTI Press, January 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0015.1801.

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Adaptive programming and management principles focused on learning, experimentation, and evidence-based decision making are gaining traction with donor agencies and implementing partners in international development. Adaptation calls for using learning to inform adjustments during project implementation. This requires information gathering methods that promote reflection, learning, and adaption, beyond reporting on pre-specified data. A focus on adaptation changes traditional thinking about program cycle. It both erases the boundaries between design, implementation, and evaluation and reframes thinking to consider the complexity of development problems and nonlinear change pathways.Supportive management structures and processes are crucial for fostering adaptive management. Implementers and donors are experimenting with how procurement, contracting, work planning, and reporting can be modified to foster adaptive programming. Well-designed monitoring, evaluation, and learning systems can go beyond meeting accountability and reporting requirements to produce data and learning for evidence-based decision making and adaptive management. It is important to continue experimenting and learning to integrate adaptive programming and management into the operational policies and practices of donor agencies, country partners, and implementers. We need to devote ongoing effort to build the evidence base for the contributions of adaptive management to achieving international development results.

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