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Littérature scientifique sur le sujet « Data Cube Technology »

Auteur : Grafiati

Publié le 27 juillet 2024

Mis à jour le 28 juillet 2024

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Articles de revues sur le sujet "Data Cube Technology"

Huang, Xinyuan, Xiaoming Gao, Sida Ouyang, and Zhengbo Fu. "Image Data Stream Organization and Online Analysis Application Based on Data Cube Technology." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 271–76. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-271-2024.

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Abstract. This study aims to explore the important role of data-like cube structures in modern remote sensing data processing and data analysis through ArcPy and Python multiprocessing techniques. A multi-scale spatial data cube is innovatively developed to improve the efficiency of remote sensing data management and optimize data analysis. The core of this study is to define and implement grid cells of different sizes that form the basis of data cube, and to quantify the efficient coverage of specific areas using Python multiprocessing techniques. Experiments were conducted in Hainan Province, and efficient data coverage of the whole Hainan Province was realized using the grid data method, which significantly reduced the amount of remote sensing data and processing time required. This shows that the method has successfully improving data coverage capacity and utilization efficiency. The results of this study not only demonstrate the effective application of data-like cubes in remote sensing data processing and analysis, but also provide new perspectives and methods for future complex spatial data analysis and large-scale remote sensing data processing.

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Picoli, M. C. A., R. Simoes, M. Chaves, et al. "CBERS DATA CUBE: A POWERFUL TECHNOLOGY FOR MAPPING AND MONITORING BRAZILIAN BIOMES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 533–39. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-533-2020.

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Abstract. Currently, the overwhelming amount of Earth Observation data demands new solutions regarding processing and storage. To reduce the amount of time spent in searching, downloading and pre-processing data, the remote Sensing community is coming to an agreement on the minimum amount of corrections satellite images must convey in order to reach the broadest range of applications. Satellite imagery meeting such criteria (which usually include atmospheric, radiometric and topographic corrections) are generically called Analysis Ready Data (ARD). Furthermore, ARD is being assembled into multidimensional data cubes, minimising preprocessing tasks and allowing scientists and users in general to focus on analysis. A particular instance of this is the Brazil Data Cube (BDC) project, which is processing remote sensing images of medium spatial resolution into ARD datasets and assembling them as multidimensional cubes of the Brazilian territory. For example, BDC users are released from performing tasks such as image co-registration , aerosol interference correction. This work presents a BDC proof of concept, by analysing a BDC data cube made with images from the fourth China-Brazil Earth Resources Satellite (CBERS-4) of one of the largest biodiversity hotspot in the world, the Cerrado biome. It also shows how to map and monitor land use and land cover using the CBERS data cube. We demonstrate that the CBERS data cube is effective in resolving land use and and land cover issues to meet local and national needs related to the landscape dynamics, including deforestation, carbon emissions, and public policies.

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Chang, Chih-Kai, Gwo-Dong Chen, and Kou-Liang Ou. "Student Portfolio Analysis by Data Cube Technology for Decision Support of Web-Based Classroom Teacher." Journal of Educational Computing Research 19, no. 3 (1998): 307–28. http://dx.doi.org/10.2190/k6x6-9fmd-yeen-kn42.

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As learners increasingly use Web-based distance learning systems over years, large amounts of learning logs are generated. An instructor needs analysis tools to manage the logs and discover patterns within them to help improve instruction. A variety of analysis tools, including descriptive statistics, statistical inference, prediction, etc., can be utilized to analyze the effects of a teaching strategy from the logs. However, logs of a Web server, as learners' portfolios, cannot satisfy the requirements of these analysis tools. To resolve this problem, a data cube model is proposed as the infrastructure to store learning logs for analysis. We also describe the method of using query language to retrieve information from a database to construct the data cube. Furthermore, user-friendly operations for manipulating a data cube can retrieve statistical information from the data cube. Although statistical tools for managing Web logs exist, none specifically address the needs of the distance learning instructor. This article uses data cubes and database technology as fundamental analysis tools to satisfy a distance learning instructor's requirements for managing and analyzing learning logs.

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Lamani, Asma, Brahim Erraha, Malika Elkyal, and Abdallah Sair. "Data mining techniques application for prediction in OLAP cube." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 2094. http://dx.doi.org/10.11591/ijece.v9i3.pp2094-2102.

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Data warehouses represent collections of data organized to support a process of decision support, and provide an appropriate solution for managing large volumes of data. OLAP online analytics is a technology that complements data warehouses to make data usable and understandable by users, by providing tools for visualization, exploration, and navigation of data-cubes. On the other hand, data mining allows the extraction of knowledge from data with different methods of description, classification, explanation and prediction. As part of this work, we propose new ways to improve existing approaches in the process of decision support. In the continuity of the work treating the coupling between the online analysis and data mining to integrate prediction into OLAP, an approach based on automatic learning with Clustering is proposed in order to partition an initial data cube into dense sub-cubes that could serve as a learning set to build a prediction model. The technique of data mining by regression trees is then applied for each sub-cube to predict the value of a cell.

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Nabibayova, Gulnara. "Expanding the intellectual capabilities of OLAP technology using neural networks." Problems of Information Society 15, no. 2 (2024): 43–48. http://dx.doi.org/10.25045/jpis.v15.i2.05.

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The article highlights the main characteristics, features and structure of Online Analytical Processing systems based on the same technology that perform online analytical processing of data. This technology allows analysts to explore and navigate a multidimensional indicator structure called an online analytical processing cube (data cube). Indicators (measures) of data cube play an important role in the decision-making process. To solve certain problems, these measures often need to be classified or grouped. Moreover, empty measures are common in data cube. This fact negatively affects strategic decision making. Unfortunately, online analytical processing itself is not well suited for classifying, clustering, and predicting empty measures of data cube in the presence of large data. In this regard, today there is a need to use new technologies to solve such problems. Such technologies include neural networks. The article discusses the problem of integrating online analytical processing and a neural network, showing the possibilities and advantages of such integration. It mentions that in the case of big data, the integration of OLAP and neural networks is very effective in solving problems of classification, clustering and empty measure prediction of data cube. An architectural and technological model for the integration of online analytical processing and neural networks is presented.

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Tardío, Roberto, Alejandro Maté, and Juan Trujillo. "A New Big Data Benchmark for OLAP Cube Design Using Data Pre-Aggregation Techniques." Applied Sciences 10, no. 23 (2020): 8674. http://dx.doi.org/10.3390/app10238674.

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In recent years, several new technologies have enabled OLAP processing over Big Data sources. Among these technologies, we highlight those that allow data pre-aggregation because of their demonstrated performance in data querying. This is the case of Apache Kylin, a Hadoop based technology that supports sub-second queries over fact tables with billions of rows combined with ultra high cardinality dimensions. However, taking advantage of data pre-aggregation techniques to designing analytic models for Big Data OLAP is not a trivial task. It requires very advanced knowledge of the underlying technologies and user querying patterns. A wrong design of the OLAP cube alters significantly several key performance metrics, including: (i) the analytic capabilities of the cube (time and ability to provide an answer to a query), (ii) size of the OLAP cube, and (iii) time required to build the OLAP cube. Therefore, in this paper we (i) propose a benchmark to aid Big Data OLAP designers to choose the most suitable cube design for their goals, (ii) we identify and describe the main requirements and trade-offs for effectively designing a Big Data OLAP cube taking advantage of data pre-aggregation techniques, and (iii) we validate our benchmark in a case study.

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Kopp, Steve, Peter Becker, Abhijit Doshi, Dawn J. Wright, Kaixi Zhang, and Hong Xu. "Achieving the Full Vision of Earth Observation Data Cubes." Data 4, no. 3 (2019): 94. http://dx.doi.org/10.3390/data4030094.

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Earth observation imagery have traditionally been expensive, difficult to find and access, and required specialized skills and software to transform imagery into actionable information. This has limited adoption by the broader science community. Changes in cost of imagery and changes in computing technology over the last decade have enabled a new approach for how to organize, analyze, and share Earth observation imagery, broadly referred to as a data cube. The vision and promise of image data cubes is to lower these hurdles and expand the user community by making analysis ready data readily accessible and providing modern approaches to more easily analyze and visualize the data, empowering a larger community of users to improve their knowledge of place and make better informed decisions. Image data cubes are large collections of temporal, multivariate datasets typically consisting of analysis ready multispectral Earth observation data. Several flavors and variations of data cubes have emerged. To simplify access for end users we developed a flexible approach supporting multiple data cube styles, referencing images in their existing structure and storage location, enabling fast access, visualization, and analysis from a wide variety of web and desktop applications. We provide here an overview of that approach and three case studies.

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Errattahi, Rahhal, Mohammed Fakir, and Fatima Zahra Salmam. "Explanation in OLAP Data Cubes." Journal of Information Technology Research 7, no. 4 (2014): 63–78. http://dx.doi.org/10.4018/jitr.2014100105.

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OLAP is an important technology that offers a fast and interactive data navigation, it also provides tools to explore data cubes in order to extract interesting information from a multidimensional data structures. However, the OLAP exploration is done manually, without tools that could automatically extract relevant information from the cube. In addition OLAP is not capable of explaining relationships that could exist within data. This paper presents a new approach to coupling between data mining and online analytical processing. Its approach provides the explanation in OLAP data cubes by using the association rules between the inter-dimensional predicates. The mining process could be done by one of the two algorithms, Apriori and Fp-Growth, in which aggregate measures to calculate support and confidence are exploited. It also evaluates the interestingness of mined association rules according to the Lift criteria.

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Jabbar, Ayad. "Design and Develop an Information system for Court Data in the Republic of Iraq by using SSRS Reports with SSAS Cubes." Iraqi Journal for Electrical and Electronic Engineering 11, no. 1 (2015): 105–9. http://dx.doi.org/10.37917/ijeee.11.1.11.

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Multidimensional Online analytical processing (MOLAP) technology is considered a good tool to produce meaningful and quality results by using a multidimensional cube. The term “multidimensional cube” is used to refer to the multiple layers of data that are used to show the result. This result is identified by high-level management to increase the Iraqi court work and to improve its quality. The Iraqi court needs an analytical report to make a strategic decision on case date, case type, case state, judge, criminal age, and criminal gender. Currently, MOLAP is known as the best and strongest technique because it provides rapid, dynamic, and multiple analyses of data; presents knowledge from different perspectives; comes up with data in time series intervals; and drills down into multiple levels of data layers to present different types of details. The SQL Server Reporting Service (SSRS) presents analysis reports based on the MOLAP cube. This paper focuses on designing and developing the analysis reports of the court data system of the Republic of Iraq by using SSRS with SQL Server Analysis Service to create the MOLAP cubes.

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Ding, Jun Shuai, Jin Guo You, and Shui Li Zhou. "Closed Cube Algorithm Research for Distributed Storage." Applied Mechanics and Materials 433-435 (October 2013): 1724–29. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1724.

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Data cube is to improve the efficiency of an important means in data query, while the enclosed data cube used by eliminating non-enclosed data cube tuples to reduce the disk space and improve the query response time. Based on the MapReduce parallel processing technology research, the MapReduce-based closed data cube generation algorithm is proposed,which can effectively improve the closed data cube massive data generation rate. The simulation results show that the proposed MCDC algorithm in practical applications can offer conditional definition by users to build closed data cube that is needed. Parallel processing capability of distribution system has been fully developed.

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Thèses sur le sujet "Data Cube Technology"

Fu, Lixin. "CubiST++ a new approach to improving the performance of ad-hoc cube queries /." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/ank7110/masterfinal0.pdf.

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Thesis (Ph. D.)--University of Florida, 2001.<br>Title from first page of PDF file. Document formatted into pages; contains x, 100 p.; also contains graphics. Vita. Includes bibliographical references (p. 95-99).

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Ahmed, Usman. "Dynamic cubing for hierarchical multidimensional data space." Phd thesis, INSA de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-00876624.

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Data warehouses are being used in many applications since quite a long time. Traditionally, new data in these warehouses is loaded through offline bulk updates which implies that latest data is not always available for analysis. This, however, is not acceptable in many modern applications (such as intelligent building, smart grid etc.) that require the latest data for decision making. These modern applications necessitate real-time fast atomic integration of incoming facts in data warehouse. Moreover, the data defining the analysis dimensions, stored in dimension tables of these warehouses, also needs to be updated in real-time, in case of any change. In this thesis, such real-time data warehouses are defined as dynamic data warehouses. We propose a data model for these dynamic data warehouses and present the concept of Hierarchical Hybrid Multidimensional Data Space (HHMDS) which constitutes of both ordered and non-ordered hierarchical dimensions. The axes of the data space are non-ordered which help their dynamic evolution without any need of reordering. We define a data grouping structure, called Minimum Bounding Space (MBS), that helps efficient data partitioning of data in the space. Various operators, relations and metrics are defined which are used for the optimization of these data partitions and the analogies among classical OLAP concepts and the HHMDS are defined. We propose efficient algorithms to store summarized or detailed data, in form of MBS, in a tree structure called DyTree. Algorithms for OLAP queries over the DyTree are also detailed. The nodes of DyTree, holding MBS with associated aggregated measure values, represent materialized sections of cuboids and tree as a whole is a partially materialized and indexed data cube which is maintained using online atomic incremental updates. We propose a methodology to experimentally evaluate partial data cubing techniques and a prototype implementing this methodology is developed. The prototype lets us experimentally evaluate and simulate the structure and performance of the DyTree against other solutions. An extensive study is conducted using this prototype which shows that the DyTree is an efficient and effective partial data cubing solution for a dynamic data warehousing environment.

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Unal, Calargun Seda. "Fuzzy Association Rule Mining From Spatio-temporal Data: An Analysis Of Meteorological Data In Turkey." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609308/index.pdf.

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Data mining is the extraction of interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Association rule mining is a data mining method that seeks to discover associations among transactions encoded within a database. Data mining on spatio-temporal data takes into consideration the dynamics of spatially extended systems for which large amounts of spatial data exist, given that all real world spatial data exists in some temporal context. We need fuzzy sets in mining association rules from spatio-temporal databases since fuzzy sets handle the numerical data better by softening the sharp boundaries of data which models the uncertainty embedded in the meaning of data. In this thesis, fuzzy association rule mining is performed on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed for fuzzy spatio-temporal data cube construction. Besides the performance criteria interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined to be the metrics for the comparison of association rule mining techniques. Fuzzy association rule mining using spatio-temporal data cubes and Apriori algorithm performed within the scope of this thesis are compared using these metrics. Real meteorological data (precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed using data cube and Apriori algorithm in order to generate the fuzzy association rules.

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Bačík, Ladislav. "Využití GPU pro akcelerované zpracování obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235888.

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This master thesis deals with modern technologies in graphic hardware and using their for general purpose computing. It is primary focused on architecture of unified processors and algorithm implementation via CUDA programming interface. Thesis base is to choose suited algorithm for GPU horsepower demonstration. Main aim of this work is implementation of multiplatform library offering algorithms for discrete volumetric data vectorization. For this purpose was chosen algorithm Marching cubes that is able to find surface of processed object. In created library will be contained algorithm runnable on graphic device and also one runnable on CPU. Finally we compare both variants and discuss their pros and cons.

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Livres sur le sujet "Data Cube Technology"

Alberto, Ferrari, Russo Marco, and ebrary Inc, eds. Expert cube development with Microsoft SQL Server 2008 Analysis Services: Design and implement fast, scalable, and maintainable cubes. Packt Publishing, 2009.

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“Data Mining Concepts & Techniques”. 3rd ed. Morgan Kaufmann Publishers, 2011.

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Wacks, Raymond. 6. The death of privacy? Oxford University Press, 2015. http://dx.doi.org/10.1093/actrade/9780198725947.003.0006.

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Privacy is under attack from several quarters. The ‘war on terror’ has amplified this pressure. The Internet, increased surveillance, and sensationalist journalism seriously undermine individuals’ control over their private lives. Many advocates believe that the protection of privacy stands in need of urgent renewal. Has the Internet sounded privacy’s death knell? The rapid advance of information technology, especially the Internet, has generated widespread concern about protection of personal data, with many jurisdictions adopting data protection legislation. Ironically, technology generates both the malady and part of the cure. While the law is rarely an effective tool against the dedicated intruder, advances in protective software, along with fair information practices of the European Directive and laws of several jurisdictions, afford a rational and sound normative framework for the collection, use, and transfer of personal data. Some of these questions—likely to dominate 21st-century discussions of privacy—are considered in this concluding chapter.

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Chapitres de livres sur le sujet "Data Cube Technology"

Geffner, Steven, Divakant Agrawal, and Amr El Abbadi. "The Dynamic Data Cube." In Advances in Database Technology — EDBT 2000. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-46439-5_17.

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Liu, Wu, Donghong Sun, Ping Ren, and Haibin Xiong. "Path-Calculation-Based XML Data Cube Model." In Convergence and Hybrid Information Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32645-5_68.

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Saxena, Vipin, Vishal Verma, Versha Verma, and Karm Veer Singh. "Data Cube Technology for Accessing of Large Database." In Proceedings of Fifth International Conference on Computer and Communication Technologies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9704-6_4.

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Shukla, Amit, Prasad M. Deshpande, and Jeffrey F. Naughton. "Materialized View Selection for Multi-cube Data Models." In Advances in Database Technology — EDBT 2000. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-46439-5_19.

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Cuzzocrea, Alfredo. "OLAP Data Cube Compression Techniques: A Ten-Year-Long History." In Future Generation Information Technology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17569-5_74.

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Kaur, Gursimran, and Harkiran Kaur. "Descriptive Data Analysis of Real Estate Using Cube Technology." In Emerging Research in Computing, Information, Communication and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6001-5_18.

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Tan, Qinwen, and Huiming Tang. "In Situ Triaxial Creep Test on Gravelly Slip Zone Soil of a Giant Landslide: Innovative Attempts and Findings." In Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18471-0_9.

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AbstractIn recent years, numerous landslides catastrophes have occurred, generating considerable financial losses and other tolls. The deformational and mechanical properties of sliding zone soil would be of primary significance to landslide research, as the sliding zone basically controls the initiation and mobility of the landslide. An in situ triaxial test was carried out on a sample from the sliding zone of the Riverside Slump 1# landslide, a subdivision of the Huangtupo landslide in the Three Gorges area of China. The test results indicate that a. the sliding zone exhibits low compressibility due to the high rock content (54.3%) and long-time consolidation by the overlying soil mass; b. only decaying creep occurs without abrupt failure, and a constitutive equation with both linear and nonlinear viscoplastic terms is deduced to accurately fit the test data; c. the surface with an orientation of 35° presents anisotropic traits in terms of displacement, possibly due to cracks that formed at similar orientations within the sample cube; and d. the creep behavior of the landslide may be closely related to the properties of the sliding zone soil. When a similar stress magnitude to that of the in situ stress environment is applied to the sample, the sliding zone soil behavior matches the landslide behavior. The test results indicate that the Huangtupo 1# landslide will continue to creep, as interpreted from the deformation and properties of the sample. However, unavoidable limitations of the test and extreme external factors, namely, unexpected rainfall and water fluctuation, cannot be ignored.

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Ahlstrøm, Kim, Katja Hose, and Torben Bach Pedersen. "Towards Answering Provenance-Enabled SPARQL Queries Over RDF Data Cubes." In Semantic Technology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50112-3_14.

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Saidi, Abdessamad, Mohamed Hadj Kacem, Imen Tounsi, and Ahmed Hadj Kacem. "Adopting the Internet of Things Technology to Remotely Monitor COVID-19 Patients." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09593-1_13.

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AbstractThe coronavirus known as COVID-19 is the topic of the hour all over the world. This virus has invaded the world with its invariants, which are characterized by their rapid spread. COVID-19 has impacted the health of people and the economy of countries. For that, laboratories, researchers, and doctors are in a race against time to find a cure for this pandemic. To combat this virus, cutting-edge technologies such as artificial intelligence, cloud computing, and big data have been put in place. In our work, we use Internet of Things (IoT) technology. The use of IoT in an efficient way can lead to detecting infected people and avoiding being contaminated. In this paper, we are interested in the remote medical monitoring of patients who have tested positive for COVID-19. We propose a meta-modeling technique to model the IoT architecture. Then we implement two IoT solutions that permit the remote medical monitoring of patients infected with COVID-19 and the respect of social distancing by instantiating correct models that conform to the proposed meta-model in order to mitigate the COVID-19 outbreak.

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Han, Jiawei, Micheline Kamber, and Jian Pei. "Data Cube Technology." In Data Mining. Elsevier, 2012. http://dx.doi.org/10.1016/b978-0-12-381479-1.00005-8.

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Actes de conférences sur le sujet "Data Cube Technology"

Godase, Abhijeet, and Vahida Attar. "Classification of data streams with skewed distribution." In the CUBE International Information Technology Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2381769.

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Nagpal, Sushama, Anjana Gosain, and Sangeeta Sabharwal. "Complexity metric for multidimensional models for data warehouse." In the CUBE International Information Technology Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2381784.

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Rajesh, P., G. Narisimha, and Ch Rupa. "Fuzzy based privacy preserving classification of data streams." In the CUBE International Information Technology Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2381865.

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Dutta, Prasun, and Rituparna Chaki. "A survey of data mining applications in water quality management." In the CUBE International Information Technology Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2381805.

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Viswanadham, Sangeeta, and Vatsavayi Valli Kumari. "An efficient approach to identify n-wMVD for eliminating data redundancy." In the CUBE International Information Technology Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2381804.

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Bhange, Gauri S., and Swapnaja R. Hiray. "Dental patient records maintenance on cloud computing using data protection model." In the CUBE International Information Technology Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2381817.

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Deshpande, Vivek, Jagdish Helonde, Vijay Wadhai, Vidyasagar Potdar, and A. J. Dinusha Rathnayaka. "Handling the Quality of Service for the data dissemination in Wireless Sensor Networks." In the CUBE International Information Technology Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2387981.

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priezzhev, I. I., D. A. Danko, and E. E. Taikulakov. "Technology for Predicting the Lithology Cube Using Kolmogorov Neural Networks." In Data Science in Oil and Gas 2021. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202156007.

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Ying Yin, Bin Zhang, Xizhe Zhang, and Yuhai Zhao. "Research on Data Cube technology of Dwarf based semantic OLAP." In 2009 International Conference on Future BioMedical Information Engineering (FBIE). IEEE, 2009. http://dx.doi.org/10.1109/fbie.2009.5405777.

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Schelkens, Peter, Joeri Barbarien, and Jan P. Cornelis. "Compression of volumetric medical data based on cube splitting." In International Symposium on Optical Science and Technology, edited by Andrew G. Tescher. SPIE, 2000. http://dx.doi.org/10.1117/12.411534.

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Rapports d'organisations sur le sujet "Data Cube Technology"

Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.

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