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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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Cao, Qianqian, Guoqing Li, Xiaochuang Yao, and Yue Ma. "China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned." Information 13, no. 9 (2022): 407. http://dx.doi.org/10.3390/info13090407.
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In the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and use earth observation data, and provides an analysis-ready form to access big spatiotemporal data, so as to realize the greater potential of earth observation data. Based on the Open Data Cube (ODC) framework, combined with analysis-ready data (ARD) generation technology, the design and implementation of CDC_DLTool, extending the support for data loading and the processing of international and Chinese imagery data covering China, this study eventually constructs the China Data Cube (CDC) framework. In the framework of this CDC grid, this study carried out case studies of water change monitoring based on international satellite imagery data of Landsat 8 in addition to vegetation change monitoring based on Chinese satellite imagery data of GF-1. The experimental results show that, compared with traditional scene-based data organization, the minimum management unit of this framework is a pixel, which makes the unified organization and management of multisource heterogeneous satellite imagery data more convenient and faster.
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Ding, Heng, Qian Yuan Zhang, Yan Ping Wang, and Yan Jiang. "Data Warehousing and Data Mining Technology Implementation in Jpeen_CRM System Design." Applied Mechanics and Materials 610 (August 2014): 769–74. http://dx.doi.org/10.4028/www.scientific.net/amm.610.769.
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In this paper, on the basis of in-depth study of data warehouse, OLAP, data mining and other key technologies, according to the characteristics of the Jpeen customer relationship management (Jpeen_CRM), developed a Web-based customer relationship management system. First of all, research the demand for Jpeen company and create Jpeen_CRM data warehouse. Second, create the OLAP multidimensional cube which is applied to the analysis of customer transactions. Using the decision tree algorithm to create mining models for customers to choose glasses, and gives pruning optimize decision tree. Using decision tree to classify users and recommend them the hot products they prefer. Finally, realize the Jpeen_CRM on J2EE platform, provide a decision support for Jpeen company to better serve customers and make the company itself have the advantages in a competitive environment.
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Hamdani, Younes, Guohui Xiao, Linfang Ding, and Diego Calvanese. "An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs." ISPRS International Journal of Geo-Information 12, no. 9 (2023): 375. http://dx.doi.org/10.3390/ijgi12090375.
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The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial data and linking the raster data cube to semantic technology standards. Many recent approaches have been attempted to address this issue, but they often lack robust formal elaboration or solely concentrate on integrating raster data cubes without considering the inclusion of semantic spatial entities along with their spatial relationships. This may constitute a major shortcoming when it comes to performing advanced geospatial queries and semantically enriching geospatial models. In this paper, we propose a framework that can enable such semantic integration and advanced querying of raster data cubes based on the virtual knowledge graph (VKG) paradigm. This framework defines a semantic representation model for raster data cubes that extends the GeoSPARQL ontology. With such a model, we can combine the semantics of raster data cubes with features-based models that involve geometries as well as spatial and topological relationships. This could allow us to formulate spatiotemporal queries using SPARQL in a natural way by using ontological concepts at an appropriate level of abstraction. We propose an implementation of the proposed framework based on a VKG system architecture. In addition, we perform an experimental evaluation to compare our framework with other existing systems in terms of performance and scalability. Finally, we show the potential and the limitations of our implementation and we discuss several possible future works.
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Cristescu, Marian Pompiliu. "Using OLAP Data Cubes in Business Intelligence." Scientific Bulletin 21, no. 2 (2016): 80–86. http://dx.doi.org/10.1515/bsaft-2016-0039.
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Abstract The purpose of this paper is to demonstrate that it is possible to develop business intelligence projects in big and medium-size organizations, only with Microsoft products, used in accordance with standard OLAP cube technology, and presented possible alternatives, in relation with the requested functions.
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Raharjo, Sigit, Astrino Purmanna, Kus Andini Purbaningrum, Riche Ramayanti, and Dea Mei Cahya Muhti. "Pengembangan Media Pembelajaran Berbasis Software Geogebra pada Kubus untuk Mengembangkan Kemampuan Spasial Siswa." MAJAMATH: Jurnal Matematika dan Pendidikan Matematika 6, no. 1 (2023): 57–68. http://dx.doi.org/10.36815/majamath.v6i1.2560.
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The development of science and technology affects the emergence of interactive learning media. Interactive learning media is expected to be able to foster students’ learning motivation and spatial abilities. This study aimed to develop learning media based on geogebra software on cubes to deveop students’ spatial ability. This study applies the ADDIE model (analysis, design, development implementation, evaluation) as a research and development model for creating interactive learning media using geogebra on cube theory. Tha sample in this study amounted to 20 students where these students had not received theory about cubes. The research data was obtained through filling out a questionnaire evaluation test for students’ responses to learning. The quality of geogebra based learning media is related to three criteria, namely validity, practicality, and effectiveness. Based on the results of the research and analysis carried out, the interactive learning media that has been developed meets good validity criteria, can be used practically in learning and is able to grow students’ spatial ability in cube theory
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Hazboun, Fadi H., Majdi Owda, and Amani Yousef Owda. "A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube." AI 2, no. 4 (2021): 720–37. http://dx.doi.org/10.3390/ai2040043.
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Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.
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Lu, Y. L., C. W. Liu, J. W. Li, and J. W. Jiang. "CONSTRUCTION METHOD OF “CELL-CUBE” SPATIO-TEMPORAL DATA MODEL FOR BIG DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 25–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-25-2020.
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Abstract. In recent years, with high accuracy, high frequency, considerable coverage of remote sensing images, map tiles, video surveillance, web crawlers, social networking platforms and other types of spatiotemporal data have exploded in geometric progression. Human society has come into the era of big data in time and space. In view of the characteristics of multi-attribute, multi-dimensional, multisource and heterogeneous spatiotemporal big data, how to make use of the emerging information technology means, combined with the geographic information data analysis means, the rapid mining and utilization of spatiotemporal big data has become a key problem to be solved. Built on the background of spatiotemporal big data and the process of geospatial cognition, this paper proposes a "cell-cube" spatiotemporal object data model. This paper constructs a model system of geo-spatiotemporal big data from the aspects of data organization, data storage and data partition, and abstracts the geo-space into an infinite number of geo-cells, and the adjacent geo-cells gather around the core cells to form geographical clusters, and the geographical clusters with similar attributes are clustered into geographical blocks. At the level of data organization, the spatial and temporal characteristics of structured data and unstructured data are considered as organizational dimensions, and a multi-factor extended cube data model is proposed. In the aspect of data storage, the organization model is further abstracted into the cell-cube structure of distributed data warehouse, and then the spatiotemporal data is stored uniformly. At the level of data segmentation, the mathematical table and space calculation method of multi-feature extended cube are proposed, and the geographical cell data division model based on connection is established. It solves the organization and management problem of spatiotemporal big data, provides a more complete data organization framework and solution for the application of geo-spatiotemporal big data, and promotes the development of deep mining of spatiotemporal extensive data in GIS. And to achieve space-time big data in the geographical space microscopic and the macroscopic unification cognition.
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Jantti, Margie, and Brian Cox. "Measuring the Value of Library Resources and Student Academic Performance through Relational Datasets." Evidence Based Library and Information Practice 8, no. 2 (2013): 163. http://dx.doi.org/10.18438/b8q89f.
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Objective – This article describes a project undertaken by the University of Wollongong Library (UWL) to identify whether a correlation exists between usage of library resources and academic performance.
 
 Methods – A multidimensional approach to systems design was implemented, requiring collaboration between among the library, university administration, Performance Indicator Project team (PIP), and information technology services. The project centers on the integration and interrogation of a series of discrete datasets containing student performance, attrition, demographic, borrowing, and electronic resources usage data. PIP built a cube for the library that links usage of library resources to student demographic data and academic performance (the “Library Cube”). Other cubes will be linked later. 
 
 Results – While initial reports are rudimentary and do not yet incorporate data on e-resource usage, results are favourable in demonstrating the value of using the library information resources in coursework. Based on the data generated to date, students who borrow library resources do outperform students who do not. Early trend data shows up to a 12-point difference in grades.
 
 Conclusion – The Library Cube signals a new milestone in the UWL’s quality assessment journey. Well-established measures of effectiveness and efficiency will be further complemented by measures of impact and value, allowing the library to step even closer to the goal of having effective and valued partnerships with the university community to realize teaching, learning, research, and internalization goals.
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Vik, Shelly, Judy Seidel, Christopher Smith, and Deborah A. Marshall. "Breaking the 80:20 rule in health research using large administrative data sets." Health Informatics Journal 29, no. 2 (2023): 146045822311805. http://dx.doi.org/10.1177/14604582231180581.
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Objective: To explore the application of online analytic processing (OLAP) to improve the efficiency of analytics using large administrative health data sets. Methods: 18 years of administrative health data (1994/95 to 2012/13) were obtained from the Alberta Ministry of Health in Canada. The data sets included hospitalization, ambulatory care and practitioner claims data. Reference files were obtained that provided information including patient demographics, resident postal code, facility, and provider details. Population counts and projections for each year, sex, age were included for rate calculations. These sources were used to develop a data cube using OLAP tools. Results: Time required for analyses was reduced to 5% of that required when comparing run-time for simple queries that did not require linkage of data sets. The data cube negated the need for many intermediary steps for data extraction and analyses for research activities. Conventional methods required over 250 GB of server space for multiple analytic subsets, compared to only 10.3 GB for the data cube. Conclusions: Cross-training in information technology and health analytics is recommended to provide capacity to better leverage OLAP tools which are available with many common applications.
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Jane, Chetan, and Vinay S. Navale. "An In-Memory Data-Cube Aware Distributed Data Discovery Across Clouds for Remote Sensing Big Data." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 2249–56. http://dx.doi.org/10.22214/ijraset.2023.55108.
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Abstract: The recent proliferation of high-resolution Earth observation data, along with the advent of open-data initiatives, has led to the availability of petabyte-scale Earth observation datasets accessible for free. However, this abundance of big data presents significant challenges for regional to global spatio-temporal analysis. The traditional approach of "downloadpreprocess-store-analyze" introduces excessive data downloading overhead and computational barriers, hindering efficient analysis. The Earth observation data cube (EODC) paradigm offers a solution by revolutionizing the storage and management of spatio-temporal RS data. Yet, the presence of multiple EODC solutions has resulted in "information silos," making the sharing and joint use of RS data across EODCs difficult.
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Song, Qiang Wei, Qing Hua Qiao, and Qing Yuan Li. "Rapid Visualization of Global Ship Tracks Data Based on 3D GIS." Applied Mechanics and Materials 229-231 (November 2012): 1759–62. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1759.
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On the basis of the analysis of spatial distribution characteristics of massive ship tracks data, the model of spatiotemporal cube was established and the “3DR-Tree” algorithm was proposed to study the efficient index building technology. The spatiotemporal query of massive ship tracks data was realized and the ship historical trajectories were also displayed in the global environment intuitively and dynamically, it can be used for maritime search and rescue effectively.
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Raditya Vivaldy, Muhammad. "Perancangan Dashboard IT Helpdesk pada PT. XYZ." COMSERVA Indonesian Jurnal of Community Services and Development 2, no. 11 (2023): 2557–67. http://dx.doi.org/10.59141/comserva.v2i11.660.
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The helpdesk dashboard functions to monitor: the number of tickets available, the average handling time for each ticket, module, system, or application that gets the most tickets, the department that submits tickets the most, which is adjusted to the needs of the helpdesk of PT. XYZ. Ticket is a trouble ticket or problem report that is used in an organization to detect, report, and provide solutions to various types of problems. In this thesis, the authors create a data mart using Kimball's nine-step methodology. Before designing the IT helpdesk dashboard, PT. XYZ. will be processed first using data integration tools, namely Pentaho. Helpdesk data will go through a three-phase process, namely (i) extract, (ii) transform, and (iii) load (ETL). The data will then be loaded into the data mart and processed into a data cube. Data cube is a tool that describes technology using a multi-dimensional image of a number of data to provide faster access to strategic information with the aim of speeding up analysis. The data cube will be visualized using an interactive dashboard design tool, namely Grafana.
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D N, Ashwini, and Soumya Dass B. "Role of Data Mining Technique: A Boon to Society." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 657–60. http://dx.doi.org/10.22214/ijraset.2022.43782.
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Abstract: Datamining is a method of finding interested patterns from huge volume of data. Datamining techniques helps to make business decisions. It analyses information from multiple sources like DataMart, databases. In this paper, we are focussing on datamining tasks and its variety of applications in different fields, which is boon to the society. Keywords: KDD, Decision Tree, OLAP servers, Cube API, ODBC, Frequent patterns
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Wardle, Julee, and Zachary Phillips. "Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube." Remote Sensing 16, no. 7 (2024): 1241. http://dx.doi.org/10.3390/rs16071241.
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The Horn of Africa has sensitive, arid ecosystems, with its vegetation commonly distressed by factors such as climate change, population increase, unstable water resources, and rarely enforced land use management practices. These factors make countries such as Djibouti highly variable locations for the growth of vegetation and agricultural products, and these countries are becoming more vulnerable to food insecurity as the climate warms. The rapid growth of satellite and digital image processing technology over the last five decades has improved our ability to track long-term agricultural and vegetation changes. Data cubes are a newer approach to managing satellite imagery and studying temporal patterns. Here, we use the cloud-based Digital Earth Africa, Open Data Cube to analyze 30 years of Landsat imagery and orthomosaics. We analyze long-term trends in vegetation dynamics by comparing annual fractional cover metrics (photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) to the Normalized Difference Vegetation Index. Investigating Djibouti-wide and regional vegetation trends, we provide a comparison of trends between districts and highlight a primary agricultural region in the southeast as a detailed example of vegetation change. The results of the Sen’s slope and Mann–Kendall regression analyses of the data cube suggest a significant decline in vegetation (p = 0.00002), equating to a loss of ~0.09 km2 of arable land per year (roughly 2.7 km2 over the 30-year period). Overall, decreases in photosynthetic vegetation and increases in both non-photosynthetic vegetation and bare soil areas indicate that the region is becoming more arid and that land cover is responding to this trend.
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Soler, L. S., D. E. Silva, C. Messias, et al. "PROMISING ADVANCES OF AMAZONIAN MONITORING SYSTEMS THROUGHOUT VANGUARD TECHNOLOGY AND SCIENTIFIC KNOWLEDGE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 29, 2021): 843–49. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-843-2021.
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Abstract. PRODES and DETER project together turned 33 years-old with an undeniably contribution to the state-of-art in mapping and monitoring tropical deforestation in Brazil. Monitoring systems all over the world have taken advantage of big data repositories of remote sensing data as they are becoming freely available together with artificial intelligence. Thus, considering the advent of new generation remote sensing data hubs, online platforms of big data that can fill in spatial and temporal resolutions gaps in current deforestation mapping, this work aims to present recent innovations at INPE´s deforestation monitoring systems in Brazil and how they are gauging new realms of technological levels. Recent innovations at INPE´s monitoring systems are: 1) the development of TerraBrasilis platform of data access and analysis; 2) the adoption of new sensors and cloud detection strategies; 3) the complementary use of multi-sensor images; 4) the complementary adoption of SAR C-band images using cloud data to sample and process Sentinel-1. Future innovations are: 1) development of a Brazilian data cube to be used in deep learning techniques of image classification; 2) Routine uncertainty analysis of PRODES data. Automatization might fasten mapping process, but the real challenge is to succeed in automatization maintaining data quality and historical series. The hyper-availability of remote sensing data, the initiative of a Brazilian Data Cube and promising machine learning techniques applied to land cover change detection, allowed INPE to reinforce its central role in tropical forest monitoring.
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Safitri, Raudhatun, Puguh Wahyu Prasetyo, Dian Eka Wijayanti, Samsul Arifin, Fariz Setyawan, and Joe Repka. "Text security by using a combination of the vigenere cipher and the rubik's cube method of size 4×4×4." Al-Jabar : Jurnal Pendidikan Matematika 14, no. 2 (2023): 281. http://dx.doi.org/10.24042/ajpm.v14i2.14276.
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Background: In the current era of technology, information security is increasingly important. The growth of technology leads to a higher level of threat to the security of data and information dissemination, and cryptography is a valuable protective tool.Aim: The primary objective of this research is to enhance text security through the fusion of the Vigenere cipher and the Rubik's cube algorithm. By leveraging this novel approach, we aim to fortify the confidentiality of textual data against potential eavesdroppers and adversaries. To demonstrate the practicality of this method, we perform a simulation using the Python programming language within the Google Colab environment. Method: This study employs a qualitative research methodology supplemented by empirical simulation. The combination of the Vigenere Cipher and the Rubik's Cube algorithm in a 4×4×4 configuration is implemented to encrypt and decrypt text. The simulation is executed using the Google Colab platform, enabling a practical illustration of the encryption process.Result: The results of our research indicate the feasibility of generating ciphertext through the amalgamation of the Vigenere Cipher and the Rubik's Cube algorithm in the specified 4×4×4 configuration. The simulation conducted in Google Colab serves as concrete evidence of the effectiveness and practicality of this combined encryption method.Conclusion: In conclusion, this research offers a compelling approach to bolstering text security in the modern era of information technology. By combining the Vigenere Cipher with the Rubik's Cube algorithm in a 4×4×4 configuration, we have demonstrated the potential to significantly enhance the confidentiality of sensitive textual data. The empirical simulation conducted in Google Colab reaffirms the practicality and viability of this innovative encryption technique, highlighting its potential as a valuable tool in the realm of information security.
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Rizki, Reza, and Handoyo Handoyo. "Estimation Microporosity Value of Fontanebleau Sandstone Using Digital Rock Physics Approach." Journal of Geoscience, Engineering, Environment, and Technology 3, no. 2 (2018): 103. http://dx.doi.org/10.24273/jgeet.2018.3.2.1544.
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The technology of digital rock physics (DRP) allowed to predict the physical properties in core data sample, for example to predict value of porosity of data sample. This research applied the digital rock physics technique to predict the microporosity in sandstone sample: Fontanebleau Sandstone. The data are digital images from Fontanebleau Sandstone with high resolution scanned from micro tomography CT-Scan processing. The result of image processing shown in 2D and 3D image. From the data, the value of microporosity Fontanebleau Sandstone are beetwen 6% - 7%. This result confirmed by the quartz cemented sample of Fontanebleau Sandstone. The scale and sub-cube give the different value of microporosity which is indicated the scale influence to value of porosity value. So the simplest and best way is to average the all result from sub-cubes.
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Aghayeva, Maleyka Agha Ali. "Joint analysis of seismic and well log data applied for prediction of oil presence in Maykop deposits in Naftalan area." AIMS Geosciences 7, no. 3 (2021): 331–37. http://dx.doi.org/10.3934/geosci.2021020.
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<abstract> <p>The paper is devoted to joint application of 3D seismic survey and well logging techniques to study oil and gas presence in Maykop deposits in Naftalan oil field. The field is located in Naftalan-Northern Naftalan area of Ganja oil and gas province, Azerbaijan. The paper brings data about Naftalan field, the ancient brachianticline type of field in Azerbaijan. The paper also considers the history of study of the field by use of geological and geophysical techniques. Despite the studies cover the area since 1873, the oil and gas presence in Maykop has not been studied sufficiently due to sparse network of wells and insufficient amount of core samples. To avoid this gap the results of previous studies have been revised. Joint interpretation of well logging data acquired from 22 wells and data of 3D seismic survey covering the area has been done. On the basis of oil-saturation cube, we have designed the map (the area between the top and the foot of I horizon of Maykop) and 3D model of target interval. It has been defined that uneven distribution of oil saturation and poor oil recovery depends also on sedimentation environment, characterized by rapid subsidence of the basin and increasing amount of clay in the process of sedimentation. Our studies revealed that the average value of oil saturation varies as 0.5–0.6 and increases towards the North-East of the study area. In the final stage of the study we have presented the oil saturation cube and the map drawn on the basis of this cube.</p> </abstract>
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Yang, Peng, and Zhe Jun Zeng. "The Application of Data Warehouse in Enterprise Resource Planning." Advanced Materials Research 179-180 (January 2011): 973–77. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.973.
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Through building a data warehouse which is used to support manufacturing inventory analysis, according to the analysis theme, this paper extracted, purified and integrated the historical data which is accumulated by the enterprise in a long term and distributed in the various heterogeneous systems, and store them in the data warehouse fact table; through the establishment of the data cube, it generate the aggregated data with different particle size, as well as provide on-line analytical processing technology (OLAP), to provide users data query analysis with multi-angle, multi-granularity, visualization, which can solve many problems difficult to solve in the inventory analysis in traditional enterprise information processing systems.
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Lee, T. Y., and C. H. Lin. "Growing-cube isosurface extraction algorithm for medical volume data." Computerized Medical Imaging and Graphics 25, no. 5 (2001): 405–15. http://dx.doi.org/10.1016/s0895-6111(00)00084-7.
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Potratz, C. J., R. K. Steinhorst, and C. L. Hanson. "Estimating the Parameters of a Cube Root Normal Distribution from Reduced Sample Data." Water Resources Research 21, no. 1 (1985): 89–90. http://dx.doi.org/10.1029/wr021i001p00089.
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Huang, Ting, Wei Huang, Kun Song, and Ming Jie Gao. "Design and Implementation of Wind Farm Visualization Intelligent Data Analysis System." Advanced Materials Research 816-817 (September 2013): 497–501. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.497.
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Building visualization intelligent data analysis system of wind farms is important for operating personnel to master the running status of wind farm in real time and research the grid operation strategy of wind farm. In this paper, the designed system collects wind farm operation data from the production management system, SCADA and so on. Based on multi-threading data acquisition, dynamic data storage, cube views, analysis technology and component/architecture design concept, design and implement the visualization intelligent data analysis system which meets requirements of wind farms. This system has been used in a certain electric power company of China.
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Wang, Dandi, Shuai Xing, Yan He, Jiayong Yu, Qing Xu, and Pengcheng Li. "Evaluation of a New Lightweight UAV-Borne Topo-Bathymetric LiDAR for Shallow Water Bathymetry and Object Detection." Sensors 22, no. 4 (2022): 1379. http://dx.doi.org/10.3390/s22041379.
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Airborne LiDAR bathymetry (ALB) has proven to be an effective technology for shallow water mapping. To collect data with a high point density, a lightweight dual-wavelength LiDAR system mounted on unmanned aerial vehicles (UAVs) was developed. This study presents and evaluates the system using the field data acquired from a flight test in Dazhou Island, China. In the precision and accuracy assessment, the local fitted planes extracted from the water surface points and the multibeam echosounder data are used as a reference for water surface and bottom measurements, respectively. For the bathymetric performance comparison, the study area is also measured with an ALB system installed on the manned aerial platform. The object detection capability of the system is examined with placed small cubes. Results show that the fitting precision of the water surface is 0.1227 m, and the absolute accuracy of the water bottom is 0.1268 m, both of which reach a decimeter level. Compared to the manned ALB system, the UAV-borne system provides higher resolution data with an average point density of 42 points/m2 and maximum detectable depth of 1.7–1.9 Secchi depths. In the point cloud of the water bottom, the existence of a 1-m target cube and the rough shape of a 2-m target cube are clearly observed at a depth of 12 m. The system shows great potential for flexible shallow water mapping and underwater object detection with promising results.
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Novreza, Vicky, Yuda Munarko, and Lailatul Husniah. "Data Warehouse Menggunakan Snowflake Schema Pada Virtual Shop." Jurnal Repositor 2, no. 1 (2020): 67. http://dx.doi.org/10.22219/repositor.v2i1.180.
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AbstrakVirtual shop adalah penerapan teknologi virtual realiy pada toko online guna meningkatkan interaksi pembeli. Penerarapan virtual reality pada online shop menimbulkan pertanyaan untuk penyimpanan data serta pengolahan data sebagai analisa kebutuhan. Data pada toko online sebatas pada data transaksional dan tidak dilakukan pengolahan yang lebih untuk dilakukan analisa. Solusi dari permasalahan tersebut dengan melakukan perancangan data warehouse untuk menyiapkan tempat penyimpanan data agar analisa dapat dilakukan guna pengembangan teknologi virtual shop. Sistem pada data warehouse dapat sebagai repository data yang digunakan oleh virtual shop. Penelitian ini menerapkan multidimensional snowflakes schema dengan tujuan me-normalisasi tabel dimensi sehingga tidak terjadi redudansi data khusus pada tabel dimensi. Menerapkan metode desain data multidimensional dari Moody pada Multidimensional snowflakes schema, from ER models to Dimensional models. Untuk mendukung penelitian ini, Peneliti melakukan analisis pada data warehouse dengan menggunakan OLAP Cube Reporting berbasis web.Abstract A Virtual Shop is kind of Online based store that implement Virtual Reality Technology for their interaction. Usually, Data inside online shop just as transactional data and there is not further process to makes that data as analyse need. So, in this research is to design we can call Data Warehouse as a solution to prepare a repository for data to make use as analyse need for Virtual Shop development. This research implement multidimensional scheme is Snowflake Scheme as Moody’s from ER models to Dimensional Models for it method. For analysis activities, user will do that activities via Web Based OLAP Cube Reporting.
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Khalil, Abdelhak, and Mustapha Belaissaoui. "NoSQL-based approach in data warehousing and OLAP cube computation." International Journal of Information and Decision Sciences 15, no. 4 (2023): 408–30. http://dx.doi.org/10.1504/ijids.2023.134794.
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Belaissaoui, Mustapha, and Abdelhak Khalil. "NoSQL-based approach in data warehousing and OLAP cube computation." International Journal of Information and Decision Sciences 15, no. 4 (2023): 408–30. http://dx.doi.org/10.1504/ijids.2023.10060225.
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Dhu, Trevor, Gregory Giuliani, Jimena Juárez, et al. "National Open Data Cubes and Their Contribution to Country-Level Development Policies and Practices." Data 4, no. 4 (2019): 144. http://dx.doi.org/10.3390/data4040144.
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The emerging global trend of satellite operators producing analysis-ready data combined with open source tools for managing and exploiting these data are leading to more and more countries using Earth observation data to drive progress against key national and international development agendas. This paper provides examples from Australia, Mexico, Switzerland, and Tanzania on how the Open Data Cube technology has been combined with analysis-ready data to provide new insights and support better policy making across issues as diverse as water resource management through to urbanization and environmental–economic accounting.
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Cao, Qianqian, Guoqing Li, Xiaochuang Yao, et al. "GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube." Applied Sciences 12, no. 15 (2022): 7816. http://dx.doi.org/10.3390/app12157816.
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With the application of big data in Earth observation, satellite imagery data are gradually becoming important means of observation for monitoring changes in vegetation, water bodies, and urbanization. Therefore, new satellite imagery data organization and management paradigms are urgently needed to fully mine the useful information from these data and provide new ways to better quantify and serve the sustainable development of resources and the environment. In this paper, a framework for processing and analyzing Chinese GF-1 satellite imagery data was developed using the latest technologies such as Open Data Cube (ODC) grids, Analysis Ready Data (ARD) generation, and space subdivision, which extended the data loading and processing capacities of the ODC grids for Chinese satellite imagery data. Using the proposed framework, we conducted a case study to investigate the spatial and temporal changes in vegetation and water mapping with GF-1 data collected from 2014 to 2021 covering the Miyun Reservoir, Beijing, China. The experimental results showed that the proposed framework had significantly improved temporal and spatial efficiency compared with the traditional scene-based data management approach, thus demonstrating the advantages and potential of the ODC grids as a new data management paradigm.
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Nabibayova, G. Ch. "Application of OLAP Technology in the Environment of Electronic Demography." Informacionnye Tehnologii 27, no. 10 (2021): 542–49. http://dx.doi.org/10.17587/it.27.542-549.
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The article proposes an approach to the development of an electronic demographic decision support system using technologies of Data Warehouse (DW) and Interactive Analytical Processing OLAP. This makes it possible to conduct high-level demographic research and provide support to decision-makers in demographic sphere. The article notes that demography is an interdisciplinary field of research and is defined as a complex science. Each industry of demography has many indicators. A sample list of these indicators is presented. The main characteristics of the DW, which should be taken into account when developing its architecture, are stated. Among these characteristics, one can find the main defining characteristics of Big Data — volume, velocity, variety, veracity, variability, visualization, value etc. For a more rational and efficient use of a large amount of information, taking into account its constant increase, to ensure the speed of execution of requests for a given system, it is proposed to use a Bus of Interconnected Data Marts (DM) as an architecture of DW. One of the advantages of using DM is that their use assumes distributed parallel data processing. This architecture allows for much faster results generation. It is based on the MapReduce distributed computing model and the Hadoop project. In addition, to effectively use large amounts of data, it is also proposed to use OLAP operations such as roll-up and drill-down, as well as fuzzy set theory, based on the technique of computing with words. The article also shows the practical application of interconnected DM. An OLAP cube is built on the basis of these DM. OLAP operations provide the ability to view cubes in different slices and provide aggregate data.
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Hardage, B. A., V. M. Pendleton, J. L. Simmons, B. A. Stubbs, and B. J. Uszynski. "3-D instantaneous frequency used as a coherency/continuity parameter to interpret reservoir compartment boundaries across an area of complex turbidite deposition." GEOPHYSICS 63, no. 5 (1998): 1520–31. http://dx.doi.org/10.1190/1.1444448.
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In previous unpublished work, we found that anomalous values of instantaneous frequency (that is, frequency values that are negative or that have positive magnitudes greater than Nyquist limit) are valuable indicators of alterations in reflection waveshape that occur commonly at stratigraphic terminations. Inspection of 3-D seismic data across Nash Draw Field on the northwest slope of the Delaware Basin showed that appreciable wavelet alterations occurred at the boundaries of distinct seismic facies within the targeted Brushy Canyon reservoirs that are being produced in this field. Based on this observation, we used instantaneous frequency as the fundamental database to define the edge positions and areal shapes of individual reservoir facies within this complex, slope‐basin distribution of siltstones and sandstones, commonly thought to be a succession of turbidite depositions. We compared the compartmentalization detail derived from this frequency‐based approach with compartmentalization models provided by an amplitude‐based interpretation and by coherency/continuity cube technology. These comparisons led us to conclude that a properly executed 3-D interpretation of instantaneous frequency behavior can provide a good first guess of the internal compartmented structure of many reservoirs. We offer our work here for peer evaluation by people who support reservoir characterization studies similar to what we describe. We have used instantaneous frequency behavior to successfully detect lateral disruptions in stratigraphic continuity for several years—long before the current concept of a coherency (or continuity) cube was publicized for this same purpose. This study shows that interpreters who do not have ready access to coherency‐cube technology, but who do have Hilbert transform algorithms available to them, can create 3-D volumes of instantaneous frequency that provide valuable indications of reflection discontinuity, much in the same way that continuity/coherency cube technology does.
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Yu, Lu, Hongbo Li, Jing Li, and Wei Li. "Lossless Compression of Large Aperture Static Imaging Spectrometer Data." Applied Sciences 13, no. 9 (2023): 5632. http://dx.doi.org/10.3390/app13095632.
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The large-aperture static imaging spectrometer (LASIS) is an interference spectrometer with high device stability, high throughput, a wide spectral range, and a high spectral resolution. One frame image of the original data cube acquired by the LASIS shows the image superimposed with interference fringes, which is distinctly different from traditional hyperspectral images. For compression studies using this new type of data, a lossless compression scheme that combines a novel data rearrange method and the lossless multispectral and hyperspectral image compression standard CCSDS-123 is presented. In the rearrange approach, the LASIS data cube is rearranged such that the interference information overlapped on the image can be separated, and the results are then processed using the CCSDS-123 standard. Then, several experiments are conducted to investigate the performance of the rearrange method and examine the impact of different CCSDS-123 parameter settings for the LASIS. The experimental results indicate that the proposed scheme provides a 32.9% higher ratio than traditional rearrange methods. Moreover, an adequate parameter combination for this compression scheme for LASIS is presented, and it yields a 19.6% improvement over the default settings suggested by the standard.
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Akimkina, E. E. "Recommendations for deploying multidimensio." Informacionno-technologicheskij vestnik 11, no. 1 (2017): 68–80. http://dx.doi.org/10.21499/2409-1650-2017-1-68-80.
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A comparative analysis of different approaches to analytical data and shows that the most ample opportunities has a multi-dimensional approach, implemented with the help of OLAP technology. Presented multidimensional OLAP-cube model with the measurements for the analysis and processing of process data. Practical recommendations for the deployment of a multidimensional data modeling systems with regard to their integration into existing enterprise management system.
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Wang, Chang, Xinyu Liu, Yang Zhang, Yan Sun, Zeqing Yu, and Zhenrong Zheng. "Dual-Channel Switchable Metasurface Filters for Compact Spectral Imaging with Deep Compressive Reconstruction." Nanomaterials 13, no. 21 (2023): 2854. http://dx.doi.org/10.3390/nano13212854.
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Spectral imaging technology, which aims to capture images across multiple spectral channels and create a spectral data cube, has been widely utilized in various fields. However, conventional spectral imaging systems face challenges, such as slow acquisition speed and large size. The rapid development of optical metasurfaces, capable of manipulating light fields versatilely and miniaturizing optical components into ultrathin planar devices, offers a promising solution for compact hyperspectral imaging (HSI). This study proposes a compact snapshot compressive spectral imaging (SCSI) system by leveraging the spectral modulations of metasurfaces with dual-channel switchable metasurface filters and employing a deep-learning-based reconstruction algorithm. To achieve compactness, the proposed system integrates dual-channel switchable metasurface filters using twisted nematic liquid crystals (TNLCs) and anisotropic titanium dioxide (TiO2) nanostructures. These thin metasurface filters are closely attached to the image sensor, resulting in a compact system. The TNLCs possess a broadband linear polarization conversion ability, enabling the rapid switching of the incidence polarization state between x-polarization and y-polarization by applying different voltages. This polarization conversion facilitates the generation of two groups of transmittance spectra for wavelength-encoding, providing richer information for spectral data cube reconstruction compared to that of other snapshot compressive spectral imaging techniques. In addition, instead of employing classic iterative compressive sensing (CS) algorithms, an end-to-end residual neural network (ResNet) is utilized to reconstruct the spectral data cube. This neural network leverages the 2-frame snapshot measurements of orthogonal polarization channels. The proposed hyperspectral imaging technology demonstrates superior reconstruction quality and speed compared to those of the traditional compressive hyperspectral image recovery methods. As a result, it is expected that this technology will have substantial implications in various domains, including but not limited to object detection, face recognition, food safety, biomedical imaging, agriculture surveillance, and so on.
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Kang, Xiaojun, Cheng Yang, Xuguang Zhao, Weiwei Chen, Sifa Zhang, and Yaping Wang. "A Short Sequence Splicing Method for Genome Assembly Using a Three-Dimensional Mixing-Pool of BAC Clones and High-throughput Technology." Open Biotechnology Journal 9, no. 1 (2015): 210–15. http://dx.doi.org/10.2174/1874070701509010210.
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Current genome sequencing techniques are expensive, and it is still a major challenge to obtain an individual whole-genome sequence. To reduce the cost of sequencing, this paper introduced a high-throughput sequencing strategy using a three-dimensional mixing-pools based on the cube. Following the strategy, BAC clones were injected into each vertex of the cube, and sequencing of each plane provided information about multiple clones, thereby significantly reducing the cost of sequencing. In addition, Velvet was used to assemble the sequencing data. The scaffold generated from Velvet contained a number of contigs, which were orderless. Therefore, to address this problem, a scaffold assembly algorithm based on multi-way trees was used. The algorithm used a multi-way tree to build the framework of chromosomes, and subsequently, the frame was filled to complete the scaffold assembly. This algorithm alone outperformed Velvet in the assembling of a scaffold.
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Navale, Vinay S., Chetan Jane, Anvita B, Kalyan B, and Satvik A. "Link Prediction on N-ary Relational Data Based on Relatedness Evaluation." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 1597–609. http://dx.doi.org/10.22214/ijraset.2023.55413.
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Abstract: The recent proliferation of high-resolution Earth observation data, along with the advent of open-data initiatives, has led to the availability of petabyte-scale Earth observation datasets accessible for free. However, this abundance of big data presents significant challenges for regional to global spatio-temporal analysis. The traditional approach of "downloadpreprocess-store-analyze" introduces excessive data downloading overhead and computational barriers, hindering efficient analysis. The Earth observation data cube (EODC) paradigm offers a solution by revolutionizing the storage and management of spatio-temporal RS data. Yet, the presence of multiple EODC solutions has resulted in "information silos," making the sharing and joint use of RS data across EODCs difficult.
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Ghosh, Debmitra, Pulak Tarafder, and Roshmikana Pal. "Measuring Human Psychology Level through Game: CU63." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (2023): 1278–82. http://dx.doi.org/10.22214/ijraset.2023.55821.
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Abstract: The game Cu63-Cube appears to be a puzzle-based game that incorporates elements of decision- making and measures players' decision-making time. The game analyzes how quickly players complete each level and uses that data to determine the most valuable player for that particular level. It emphasizes real-time IQ-based gameplay and aims to detect players' basic IQ and decision-making abilities. In addition to assessing decision-making time, the game also tracks the movement of the player's hands. This feature allows for the recording of players' hand movements during gameplay, which can provide additional insights or data related to their performance. It seems that Cu63-Cube aims to combine puzzle-solving, decision-making, and hand movement tracking to create a game that assesses players' cognitive abilities and potentially provides a measure of their IQ. However, it is important to note that without further information or research on the game, its validity as an IQ assessment tool cannot be determined. It's worth mentioning that the validity and reliability of any IQ assessment or game-based assessment tool require rigorous testing and validation. It would be necessary to conduct research studies to evaluate the effectiveness, accuracy, and fairness of Cu63-Cube as an IQ assessment tool before making any claims about its ability to measure intelligence or make predictions about job performance.
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DELGADO, MIGUEL, CARLOS MOLINA, LÁZARO RODRÍGUEZ-ARIZA, DANIEL SÁNCHEZ, and M. AMPARO VILA. "F-CUBE FACTORY: A FUZZY OLAP SYSTEM FOR SUPPORTING IMPRECISION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, supp01 (2007): 59–81. http://dx.doi.org/10.1142/s0218488507004467.
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The special needs of the OLAP technology were the main cause of the use of a multidimensional view of the data. Crisp models are not suitable to model complex or non well defined domains. They also fail to integrate data from semi/non-structured sources (e.g. Internet) or with incompatibilities in their schemata. In these situations, as a result of the modelling and/or integration, imprecision appears. So, we need a model able to manage imprecision in the structures and data. If we want to use expert's knowledge in the analysis, we have to keep in mind that expert users are more comfortable when they use linguistic expressions instead of exact values. In this paper we present an extension of a fuzzy multidimensional model to support the use of linguistic labels in the definition of the hierarchies and the OLAP system that implements this model.
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Liu, Feng-Jung, and Chung-Wei Tseng. "Design and Practice of STEAM Interactive LED-Cube Teaching Aids." Educational Innovations and Emerging Technologies 4, no. 1 (2024): 20–27. http://dx.doi.org/10.35745/eiet2024v04.01.0003.
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STEAM education endeavors to integrate Science, Technology, Engineering, Arts, and Mathematics into interdisciplinary learning. The development of interactive media design requires proficiency not only in programming and art but also in integrating emerging technologies and artistic concepts across various disciplines. However, existing curriculum frameworks frequently face challenges in adequately instructing and integrating skills relevant to interactive media development, including programming, 3D modeling, animation, and presentation. Due to limited knowledge, abilities, and time, students are hesitant to experiment with different display methods for showcasing their work. Despite recognizing the importance of cross-disciplinary integration, students' limited resources and learned abilities reduce their willingness to put this into practice. Therefore, we propose to provide a toolkit that allows students to learn and simplify the integration process of interactive media development, thereby encouraging students to have the intention of cross-domain collaborative creation and to implement the STEAM educational approach. The project focuses on designing Arduino modules integrated with Unity to implement a real-time display LED cube system, RLDS. Bluetooth or WiFi technology will be utilized to achieve the scalability of the LED cube modules. A communication package facilitates transmission of interactive data between the 3D content system and the Arduino-based LED cube display system. We anticipate that through the use of such RLDS teaching aids, students can learn various integrated applications of digital media technologies, which can be beneficial for their future interactive media creations.
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Al-amri, Redhwan, Raja Kumar Murugesan, Mubarak Almutairi, Kashif Munir, Gamal Alkawsi, and Yahia Baashar. "A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube." Applied Sciences 12, no. 13 (2022): 6523. http://dx.doi.org/10.3390/app12136523.
Texte intégralRésumé :
As applications generate massive amounts of data streams, the requirement for ways to analyze and cluster this data has become a critical field of research for knowledge discovery. Data stream clustering’s primary objective and goal are to acquire insights into incoming data. Recognizing all possible patterns in data streams that enter at variable rates and structures and evolve over time is critical for acquiring insights. Analyzing the data stream has been one of the vital research areas due to the inevitable evolving aspect of the data stream and its vast application domains. Existing algorithms for handling data stream clustering consider adding various data summarization structures starting from grid projection and ending with buffers of Core-Micro and Macro clusters. However, it is found that the static assumption of the data summarization impacts the quality of clustering. To fill this gap, an online clustering algorithm for handling evolving data streams using a tempo-spatial hyper cube called BOCEDS TSHC has been developed in this research. The role of the tempo-spatial hyper cube (TSHC) is to add more dimensions to the data summarization for more degree of freedom. TSHC when added to Buffer-based Online Clustering for Evolving Data Stream (BOCEDS) results in a superior evolving data stream clustering algorithm. Evaluation based on both the real world and synthetic datasets has proven the superiority of the developed BOCEDS TSHC clustering algorithm over the baseline algorithms with respect to most of the clustering metrics.
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Zhang, Fan, Geng Ma, Yunqi Tao, Xiao Liu, Yixin Liu, and Rui Li. "Characteristics of hydraulic fracture surface based on 3D scanning technology." Royal Society Open Science 5, no. 6 (2018): 171845. http://dx.doi.org/10.1098/rsos.171845.
Texte intégralRésumé :
The surface characteristics of fractured specimens are important in hydraulic fracturing laboratory experiments. In this paper, we present a three-dimensional (3D) scanning device assembled to study these surface characteristics. Cube-shaped rock specimens were produced in the laboratory and subjected to triaxial loading until the specimen split in two in a hydraulic fracturing experiment. Each fractured specimen was placed on a rotating platform and scanned to produce 3D superficial coordinates of the surface of the fractured specimen. The scanned data were processed to produce high-precision digital images of the fractured model, a surface contour map and accurate values of the superficial area and specimen volume. The images produced by processing the 3D scanner data provided detailed information on the morphology of the fractured surface and mechanism of fracture propagation. High-precision 3D mapping of the fractured surfaces is essential for quantitative analysis of fractured specimens. The 3D scanning technology presented here is an important tool for the study of fracture characteristics in hydraulic fracturing experiments.