scholarly journals GeohashTile: Vector Geographic Data Display Method Based on Geohash

2020 ◽  
Vol 9 (7) ◽  
pp. 418
Author(s):  
Chang Zhou ◽  
Huimei Lu ◽  
Yong Xiang ◽  
Jingbang Wu ◽  
Feng Wang
Keyword(s):  
Data Compression ◽  
Large Scale ◽  
Direct Conversion ◽  
Data Display ◽  
Visual Effects ◽  
Main Research ◽  
Data Indexing ◽  
Geographic Data ◽  
Data Volume ◽  
World Environment

In the development of geographic information-based applications for mobile devices, achieving better access speed and visual effects is the main research aim. In this paper, we propose a new geographic data display method based on Geohash, namely GeohashTile, to improve the performance of traditional geographic data display methods in data indexing, data compression, and the projection of different granularities. First, we use the Geohash encoding system to represent coordinates, as well as to partition and index large-scale geographic data. The data compression and tile encoding is accomplished by Geohash. Second, to realize a direct conversion between Geohash and screen-pixel coordinates, we adopt the relative position projection method. Finally, we improve the calculation and rendering efficiency by using the intermediate result caching method. To evaluate the GeohashTile method, we have implemented the client and the server of the GeohashTile system, which is also evaluated in a real-world environment. The results show that Geohash encoding can accurately represent latitude and longitude coordinates in vector maps, while the GeohashTile framework has obvious advantages when requesting data volume and average load time compared to the state-of-the-art GeoTile system.

scholarly journals Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

2018 ◽  
Vol 4 (12) ◽  
pp. 142 ◽  
Author(s):  
Hongda Shen ◽  
Zhuocheng Jiang ◽  
W. Pan
Keyword(s):  
Data Compression ◽  
Hyperspectral Image ◽  
Image Data ◽  
Lossless Compression ◽  
Large Data ◽  
Hyperspectral Data ◽  
Compression Performance ◽  
Temporal Correlations ◽  
Sensing Applications ◽  
Data Volume

Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

scholarly journals Maximum Variance Hashing via Column Generation

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Lei Luo ◽  
Chao Zhang ◽  
Yongrui Qin ◽  
Chunyuan Zhang
Keyword(s):  
Column Generation ◽  
Large Scale ◽  
Web Search ◽  
Nearest Neighbor ◽  
Computational Cost ◽  
Multimedia Retrieval ◽  
Training Data ◽  
Nonlinear Dimensionality Reduction ◽  
Maximum Variance ◽  
Data Volume

With the explosive growth of the data volume in modern applications such as web search and multimedia retrieval, hashing is becoming increasingly important for efficient nearest neighbor (similar item) search. Recently, a number of data-dependent methods have been developed, reflecting the great potential of learning for hashing. Inspired by the classic nonlinear dimensionality reduction algorithm—maximum variance unfolding, we propose a novel unsupervised hashing method, named maximum variance hashing, in this work. The idea is to maximize the total variance of the hash codes while preserving the local structure of the training data. To solve the derived optimization problem, we propose a column generation algorithm, which directly learns the binary-valued hash functions. We then extend it using anchor graphs to reduce the computational cost. Experiments on large-scale image datasets demonstrate that the proposed method outperforms state-of-the-art hashing methods in many cases.

scholarly journals HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time

2018 ◽  
Vol 7 (12) ◽  
pp. 467 ◽  
Author(s):  
Mengyu Ma ◽  
Ye Wu ◽  
Wenze Luo ◽  
Luo Chen ◽  
Jun Li ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
High Performance ◽  
Large Scale ◽  
Computation Time ◽  
Buffer Analysis ◽  
Data Volume ◽  
Time Buffer ◽  
Real World Datasets ◽  
Spatial Indexes

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.

Could lean and green be the driver to integrate business improvement throughout the organisation?

2018 ◽  
Vol 67 (1) ◽  
pp. 207-219 ◽  
Author(s):  
Lara Chaplin ◽  
Simon T.J. O’Rourke
Keyword(s):  
Case Studies ◽  
Large Scale ◽  
Participant Observation ◽  
Integrated Approach ◽  
Thick Description ◽  
Content Type ◽  
Main Research ◽  
Lean And Green ◽  
Multiple Case ◽  
Multiple Case Studies

Purpose It seems to be the consensus (Zhang et al., 2012; George et al., 2003; Arumugam et al., 2013) that Lean Six Sigma (LSS) has become a beneficial improvement initiative used in a variety of industries. There is a move towards integrating any high-level business improvement methods holistically throughout the whole organisation. Indeed, Hoerl (2014) explored the idea that when using LSS for business improvement, the programme should engage the whole organisation in much the same way as the financial function is present throughout each department. The purpose of this paper is to posit that using the lean and green agenda may be the driver to achieve integration. Design/methodology/approach The research adopted a subjective ontological perspective with the researcher using participant observation as the main research instrument. Denzin and Lincoln (2005) note that it is now common for scholars to argue that the only relevant data are those based upon the personal experience of the researcher; this served as an informing foundation for the approach for the exploration of the topic. Based on multiple case studies, chosen because they operate in different sectors, the paper adopted an extended case method (Burawoy, 1998) to analyse and gather the research. The organisations were chosen because they both were at a similar stage in their continuous improvement (CI) journey. The main reasoning behind the selection of the two different organisations is to reach “Thick Description” (Geertz, 1973, p. 3, 2001). Findings The findings suggest that there are still significant benefits of implementing a large-scale lean agenda in particular when using an LSS methodology. The paper finds that there are also significant gaps in achieving full integration within the organisation and argues that lean and CI are still the remit of the operations manager. The document goes on to argue that if the CI initiative is driven by the corporate social responsibility (CSR) plan, then any lean/lean green implementation will enable the company to drive CI integration with all stakeholders. Research limitations/implications The research has implications for those responsible for the CSR function within the organisation and the operations manager who is charged with implementing any lean/lean and green CI. Practical implications The paper argues that the lean and green agenda can drive integration of any CI activity throughout the organisation and suggests that the way this can be achieved is any CI activity that is included in the wider CSR plan. Social implications This paper contributes to the “lean and green” agenda and offers a solution for the problem of integrating LSS activities throughout the whole organisation by placing CI and LSS within the CSR remit. Originality/value There is little consensus how this holistic integrated approach should be implemented by the company. This research uses multiple case studies to critically examine the application of LSS as an improvement programme within two large UK-based organisations, each company operating in very different industry sectors to identify the benefits of LSS but also the missed “green/societal” opportunities and argues that if any lean and lean and green agenda is to be holistically adopted, then any CI activity should be driven by the CSR department.

scholarly journals Aerosol-cloud-turbulence interactions in well-coupled Arctic boundary layers over open water

2021 ◽  
Author(s):  
Jan Chylik ◽  
Dmitry Chechin ◽  
Regis Dupuy ◽  
Birte S. Kulla ◽  
Christof Lüpkes ◽  
...  
Keyword(s):  
Large Scale ◽  
Convective Cloud ◽  
Open Water ◽  
Convective Transport ◽  
Cloud Layer ◽  
Mixed Phase ◽  
State Variables ◽  
Strongly Coupled ◽  
Main Research ◽  
Weather Model

Abstract. Late springtime Arctic mixed-phase convective clouds over open water in the Fram Strait as observed during the recent ACLOUD field campaign are simulated at turbulence-resolving resolutions. The main research objective is to gain more insight into the coupling of these cloud layers to the surface, and into the role played by interactions between aerosol, hydrometeors and turbulence in this process. A composite case is constructed based on data collected by two research aircraft on 18 June 2017. The boundary conditions and large-scale forcings are based on weather model analyses, yielding a simulation that freely equilibrates towards the observed thermodynamic state. The results are evaluated against a variety of independent aircraft measurements. The observed cloud macro- and microphysical structure is well reproduced, consisting of a stratiform cloud layer in mixed-phase fed by surface-driven convective transport in predominantly liquid phase. Comparison to noseboom turbulence measurements suggests that the simulated cloud-surface coupling is realistic. A joint-pdf analysis of relevant state variables is conducted, suggesting that locations where the mixed-phase cloud layer is strongly coupled to the surface by convective updrafts act as hot-spots for invigorated interactions between turbulence, clouds and aerosol. A mixing-line analysis reveals that the turbulent mixing is similar to warm convective cloud regimes, but is accompanied by hydrometeor transitions that are unique for mixed-phase cloud systems. Distinct fingerprints in the joint-pdf diagrams also explain i) the typical ring-like shape of ice mass in the outflow cloud deck, ii) its slightly elevated buoyancy, and iii) an associated local minimum in CCN.

From Entrepreneur to Big Player

2013 ◽  
Vol 4 (2) ◽  
pp. 21-34
Author(s):  
Ruey-Shiang Shaw ◽  
Sheng-Pao Shih ◽  
Ta-Yu Fu ◽  
Chia-Wen Tsai
Keyword(s):  
Business Model ◽  
Large Scale ◽  
Software Industry ◽  
Stable Phase ◽  
Data Set ◽  
Main Research ◽  
Software Products ◽  
Ex Post ◽  
Major Player

The software industry faces drastic changes in technology and business operations. The research structure of this study is based on the business model for software industries proposed by Rajala in 2003. The researcher employed an ex post facto research design to conduct a case study of the Galaxy Software Service Co., a company that is representative of the software industry in Taiwan. The main research goal of this study is to explore how this particular company developed into a large software company in the Taiwanese software sector, which is characterized by a prevalence of small- and medium-sized businesses, over a period of 25 years. This study employs a case study design and relies on in-depth participation and interviews to acquire a complete data set of the company’s internal operations. The evolution of the business model from the company’s inception until the present day has been divided into four phases: the entrepreneur phase, the growth phase, the stable phase, and the innovative breakthrough phase. The company developed into a major player in the software industry for 3 reasons: it has always insisted on a product differentiation strategy based on the sole reliance on software products, it started out as a software products dealer and gradually developed its own research and development capability, and it built a large-scale project management capability and received CMMI certification. These factors make the company stand out from other System Integrated businesses in the Taiwanese software sector offering both hardware and software products.

scholarly journals Agricultural land use and cover change in the Cerrado/Amazon ecotone: A case study of the upper Teles Pires River basin

2018 ◽  
Vol 48 (2) ◽  
pp. 168-177 ◽  
Author(s):  
Ana Paula Sousa Rodrigues ZAIATZ ◽  
Cornélio Alberto ZOLIN ◽  
Laurimar Goncalves VENDRUSCULO ◽  
Tarcio Rocha LOPES ◽  
Janaina PAULINO
Keyword(s):  
Land Use ◽  
River Basin ◽  
Large Scale ◽  
Agricultural Land ◽  
Temporal Dynamics ◽  
Direct Conversion ◽  
Landsat 8 ◽  
Error Matrix ◽  
Mato Grosso ◽  
Land Use And Cover

ABSTRACT The upper Teles Pires River basin is a key hydrological resource for the state of Mato Grosso, but has suffered rapid land use and cover change. The basin includes areas of Cerrado biome, as well as transitional areas between the Amazon and Cerrado vegetation types, with intensive large-scale agriculture widely-spread throughout the region. The objective of this study was to explore the spatial and temporal dynamics of land use and cover change from 1986 to 2014 in the upper Teles Pires basin using remote sensing and GIS techniques. TM (Thematic Mapper) and TIRS (Thermal Infrared Sensor) sensor images aboard the Landsat 5 and Landsat 8, respectively, were employed for supervised classification using the “Classification Workflow” in ENVI 5.0. To evaluate classification accuracy, an error matrix was generated, and the Kappa, overall accuracy, errors of omission and commission, user accuracy and producer accuracy indexes calculated. The classes showing greatest variation across the study period were “Agriculture” and “Rainforest”. Results indicated that deforested areas are often replaced by pasture and then by agriculture, while direct conversion of forest to agriculture occured less frequently. The indices with satisfactory accuracy levels included the Kappa and Global indices, which showed accuracy levels above 80% for all study years. In addition, the producer and user accuracy indices ranged from 59-100% and 68-100%, while the errors of omission and commission ranged from 0-32% and 0-40.6%, respectively.

scholarly journals Three-Dimensional Magnetic Inversion Based on an Adaptive Quadtree Data Compression

2020 ◽  
Vol 10 (21) ◽  
pp. 7636
Author(s):  
Dandan Jiang ◽  
Zhaofa Zeng ◽  
Shuai Zhou ◽  
Yanwu Guan ◽  
Tao Lin ◽  
...  
Keyword(s):  
Data Compression ◽  
Large Scale ◽  
Mineral Exploration ◽  
Three Dimensional ◽  
Original Data ◽  
Magnetic Data ◽  
The Best Approximation ◽  
Magnetic Inversion ◽  
Iterative Inversion ◽  
Computationally Intensive

Three-dimensional magnetic inversion allows the distribution of magnetic parameters to be obtained, and it is an important tool for geological exploration and interpretation. However, because of the redundancy of the data obtained from large-scale investigations or high-density sampling, it is very computationally intensive to use these data for iterative inversion calculations. In this paper, we propose a method for compressing magnetic data by using an adaptive quadtree decomposition method, which divides the two-dimensional data region into four quadrants and progressively subdivides them by recursion until the data in each quadrant meets the regional consistency criterion. The method allows for dense sampling at the abnormal boundaries with large amplitude changes and sparse sampling at regions with small amplitude changes, and achieves the best approximation to the original data with the least amount of data, thus retaining more anomalous information while achieving the purpose of data compression. In addition, assigning values to the data in the quadrants using the averaging method is essentially equivalent to average filtering, which reduces the noise of the magnetic data. Testing the synthetic model and applying the method to mineral exploration a prove that it can effectively compress the magnetic data and greatly improve the computational efficiency.

scholarly journals Compression Challenges in Large Scale Partial Differential Equation Solvers

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 197 ◽  
Author(s):  
Sebastian Götschel ◽  
Martin Weiser
Keyword(s):  
Data Compression ◽  
Large Scale ◽  
Memory Hierarchy ◽  
Main Memory ◽  
Computational Science ◽  
Limiting Factor ◽  
Mass Storage ◽  
Partial Differential ◽  
Computing Power ◽  
Pde Solvers

Solvers for partial differential equations (PDEs) are one of the cornerstones of computational science. For large problems, they involve huge amounts of data that need to be stored and transmitted on all levels of the memory hierarchy. Often, bandwidth is the limiting factor due to the relatively small arithmetic intensity, and increasingly due to the growing disparity between computing power and bandwidth. Consequently, data compression techniques have been investigated and tailored towards the specific requirements of PDE solvers over the recent decades. This paper surveys data compression challenges and discusses examples of corresponding solution approaches for PDE problems, covering all levels of the memory hierarchy from mass storage up to the main memory. We illustrate concepts for particular methods, with examples, and give references to alternatives.

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