Enabling In Situ Pre- and Post-processing for Exascale Hemodynamic Simulations - A Co-design Study with the Sparse Geometry Lattice-Boltzmann Code HemeLB

A seven-tuple representation for hierarchical data structures

Manifold learning methods are promising data analysis tools. However, if we locate a new test sample on the manifold, we have to find its embedding by making use of the learned embedded representation of the training samples. This process often involves accessing considerable volume of data for large sample set. In this paper, an approach of selecting "landmark points" from the given samples is proposed for hierarchical structuring of data on manifolds. The selection is made such that if one use the Voronoi diagram generated by the landmark points in the ambient space to partition the embeded manifold, the topology of the manifold is preserved. The landmark points then are used to recursively construct a hierarchical structure of the data. Thus it can speed up queries in a manifold data set. It is a general framework that can fit any manifold learning algorithm as long as its result of an input can be predicted by the results of the neighbor inputs. Compared to the existing techniques of organizing data based on spatial partitioning, our method preserves the topology of the latent space of the data. Different from manifold learning algorithms that use landmark points to reduce complexity, our approach is designed for fast retrieval of samples. It may find its way in high dimensional data analysis such as indexing, clustering, and progressive compression. More importantly, it extends the manifold learning methods to applications in which they were previously considered to be not fast enough. Our algorithm is stable and fast, and its validity is proved mathematically.

Data Science is associated with new discoveries, the discovery of value from the data. It is a practice of deriving insights and developing business strategies through transformation of data in to useful information. It has been evaluated as a scientific field and research evolution in disciplines like statistics, computing science, intelligence science, and practical transformation in the domains like science, engineering, public sector, business and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. In this paper we entitled epicycles of analysis, formal modeling, from data analysis to data science, data analytics -A keystone of data science, The Big data is not a single technology but an amalgamation of old and new technologies that assistance companies gain actionable awareness. The big data is vital because it manages, store and manipulates large amount of data at the desirable speed and time. Big data addresses detached requirements, in other words the amalgamate of multiple un-associated datasets, processing of large amounts of amorphous data and harvesting of unseen information in a time-sensitive generation.
 As businesses struggle to stay up with changing market requirements, some companies are finding creative ways to use Big Data to their growing business needs and increasingly complex problems. As organizations evolve their processes and see the opportunities that Big Data can provide, they struggle to beyond traditional Business Intelligence activities, like using data to populate reports and dashboards, and move toward Data Science- driven projects that plan to answer more open-ended and sophisticated questions.
 Although some organizations are fortunate to have data scientists, most are not, because there is a growing talent gap that makes finding and hiring data scientists in a timely manner is difficult. This paper, aimed to demonstrate a close view about Data science, big data, including big data concepts like data storage, data processing, and data analysis of these technological developments, we also provide brief description about big data analytics and its characteristics , data structures, data analytics life cycle, emphasizes critical points on these issues.

Described in this paper are the data structure and encoding rules of digital imaging and communication in medicine radiotherapy in tumor radiation therapy. The process and the actual distribution of the radiation data integrated. Expanded the relevant issues of data base standardization construction, focus on the logical relationship of data analysis and the details of the data construction process, combined the data processing in clinic with the method of software technology in data analysis, further explore the issues related to data integration and application in radiation therapy. Key words: Antineoplastic protocols; Radiotherapy, computer-assisted; DICOM RT; Data analysis; Hospital information; Data integration

Automatic parallelization is a promising strategy to improve application performance in the multicore era. However, common programming practices such as the reuse of data structures introduce artificial constraints that obstruct automatic parallelization. Privatization relieves these constraints by replicating data structures, thus enabling scalable parallelization. Prior privatization schemes are limited to arrays and scalar variables because they are sensitive to the layout of dynamic data structures. This work presents Privateer, the first fully automatic privatization system to handle dynamic and recursive data structures, even in languages with unrestricted pointers. To reduce sensitivity to memory layout, Privateer speculatively separates memory objects. Privateer's lightweight runtime system validates speculative separation and speculative privatization to ensure correct parallel execution. Privateer enables automatic parallelization of general-purpose C/C++ applications, yielding a geomean whole-program speedup of 11.4x over best sequential execution on 24 cores, while non-speculative parallelization yields only 0.93x.

The uniform grid data structure is a flat (non-hierarchical) grid whose resolution adapts to the data. An exhaustive analysis of the uniform grid data structure for determining intersections in a set of many small line segments is presented. Databases from cartography, VLSI, and graphics with up to 115,973 edges are used. For each data set the intersection time, the ratio of edge pairs tested to pairs found to intersect, and size of intermediate data structures was measured as a function of grid resolution. The execution time was relatively insensitive to the grid size over a range of up to a factor of 10. 115,973 edges were processed to find 135,050 intersections in 683 seconds on a Sun 3/50 workstation. This data structure is also ideally suited for implementation on a parallel machine. When executing on a 16 processor Sequent Balance 21000, total times averaged ten times faster than when using only one processor. Finding all 81,373 intersections in a 62,045 edge database took only 28 seconds elapsed time. This research shows that more complicated, hierarchical data structures, such as quadtrees, are not necessary for this problem.

The aim of this paper is to present the first part of a new approach devoted to the generation of a data structure and operators for the hierarchical representation of 3D polyhedra. Here are described the treatments which allow to create some of the elements of this hierarchical model. At first, partitions of the initial polyhedron are mapped into planar connex hulls. Then, these domains are used like a piecewise parametric 2D space for subsequent polyhedra generations. In order to create such a mapping, the initial 3D polyhedron is partitioned to produce simply convex subsets which can be submitted to the parametrization process. The next step consists in the generation of a minimum representation of the initial 3D polyhedron. This representation forms the root of the hierarchical data structure. Then, the mapping obtained allows the construction of various polyhedral representations of the initial geometry. Criteria related to 3D parameters are used to generate the range of polyhedra. The reverse mapping (from 2D to 3D) helps reduce the computing cost required to generate 3D polyhedra. Each 3D polyhedron generation is carried out under 3D geometric criteria depending on the context. i.e.: structural analysis, levels of details of a geometric model, ... Among the goals of the hierarchical data structure, the unification and the inter dependency of the meshes required to carry out the structural analysis of a part occupies a central position.

Context. The urgent task of increasing the functional efficiency of machine learning of decision support system (DSS) for assessing compliance with content modern requirements of the educational disciplines of the graduation department based on the results of the employer survey has been solved.
 Objective. Increasing the functional efficiency of machine learning of DSS for assessing compliance with modern requirements of the educational disciplines content of the first (bachelor’s) level specialty educational and professional program based on machine learning and pattern recognition.
 Method. The method of machine learning of DSS is proposed for adapting the educational content of the graduation department to the labor market requirements. The idea of the method is to maximize the information capacity of the DSS in the machine learning process, which allows in the monitoring mode to guarantee a high full probability of making the correct classification decisions. The method was developed as part of a functional approach to modeling cognitive processes of natural intelligence, which makes it possible to provide DSS with flexibility when retraining the system due to increasing the power of the recognition classes alphabet. The method is based on the principle of maximizing the amount of information in the machine learning process. The modified Kullback information measure, which is a functional of the accuracy characteristics of classification solutions, is considered as a criterion for optimizing machine learning parameters. According to the proposed functional category model, an information-extreme machine learning algorithm was developed based on the hierarchical data structure in the form of a binary decursive tree. The use of such a data structure allows you to automatically divide a large number of recognition classes into pairs of nearest neighbors, for which optimization of machine learning parameters is carried out according to a linear algorithm of the required depth. The geometric parameters of hyperspherical containers of recognition classes were considered as optimization parameters, which were restored in the radial basis of the binary space of Hamming features in the machine learning process. At the same time, the input traning matrix was transformed into a working binary training matrix, which was changed in the machine learning process through admissible transformations in order to adapt the input information description of the DSS to the maximum reliability of classification decisions.
 Results. The informational, algorithmic, and software of the DSS was developed to assess the educational content quality based on the machine analysis results of respondents’ answers. Within the framework of the geometric approach, based on the informationextreme machine learning results, highly reliable decisive rules, practically invariant to the multidimensionality of the recognition features space, were constructed based on the hierarchical data structure in the form of a binary decursive tree. The influence of machine learning parameters on the functional effectiveness of machine learning of the DSS was studied on the evaluation example of the educational content of the educational and professional bachelor’s program of the specialty 122 Computer Science.
 Conclusions. The computer modeling results confirm the high functional efficiency of the proposed method of informationextreme hierarchical machine learning and can be recommended for practical use in institutions of higher education to assess compliance with modern requirements of the educational content of graduation departments.

In this paper, a solution to the visualization of huge amount of data provided by solvers using tree based AMR method is proposed. This approach strongly relies on the hierarchical structure of data and view dependent arguments: only the visible cells will be drawn, reducing consequently the amount of rendered data, selecting only the cells that intersect the screen and whose size is bigger than one pixel.After a brief statement of the problem, we recall the main principles of AMR methods.We then proceed to the data analysis which shows notable differences related to the dimension (2 or 3). A natural view dependent decimation algorithm is derived in the 2D case (only visible cells are plotted), while in 3D the treatment is not straightforward. The proposed solution relies then on the use of perspective in order to keep the same guidelines that were used in 2D. We then give a few hints about implementation and perform numerical experiments which confirm the efficiency of the proposed algorithms.We finally discuss this approach and give the sketch for future improvements.

We describe an important memory optimization that arises in the presence of aggregate data structures such as arrays and structs in a C/C++ based system design methodology. We present an algorithm for determining an optimized memory layout of such data. Our implementation consists of a pointer analysis and resolution phase, followed by memory layout optimization. Experiments on typical applications from the DSP domain result in up to 44% improvement in memory performance.

Visualisation techniques have been one of the best data processing and analysis methods in recent decades, and they have assisted in data understanding efforts in various fields. Visualisation techniques for low-dimensional data are well developed and applied in multiple sectors; however, multidimensional data visualisation techniques still present some limitations, such as inaccurate data comparison and perception, exaggerated visual differences, label occlusion, and overlap. This study addresses the pros and cons and proposes a novel graphical drawing method, the multidimensional rose chart. It adopts the design idea of the Nightingale rose chart, but overcomes relevant limitations. The main challenges of this area include the incomplete presentation of multidimensional data, the neglect of the linkage of multiple attributes, the inefficient use of space, and the lack of simplicity of the interface. Contributions include enriching the representations of multidimensional data through the use of colour shades, area, and height sizes to represent values; straightforward data attribute comparisons via graph nesting; and detailed attributes showing the use of specific value labels. To verify the preliminary validity of this method, we imported COVID-19 data into experiments and further compared the final layouts with traditional methods, such as the line chart, bar chart, tree, parallel coordinate chart, and Nightingale rose chart, as well as their structures, functionalities, clear advantages, and disadvantages. The experimental results show that multidimensional rose diagrams perform effectively in presenting multidimensional data when comparing other graph drawing methods in our case, and the outcomes match existing works' conclusions in related COVID-19 research sectors. This work has the potential to provide a suitable supplemental approach to the multidimensional data analysis.

This paper develops a formalism that precisely characterizes when class tables are required for C++ memory layouts. A memory layout is a particular choice of data structures for implementing run‐time support for object‐oriented languages. We use this formalism to quantify and evaluate, on a set of benchmarks, the space overhead for a set of C++ memory layouts. In particular, this paper studies the space overhead due to three language features: virtual dispatch, virtual inheritance, and dynamic typing. To date, there has been no scientific quantification or evaluation of C++ memory layouts. Our approach can help C++ implementors. This work has already influenced the memory layout design choices in IBM's Visual Age C++ V5 compiler.Applying our approach to a set of five benchmarks, we demonstrate that the impact of object‐oriented space overhead can vary dramatically between applications (ranging from 0.42% to 99.79% for our benchmarks). In particular, applications whose object space is dominated by instances of classes that heavily use object‐oriented language features will be significantly impacted by the choice of a memory layout. Copyright © 2003 John Wiley & Sons, Ltd.

Total‐body positron emission tomography (TB‐PET) has ultra‐high sensitivity and the unique ability to conduct dynamic imaging of the entire body. Both the hardware configuration and the data acquired from a TB‐PET scanner differ from those of the conventional short axial field‐of‐view scanners. Therefore, various aspects concerning data processing need careful consideration when implementing TB‐PET in clinical settings. Additionally, advances in data analysis are needed to fully uncover the potential of these systems. Although some progress has been achieved, further research and innovation in scan data management are necessary. In this report, we provide a comprehensive overview of the current progress, challenges, and possible future directions for TB‐PET data processing and analysis. For a review of clinical applications, please find the other review accompanying this paper.

When representing the geometry of voxelized three-dimensional scenes (especially if they have been voxelized to high resolutions) in a naive—uncompressed—form, one may end up using vast amounts of data. These can easily attack the available memory capacity of the graphics card, the operating memory or even secondary storage of computer. A viable solution to this problem is to use domain-specific hierarchical data structures, based on octant trees or directed acyclic graphs, which, among other advantages, provide a compact binary representation that can thus be considered to be their compressed encoding. These data structures include—inter alia—sparse voxel octrees, sparse voxel directed acyclic graphs and symmetry-aware sparse voxel directed acyclic graphs. The paper deals with the proposal of a new domain-specific hierarchical data structure: the clustered sparse voxel octrees. It is designed to represent the geometry of voxelized three-dimensional scenes and can be constructed using the out-of-core algorithm proposed in the paper. The advantage of the presented data structure is in its compact binary representation, achieved by omitting a significant number of pointers to child nodes (82.55% in case of Angel Lucy model in 1283 voxels resolution) and by using a wider range of child node pointer lengths, including 8b, 16b and 32b. We achieved from 6.57 to 6.82 times more compact encoding, compared to sparse voxel octrees, whose all node components were 32b aligned, and from 4.11 to 4.27 times more compact encoding, when not all node components were 32b aligned.

One of the important pre-processing stages in the analysis of jointed rock masses is the identification of rock blocks from discontinuities in the field. In 3D, the identification of polyhedral blocks usually involve tedious housekeeping algorithms, because one needs to establish their vertices, edges and faces, together with a hierarchical data structure: edges by pairs of vertices, faces by bounding edges, polyhedron by bounding faces.In this paper, we present a novel rock slicing method, based on the subdivision approach and linear programming optimisation, which requires only a single level of data structure rather than the current 2 or 3 levels presented in the literature. This method exploits the novel mathematical framework for contact detection introduced in Boon et al. (2012). In the proposed method, it is not necessary to calculate the intersections between a discontinuity and the block faces, because information on the block vertices and edges is not needed. The use of a simpler data structure presents obvious advantages in terms of code development, robustness and ease of maintenance. Non-persistent joints are also introduced in a novel way within the framework of linear programming. Advantages and disadvantages of the proposed modelling of non-persistent joints are discussed in this paper. Concave blocks are generated using established methods in the sequential subdivision approach, i.e. through fictitious joints.