Tree Data Structure Research Articles

Correction to: HARE: A new Hash-based Authenticated Reliable and Efficient Modified Merkle Tree Data Structure to Ensure Integrity of Data in the Healthcare Systems

A correction to this paper has been published: https://doi.org/10.1007/s12652-021-03300-y

Efficient and Scalable Initialization of Partitioned Coupled Simulations with preCICE

preCICE is an open-source library, that provides comprehensive functionality to couple independent parallelized solver codes to establish a partitioned multi-physics multi-code simulation environment. For data communication between the respective executables at runtime, it implements a peer-to-peer concept, which renders the computational cost of the coupling per time step negligible compared to the typical run time of the coupled codes. To initialize the peer-to-peer coupling, the mesh partitions of the respective solvers need to be compared to determine the point-to-point communication channels between the processes of both codes. This initialization effort can become a limiting factor, if we either reach memory limits or if we have to re-initialize communication relations in every time step. In this contribution, we remove two remaining bottlenecks: (i) We base the neighborhood search between mesh entities of two solvers on a tree data structure to avoid quadratic complexity, and (ii) we replace the sequential gather-scatter comparison of both mesh partitions by a two-level approach that first compares bounding boxes around mesh partitions in a sequential manner, subsequently establishes pairwise communication between processes of the two solvers, and finally compares mesh partitions between connected processes in parallel. We show, that the two-level initialization method is fives times faster than the old one-level scheme on 24,567 CPU-cores using a mesh with 628,898 vertices. In addition, the two-level scheme is able to handle much larger computational meshes, since the central mesh communication of the one-level scheme is replaced with a fully point-to-point mesh communication scheme.

Research Summary of Blockchain Fragmentation Propagation Mechanism Based on Merkel Tree

At present, when the blockchain system propagates blocks, it is a point-to-point propagation of complete blocks. This method has three shortcomings: the propagation load is large; the message can only be received from one node; only after the complete block is received, can it be propagated to other nodes. Therefore, this article considers changing the block’s Merkel tree data structure to minimize the data size during block propagation while ensuring that the receiving node can reconstruct the correct and complete block. On the other hand, in order to be able to receive blocks from multiple nodes at the same time and relay to other nodes when incompletely received, the blocks are fragmented and then reorganized. At the same time, the correctness of a single fragment can also be verified.

A blockchain based patient centric electronic health record storage and integrity management for e-Health systems

ObjectivesIn the digitization of health records, patients and doctors find difficulty in accessing their health records, especially when the health records are fragmented with different health service providers. In this paper, Patient Centric blockchain smart contracts are designed to provide a regulated solution to the requirements of patients, doctors and health service providers with integrity management.MethodsThe proposed system launches smart contracts namely i) Registration contract for immutable patient log creation. ii) Health Record Creation Contact to generate digital health records. iii) Health Record Storage Contract for secure storage and rapid access with a new Modified Merkle Tree data structure. iv) Update permission Contract that can provide access at emergency situations. v) Data sharing Permission Contract for exchange of health records between different stakeholders and vi) Viewership Permission Contract for viewing the health information by the patients for home care and future care.ResultsThe work has been carried out on a number of trials to check the effectiveness of the proposed system. The qualitative and quantitative metrics of the proposed system have been measured to evaluate the performance of resources, transactions per second, and the latency of transaction.ConclusionThe proposed system provides high security and integrity through cryptographic hash functions. The results are encouraging. The designed system is implemented and found to outperform the existing systems.

Interactive Path Tracing and Reconstruction of Sparse Volumes

We combine state-of-the-art techniques into a system for high-quality, interactive rendering of participating media. We leverage unbiased volume path tracing with multiple scattering, temporally stable neural denoising and NanoVDB [Museth 2021], a fast, sparse voxel tree data structure for the GPU, to explore what performance and image quality can be obtained for rendering volumetric data. Additionally, we integrate neural adaptive sampling to significantly improve image quality at a fixed sample budget. Our system runs at interactive rates at 1920 × 1080 on a single GPU and produces high quality results for complex dynamic volumes.

HARE: A new Hash-based Authenticated Reliable and Efficient Modified Merkle Tree Data Structure to Ensure Integrity of Data in the Healthcare Systems

In the digital world, systems are usually distributed, where health data are fragmented across multiple healthcare providers’ data may be accidentally corrupted or intentionally tampered. A mechanism is important to verify the integrity of the content in the network to ensure the security. The main objective of this paper is to propose a Modified Merkle Tree (MMT) data structure for data storage and data authentication. In this paper, a new Modified Merkle Tree data structure that can ensure storage security with integrity management is proposed. The Merkle Tree data structure, utilized in Blockchain technology is modified in this work for the purpose of designing a new three-stage Modified Merkle Tree based on hashed transactions. The objective of the proposed hashed transaction is to verify the authenticity and integrity of the content. The use of the Modified Merkle Tree data structure is highlighted in designing a system that takes care of Data Authentication, Consistency Verification and Data Synchronization. The effectiveness of the proposed system has been experimented in the healthcare scenario and proved to store and verify the content integrity. The quantitative metrics of the proposed system have been evaluated and compared with the binary Merkle Tree data structure.

Exploring Archetypes of Tropical Fire-Related Forest Disturbances Based on Dense Optical and Radar Satellite Data and Active Fire Alerts

Tropical forest disturbances linked to fire usage cause large amounts of greenhouse gas (GHG) emissions and environmental damages. Supporting precise GHG estimations and counteracting illegal fire usages in the tropics require timely and thematically detailed large-scale information on fire-related forest disturbances. Multi-sensor optical and radar detection and ranging (radar) remote sensing data combined with active fire alerts shows the potential for a more in-depth characterization of fire-related forest disturbances. We utilized dense optical (Landsat-7, Landsat-8 and Sentinel-2) and radar (Sentinel-1) time series to individually map forest disturbances in the province of Riau (Indonesia) for 2018–2019. We combined the sensor-specific optical and radar forest disturbance maps with daily active fire alerts and classified their temporal relationship (predating, coinciding, postdating) into seven so-called archetypes of fire-related forest disturbances. The archetypes reflect sensor-specific sensitives of optical (e.g., changes in tree foliage) and radar (e.g., changes in tree structure) data to detect varying types of forest disturbances, ranging from either a loss of tree foliage and/or structure predating, coinciding or postdating fires. These can be related to different magnitudes of fire-related forest disturbances and burn severities and can be associated with specific land management practices, such as slash-and-burn agriculture and salvage logging. This can support policy development, local and regional forest management and law enforcement to reduce illegal fire usage in the tropics. Results suggest that a delayed or opposing forest disturbance detection in the optical and radar signal is not only caused by environmental influences or different observation densities but, in some cases, such as fire-related forest disturbances, can be related to their different sensitives to detect changes in tree foliage and structure. Multi-sensor-based forest monitoring approaches should, therefore, not simply combine optical and radar time series on a data level, as it bears the risk of introducing artefacts.

A novel blockchain based electronic health record automation system for healthcare

Technology has widespread health records that may have been digitized into electronic health records (EHRs). In the present scenario, patients ought to have their health records across multiple hospitals in the geographical area. In such case accessibility of health data from one provider to another at the right time remains a major challenge, especially when patients are in a critical condition having access to fragmented health records from multiple sources into a single chain. In this paper, blockchain smart contracts are designed so as to provide a regulated solution to the need of patients, physicians, and health service providers. The proposed system aims to exchange health information on a blockchain platform to build a smart e-health system. The proposed system launches health models namely immutable patient log creation with Modified Merkle Tree data structure for secure storage and rapid access of health records, update medical records, health information exchange between different providers, and viewership contracts on the peer to peer blockchain network. In this system, blockchain is a clinical data repository that provides patients a complete, distributed ledger record containing records of all the events and seamless access to their electronic health records through healthcare providers. As an important feature, this system provides high security and integrity through cryptographic hash functions. The work has been carried out on a number of trials to check the effectiveness of the proposed system. The qualitative and quantitative metrics of the proposed system has been measured to evaluate the performance of resources, transactions per second, and the latency of a transaction.

Засіб вимірювання метрик вихідного коду FORTRAN за допомогою синтаксичного аналізу

The rapid development of software quality measurement methods, the need in efficient and versatile reengineering automatization tools becomes increasingly bigger. This becomes even more apparent when the programming language and respective coding practices slowly develop alongside each other for a long period of time, while the legacy code base grows bigger and remains highly relevant. In this paper, a source code metrics measurement tool for Fortran program quality evaluation is developed. It is implemented as a code module for Photran integrated development environment and based on a set of syntax tree walking algorithms. The module utilizes the built-in Photran syntax analysis engine and the tree data structure which it builds from the source code. The developed tool is also compared to existing source code analysis instruments. The results show that the developed tool is most effective when used in combination with Photran’s built-in refactoring system, and that Photran’s application programming interface facilitates easy scaling of the existing infrastructure by introducing other code analysis methods.

Is Single Scan based Restructuring Always a Suitable Approach to Handle Incremental Frequent Pattern Mining?

Incremental mining of frequent patterns has attracted the attention of researchers in the last two decades. The researchers have explored the frequent pattern mining from incremental database problems by considering that the complete database to be processed can be accommodated in systems’ main memory even after the database gets updated very frequently. The FP-tree-based approaches were able to draw more interest because of their compact representation and requirement of a minimum number of database scans. The researchers have developed a few FP-tree based methods to handle the incremental scenario by adjusting or restructuring the tree prefix paths. Although the approaches have managed to solve the re-computation problem by constructing a complete pattern tree data structure using only one database scan, restructuring the prefix paths for each transaction is a computationally costly task, leading to the high tree construction time. If the FP-tree construction process can be supported with suitable data structures, reconstruction of the FP-tree from scratch may be less time consuming than the restructuring approaches in case of incremental scenario. In this study, we have proposed a tree data structure called Improved Frequent Pattern tree (Improved FP-tree). The proposed Improved FP-tree construction algorithm has immensely improved the performance of tree construction time by resourcefully using node links, maintained in header table to manage the same item node list in the FP-tree. The experimental results emphasize the significance of the proposed Improved FP-tree construction algorithm over a few conventional incremental FP-tree construction algorithms with prefix path restructuring.

E-ART: A New Encryption Algorithm Based on the Reflection of Binary Search Tree

Data security has become crucial to most enterprise and government applications due to the increasing amount of data generated, collected, and analyzed. Many algorithms have been developed to secure data storage and transmission. However, most existing solutions require multi-round functions to prevent differential and linear attacks. This results in longer execution times and greater memory consumption, which are not suitable for large datasets or delay-sensitive systems. To address these issues, this work proposes a novel algorithm that uses, on one hand, the reflection property of a balanced binary search tree data structure to minimize the overhead, and on the other hand, a dynamic offset to achieve a high security level. The performance and security of the proposed algorithm were compared to Advanced Encryption Standard and Data Encryption Standard symmetric encryption algorithms. The proposed algorithm achieved the lowest running time with comparable memory usage and satisfied the avalanche effect criterion with 50.1%. Furthermore, the randomness of the dynamic offset passed a series of National Institute of Standards and Technology (NIST) statistical tests.

Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology.

Scatterplots are used for a variety of visual analytics tasks, including cluster identification, and the visual encodings used on a scatterplot play a deciding role on the level of visual separation of clusters. For visualization designers, optimizing the visual encodings is crucial to maximizing the clarity of data. This requires accurately modeling human perception of cluster separation, which remains challenging. We present a multi-stage user study focusing on four factors-distribution size of clusters, number of points, size of points, and opacity of points-that influence cluster identification in scatterplots. From these parameters, we have constructed two models, a distance-based model, and a density-based model, using the merge tree data structure from Topological Data Analysis. Our analysis demonstrates that these factors play an important role in the number of clusters perceived, and it verifies that the distance-based and density-based models can reasonably estimate the number of clusters a user observes. Finally, we demonstrate how these models can be used to optimize visual encodings on real-world data.

HOVA-FPPM: Flexible Periodic Pattern Mining in Time Series Databases Using Hashed Occurrence Vectors and Apriori Approach

Finding flexible periodic patterns in a time series database is nontrivial due to irregular occurrence of unimportant events, which makes it intractable or computationally intensive for large datasets. There exist various solutions based on Apriori, projection, tree, and other techniques to mine these patterns. However, the existence of constant size tree structure, i.e., suffix tree, with extra information in memory throughout the mining process, redundant and invalid pattern generation, limited types of mined flexible periodic patterns, and repeated traversal over tree data structure for pattern discovery, results in unacceptable space and time complexity. In order to overcome these issues, we introduce an efficient approach called HOVA-FPPM based on Apriori approach with hashed occurrence vectors to find all types of flexible periodic patterns. We do not rely on complex tree structure rather manage necessary information in a hash table for efficient lookup during the mining process. We measured the performance of our proposed approach and compared the results with the baseline approach, i.e., FPPM. The results show that our approach requires lesser time and space, regardless of the data size or period value.

R-tree data structure implementation for Computer Aided Engineering (CAE) tools

Searching and handling geometric data are basic requirements of any Computer Aided Engineering application (CAE). Spatial search and local search has greater importance in CAD and CAE applications for reducing the model preparation time. There are many efficient algorithms being made to search geometrical data. Current neighbour search strategy is limited and not efficient in different CAE platforms. R-tree is tree data structure used for spatial access methods. This paper presents a review of R-tree data structure with its implementation in one of the CAE tool for neighbour search and local search. It satisfies current neighbour search requirements in CAE tools. Results shows considerable amount of time saving compared to the conventional approach. This work concludes that R-tree implementation can be helpful in identifying neighbour part and reducing model preparation time in CAD and CAE tools.

Механізми інтеграції блокчейнів між собою

This paper considers the need for blockchain integration and the problems that arise along the way. The idea of transporting bitcoins by destroying them on one blockchain and creating a corresponding value on another blockchain is considered. A brief description of such concepts as bitcoin transaction and bitcoin block is given. The Merkle Tree data structure, its internal organization, and its benefits are described in detail. The algorithm for constructing a “Merkle Tree” for a block of transactions and the principle of constructing and verifying a “Merkle Path” for a given transaction are presented. Describes what is behind the concept of "proof of work" (Proof of Work) from both a technical and economic point of view. For the first time, a detailed algorithm for transporting bitcoins from one blockchain to another was proposed, which is mostly based on the data structure of the "Merkle tree" and the idea of "proof of work". The four main stages of this algorithm are given and explained. The scheme of the transaction destroying one bitcoin is resulted, its locking script is considered. A real example illustrates the creation and verification proof of existence of a transaction based on the Merkle tree. Given the detailed scheme of the transaction on the auxiliary blockchain, with its basic elements, namely: a chain of block headers, proof of existence based on the Merkle tree, encoded transaction from bitcoin blockchain. The connections between these elements are given and the algorithm of formation of the above-mentioned transaction is considered. The algorithm of transaction validation on the auxiliary blockchain from the point of view of the smart contract is considered step by step. The disadvantages of the idea of integrating blockchains with each other are described.

Multidimensional segment trees can do range updates in poly-logarithmic time

Updating and querying on a range is a classical algorithmic problem with a multitude of applications. The Segment Tree data structure is particularly notable in handling the range query and update operations. A Segment Tree divides the range into disjoint segments and merges them together to perform range queries and range updates elegantly. Although this data structure is remarkably potent for 1-dimensional problems, it falls short in higher dimensions. Lazy Propagation enables the operations to be computed in O(logn) time in a single dimension. However, the concept of lazy propagation could not be translated to higher-dimensional cases, which imposes a time complexity of O(nk−1logn) for operations on k-dimensional data. In this work, we have made an attempt to emulate the idea of lazy propagation differently so that it can be applied for 2-dimensional cases. Moreover, the proposed modification should be capable of performing most general aggregate functions similar to the original Segment Tree, and can also be extended to even higher dimensions. Our proposed algorithm manages to perform range sum queries and updates in O(log2⁡n) time for a 2-dimensional problem, which becomes O(logd⁡n) for a d-dimensional situation.

Nearest neighbour distributions: New statistical measures for cosmological clustering

ABSTRACT The use of summary statistics beyond the two-point correlation function to analyse the non-Gaussian clustering on small scales, and thereby, increasing the sensitivity to the underlying cosmological parameters, is an active field of research in cosmology. In this paper, we explore a set of new summary statistics – the k-Nearest Neighbour Cumulative Distribution Functions (kNN-CDF). This is the empirical cumulative distribution function of distances from a set of volume-filling, Poisson distributed random points to the k-nearest data points, and is sensitive to all connected N-point correlations in the data. The kNN-CDF can be used to measure counts in cell, void probability distributions, and higher N-point correlation functions, all using the same formalism exploiting fast searches with spatial tree data structures. We demonstrate how it can be computed efficiently from various data sets – both discrete points, and the generalization for continuous fields. We use data from a large suite of N-body simulations to explore the sensitivity of this new statistic to various cosmological parameters, compared to the two-point correlation function, while using the same range of scales. We demonstrate that the use of kNN-CDF improves the constraints on the cosmological parameters by more than a factor of 2 when applied to the clustering of dark matter in the range of scales between 10 and $40\, h^{-1}\, {\rm Mpc}$. We also show that relative improvement is even greater when applied on the same scales to the clustering of haloes in the simulations at a fixed number density, both in real space, as well as in redshift space. Since the kNN-CDF are sensitive to all higher order connected correlation functions in the data, the gains over traditional two-point analyses are expected to grow as progressively smaller scales are included in the analysis of cosmological data, provided the higher order correlation functions are sensitive to cosmology on the scales of interest.

Truncated Tree Based Hybrid Beamforming for Multi-Panel MIMO

Multi-panel MIMO is a practical antenna array structure for massive MIMO, but the optimal hybrid beamforming design has a complexity exponential to the number of antenna panels. In this letter, we propose a truncated tree based hybrid beamforming design with linear complexity for multi-panel MIMO transmission. We convert the hybrid beamforming design problem into the shortest path finding problem that is visualized by tree data structure. To reduce complexity, the tree is truncated by deleting the intermediate paths that are less likely to be the shortest path. By this means, the truncated tree based design reduces exponential time complexity to linear time complexity. Numerical results show that the proposed truncated tree based design far outperforms the heuristic max power gain beamforming design and achieves near optimal performance with a proper truncation ratio.

Exploring Network-Wide Flow Data With Flowyager

Many network operations, ranging from attack investigation and mitigation to traffic management, require answering network-wide flow queries in seconds. Although flow records are collected at each router, using available traffic capture utilities, querying the resulting datasets from hundreds of routers across sites and over time, remains a significant challenge due to the sheer traffic volume and distributed nature of flow records. In this paper, we investigate how to improve the response time for a priori unknown network-wide queries. We present Flowyager, a system that is built on top of existing traffic capture utilities. Flowyager generates and analyzes tree data structures, that we call Flowtrees, which are succinct summaries of the raw flow data available by capture utilities. Flowtrees are self-adjusted data structures that drastically reduce space and transfer requirements, by 75% to 95%, compared to raw flow records. Flowyager manages the storage and transfers of Flowtrees, supports Flowtree operators, and provides a structured query language for answering flow queries across sites and time periods. By deploying a Flowyager prototype at both a large Internet Exchange Point and a Tier-1 Internet Service Provider, we showcase its capabilities for networks with hundreds of router interfaces. Our results show that the query response time can be reduced by an order of magnitude when compared with alternative data analytics platforms. Thus, Flowyager enables interactive network-wide queries and offers unprecedented drill-down capabilities to, e.g., identify DDoS culprits, pinpoint the involved sites, and determine the length of the attack.

Postfire Tree Structure from High-Resolution LiDAR and RBR Sentinel 2A Fire Severity Metrics in a Pinus halepensis-Dominated Burned Stand

Tree and plant structures remaining after fires reflect well their degree of consumption, and are therefore good indicators of fire severity. Satellite optical images are commonly used to estimate fire severity. However, depending on the severity of a fire, these sensors have a limited ability to penetrate the canopy down to the ground. Airborne light detection and ranging (LiDAR) can overcome this limitation. Assessing the differences between areas that have been burned in different fire severities based on satellite images of plant and tree structures remaining after fires is important, given its widespread use to characterize fires and fire impacts (e.g., carbon emissions). Here, we measured the remaining tree structures after a fire in a forest stand burned in SE Spain in the summer of 2017. We used high-resolution LiDAR data, acquired from an unmanned aerial vehicle (UAV) six months after the fire. This information was crossed with fire severity levels based on the relativized burnt ratio (RBR) derived from Sentinel 2A images acquired a few months before and after fire. LiDAR tree structure data derived from vertical canopy profiles (VCPs) were classified into three clusters, using hierarchical principal component analysis (HPCA), followed by a random forest (RF) to select the most important variables in distinguishing the cluster groups. Among these, crown leaf area index (LAI), crown leaf area density (LAD), crown volume, tree height and tree height skewness, among others, were the most significant variables, and reflected well the degree of combustion undergone by the trees based on the response of these variables to variations in fire severity from RBR Sentinel 2A. LiDAR metrics were able to distinguish crown fire from surface fire through changes in the understory LAI and understory and midstory vegetation. The three tree structure clusters were well separated among each other and significantly related with the RBR Sentinel 2A-derived fire severity categories. Unburned and low-severity burned areas were more diverse in tree structures than moderate and high severity burned ones. The LiDAR metrics derived from VCPs demonstrated promising potential for characterizing fine-grained post-fire plant structures and fire damage when crossed with satellite-based fire severity metrics, turning into a promising approach for better characterizing fire impacts at a resolution needed for many ecological processes.