Efficient Dynamic Data Research Articles

A Novel Skip Orthogonal List for Dynamic Optimal Transport Problem

Optimal transport is a fundamental topic that has attracted a great amount of attention from the optimization community in the past decades. In this paper, we consider an interesting discrete dynamic optimal transport problem: can we efficiently update the optimal transport plan when the weights or the locations of the data points change? This problem is naturally motivated by several applications in machine learning. For example, we often need to compute the optimal transport cost between two different data sets; if some changes happen to a few data points, should we re-compute the high complexity cost function or update the cost by some efficient dynamic data structure? We are aware that several dynamic maximum flow algorithms have been proposed before, however, the research on dynamic minimum cost flow problem is still quite limited, to the best of our knowledge. We propose a novel 2D Skip Orthogonal List together with some dynamic tree techniques. Although our algorithm is based on the conventional simplex method, it can efficiently find the variable to pivot within expected O(1) time, and complete each pivoting operation within expected O(|V|) time where V is the set of all supply and demand nodes. Since dynamic modifications typically do not introduce significant changes, our algorithm requires only a few simplex iterations in practice. So our algorithm is more efficient than re-computing the optimal transport cost that needs at least one traversal over all |E|=O(|V|^2) variables, where |E| denotes the number of edges in the network. Our experiments demonstrate that our algorithm significantly outperforms existing algorithms in the dynamic scenarios.

Design and Simulation of Efficient Dynamic Data Forwarding Protocol towards Route Optimization in VANET Infrastructure

The VANET, which consists of the Internet and the Internet of Things (IoT), has recently been implemented with the Internet of Vehicles (IoV). Through the use of a car network and an AdHoc approach, it provides the user with environmental traffic information. Combining the networks of cars, known as VANETs, allows for infrastructure-free data transfer, particularly for entertainment and emergency vehicle notification. The data or packet delivery ratio, data communication latency, data transmission faults, and details of vehicle collisions are among the traffic information provided by the internet of vehicles. In this work, an unmanned aerial vehicle (UAV) is used to solve the problem of route optimisation in VANET topologies for effective data transfer. Many routing protocols are used to improve the effectiveness of the packet or data routing process. In order to alleviate the routing issue and generate an effective way for efficient data transfer with strong network stability, the Optimised Link State Routing (OLSR) Protocol is used in this study with energy-saving techniques on many parameters. To evaluate the efficacy of the suggested routing technique on various network sizes, extensive simulations are carried out on the NS2 simulator with various network parameters. When compared to traditional routing protocols along UAV-based protocols for data transmission, the performance results of the suggested data routing solution employing OLSR protocol achieve higher efficient pathways. Ultimately, the suggested routing system produces superior outcomes in terms of message throughput, message delivery ratio, latency and network overhead on comparing with existing routing protocols.

A novel improved hybrid optimization algorithm for efficient dynamic medical data scheduling in cloud-based systems for biomedical applications

A novel improved hybrid optimization algorithm for efficient dynamic medical data scheduling in cloud-based systems for biomedical applications

Multi-source dynamic time series data processing method based on comprehensive weighted fusion

Aiming at the fusion problem of multi-source heterogeneous dynamic data sources, based on the subjective and objective comprehensive weighting idea, an efficient multi-source dynamic data fusion algorithm is proposed. First, the outlier detection method based on linear regression model is used to preprocess the multi-source dynamic time series data. Then, determine the weight of the dynamic data. The prior weight of the data source is determined by the AHP method, cyclic scoring method, etc., and the posterior weight is determined according to the similarity between the time series data. Based on the Euclidean distance, a similarity measure between different data sources and a similarity measure between different dynamic data samples are constructed respectively. The former is used for the fusion of dynamic data standard deviation expectations, and the latter is used for the fusion of mean expectations. Finally, the comprehensive weight of each dynamic time series of each data source is obtained, and the comprehensive weighted fusion method is used to obtain the expected value and standard deviation of the dynamic response. The analysis of the calculation example shows that the proposed algorithm has higher computational efficiency than the method that treats dynamic data as several discrete static data.

Maintaining the Union of Unit Discs under Insertions with Near-Optimal Overhead

We present efficient dynamic data structures for maintaining the union of unit discs and the lower envelope of pseudo-lines in the plane. More precisely, we present three main results in this paper: (i) We present a linear-size data structure to maintain the union of a set of unit discs under insertions. It can insert a disc and update the union in O (( k +1)log 2 n ) time, where n is the current number of unit discs and k is the combinatorial complexity of the structural change in the union due to the insertion of the new disc. It can also compute, within the same time bound, the area of the union after the insertion of each disc. (ii) We propose a linear-size data structure for maintaining the lower envelope of a set of x -monotone pseudo-lines. It can handle insertion/deletion of a pseudo-line in O (log 2 n ) time; for a query point x 0 ∈ ℝ, it can report, in O (log n ) time, the point on the lower envelope with x -coordinate x 0 ; and for a query point q ∈ ℝ 2 , it can return all k pseudo-lines lying below q in time O (log n + k log 2 n ). (iii) We present a linear-size data structure for storing a set of circular arcs of unit radius (not necessarily on the boundary of the union of the corresponding discs), so that for a query unit disc D , all input arcs intersecting D can be reported in O ( n 1/2+ɛ + k ) time, where k is the output size and ɛ > 0 is an arbitrarily small constant. A unit-circle arc can be inserted or deleted in O (log 2 n ) time.

Provable data deletion from efficient data integrity auditing and insertion in cloud storage

With the widespread popularity of cloud storage, a growing number of data owners are willing to outsource their massive data to remote cloud servers, thus effectively avoiding the heavy burden of storing and managing the massive data by themselves in local physical storage mediums. Despite the tremendous advantages, cloud storage inescapably suffers from some severe security challenges resulting from the separation of outsourced data management and its ownership, such as data integrity, data deletion, and data insertion. In this paper, we aim to deal with the problems of integrity auditing, dynamic insertion and provable deletion over outsourced data. Specifically, we design a novel authentication data structure, namely, number-rank-based Merkle hash tree (NR-MHT), which can achieve data dynamic operations. Meanwhile, NR-MHT can maintain many data blocks in each leaf node, thus effectively solving the problem of the height indefinitely increases with the total number of data blocks. Subsequently, we adopt NR-MHT to propose a new provable data deletion scheme based on efficient data integrity auditing and dynamic data insertion. In our presented scheme, the data owner can not only permanently delete the unnecessary outsourced data blocks to save storage expenditures but also efficiently insert new data blocks to dynamically update the outsourced data set. Meanwhile, we analyze the security to formally demonstrate that our presented scheme can satisfy all of the expected security requirements without interacting with a third party auditor. Finally, we also develop a prototype implementation of our presented scheme and provide an accurate efficiency evaluation. The numerical analysis and experimental results show that compared with the existing schemes, our presented scheme is more attractive in efficiency and practicability.

각종 주름판 코어 샌드위치 패널의 강성 및 진동 해석

Corrugated core sandwich panels are relatively lightweight structure components compared to their large flexural rigidity, and are widely used in automotive, aerospace, ship, and plant industries. However, research on rigidity and vibration for these sandwich panels remains fairly poor. In this study, the optimal sandwich panel is determined by presenting the rigidities of corrugated core sandwich panels with sinusoidal, triangular, trapezoidal, and rectangular waves. Furthermore, by analyzing the vibration of the sandwich panels for arbitrary boundary conditions, a basic visual program for practical and efficient static and dynamic design data extraction of the sandwich panels is completed.

Local influence analysis for GMM estimation

The generalized method of moments (GMM) is an important estimation procedure in many areas of economics and finance, and it is well known that this estimation is highly sensitive to the presence of outliers and influential observations. Case-deletion diagnostic has been studied in GMM estimation; however, it is surprised that local influence analysis is under explored. To this end, a local influence method is proposed to assess the effect of minor perturbation on GMM estimation. The local diagnostic measures of GMM estimators under the perturbations of empirical distribution and moment condition are derived to study the joint influence of observations. The obtained results are applied to efficient instrumental variable estimation and dynamic panel data model. Two real data sets are used for illustration, and a simulation study is conducted to examine the effectiveness of the proposed methodology. The advantage of local influence method is analyzed in detail through comparison with the case-deletion method.

A chameleon hash authentication tree optimisation audit for data storage security in cloud calculation

In order to improve the security of data storage in cloud calculation, a chameleon hash authentication tree optimisation audit method for data storage security in cloud calculation is proposed. First, an optimised public audit agreement is proposed. By storing homomorphic linear validator for user data on TPA sites, the response size of cloud storage server (CSS) is optimised. At the same time, the quasi-random function is used to optimise the query request to CSS; secondly, the chameleon hash and an improved chameleon authentication tree are used to perform efficient dynamic data updating on client data (cloud calculation) to support block-level updating and fine-grained updating; finally, through thorough security and performance analysis, it is clearly verified that the proposed method is safe and efficient.

Dynamic Data Allocation and Task Scheduling on Multiprocessor Systems With NVM-Based SPM

Low-power and short-latency memory access is critical to the performance of chip multiprocessor (CMP) system devices, especially to bridge the performance gap between memory and CPU. Together with increased demand for low-energy consumption and high-speed memory, scratch-pad memory (SPM) has been widely adopted in multiprocessor systems. In this paper, we employ a hybrid SPM, composed of a static random-access memory and a nonvolatile memory (NVM), to replace the cache in CMP. However, there are several challenges related to the CMP that need to be addressed, including how to dynamically assign processors to application tasks and dynamically allocate data to memories. To solve these problems based on this architecture, we propose a novel dynamic data allocation and task scheduling algorithm, i.e., dynamic greedy data allocation and task scheduling (DGDATS). Experiments on DSP benchmarks demonstrate the effectiveness and efficiency of our proposed algorithms; namely, our proposed algorithm can generate a highly efficient dynamic data allocation and task scheduling approach to minimize the total execution cost and produce the least amount of write operations on NVMs. Our extensive simulation study demonstrates that our proposed algorithm exhibits an excellent performance compared with the heuristic allocation (HA) and adaptive genetic algorithm for data allocation (AGADA) algorithms. Based on the CMP systems with hybrid SPMs, DGDATS reduces the total execution cost by 22.18% and 51.37% compared with those of the HA and AGADA algorithms, respectively. Additionally, the average number of write operations on NVM is 19.82% lower than that of HA.

Secure Data Storage and Searching for Industrial IoT by Integrating Fog Computing and Cloud Computing

With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices’ energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.

Cloud-Based Fine-Grained Health Information Access Control Framework for LightweightIoT Devices with Dynamic Auditing andAttribute Revocation

The eHealth trend has spread globally. Internet of Things (IoT) devices for medical service and pervasive Personal Health Information (PHI) systems play important roles in the eHealth environment. A cloud-based PHI system appears promising but raises privacy and information security concerns. We propose a cloud-based fine-grained health information access control framework for lightweight IoT devices with data dynamics auditing and attribute revocation functions. Only symmetric cryptography is required for IoT devices, such as wireless body sensors. A variant of ciphertext-policy attribute-based encryption, dual encryption, and Merkle hash trees are used to support fine-grained access control, efficient dynamic data auditing, batch auditing, and attribute revocation. Moreover, the proposed scheme also defines and handles the cloud reciprocity problem wherein cloud service providers can help each other avoid fines resulting from data loss. Security analysis and performance comparisons show that the proposed scheme is an excellent candidate for a cloud-based PHI system.

Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics.

Compressed sensing (CS) is an emerging sampling paradigm in data acquisition. Its integrated analog-to-information structure can perform simultaneous data sensing and compression with low-complexity hardware. To date, most of the existing CS implementations have a fixed architectural setup, which lacks flexibility and adaptivity for efficient dynamic data sensing. In this paper, we propose a dynamic knob (DK) design to effectively reconfigure the CS architecture by recognizing the biosignals. Specifically, the dynamic knob design is a template-based structure that comprises a supervised learning module and a look-up table module. We model the DK performance in a closed analytic form and optimize the design via a dynamic programming formulation. We present the design on a 130 nm process, with a 0.058 mm (2) fingerprint and a 187.88 nJ/event energy-consumption. Furthermore, we benchmark the design performance using a publicly available dataset. Given the energy constraint in wireless sensing, the adaptive CS architecture can consistently improve the signal reconstruction quality by more than 70%, compared with the traditional CS. The experimental results indicate that the ultra-low power dynamic knob can provide an effective adaptivity and improve the signal quality in compressed sensing towards biosignal dynamics.

Case deletion diagnostics for GMM estimation

Generalized method of moment (GMM) is an important estimation method for econometric models. However, it is highly sensitive to the outliers and influential observations. This paper studies the detection of influential observations using GMM estimation and establishes some useful diagnostic tools, such as residual and leverage measures. The case deletion technique is employed to derive diagnostic measures. Under linear moment conditions, an exact deletion formula is derived, and under nonlinear moment condition an approximate formula is suggested. The results are applied to efficient instrumental variable estimation and dynamic panel data models. In addition, generalized residuals and leverage measure for GMM estimator are defined and discussed. Two real data sets are used for illustration and a simulation study is conducted to confirm the usefulness of the proposed methodology.

Towards comprehensive provable data possession in cloud computing

To check the remote data integrity in cloud computing, we have proposed an efficient and full data dynamic provable data possession (PDP) scheme that uses a SN (serial number)-BN (block number) table to support data block update. In this article, we first analyze and test its performance in detail. The result shows that our scheme is efficient with low computation, storage, and communication costs. Then, we discuss how to extend the dynamic scheme to support other features, including public auditability, privacy preservation, fairness, and multiple-replica checking. After being extended, a comprehensive PDP scheme that has high efficiency and satisfies all main requirements is provided.

Parallel reactive molecular dynamics: Numerical methods and algorithmic techniques

Molecular dynamics modeling has provided a powerful tool for simulating and understanding diverse systems - ranging from materials processes to biophysical phenomena. Parallel formulations of these methods have been shown to be among the most scalable scientific computing applications. Many instances of this class of methods rely on a static bond structure for molecules, rendering them infeasible for reactive systems. Recent work on reactive force fields has resulted in the development of ReaxFF, a novel bond order potential that bridges quantum-scale and classical MD approaches by explicitly modeling bond activity (reactions) and charge equilibration. These aspects of ReaxFF pose significant challenges from a computational standpoint, both in sequential and parallel contexts. Evolving bond structure requires efficient dynamic data structures. Minimizing electrostatic energy through charge equilibration requires the solution of a large sparse linear system with a shielded electrostatic kernel at each sub-femtosecond long time-step. In this context, reaching spatio-temporal scales of tens of nanometers and nanoseconds, where phenomena of interest can be observed, poses significant challenges. In this paper, we present the design and implementation details of the Purdue Reactive Molecular Dynamics code, PuReMD. PuReMD has been demonstrated to be highly efficient (in terms of processor performance) and scalable. It extends current spatio-temporal simulation capability for reactive atomistic systems by over an order of magnitude. It incorporates efficient dynamic data structures, algorithmic optimizations, and effective solvers to deliver low per-time-step simulation time, with a small memory footprint. PuReMD is comprehensively validated for performance and accuracy on up to 3375 cores on a commodity cluster (Hera at LLNL-OCF). Potential performance bottlenecks to scalability beyond our experiments have also been analyzed. PuReMD is available over the public domain and has been used to model diverse systems, ranging from strain relaxation in Si-Ge nanobars, water-silica surface interaction, and oxidative stress in lipid bilayers (bio-membranes).

Hausdorff distance under translation for points and balls

We study the shape matching problem under the Hausdorff distance and its variants. In the first part of the article, we consider two sets A,B of balls in R d , d =2,3, and wish to find a translation t that minimizes the Hausdorff distance between A+t , the set of all balls in A shifted by t , and B . We consider several variants of this problem. First, we extend the notion of Hausdorff distance from sets of points to sets of balls, so that each ball has to be matched with the nearest ball in the other set. We also consider the problem in the standard setting, by computing the Hausdorff distance between the unions of the two sets (as point sets). Second, we consider either all possible translations t (as is the standard approach), or consider only translations that keep the balls of A+t disjoint from those of B . We propose several exact and approximation algorithms for these problems. In the second part of the article, we note that the Hausdorff distance is sensitive to outliers, and thus consider two variants that are more robust: the root-mean-square (rms) and the summed Hausdorff distance. We propose efficient approximation algorithms for computing the minimum rms and the minimum summed Hausdorff distances under translation, between two point sets in R d . In order to obtain a fast algorithm for the summed Hausdorff distance, we propose a deterministic efficient dynamic data structure for maintaining an ϵ-approximation of the 1-median of a set of points in R d , under insertions and deletions.

Design of Instrumentation Sensor Networks for Non-Linear Dynamic Processes Using Extended Kalman Filter

This paper presents a methodology for design of instrumentation sensor networks in non-linear chemical plants. The method utilizes a robust extended Kalman filter approach to provide an efficient dynamic data reconciliation. A weighted objective function has been introduced to enable the designer to incorporate each individual process variable with its own operational importance. To enhance the evaluation accuracy of the weighted objective function, a true relative standard deviation measure has been employed in the presented formulation. A Genetic Algorithm (GA) has been used to solve the resulting constrained optimization problem due to cost-optimal and performance-optimal design objectives. The proposed method has been tested on a non-linear continuous-stirred tank reactor (CSTR) benchmark plant, illustrating its effective design capabilities.

Applying Data Mapping Techniques to Vector DSPs

Vector digital signal processors (DSPs) offer a good performance to power consumption ratio. Therefore, they are suitable for mobile devices in software defined radio applications. These vector DSPs require input algorithms with vector operations. The performance of vectorized algorithms to a great extent depends on the distribution of data on vector elements. Traditional algorithms for vectorization focus on the extraction of parallelism from a program; we propose an analysis tool that focuses on the selection of an efficient dynamic data mapping for vector DSPs. We transferred Garcia's communication parallelism graph (Garcia et al., IEEE Trans Parallel Distrib Syst 12: 416---431, 2001) for distributed memory multiprocessor systems to vector DSPs. By alternating the representation of two-dimensional data distributions and the cost models, we are able to determine a dynamic mapping of data on vector elements on the Embedded Vector Processor (EVP) (van Berkel et al., Proceedings of the 2004 software-defined radio technical conference SDR'04, 2004). Additionally, we propose a new efficient algorithm for processing the graph representation that operates in two steps. We demonstrate the capabilities of our tool by describing the vectorization of some MIMO OFDM algorithms.

Space Efficient Dynamic Orthogonal Range Reporting

In this paper we present new space efficient dynamic data structures for orthogonal range reporting. The described data structures support planar range reporting queries in time O(log n+klog log (4n/(k+1))) and space O(nlog log n), or in time O(log n+k) and space O(nlog en) for any e>0. Both data structures can be constructed in O(nlog n) time and support insert and delete operations in amortized time O(log 2n) and O(log nlog log n) respectively. These results match the corresponding upper space bounds of Chazelle (SIAM J. Comput. 17, 427–462, 1988) for the static case. We also present a dynamic data structure for d-dimensional range reporting with search time O(log d−1n+k), update time O(log dn), and space O(nlog d−2+en) for any e>0. The model of computation used in our paper is a unit cost RAM with word size log n.