Cloud Data Management Systems Research Articles

Evaluation of Spatial Spillover Effect of Multidimensional Hybrid Financial Risk Contagion Based on the DAI Spatial Econometric Model

The process of global integration has accelerated, and the international financial market has become increasingly closely linked. The financial risks that come with them are becoming more complex and difficult to guard against. Multimedia modeling in Health Cloud biometric authentication and data management systems can be applied to the analysis of financial markets. Most of the current financial risk analysis models are based on a single time, and the models are relatively simple and cannot adapt to the current complex multidimensional mixed financial risk environment. Therefore, this paper aimed to analyze the spatial spillover effect of financial risk contagion based on the directed asymmetric (DAI) spatial econometric model. This paper proposed to transfer entropy information weight information and introduce the GARCH (generalized auto-regressive conditional heteroskedasticity) model to improve the traditional econometric model. By constructing a DAI measurement model, the spatial contagion of multidimensional mixed financial risks was analyzed, and on this basis, a generalized multidimensional economic space was established to analyze spatial spillover effects and analyze the specific path of spatial spillover effects. The model results in this paper showed that the degree of correlation between the stock and bond market varied greatly between countries. Among them, the change coefficient W s ⟶ b r s D of the event period was judged to have a large degree of negative change in the United Kingdom, Germany, and France in the European Union, which were −0.9885, −0.9876, and −0.9748, respectively. This showed that the model in this paper had a good and reliable ability to cope with the current multidimensional mixed complex financial risk environment and could be used as a reference for financial risk-related research. At the same time, it also proved that multimedia modeling in health cloud biometric authentication and data management system could provide a role in financial risk contagion analysis.

Evaluation of the Construction of a Data Center-Driven Financial Shared Service Platform From the Remote Multimedia Network Perspective

The financial shared service (FSS) platform under the remote multimedia network perspective is a newly proposed accounting and reporting business management mode in recent years that plays a good role in the operation of enterprises. The multimedia technology in the health cloud biological authentication and data management system can be applied to the construction of the data center-driven FSS platform. This study used multimedia in the data management system to build a data center-driven FSS platform to maintain the normal operation of the enterprise. Moreover, the study analyzed from the perspective of employee satisfaction, work efficiency, enterprise cost, and work continuity and drew a conclusion. The survey of employee satisfaction showed that the satisfaction of employees after modeling had been greatly improved compared with that before modeling. Moreover, according to the survey of work efficiency, the work efficiency of employees in the five enterprises after modeling has been greatly improved compared with that before modeling. Furthermore, in the survey of enterprise cost, the cost reduction effect of postal enterprises is best after using multimedia technology to model the data center-driven FSS platform. Then, according to the survey on work continuity, the work continuity of the five enterprises has been improved to varying degrees after the establishment of the FSS platform. Among them, the work continuity of Enterprise 2 has been improved by 11%, with the best effect. The multimedia modeling in the health cloud biological authentication and data management system plays a role in promoting the development of enterprises.

Scalability and Performance of LiDAR Point Cloud Data Management Systems: A State-of-the-Art Review

Current state-of-the-art point cloud data management (PCDM) systems rely on a variety of parallel architectures and diverse data models. The main objective of these implementations is achieving higher scalability without compromising performance. This paper reviews the scalability and performance of state-of-the-art PCDM systems with respect to both parallel architectures and data models. More specifically, in terms of parallel architectures, shared-memory architecture, shared-disk architecture, and shared-nothing architecture are considered. In terms of data models, relational models, and novel data models (such as wide-column models) are considered. New structured query language (NewSQL) models are considered. The impacts of parallel architectures and data models are discussed with respect to theoretical perspectives and in the context of existing PCDM implementations. Based on the review, a methodical approach for the selection of parallel architectures and data models for highly scalable and performance-efficient PCDM system development is proposed. Finally, notable research gaps in the PCDM literature are presented as possible directions for future research.

Optimization of Data Processing System for Exercise and Fitness Process Based on Internet of Things

In the digital network era, people have higher requirements for physical fitness. In the future, physical fitness requires not only good fitness equipment and fitness environment but also more convenient and intelligent health management, service guidance, social entertainment, and other refined fitness services. The innovation of sports and fitness equipment for the digital network era will definitely depend on the development of information technology and network technology. Based on the cutting-edge Internet of Things technology, this thesis focuses on the development and application of a new generation of digital fitness equipment adapted to future development, advocating the new concept of seamless integration of fitness exercise and information services through human-oriented systematic design thinking and providing implementable solutions to realize the science, convenience, and life of public fitness. This thesis uses modern science and technology, especially the Internet of Things (IoT) technology, to fully meet the diversified fitness needs of the fitness crowd as the guide; IoT digital fitness equipment design and application research was newly generated, using a variety of research methods to explore the functional design and application of IoT fitness equipment; the goal is to create a more intelligent and three-dimensional IoT fitness service model in the future. Through the application research of intelligent devices in IoT fitness equipment, the realization of the functions of identity identification, environment perception, and data transmission of IoT fitness equipment is made faster. Intelligent devices can become the interaction channel between fitness service personnel, fitness equipment, and fitness users and also reduce the development cost of IoT fitness equipment. The construction of an IoT fitness cloud service platform and data management system integrates the application of IoT, cloud computing, mobile communication, and other technologies to make IoT fitness service supply remote, real-time, and diversified. While providing convenient and value-added fitness services for fitness people, it also brings sustainable development space for the health service industry.

Choosing the best Bin Packing Algorithm for Replica Placement in Multi-Tenant Cloud System

Of late, Cloud Computing is visibly seen to reduce infrastructure costs with high data availability and performance conforming to service level agreement for both the service providers and the users. With the rapid and explosive growth of the number of cloud users, Cloud Data Management System must serve an array of different analytical and transactional workloads. Hence, to ensure the scalability in a multi-tenant system, replica placement algorithms always come into the picture appropriately. In our work, we have vividly analyzed various replica placement algorithms in terms of their performance and tried to find the beneficial aspects to be the fittest one to tackle the situation when the actual observed workloads are immensely deviated from the estimated workloads.

Consistency in Cloud-based database systems

Cloud computing covers the large spectrum of services available on the internet. Cloud services use replication to ensure high availability. Within database replication; various copies of the same data item are stored in different sites, this situation requires managing the consistency of the multiple copies. In fact, the requirement for consistency level can be different following to application natures and other metrics; a delay of some minutes in visualizing latest posts in social networks can be tolerated, while some seconds can make a loss of a bid in an auction system. Wide variety of database management systems are used actually by cloud services. They support different level of consistency to meet the diverse needs of consistency levels. This paper draws a presentation of the main characteristics of cloud computing and data management systems and describes different consistency models. Then it discusses the most famous cloud-based database management systems from the point of view of their data and consistency models.

Model of Point Cloud Data Management System in Big Data Paradigm

Modern geoinformation technologies for collecting and processing data, such as laser scanning or photogrammetry, can generate point clouds with billions of points. They provide abundant information that can be used for different types of analysis. Due to its characteristics, the point cloud is often viewed as a special type of geospatial data. In order to efficiently manage such volumes of data, techniques based on a computer cluster have to be used. The Apache Spark framework has proven to be a solution for efficient processing of large volumes of data. This paper thoroughly examines the representation of point cloud data type using Apache Spark constructs. The common operations over point clouds, range queries and k-nearest neighbors queries (kNN) are implemented using Apache Spark DataFrame Application Programming Interface (API). It enabled the design of point cloud related user defined types (UDT) and user defined functions (UDF). The structure of the point cloud for efficient storing in Big Data key-value stores was analyzed and described. The methods presented in this paper were compared to PostgreSQL RDBMS, and the results were discussed.

A PR-quadtree based multi-dimensional indexing for complex query in a cloud system

The state-of-the-art indexing mechanisms for distributed cloud data management systems can not support complex queries, such as multi-dimensional query and range query. To solve this problem, we propose a multi-dimensional indexing mechanism named PR-Chord to support complex queries. PR-Chord is composed of the global index named PR-Index and the Chord network. The multi-dimensional space formed by the range of the multi-dimensional data is divided into hyper-rectangle spaces equally. The PR-Index is a hierarchical index structure based on the improved PR quadtree to index these spaces. The complex query is transformed into the query of leaf nodes of PR-Index. We design the algorithms of query, insertion and deletion to support complex queries. Since PR-Index does not store the multi-dimensional data, its maintenance cost is zero. PR-Chord has the advantages of load balancing and simple algorithm. The experiment results demonstrate that PR-Chord has good query efficiency.

Concurrency Control Method to Provide Transactional Processing for Cloud Data Management System

As new applications of cloud data management system (CDMS) such as online games, cooperation edit, social network, and so on, are increasing, transaction processing capabilities for CDMS are required. Several transaction processing methods for cloud data management system (CDMS) have been proposed. However, existing transaction processing methods have some problems. Some of them provide limited transaction processing capabilities. Some of them are hard to be integrated with existing CDMSs. In this paper, we proposed a new concurrency control method to support transaction processing capability for CDMS to solve these problems. The proposed method was designed and implemented based on Spark, an in-memory distributed processing framework. It uses RDD (Resilient Distributed Dataset) model to provide fault tolerant to data in the main memory. In our proposed method, database stored in CDMS is loaded to main memory managed by Spark. The loaded data set is then transformed to RDD. In addition, we proposed a multi-version concurrency control method through immutable characteristics of RDD. Finally, we performed experiments to show the feasibility of the proposed method.

Benchmarking and improving point cloud data management in MonetDB

The popularity, availability and sizes of point cloud data sets are increasing, thus raising interesting data management and processing challenges. Various software solutions are available for the management of point cloud data. A benchmark for point cloud data management systems was defined and it was executed for several solutions. In this paper we focus on the solutions based on the column-store MonetDB, the generic out-of-the-box approach is compared with two alternative approaches that exploit the spatial coherence of the data to improve the data access and to minimize the storage requirements.

Dynamic partition lock method to reduce transaction abort rates of cloud database

Generally, existing cloud data management systems (CDMSs) support partition tolerance in mandatory and sacrifice consistency according to the CAP theory. Partition tolerance means that the database...

SWORD: workload-aware data placement and replica selection for cloud data management systems

Cloud computing is increasingly being seen as a way to reduce infrastructure costs and add elasticity, and is being used by a wide range of organizations. Cloud data management systems today need to serve a range of different workloads, from analytical read-heavy workloads to transactional (OLTP) workloads. For both the service providers and the users, it is critical to minimize the consumption of resources like CPU, memory, communication bandwidth, and energy, without compromising on service-level agreements if any. In this article, we develop a workload-aware data placement and replication approach, called SWORD, for minimizing resource consumption in such an environment. Specifically, we monitor and model the expected workload as a hypergraph and develop partitioning techniques that minimize the average query span, i.e., the average number of machines involved in the execution of a query or a transaction. We empirically justify the use of query span as the metric to optimize, for both analytical and transactional workloads, and develop a series of replication and data placement algorithms by drawing connections to several well-studied graph theoretic concepts. We introduce a suite of novel techniques to achieve high scalability by reducing the overhead of partitioning and query routing. To deal with workload changes, we propose an incremental repartitioning technique that modifies data placement in small steps without resorting to complete repartitioning. We propose the use of fine-grained quorums defined at the level of groups of data items to control the cost of distributed updates, improve throughput, and adapt to different workloads. We empirically illustrate the benefits of our approach through a comprehensive experimental evaluation for two classes of workloads. For analytical read-only workloads, we show that our techniques result in significant reduction in total resource consumption. For OLTP workloads, we show that our approach improves transaction latencies and overall throughput by minimizing the number of distributed transactions.

Benchmarking and Analyzing the Energy Consumption of Cloud Data Management System

Cloud Data Management System(CDMS)is a new rising data management system.To achieve the"green computing"and study the Energy Consumption(EC)of CDMS,firstly,the measurement and benchmarking approaches of EC should be defined,and the EC characteristics of CDMS should be studied.Currently,the benchmarking of CDMS is mainly focuses on performance issue,and few of them are about evaluation and optimization of EC,or introduce the measuring instrument,measuring approach,test cases and basic features of EC.In this paper,the measurement of EC and its mathematical expression are proposed;the benchmarking cases including data loading,querying and analyzing are defined;the evaluation approaches of EC of CDMS are designed;the EC of executing loading,retrieving,querying,aggregation and join in several CDMSs are analyzed;the approach of"reducing awaiting EC"is also proposed for optimizing the EC of CDMS.Plenties of experiment results show that,despite that CDMS is an application of"green computing",the EC of the selected CDMSs are in great difference,and some of them should be well optimized.

A Survey of Query Techniques in Cloud Data Management Systems

As a revolutionary application mode in the internet,cloud computing has attracted more and more attentions from both industry and academia.Users can obtain cloud service conveniently through terminals,and access resources of storage,computing and hardware in the Pay-As-You-Go model.The development of cloud computing brings about a series of challenging problems,data management in the cloud is of great importance.In this paper,we propose a framework of cloud data management system.Based on this framework,the key research works of query techniques in cloud data management system are classified and surveyed from several aspects: index management,query processing,query optimization and online aggregation.At last,the suggestions for future research are put forward.

Towards a Generic Security Framework for Cloud Data Management Environments

Providing an adequate security level in Cloud Environments is currently an extremely active research area. More specifically, malicious behaviors targeting large-scale Cloud data repositories (e.g., Denial of Service attacks) may drastically degrade the overall performance of such systems and cannot be detected by typical authentication mechanisms. This article proposes a generic security management framework allowing providers of Cloud data management systems to define and enforce complex security policies. This security framework is designed to detect and stop a large array of attacks defined through an expressive policy description language and to be easily interfaced with various data management systems. The authors show that they can efficiently protect a data storage system by evaluating the security framework on top of the BlobSeer data management platform. The authors evaluate the benefits of preventing a DoS attack targeted towards BlobSeer through experiments performed on the Grid’5000 testbed.