Cloud Database Systems Research Articles

Hybrid Shared-Buffer for Multi-Master Databases

Distributed shared buffer (DSB) is a well-known solution to support multi-master database systems. DSB involves controlling access to shared data among different nodes through a distributed shared buffer and lock-based cache coherence protocols. Existing DSB implementations resolve conflicts at the page level and lack the flexibility required by modern cloud database systems. Authors present HyBuffer which mixes rows and pages in the distributed shared buffer. It enables multiple masters to independently modify different rows on the same page, thereby enhancing concurrency and performance. HyBuffer adopts the hybrid design to track the location information of cached data. A centralized node maintains page locations indicating which masters have cached which pages, and the record locations of a cached page are maintained by the master which has cached this page. This design eliminates the need to redistribute metadata when dynamically adding or removing masters. Experimental results demonstrate that HyBuffer achieves better scalability and performance compared to existing approaches.

Membrane - Safe and Performant Data Access Controls in Apache Spark in the Presence of Imperative Code

Data Governance is an increasingly critical feature of modern cloud database systems, enabling administrators to set granular access policies on their data. AWS customers want to define row or column filtering on their blob storage data and access it using popular tools such as Apache Spark. AWS EMR provides a managed and serverless solution that lets users run Spark jobs in the AWS cloud with imperative and declarative programming against their data, while securely enforcing the fine-grained access controls defined on those datasets. Spark runs its compiler and scheduler alongside the user application and embeds user-defined functions in query plans, giving a threat actor direct access to its memory space. This introduces attack vectors such as information disclosure or privilege escalation during policy enforcement, in addition to well-researched threats such as SQL side channel attacks. In this paper, we present Membrane: a novel approach to secure query plans with declarative and imperative code. The innovation comes from splitting the Spark driver in two in order to rewrite query plans with security boundaries while avoiding traditional tradeoffs when using container isolation techniques. The approach described herein enables applying fine grained data access controls to both SQL and map-reduce Spark jobs, with negligible performance and cost differences.

Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems.

QOTUM: The Query Optimizer for Distributed Database in Cloud Environment

Abstract: In distributed and cloud database system, large amount of resources are used by complex queries that increase in payment overheads of cloud users. These resource needs can be minimised by evaluating common join operations and caching their results so that they could be applied to the execution of additional queries. In this research, the Query Optimization Technique Using Materialization (QOTUM) system is designed and developed that uses the mechanism of materialized views during query execution at distributed database in cloud environment. Extensive experiments are conducted with the help of standard benchmarks datasets and query workloads in cloud environment. The performance of QOTUM system is studied and evaluated against conventional SQL system based on various performance parameters.

TECHNOLOGY-ASSISTED CARE PLANNING IN PRIMARY CARE OF A PROVIDER SHORTAGE AREA

Abstract We applied faireness algorithm to identify and mitigate bias in healthcare system. We also developed DeepPlan soft that can assist primary care providers, patients and caregivers with providing real-time risk prediction system, for example, discussing nursing home placement and designation of power of attorney. Our DeepPlan was built using cloud database system than can be exchangeable in secure way between patients and primary care providers. We linked DeepPlan use to workforce education and now we are ready for presenting education and patient clinical outcomes of using DeepPlan in primary care in a provider shortage area.

Exploiting Cloud Object Storage for High-Performance Analytics

Elasticity of compute and storage is crucial for analytical cloud database systems. All cloud vendors provide disaggregated object stores, which can be used as storage backend for analytical query engines. Until recently, local storage was unavoidable to process large tables efficiently due to the bandwidth limitations of the network infrastructure in public clouds. However, the gap between remote network and local NVMe bandwidth is closing, making cloud storage more attractive. This paper presents a blueprint for performing efficient analytics directly on cloud object stores. We derive cost- and performance-optimal retrieval configurations for cloud object stores with the first in-depth study of this foundational service in the context of analytical query processing. For achieving high retrieval performance, we present AnyBlob , a novel download manager for query engines that optimizes throughput while minimizing CPU usage. We discuss the integration of high-performance data retrieval in query engines and demonstrate it by incorporating AnyBlob in our database system Umbra. Our experiments show that even without caching, Umbra with integrated AnyBlob achieves similar performance to state-of-the-art cloud data warehouses that cache data on local SSDs while improving resource elasticity.

MARKETING INNOVATIONS IN THE FIELD OF SERVICES

The article pays attention to the theoretical aspects of marketing innovations in the field of services, considers the difference between a product and a service, innovative marketing and marketing innovations, defines their features, highlights the features of a service that affect the difference between service and product marketing, considered the development of the marketing mix in relation to services, defines the directions of application of marketing innovations, their importance and the need for application in the service sector. For a comprehensive consideration of marketing innovations in the field of services, the authors provide the most common examples of the use of marketing innovations and indicate limitations and difficulties in their application. The influence of information and computer technologies and the Internet on the application of innovative marketing technologies in the service sector is analyzed. Attention is paid to the peculiarities of the application of innovative marketing technologies in the field of hospitality services, in the field of tourism, and in the field of financial services. In particular, attention is focused on sensory marketing, neuromarketing, lateral marketing, WOW-call technology, ZMET, the possibilities of digital technologies, such as the combination of 3d panoramas, photos, videos and animations with their application in electronic catalogs, multimedia guides, as well as virtual tours, internet banking and mobile banking, creation of neobanks. The possibilities of innovative technologies when using GDS-systems, CRM, DXP, cloud database systems and digital platforms have been established. The relevance of further research on this issue has been determined.

Technical Perspective for Sherman: A Write-Optimized Distributed B+Tree Index on Disaggregated Memory

Separation of compute and storage has become the defacto standard for cloud database systems. First proposed in 2007 for database systems [2], it is now widely adopted by all major cloud providers such as Amazon Redshift, Google BigQuery, and Snowflake. Separation of compute and storage adds enormous value for the customer. Users can scale storage independently of compute, which enables them to only pay for what they really uses. Consider a scenario in which data grows linearly over time, but most queries only access the last month of data, which remains relatively stable. Without the separation of compute and storage, the user would gradually be forced to significantly increase the database cluster capacity. In contrast, modern cloud database systems allow scaling the storage separately from compute; the compute cluster stays the same over time, whereas the data is stored on cheap cloud storage services, like Amazon S3.

Impact of TLS Overhead on Segmented Network for Cloud Database Systems

Cloud database serves flexible, affordable, and scalable database system. Even the cloud database is secure with Transport Layer Security (TLS), but the performance overhead that TLS introduces when executing operations on one of the major No SQL databases: Mongo DB in terms of latency. To explore TLS performance overhead for Mongo DB, we performed two tests simulating common database usage patterns. We first investigated connection pooling, where an application uses a single connection for many database operations. Then, we considered one request per connection in which an application opens a connection, executes a process, and immediately closes the connection after completing the operation. Our experimental result shows that applications that cannot endure significant performance overhead should be deployed within a properly segmented network, rather than enabling TLS. Applications using TLS should use a connection pool rather than a connection-per-request.

Development of pneumatic shoe press machine with integrated real-time cloud database system for SMEs footwear productivity

Abstract. This innovative effort focused on advancements in the shoe-making process by introducing a state-of-the-art pneumatic shoe press integrated with a real-time cloud database system tailored to the needs of small and medium-sized enterprises (SMEs). This research aims to design and engineer a state-of-the-art pneumatic shoe press capable of printing complex designs on leather footwear. This machine can collect, send, and store production data in a cloud-based database, so as to monitor and analyze their manufacturing operations in real-time. The research methodology utilizes a structured approach from the design, manufacturing, assembly, operational testing, performance evaluation, and data analysis stages. The application of a pneumatic shoe press integrated with a real-time cloud database system provides various benefits for SMEs in the footwear industry. The system’s advantage lies in the use of a real-time cloud database that allows SMEs to gain immediate access to production data. The machine is capable of printing complex designs with high precision in one minute per pair of shoes. The product quality is good, so it can meet the standards of the shoe industry. This research successfully developed a pneumatic shoe press integrated with a real-time cloud database system, which can improve the productivity and quality of SMEs in the shoe industry. This technological innovation simplifies the manufacturing process, ensures the best product quality, and provides real-time production data.

Statistical analysis of query processing time in cache-based cloud database systems

With the proliferation of data in cloud-based systems, the performance of the data retrieval process from the database management system is becoming indispensable. Caching is one of the techniques to retrieve the data faster. It reduces the number of database accesses for similar queries, which in turn, reduces the processing time. It also facilitates in reducing the load on database servers, which results in the reduction of the overall response time. This paper is based on our caching framework for NOSQL datastores, which seeks to speed up the processing of many requests. The frequently used queries, which are expensive to reevaluate, are cached by this framework on top of a column-based datastore. In order to speed up query processing, certain queries are cached. In this study, query processing times without the influence of the database or system cache are used to evaluate the framework’s performance.

When Does AI Pay Off? AI-Adoption Intensity, Complementary Investments, and R&D Strategy

This paper examines how high-tech venture performance varies with AI-adoption intensity. We find that firm revenue increases only after sufficient investment in AI, and the benefits of AI adoption are greater at firms that also invest in complementary technologies and pursue internal R&D strategy. Specifically, AI adoption at low levels does not suggest significant revenue growth, but, as the intensity of AI adoption increases revenue growth occurs. We find that such performance gains from adoption are larger among firms that invest in complementary technologies such as cloud computing and database systems. Moreover, the positive relationship between AI adoption intensity and revenue growth is stronger among firms that pursue a more exclusive R&D strategy specific to the venture.

When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy

This paper examines how high-tech venture performance varies with AI-adoption intensity. We find that firm revenue increases only after sufficient investment in AI, and the benefits of AI adoption are greater at firms that also invest in complementary technologies and pursue internal R&D strategy. Specifically, AI adoption at low levels does not suggest significant revenue growth, but, as the intensity of AI adoption increases revenue growth occurs. We find that such performance gains from adoption is larger among firms that invest in complementary technologies such as cloud computing and database systems. Moreover, the positive relationship between AI adoption intensity and revenue growth is stronger among firms that pursue a more exclusive R&D strategy specific to the venture.

Transparent Ciphertext Retrieval System Supporting Integration of Encrypted Heterogeneous Database in Cloud-Assisted IoT

Ciphertext retrieval under heterogeneous data sets is a critical concern in the cloud-assisted Internet of Things (IoT). Previous ciphertext retrieval schemes eliminate computation burden and security concerns to some extent, but cannot support the encrypted heterogeneous data sets. To solve this problem, in this article we propose a transparent ciphertext retrieval system regardless of programming language, accessible platforms, and heterogeneity of data sets. The retrieval system is a novel combination of the proposed access and integration middleware with a current encrypted cloud database system. The middleware enables cross-language and cross-platform queries over various database systems, besides, it schedules the designed data integration method in heterogeneous databases to realize cross-database queries over the encrypted cloud data sets. The proposed ciphertext retrieval system achieves efficient data sharing among different IoT applications with various database systems. We prove that the system guarantees data security by avoiding malicious hackers. Extensive experiments show that our design is efficient and feasible in practice.

Smart Automation Technique to Collect Dry and Wet Waste using IOT Module: to Achieve our ‘SBM’ Mission

The global waste production in now a days is increasing at a rapid rate, it is predicated that it will give rise to 28 billion tonnes per year by 2051, one-third of the whole global Asian continent is majorly contributed by India and china. The Main objective of the proposed method is to achieve clean India mission abbreviated as SBM. To make our urban and rural areas surrounding to be clean without any dry and wet waste. So our proposed method is designed to collect dry and wet Waste using IOT and embedded system Technology. The government has provided two dustbins in every place to dump the waste, one for dry waste and other for wet waste. The sensors node is kept inside the dustbins. When it becomes full it sends the signal to the Transmitter node. After Receiving the signal from sensor node, it updates details area and location in the common cloud IOT database. Then it sends the information to particular Location vehicle Driver to collect the waste.in the vehicle it has separate provision to collect dry and wet wate using conveyor belt and hbridge motor driver circuit. After collecting the waste it updates the information to the common cloud database system.

Frequency Selection Approach for Energy Aware Cloud Database

A lot of cloud systems are adopted in industry and academia to face the explosion of the data volume and the arrival of the big data era. Meanwhile, energy efficiency and energy saving become major concerns for data centers where massive cloud systems are deployed. However, energy waste is quite common due to resource provisioning. In this paper, using dynamic voltage and frequency scaling (DVFS), a frequency selection approach is introduced to improve the energy efficiency of the cloud systems in terms of resource provisioning. In the approach, two algorithms, genetic algorithm (GA) and Monte Carlo tree search algorithm (MCTS), are proposed. A cloud database system is taken as an example to evaluate the approach. The results of the experiments show that both algorithms have its advantages. The algorithms have great scalability, in which the GA can be applied to thousands of nodes and the MCTS can be applied to hundreds of nodes. Both algorithms have high accuracy compared with optimal solutions (up to 99.9% and 99.6% for GA and MCTS, respectively). According to an optimality bound analysis, 26% of energy can be saved at most using our frequency selection approach.

The Development of Prototype Equipment for Information System of Environmental Monitoring and Production Factor in Agroforestry Area, Uttaradit, Thailand

This research aimed to develop the prototype equipment for an information system of environmental monitoring and production factors in an agroforestry area with concepts of wireless sensor network technology controlled by microcontroller. There were 2 parts: 1) the sensor node functions various environmental monitoring such as temperature, humidity, smoke in an agroforestry area of agriculturists, and 2) the gateway station functions data collection from the sensor node via the Internet network stored in the cloud database system. Results from the wireless sensor network is the data of the inputs in the agroforestry area, such as temperature, humidity and CO2which displays data on the information system that agriculturists can use information to monitoring the environment for agriculture and support an agriculture system management. Improve production processes to increase productivity and predict future output

Efficient identity-based broadcast encryption with keyword search against insider attacks for database systems

Public key encryption with keyword search (PEKS) is a promising technique for data confidentiality and utilization. In cloud database systems, the user (sender) could want to share his data with multiple users (receivers), while the ciphertext complexity increases linearly with the number of receivers if directly employing PEKS. Also, the security of PEKS is weakly defined in presence of outsider attacks, where the untrusted server infers the keyword in trapdoor by insider attacks. Therefore, how to reduce the size of the ciphertext and resist insider attacks remain unresolved. In this paper, we propose the first efficient identity-based broadcast encryption with keyword search against insider attacks (IBEKS-IA) for cloud database systems. We build the IBEKS-IA framework to provide data retrieval for multiple receivers and resist insider attacks. Based on this framework, we construct an IBEKS-IA scheme featuring with constant size ciphertext, which is proved to be semantically secure against the chosen keyword attack and the server keyword guessing attack. We further give an extended version featuring with both the constant size ciphertext and constant size online trapdoor. Performance evaluation presents the practicality of the proposal in the cloud database systems.

Hot-N-Cold model for energy aware cloud databases

A lot of cloud computing and cloud database techniques are adopted in industry and academia to face the explosion of the arrival of the big data era. Meanwhile, energy efficiency and energy saving become a major concern in data centers, which are in charge of large distributed systems and cloud databases. However, the phenomenon of energy wasting is related to resource provisioning. Hot-N-Cold model is introduced in this paper, which uses workload predictions and DVFS(Dynamic Voltage and Frequency Scaling) to cope with the resource provisioning problem within energy aware cloud database systems. In this model, the resource provisioning problem is considered as two bounded problems. A nonlinear programming algorithm and a multi-phase algorithm are proposed to solve them. The experimental results show that one of the proposed algorithms has great scalability which can be applied to a cloud database system deployed on 70 nodes. Using Hot-N-Cold model can save up to 21.5% of the energy of the running time.

IDENTIFYING SKYLINES IN CLOUD DATABASES WITH INCOMPLETE DATA

Skyline queries is a rich area of research in the database community. Due to its great benefits, it has been integrated into many database applications including but not limited to personalized recommendation, multi-objective, decision support and decision-making systems. Many variations of skyline technique have been proposed in the literature addressing the issue of handling skyline queries in incomplete database. Nevertheless, these solutions are designed to fit with centralized incomplete database single access. However, in many real-world database systems, this might not be the case, particularly for a database witha large amount of incomplete data distributed over various remote locations such as cloud databases. It is inadequate to directly apply skyline solutions designed for the centralized incomplete database to work on cloud due to the prohibitive cost. Thus, this paper introduces a new approach called Incomplete-data Cloud Skylines (ICS) aiming at processing skyline queries in cloud databases with incomplete data. This approach emphasizes on reducing the amount of data transfer and domination tests during skyline process. It incorporates sorting technique that assists in arranging the data items in a way where dominating data items will be placed at the top of the list helping in eliminate dominated data items. Besides, ICS also employs a filtering technique to prune the dominated data items before applying skyline technique. It comprises a technique named local skyline joiner that helps in reducing the amount of data transfer between datacenters when deriving the final skylines. It limit the amount of data items to be transferred to only those local skylines of each relation. A comprehensive experiment have been performed on both synthetic and real-life datasets, which demonstrate the effectiveness and versatility of our approach in comparison to the current existing approaches. We argue that our approach is practical and can be adopted in many contemporary cloud database systems with incomplete data to process skyline queries.