DaaS Model Research Articles

Efficient Data as a Service in Fog Computing: An Adaptive Multi-Agent Based Approach

Data as a Service (DaaS) offers an effective provisioning model able to exploit the advantages of cloud computing in terms of accessibility and scalability when data providers need to make their data available to different data consumers. Nevertheless, in settings where data are generated at the edge and they need to be propagated (e.g., Industry 4.0, Smart Cities), DaaS model suffers of some limitations: data transfer from the edge to the cloud – and viceversa – could require a significant time and privacy issues could hamper the possibility to move the data. Goal of this paper is to propose a DaaS model based on the Fog Computing paradigm, which combines the advantages of both cloud and edge computing. The proposed solution implements an adaptive multi-agent system where each agent autonomously manages the placement of data in the most convenient location considering the quality of service requirements of the user that it is serving. To guarantee the collaboration of the agents without imposing a centralized control, a reinforcement learning algorithm will be enacted to balance between the local optimum for the single data consumers and the satisfaction of the global requirements of all consumers.

Design-as-a-Service Framework for Enabling Innovations in Small- and Medium-Sized Enterprises

Abstract Modern manufacturing enterprises must be agile to cope with sudden demand changes arising from increased global competition, geopolitical factors, and unforeseen circumstances such as the Covid-19 pandemic. Small- and Medium-Sized Enterprises (SMEs) in the manufacturing sector lack agility due to lower penetration of Information Technology (IT) and Operational Technology (OT), the inability to employ highly skilled human capital, and the absence of a formal innovation ecosystem for new products or solutions. In recent years, Cloud-based Design and Manufacturing (CBDM) has emerged as an enabler for product realization by integrating various service-based models. However, the existing framework does not thoroughly support the innovation ecosystem from concept to product realization by formally addressing economic challenges and human skillset requirements. The present work considers the augmentation of the Design-as-a-Service (DaaS) model into the existing CBDM framework for enabling systematic product innovations. The DaaS model proposes to connect skilled human resources with enterprises interested in transforming an idea into a product or solution through the CBDM framework. The model presents an approach for integrating human resources with various CBDM elements and end-users through a service-based model. The challenges associated with successfully implementing the proposed model are also discussed. It is established that the DaaS has the potential for rapid and economical product discovery and can be readily accessible to SMEs or independent individuals.

Adoption of Precision Agriculture by Detecting and Spraying Herbicide using UAV

Farmers are facing the VUCA environment (volatile, uncertain, complex and ambiguous) and data indicating the contribution of farming to India's GDP has come down from 52% to 18% between 1951 and 2018, which is alarming. At this juncture, developing countries like India, where over 70% of the rural people depend upon the agriculture fields, adoption of disruptive technology (creative destruction) becomes the need of the hour, to enhance the crop yield and quality. Weeds are one of the major issues which severely affect the crop output. Unmanned Aerial Vehicle (UAV) or drone is recommended, to address the problem. Globally, the market for agriculture drones to move from $1.3 billion to $ 6.52 billion by 2026. Globally agriculture is the second largest industry after construction in terms of drone adoption. But Indian farmers have difficulty in adopting (or) procuring UAV's, as the size of their farm is small, income is very less. Other problems associated with the adoption of UAV include knowledge transfer and training to farmers, service support and maintenance cost. DaaS (Drone as a service) model is proposed, for rural areas. This paper aims to focus on weed management by providing a safer and cost-effective solution. By integrating technologies like visible light (VIS), near-infrared (NIR) light on an Unmanned Ariel Vehicle along with a precise sprayer and a weed detection system backed up by a lithium-ion battery (for longer flight duration), can help the process of spraying weedicide efficiently. The accuracy of the tested model is 92.6% for far away detection module and 95.4 for close range detection. UAV's with sprayer protects the farmer and consumers from odour and side effects.

Integration Model between Heterogeneous Data Services in a Cloud

With the growth of cloud services, many companies have begun to persist and make their data available through services such as Data as a Service (DaaS) and Database as a Service (DBaaS). The DaaS model provides on-demand data through an Application Programming Inter- face (API), while DBaaS model provides on-demand database management systems. Different data sources require efforts to integrate data from different models. These model types include unstructured, semi-structured, and structured data. Heterogeneity from DaaS and DBaaS makes it challenging to integrate data from different services. In response to this problem, we developed the Data Join (DJ) method to integrate heterogeneous DaaS and DBaaS sources. DJ was described through canonical models and incorporated into a middleware as a proof-of-concept. A test case and three experiments were performed to validate our DJ method: the first experiment tackles data from DaaS and DBaaS in isolation; the second experiment associates data from different DaaS and DBaaS through one join clause; and the third experiment integrates data from three sources (one DaaS and two DBaaS) based on different data type (relational, NoSQL, and NewSQL) through two join clauses. Our experiments evaluated the viability, functionality, integration, and performance of the DJ method. Results demonstrate that DJ method outperforms most of the related work on selecting and integrating data in a cloud environment.

Integrity Authentication for SQL Query Evaluation on Outsourced Databases: A Survey

Spurred by the development of cloud computing, there has been considerable recent interest in the Database-as-a-Service (DaaS) paradigm. Users lacking in expertise or computational resources can outsource their data and database management needs to a third-party service provider. Outsourcing, however, raises an important issue of result integrity: how can the client verify with lightweight overhead that the query results returned by the service provider are correct (i.e., the same as the results of query execution locally)? This survey focuses on categorizing and reviewing the progress on the current approaches for result integrity of SQL query evaluation in the DaaS model. The survey also includes some potential future research directions for result integrity verification of the outsourced computations.

A Database as a Service for the Healthcare System to Store Physiological Signal Data.

Wearable devices that measure physiological signals to help develop self-health management habits have become increasingly popular in recent years. These records are conducive for follow-up health and medical care. In this study, based on the characteristics of the observed physiological signal records– 1) a large number of users, 2) a large amount of data, 3) low information variability, 4) data privacy authorization, and 5) data access by designated users—we wish to resolve physiological signal record-relevant issues utilizing the advantages of the Database as a Service (DaaS) model. Storing a large amount of data using file patterns can reduce database load, allowing users to access data efficiently; the privacy control settings allow users to store data securely. The results of the experiment show that the proposed system has better database access performance than a traditional relational database, with a small difference in database volume, thus proving that the proposed system can improve data storage performance.

Distribution-based query scheduling

Query scheduling, a fundamental problem in database management systems, has recently received a renewed attention, perhaps in part due to the rise of the "database as a service" (DaaS) model for database deployment. While there has been a great deal of work investigating different scheduling algorithms, there has been comparatively little work investigating what the scheduling algorithms can or should know about the queries to be scheduled. In this work, we investigate the efficacy of using histograms describing the distribution of likely query execution times as input to the query scheduler. We propose a novel distribution-based scheduling algorithm, Shepherd, and show that Shepherd substantially outperforms state-of-the-art point-based methods through extensive experimentation with both synthetic and TPC workloads.

Data contracts for cloud-based data marketplaces

Currently, rich and diverse data types have been increasingly provided using the dataas- a-service (DaaS) model, a form of cloud computing services and the core element of data marketplaces. This facilitates the on-the-fly data composition and utilisation for several data-intensive applications in e-science and business domains. However, data offered by DaaS are constrained by several data concerns that, if not automatically being reasoned properly, will lead to a wrong way of using them. In this paper, we support the view that data concerns should be explicitly modelled and specified in data contracts to support concern-aware data selection and utilisation. We perform a detailed analysis of current techniques for data contracts in the cloud. Instead of relying on a specific representation of data contracts, we introduce an abstract model for data contracts that can be used to build different types of data contracts for specific types of data. Based on the abstract model, we propose several techniques for evaluating data contracts that can be integrated into data service selection and composition frameworks. We also illustrate our approach with some real-world scenarios and show how data contracts can be integrated into data agreement exchange services in the cloud.