Showing papers on "Software as a service published in 2018"

A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms

Abstract: Real estate needs to improve its adoption of disruptive technologies to move from traditional to smart real estate (SRE). This study reviews the adoption of disruptive technologies in real estate. It covers the applications of nine such technologies, hereby referred to as the Big9. These are: drones, the internet of things (IoT), clouds, software as a service (SaaS), big data, 3D scanning, wearable technologies, virtual and augmented realities (VR and AR), and artificial intelligence (AI) and robotics. The Big9 are examined in terms of their application to real estate and how they can furnish consumers with the kind of information that can avert regrets. The review is based on 213 published articles. The compiled results show the state of each technology’s practice and usage in real estate. This review also surveys dissemination mechanisms, including smartphone technology, websites and social media-based online platforms, as well as the core components of SRE: sustainability, innovative technology and user centredness. It identifies four key real estate stakeholders—consumers, agents and associations, government and regulatory authorities, and complementary industries—and their needs, such as buying or selling property, profits, taxes, business and/or other factors. Interactions between these stakeholders are highlighted, and the specific needs that various technologies address are tabulated in the form of a what, who and how analysis to highlight the impact that the technologies have on key stakeholders. Finally, stakeholder needs as identified in the previous steps are matched theoretically with six extensions of the traditionally accepted technology adoption model (TAM), paving the way for a smoother transition to technology-based benefits for consumers. The findings pertinent to the Big9 technologies in the form of opportunities, potential losses and exploitation levels (OPLEL) analyses highlight the potential utilisation of each technology for addressing consumers’ needs and minimizing their regrets. Additionally, the tabulated findings in the form of what, how and who links the Big9 technologies to core consumers’ needs and provides a list of resources needed to ensure proper information dissemination to the stakeholders. Such high-quality information can bridge the gap between real estate consumers and other stakeholders and raise the state of the industry to a level where its consumers have fewer or no regrets. The study, being the first to explore real estate technologies, is limited by the number of research publications on the SRE technologies that has been compensated through incorporation of online reports.

A Workflow Management System for Scalable Data Mining on Clouds

Abstract: The extraction of useful information from data is often a complex process that can be conveniently modeled as a data analysis workflow. When very large data sets must be analyzed and/or complex data mining algorithms must be executed, data analysis workflows may take very long times to complete their execution. Therefore, efficient systems are required for the scalable execution of data analysis workflows, by exploiting the computing services of the Cloud platforms where data is increasingly being stored. The objective of the paper is to demonstrate how Cloud software technologies can be integrated to implement an effective environment for designing and executing scalable data analysis workflows. We describe the design and implementation of the Data Mining Cloud Framework (DMCF), a data analysis system that integrates a visual workflow language and a parallel runtime with the Software-as-a-Service (SaaS) model. DMCF was designed taking into account the needs of real data mining applications, with the goal of simplifying the development of data mining applications compared to generic workflow management systems that are not specifically designed for this domain. The result is a high-level environment that, through an integrated visual workflow language, minimizes the programming effort, making easier to domain experts the use of common patterns specifically designed for the development and the parallel execution of data mining applications. The DMCF's visual workflow language, system architecture and runtime mechanisms are presented. We also discuss several data mining workflows developed with DMCF and the scalability obtained executing such workflows on a public Cloud.

MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning

Abstract: Mobile learning in massive open online course (MOOC) evidently differs from its traditional ways as it relies more on collaborations and becomes more fragmented. We present a cloud-based virtual learning environment (VLE) which can organize learners into a better teamwork context and customize micro learning resources in order to meet personal demands in real time. Particularly, a smart micro learning environment was built by a newly designed Software as a Service (SaaS), namely Micro Learning as a Service (MLaaS). It aims to provide adaptive micro learning contents as well as learning path identifications customized for each individual learner. To personalize the micro learning, a dynamic learner model is constructed with regards to the internal and external factors that can affect learning experience and outcomes. Educational data mining (EDM) techniques are employed as the main method to understand learners’ behaviors and recognize learning resource features. A solution of learning path optimization is also proposed towards assembling a complete MOOC learning experience.

Cloud Computing Architecture: A Critical Analysis

Abstract: Cloud computing has evolved to emerge the most topical IT paradigm in recent times. Cloud computing is rapidly transforming the IT landscape. On a pay-as-you-use basis, cloud consumers can access resources, applications and infrastructure provided by cloud providers. Such access could be in form of applications already deployed by cloud providers for use by the cloud users. It could be in form of the capability to develop and deploy user applications using services of a cloud provider. In addition, massive storage infrastructure is available for database and data supplied by the user. The cloud has several unique architectures and many more are still evolving. The primary ones are the SaaS, PaaS and the IaaS that can be deployed on private, public, community and hybrid clouds. This paper examines present developments in the cloud computing architecture and presents guidance for additional research. Papers published in journals, conferences, white papers were analyzed. The objective of this present work is to identify, examine and explain the current trends and development in cloud computing architecture. However, only 13% of the papers examined discussed Others-as-a-Service, while only 26% of the papers reviewed considered issues relating to the major actors involved in cloud computing. This will beneficial to cloud providers, users, and researchers alike

A Decision-Making Model for Adopting a Cloud Computing System

Abstract: The use of big data, artificial intelligence, and new information and communication technologies has led to sustainable developments and improved business competitiveness. Until recently cloud services were classified as having special system requirements for a business organization, and was represented by different cloud computing architecture layers like infrastructure, platform, or software as a service. However, as the environment of IT services undergoes successive changes, companies have been required to reconsider their business models and consider adopting a cloud computing system, which can bring on business achievements and development. Regarding a decision-making model for adopting a cloud computing system, this paper analyzes critical variables in a hierarchical structure of decision areas: technology, organization, and environment, as well as seven factors and 23 attributes based on underlying decision factors of cloud computing adoption by AHP (Analytic Hierarchy Process) and Delphi analysis. Furthermore, this research explores a comparative analysis between demanders and providers of cloud computing adoption. Resultantly, this study suggests several important factors for adopting a cloud computing system: top management support, competitive pressure, and compatibility. From the demander side, the high priority factor was compatibility and competitive pressure; in contrast, related advantage and top management support were regarded as priority factors for providers to service their cloud computing systems.

Multi-Tenant Cloud Service Composition Using Evolutionary Optimization

Abstract: In Software as a Service (SaaS)cloud marketplace, several functionally equivalent services tend to be available with different Quality of Service (QoS)values. For processing end-users multi-dimensional QoS and functional requirements, the application engineers are required to choose suitable services and optimize the service composition plans for each category of users. However, existing approaches for dynamic services composition tend to support execution plans that search for service provisions of equivalent functionalities with varying QoS or cost constraints to meet the tenants' QoS requirements or to dynamically respond to changes in QoS. These approaches tend to ignore the fact that multi-tenant execution plans need to provide variant execution plans, each offering a customized plan for a given tenant with its functionality, QoS and cost requirements. Henceforth, the dynamic selection and composition of multi-tenant service composition is a NP-hard dynamic multiobjective optimization problem. To address these challenges, we propose a novel multi-tenant middleware for dynamic service composition in the SaaS cloud. In particular, we present new encoding representation and fitness functions that model the service selection and composition as an evolutionary search. We incorporate our approach with two Multi-Objective Evolutionary Algorithms (MOEA), i.e., MOEA/D-STM and NSGA-II, to perform a comparative study. The experiment results show that the MOEA/D-STM outperforms NSGA-II in terms of quality of solutions and computation time.

Open source ERP business model framework

Abstract: ERP systems became popular with large organizations in the 1990s. In the 21st Century, these products were expanded by addition of supply chain management (SCM) and customer relationship management (CRM), as well as access through the Web, creating the ERP II concept. Efforts to increase the market led vendors to serve not only large organizations, but also focus more on small-to-medium sized enterprises (SMEs).Open source software has become a player in the field of enterprise resource planning (ERP) systems. While it is still unclear to what extent it has diffused among organizations, it is clear that opportunities exist. New ways of delivering ERP software, such as software as a service (SaaS) have appeared. Some smaller vendors utilized a free distribution system (Free/Open Source ERP, FOS-ERP) for their source code, relying on various business models for corporate success. There also have been attempts to generate FOS-ERP components found on sites such as SourceForge.com that are not only distributed freely, but also were developed through community participation much as Linux has been developed. Some ERP vendors use community developed components for various purposes to support their proprietorial software. Thus one dimension of ERP systems is based upon who directs the development process. Proprietorial ERP refers to systems with closely held intellectual property rights, such as the leading market products by SAP and Oracle as well as many smaller proprietorial competitors. FOS-ERP can be community based, or sponsored by some organization.In this paper we present a framework that aims at analyzing FOS-ERP business models. Goals include discussing the differences between FOS-ERP and their proprietary equivalents (P-ERP) in terms of business models, selection, customization, and evolution. We will discuss challenges and opportunities that they offer to adopters and vendors. Description of open source ERP options.Framework of open source ERP models.Identification of tradeoffs in open source ERP options for small-to-medium enterprises.

A Model of Competition Between Perpetual Software and Software as a Service

Abstract: Software as a service (SaaS) has grown to be a significant segment of many software product markets. SaaS vendors, which charge customers based on use and continuously improve the quality of their products, have put competitive pressure on traditional perpetual software vendors, which charge a licensing fee and periodically upgrade the quality of their software. We develop an analytical model to study the competitive pricing strategies of an incumbent perpetual software vendor in the presence of a SaaS competitor. We find that, depending on both the SaaS quality improvement rate and the network effect, the perpetual software vendor adopts one of three different strategies: (1) an entry deterrence strategy, (2) a market segmentation strategy, or (3) a sequential dominance strategy. Surprisingly, we find that vendor competition does not always result in higher consumer surplus, and it might lead to a socially inefficient outcome under certain conditions. We further show insights into how the incumbent perpetual software vendor can defend its market position by providing incremental quality improvement through patching and/or by releasing consecutive versions with major quality upgrades. Finally, we extend our model to include the vendor’s quality improvement cost and users’ switching cost. These additional analyses help to identify the effect of different quality and cost factors on the market competitive equilibrium.

Systems, methods, and devices for an enterprise AI and internet-of-things platform

Abstract: Systems, methods, and devices for a cyberphysical (IoT) software application development platform based upon a model driven architecture and derivative IoT SaaS applications are disclosed herein. The system may include concentrators to receive and forward time-series data from sensors or smart devices. The system may include message decoders to receive messages comprising the time-series data and storing the messages on message queues. The system may include a persistence component to store the time-series data in a key-value store and store the relational data in a relational database. The system may include a data services component to implement a type layer over data stores. The system may also include a processing component to access and process data in the data stores via the type layer, the processing component comprising a batch processing component and an iterative processing component.

Model-Driven Development Patterns for Mobile Services in Cloud of Things

Abstract: Cloud of Things (CoT) is an integration of Internet of Things (IoT) and cloud computing for intelligent and smart application especially in mobile environment. Model Driven Architecture (MDA) is used to develop Software as a Service (SaaS) so as to facilitate mobile application development by relieving developers from technical details. However, traditional service composition or mashup are somewhat unavailable due to complex relations and heterogeneous deployed environments. For the purpose of building cloud-enabled mobile applications in a configurable and adaptive way, Model-Driven Development Patterns based on semantic reasoning mechanism are provided towards CoT application development. Firstly, a meta-model covering both multi-view business elements and service components are provided for model transformation. Then, based on formal representation of models, three patterns from different tiers of Model-View-Controller (MVC) framework are used to transform business models into service component system so as to configure cloud services rapidly. Lastly, a related software platform is also provided for verification. The result shows that the platform is applicable for rapid system development by means of various service integration patterns.

Cloud Computing and Internet of Things Integration: Architecture, Applications, Issues, and Challenges

Abstract: The Internet of Things (IoT) and Cloud Computing both are developing technologies. Cloud Computing blows up to provide support to IoT by working as a sort of front-end and it is based on the concept of permitting users to do computing tasks using services delivered with internet. The cloud computing empower an appropriate, on-demand, and scalable network access to a shared pool of configurable computing resources. The cloud-based IoT architecture includes features of cloud-based IoT platform and its interaction with three main cloud computing models: IaaS (infrastructure as a service), Paas (platform as a service), and SaaS (software as a service). The cloud and IoT integration empowers new scenarios, for smart services and applications, as Sensing as a Service (SaaS), DataBase as a Service (DBaaS), Video Surveillance as a Service (VSaaS), and many more. Various live company products, research projects, and projects with freely available source code in various areas of Cloud Computing and IoT integration are Nimbits, ThingSpeak, Paraimpu, Device Cloud, Sensor Cloud. REpresentational State Transfer (REST) architectural style web services and Constrained Application Protocol (COAP), Message Queue Telemetry Transport (MQTT), web transfer protocols are used for communication for the IoT resource-constrained things. Networking protocols like IPv6 over Low power Wireless Personal Area Network (6LoWPAN) and IPv6 over Bluetooth Low Energy are used for constrained networks in IoT and cloud integration. The data link layer protocols for IoT devices like IEEE 802.15.4, IEEE 802.11ah, Z-Wave, WirelessHART, Bluetooth, Zigbee are used for short range communication for IoT things. The applications of integrated cloud and IoT include agriculture, video surveillance, healthcare, smart city, smart home and smart metering, etc. IoT and cloud integration involves several challenges and issues as standardization of machine to machine (M2M) communication and interoperability, power and energy efficiency of devices for data transmission and processing, big data generated by several devices, security and privacy, integration methodology, pricing and billing, network communications, storage, etc. In this chapter, the introduction of cloud and IoT, their integration architecture, integration applications, and challenges and issues involved are discussed.

Optimal Distribution Strategy for Enterprise Software: Retail, SaaS, or Dual Channel?

Abstract: While the optimal distribution channel strategy of physical goods has been extensively studied, there is a lack of research for that of enterprise software as a digital good. This research analyzes the optimal distribution strategy of enterprise software by taking into account the distinct features of enterprise software for both the short‐run problem, in which the software quality is fixed, and the long‐run problem, in which the software quality becomes part of the strategic decisions. Our results indicate that in the presence of high unfit cost relative to the customization cost, the dual channel strategy exists and generates the highest profit for the firm and the highest social welfare. When the unfit cost is low relative to the customization cost, the SaaS channel strategy becomes the best strategy for both the firm in terms of profitability and society in terms of social welfare. This key finding is robust in that it holds for both the short‐run problem and the long‐run problem.

A Cloud-Based System for Improving Retention Marketing Loyalty Programs in Industry 4.0: A Study on Big Data Storage Implications

Abstract: Nowadays, the growing global economy and demand for customized products are bringing the manufacturing industry from a sellers’ market toward a buyers’ market. In this context, the smart manufacturing enabled by Industry 4.0 is changing the whole production cycle of companies specialized on different kinds of products. On one hand, the advent of cloud computing and social media makes the customers’ experience more and more inclusive, whereas on the other hand cyber-physical system technologies help industries to change in real time the cycle of production according to customers’ needs. In this context, “retention” marketing strategies aimed not only at the acquisition of new customers but also at the profitability of existing ones allow industries to apply specific production strategies so as to maximize their revenues. This is possible by means of the analysis of various kinds of information coming from customers, products, purchases, and so on. In this paper, we focus on customer loyalty programs. In particular, we propose cloud-based software as a service architecture that store and analyses big data related to purchases and products’ ranks in order to provide customers a list of recommended products. Experiments focus on a prototype of human to machine workflow for the pre-selection of customers deployed on both private and hybrid cloud scenarios.

What is “Cloud”? It is time to update the NIST definition?

Abstract: IaaS, PaaS, and SaaS were formally defined in 2011. Have these definitions held up in the fast-moving world of Cloud Computing?

Identifying emerging topics and difficulties in software engineering education in Brazil

Abstract: Technological evolution has contributed to the emergence of new paradigms and trends that meet demands of a dynamic market In this context, teaching software engineering (SE) becomes a challenge SE professors in turn seek strategies to prepare students for dealing with industry's demands Therefore, it is important to know how emerging SE topics has affected the teaching-learning process This paper aims to identify the emerging SE topics in undergraduate courses in the Brazilian scenario and difficulties in its teaching-learning process A survey with SE professors was carried out and allowed us to identify a set of emerging topics explored in Brazilian SE courses as well as underlying difficulties in the teaching-learning process Qualitative analysis was applied Some topics identified from the dataset were agile methods, reuse, software architecture, software product lines, SE for software as a service, SE for startups, among others Relations between such emerging topics and difficulties regarding pedagogy, materials, students and content were identified as well

Risk-Aware Management of Virtual Resources in Access Controlled Service-Oriented Cloud Datacenters

Abstract: For economic benefits and efficient management of resources, organizations are increasingly moving towards the paradigm of “cloud computing” by which they are allowed on-demand delivery of hardware, software and data as services. However, there are many security challenges which are particularly exacerbated by the multitenancy and virtualization features of cloud computing that allow sharing of resources among potentially untrusted tenants in access controlled cloud datacenters. This can result in increased risk of data leakage. To address this risk vulnerability, we propose an efficient risk-aware virtual resource assignment mechanism for clouds multitenant environment. In particular, we introduce the notion of sensitivity in datacenters and the objective is to minimize the risk of data leakage. In addition, the risk should not exceed in high sensitivity datacenters in comparison to low sensitivity datacenters. We present three assignment heuristics and compare their relative performance.

Supporting Science Gateways Using Apache Airavata and SciGaP Services

Abstract: The Science Gateways Platform as a service (SciGaP.org) project provides a rapid development and stable hosting platform for a wide range of science gateways that focus on software as a service. Based on the open source Apache Airavata project, SciGaP services include user management, workflow execution management, computational experiment archiving and access, and sharing services that allow users to share results and other digital artifacts. SciGaP services are multi-tenanted, with clients accessing services through a well-defined, programming language-independent API. SciGaP services can be integrated into web, mobile, and desktop clients. To simplify development for new clients, SciGaP includes the PGA, a generic PHP-based gateway client for SciGaP services that also acts as a reference implementation of the API. Several example gateways using these services are summarized.

A Survey on Cloud Computing

Abstract: Cloud computing technology is the way to provide everything to clients as services through internet connection. Using this technology the clients would be able to rent the required services via web browsers. This study gives a proper definition to cloud computing, highlighted the related technologies, the essential characteristics, cloud architecture and components. Comparison among three service models (SaaS, PaaS, and IaaS) as well as deployment models: private, public, and community cloud has been given. Furthermore, the chapter includes information security requirements of public and private cloud according to different service models. The aim of this chapter is to giving the researchers a clear vision about this technology and the information security requirements for private and public cloud as well as the main security issues for future researches.

Risk Analysis of Cloud Sourcing in Healthcare and Public Health Industry

Abstract: The task of protecting healthcare information systems (HIS) from immediate cyber security risks has been intertwined with cloud computing adoption. The data and resources of HISs are inherently shared with other systems for remote access, decision making, emergency, and other healthcare related perspectives. In the case of a multitude of requirements by multiple stakeholders, various, and diverse cloud models are being adopted across the healthcare and public health industry, which defies the real essence of sharing and using cloud computing in this domain. The misconception of security is one of the key hurdles in the adoption of cloud as a de facto standard in the healthcare and public health sector. In this paper, we demonstrate the similarity of the security aspects of the cloud computing models, by identifying the critical assets in the HIS, and by assessing their impact on the HIS. We also evaluate the risk exposure of the cloud computing models by performing a critical analysis. To the best of our knowledge, this is the first study of its kind for risk analysis of cloud computing models in order to demonstrate their suitability for the HIS.

Quality, pricing, and release time

Abstract: As a new software licensing model, software-as-a-service (SaaS) is gaining tremendous popularity across the globe. In this study, we investigate the competition between a new entrant and an incumbe...

Block-Stream as a Service: A More Secure, Nimble, and Dynamically Balanced Cloud Service Model for Ambient Computing

Abstract: Cloud computing has become mainstream in the last few years. Diverse services based on IaaS, PaaS, SaaS, and app store models have been widely available to millions of users worldwide. At the same time, transparent computing (TC) has also gained strong interest in China. With the rapid development of IoT, increasing IoT devices will be deployed to provide information services for end users. As we are heading into the era of ambient computing, where end users are immersed in seamless computing devices and services, the boundary between cloud and devices is getting blurry, and more devices and services need to be securely managed. The existing service models that are defined for user-cloud interaction should be extended to serve more diverse and lightweight devices with nimble and fluid services. With this evolution trend, it is paramount for both cloud service providers and IoT service operators to manage the security and integrity of these services. In this article, we propose a new cloud service model, named block-stream as a service (BaaS), based on our previous study on TC. BaaS is nimbler than SaaS and has better security management than an app store. It is expected that this new cloud service model has great potential to support the vision of ambient computing and securely manage diverse applications on lightweight IoT devices.

A composite particle swarm optimization approach for the composite SaaS placement in cloud environment

Abstract: Cloud computing has emerged as a new powerful service delivery model to cope with resource challenges and to offer on-demand various types of services (e.g., software, storage, network). One of the most popular service models is Software as a Service (SaaS). To allow flexibility and reusability, SaaS can be offered in a composite form, where a set of interacting application and data components cooperate to form a higher-level functional SaaS. However, this approach introduces new challenges to resource management in the cloud, especially finding the optimal placement for SaaS components to have the best possible SaaS performance. SaaS Placement Problem (SPP) refers to this challenge of determining which servers in the cloud’s data center can host which components without violating SaaS constraints. Most existing SPP approaches only addressed homogenous SaaS components placement and only considered one type of constraints (i.e., resource constraint). In addition, none of them has considered the objective of maintaining a good machine performance by minimizing the resource usage for the hosting machines. To allow finding the optimal placement of a composite SaaS, we adopt a new variation of PSO called ’Particle Swarm Optimization with Composite Particle (PSO-CP).’ In the proposed PSO-CP-based approach, each composite particle in the swarm represents a candidate SaaS placement scheme. Composite particles adopt a collective behavior to explore and evaluate the search space (i.e., data center) and adjust their structures by collaborating with other composite or independent particles (i.e., servers). The implementation and experimental results show the feasibility and efficiency of the proposed approach.

QoS-based ranking and selection of SaaS applications using heterogeneous similarity metrics

Abstract: The plethora of cloud application services (Apps) in the cloud business apps e-marketplace often leads to service choice overload Meanwhile, existing SaaS e-marketplaces employ keyword-based inputs that do not consider both the quantitative and qualitative quality of service (QoS) attributes that characterise cloud-based services Also, existing QoS-based cloud service ranking approaches rank cloud application services are based on the assumption that the services are characterised by quantitative QoS attributes alone, and have employed quantitative-based similarity metrics for ranking However, the dimensions of cloud service QoS requirements are heterogeneous in nature, comprising both quantitative and qualitative QoS attributes, hence a cloud service ranking approach that embrace core heterogeneous QoS dimensions is essential in order to engender more objective cloud selection In this paper, we propose the use of heterogeneous similarity metrics (HSM) that combines quantitative and qualitative dimensions for QoS-based ranking of cloud-based services By using a synthetically generated cloud services dataset, we evaluated the ranking performance of five HSM using Kendall tau rank coefficient and precision as accuracy metrics benchmarked with one HSM The results show significant rank order correlation of Heterogeneous Euclidean-Eskin Metric, Heterogeneous Euclidean-Overlap Metric, and Heterogeneous Value Difference Metric with human similarity judgment, compared to other metrics used in the study Our results confirm the applicability of HSM for QoS ranking of cloud services in cloud service e-marketplace with respect to users’ heterogeneous QoS requirements