Profit Maximization Auction and Data Management in Big Data Markets

A big data service is any data-originated resource that is offered over the Internet. The performance of a big data service depends on the data bought from the data collectors. However, the problem of optimal pricing and data allocation in big data services is not well-studied. In this paper, we propose an auction-based big data market model. We first define the data cost and utility based on the impact of data size on the performance of big data analytics, e.g., machine learning algorithms. The big data services are considered as digital goods and uniquely characterized with "unlimited supply" compared to conventional goods which are limited. We therefore propose a Bayesian profit maximization auction which is truthful, rational, and computationally efficient. The optimal service price and data size are obtained by solving the profit maximization auction. Finally, experimental results on a real-world taxi trip dataset show that our big data market model and auction mechanism effectively solve the profit maximization problem of the service provider.Comment: 6 pages, 9 figures. This paper was accepted by IEEE WCNC conference in Dec. 201

Market Model and Optimal Pricing Scheme of Big Data and Internet of Things (IoT)

Big data has been emerging as a new approach in utilizing large datasets to optimize complex system operations. Big data is fueled with Internet-of-Things (IoT) services that generate immense sensory data from numerous sensors and devices. While most current research focus of big data is on machine learning and resource management design, the economic modeling and analysis have been largely overlooked. This paper thus investigates the big data market model and optimal pricing scheme. We first study the utility of data from the data science perspective, i.e., using the machine learning methods. We then introduce the market model and develop an optimal pricing scheme afterward. The case study shows clearly the suitability of the proposed data utility functions. The numerical examples demonstrate that big data and IoT service provider can achieve the maximum profit through the proposed market model

IoT mashups : From IoT big data to IoT big service

Internet of Things (IoT) addresses the challenge to provide a transparent access to a huge number of IoT resources that can be either physical devices or just data resources. Moreover, because of the large number of resource-constrained devices and the dynamic nature of IoT environments, integrating the resulted data becomes a non trivial task. We believe that the use of mashups, a way to compose new services from existing ones, can be a solution to the above challenge if each resource exposes its functionalities as a Web service. In the IoT environment, this will constitute a Web of Things where mashups development will take advantage of the connected physical world. The huge amounts of IoT-generated data from physical devices and data sources, called IoT Big Data, requires new design solutions to speed up data processing, scale up with the data volume and improve data adaptability. Besides existing techniques for IoT data collection, filtering, and analytics, we present in this article a mashup oriented model, called IoT Big Services, for provisioning data-centric IoT services in the context of IOT mashups. These IoT services are organized in tree structure where each node, called an IoT Big Service, acts as an integrator that collects data from lower level, processes them and delivers the results to higher level in the architecture

Applying Big Data Technology to University Libraries: A Perspective Based on Service Context

In the era of big data, the construction of university libraries cannot be separated from the support of big data technology. The collection and analysis of data can improve the management efficiency and service quality of university libraries. On the basis of explaining the characteristics of big data and big data related technologies, our study puts forward the application framework of big data technology in university libraries based on the characteristics of university libraries. Based on the perspective of the context, we explore the effects of the application form of big data technology in university libraries on students \u27perception of the context, and establish a theoretical model of how it will affect students\u27 perceived service quality, perceived value and satisfaction the library. Our research can give suggestions to the service innovation practice of university libraries

Big Data analysis and Finance: a literature review

In this paper, we discuss the exploitation of Big Data in finance, particularly; we discuss financial opportunities to better management and challenges related to the emergence of big data. We review various works putting big data at the service of finance using analytical or predictive techniques. Furthermore, we recall some methods suitable to handle and extract relevant information from big data

A Framework for Accessing Patient Big Data: ANT View of a South African Health Facility

This study was undertaken to develop a framework that can be used to guide analysis when translating and transforming big data into a more purposeful resource for healthcare service delivery in South Africa. It is intended to assist in addressing some of the challenges encountered by healthcare facilities. Qualitative data were collected, with a focus on the types of interactions that happen between actors when accessing and using patients’ big data for healthcare services. Actor-network theory (ANT) was applied as a lens in the analysis of the data, to examine the relationships between actors when accessing and using big data for healthcare services. From the analysis, factors influencing the accessing and use of patient’s big data were found. These factors provided the basis from which a framework was developed. The framework can be used to guide the assessment of big data and improve healthcare and service delivery to the community