Investor protection, firm informational problems, big N auditors, and cost of debt around the world

This paper examines the effects of investor protection, firm informational problems (proxied by firm size, firm age, and the number of analysts following), and Big N auditors on firms' cost of debt around the world. Using data from 1994 to 2006 and over 90,000 firm-year observations, we find that the cost of debt is lower when firms are audited by Big N auditors, especially in countries with strong investor protection. Second, we find that firms with more informational problems (i.e., higher information asymmetry problems) benefit more from Big N auditors in terms of lower cost of debt only in countries with stronger investor protection.

A secure cloud computing based framework for big data information management of smart grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call 'Smart-Frame.' The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.

Aggregation on the fly: Reducing traffic for big data in the cloud

As a leading framework for processing and analyzing big data, MapReduce is leveraged by many enterprises to parallelize their data processing on distributed computing systems. Unfortunately, the all-to-all data forwarding from map tasks to reduce tasks in the traditional MapReduce framework would generate a large amount of network traffic. The fact that the intermediate data generated by map tasks can be combined with significant traffic reduction in many applications motivates us to propose a data aggregation scheme for MapReduce jobs in cloud. Specifically, we design an aggregation architecture under the existing MapReduce framework with the objective of minimizing the data traffic during the shuffle phase, in which aggregators can reside anywhere in the cloud. Some experimental results also show that our proposal outperforms existing work by reducing the network traffic significantly.

‘It should be a big responsibility’: Separated low-income mothers’ evaluation of child support arrangements and the conduct of fathers

The Australian Child Support Scheme was established as a means of ensuring adequate financial support for children of separated parents. However, within the financial transfer of child support exist notions of ‘trust’ and ‘fairness’ based on parents navigating their changed relationship post-separation. Previous research has explored the assessment and outcomes of child support for both payee and payer parents, however little attention has been given to how women evaluate the assessment and outcomes of child support. As such, this research aimed to explore payee mothers’ evaluation of their child support experiences based on the value of their child support assessment and the extent to which these payments were received. Following the methods of constructivist grounded theory, in-depth interviews were conducted with 20 low-income single mothers. Analysis revealed that payee mothers evaluated child support based on the moral assumptions and the rationalities they perceived were underlying payer fathers’ child support compliance. While payee mothers desired arrangements that reflected joint parental financial responsibility, in reality many experienced problematic child support payments, which may ultimately undermine payee parents’ confidence in the Child Support Scheme.

A data as a product model for future consumption of big stream data in clouds

Data is becoming the world’s new natural resourceand big data use grows quickly. The trend of computingtechnology is that everything is merged into the Internet and‘big data’ are integrated to comprise completeinformation for collective intelligence. With the increasingsize of big data, refining big data themselves to reduce data sizewhile keeping critical data (or useful information) is a newapproach direction. In this paper, we provide a novel dataconsumption model, which separates the consumption of datafrom the raw data, and thus enable cloud computing for bigdata applications. We define a new Data-as-a-Product (DaaP)concept; a data product is a small sized summary of theoriginal data and can directly answer users’ queries. Thus, weseparate the mining of big data into two classes of processingmodules: the refine modules to change raw big data into smallsizeddata products, and application-oriented mining modulesto discover desired knowledge further for applications fromwell-defined data products. Our practices of mining big streamdata, including medical sensor stream data, streams of textdata and trajectory data, demonstrated the efficiency andprecision of our DaaP model for answering users’ queries

Wielding the big stick – Lessons for enforcing anti-discrimination law from the Fair Work Ombudsman

Anti-discrimination law is enforced by a person who has experienced discrimination by lodging a complaint at a statutory equal opportunity agency. The agency is responsible for receiving and resolving discrimination complaints and educating the community; it does not play a role in enforcing the law. The agency relies on ‘carrots’ to encourage voluntary compliance, but it does not wield any ‘sticks’. This is not the case in other areas of law, such as industrial relations, where the Fair Work Ombudsman is charged with enforcing the law — including the prohibition of discrimination in the workplace — and possesses the necessary powers to do so. British academics Hepple, Coussey and Choudhury developed an enforcement pyramid for equal opportunity. This article shows that the model used by the Fair Work Ombudsman reflects what Hepple, Coussey and Choudhury propose, while anti-discrimination law enforcement would be represented as a flat, rectangular structure. The article considers the Fair Work Ombudsman’s discrimination enforcement work to date and identifies some lessons that anti-discrimination law enforcement can learn from its experience.

Big data related technologies, challenges and future prospects

Big data is the large or complex data that exceed the processing capacity of conventional data processing systems. This book provides a big picture in this broad research area, covering all the phases of its value chains. The authors have attempted to survey most of the relevant technologies in each phrase of big data. The book is recommended for readers interested in advanced research in big data, also for industry practitioners who are interested in building big data applications. If the reader is not with necessary technical background, complementary readings may be needed.

Networking for big data

Big data is an emerging hot research topic due to its pervasive application in human society, such as government, climate, finance, and science. Currently, most research work on big data falls in data mining, machine learning, and data analysis. However, these amazing top-level killer applications would not be possible without the underneath support of networking due to their extremely large volume and computing complexity, especially when real-time or near-real-time applications are demanded.

Big data for networking

Big data analytics has shown great potential in optimizing operations, making decisions, spotting business trends, preventing threats, and capitalizing on new sources of revenues in various fields such as manufacturing, healthcare, finance, insurance, and retail. The management of various networks has become inefficient and difficult because of their high complexities and interdependencies. Big data, in forms of device logs, software logs, media content, and sensed data, provide rich information and facilitate a fundamentally different and novel approach to explore, design, and develop reliable and scalable networks. This Special Issue covers the most recent research results that address challenges of big data for networking. We received 45 submissions, and ultimately nine high quality papers, organized into two groups, have been selected for inclusion in this Special Issue.

Big data sensing and service: a tutorial

As the advance of the Internet of Things (IoT), more M2M sensors and devices are connected to the Internet. These sensors and devices generate sensor-based big data and bring new business opportunities and demands for creating and developing sensor-oriented big data infrastructures, platforms and analytics service applications. Big data sensing is becoming a new concept and next technology trend based on a connected sensor world because of IoT. It brings a strong impact on many sensor-oriented applications, including smart city, disaster control and monitor, healthcare services, and environment protection and climate change study. This paper is written as a tutorial paper by providing the informative concepts and taxonomy on big data sensing and services. The paper not only discusses the motivation, research scope, and features of big data sensing and services, but also exams the required services in big data sensing based on the state-of-the-art research work. Moreover, the paper discusses big data sensing challenges, issues, and needs.