‘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 general communication cost optimization framework for big data stream processing in geo-distributed data centers

With the explosion of big data, processing large numbers of continuous data streams, i.e., big data stream processing (BDSP), has become a crucial requirement for many scientific and industrial applications in recent years. By offering a pool of computation, communication and storage resources, public clouds, like Amazon's EC2, are undoubtedly the most efficient platforms to meet the ever-growing needs of BDSP. Public cloud service providers usually operate a number of geo-distributed datacenters across the globe. Different datacenter pairs are with different inter-datacenter network costs charged by Internet Service Providers (ISPs). While, inter-datacenter traffic in BDSP constitutes a large portion of a cloud provider's traffic demand over the Internet and incurs substantial communication cost, which may even become the dominant operational expenditure factor. As the datacenter resources are provided in a virtualized way, the virtual machines (VMs) for stream processing tasks can be freely deployed onto any datacenters, provided that the Service Level Agreement (SLA, e.g., quality-of-information) is obeyed. This raises the opportunity, but also a challenge, to explore the inter-datacenter network cost diversities to optimize both VM placement and load balancing towards network cost minimization with guaranteed SLA. In this paper, we first propose a general modeling framework that describes all representative inter-task relationship semantics in BDSP. Based on our novel framework, we then formulate the communication cost minimization problem for BDSP into a mixed-integer linear programming (MILP) problem and prove it to be NP-hard. We then propose a computation-efficient solution based on MILP. The high efficiency of our proposal is validated by extensive simulation based studies.

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.

Troca de informação e ações conjuntas desenvolvidas pelo grupo de Big Bands Movimento Elefantes

Dissertação apresentada ao Programa de Pós-graduação em Administração da Universidade Municipal de São Caetano do Sul

Estratégias de Compras Governamentais no Brasil - Teoria dos Leilões e "Big Data"

O estudo da FGV Projetos, coordenado pelo economista Fernando Blumenschein, desenvolve um quadro metodológico específico para compras governamentais com base em pesquisa realizada para o Fundo Nacional de Desenvolvimento da Educação (FNDE). Este estudo destaca o potencial de uso dos conceitos da teoria dos leilões, juntamente com métodos de análise de "Big Data" na formação de sessões públicas.