622,988 research outputs found
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Breaking the Barriers to Specialty Care: Practical Ideas to Improve Health Equity and Reduce Cost - Striving for Equity in Specialty Care Full Report
Tremendous health outcome inequities remain in the U.S. across race and ethnicity, gender and sexual orientation, socio-economic status, and geography—particularly for those with serious conditions such as lung or skin cancer, HIV/AIDS, or cardiovascular disease.These inequities are driven by a complex set of factors—including distance to a specialist, insurance coverage, provider bias, and a patient's housing and healthy food access. These inequities not only harm patients, resulting in avoidable illness and death, they also drive unnecessary health systems costs.This 5-part series highlights the urgent need to address these issues, providing resources such as case studies, data, and recommendations to help the health care sector make meaningful strides toward achieving equity in specialty care.Top TakeawaysThere are vast inequalities in access to and outcomes from specialty health care in the U.S. These inequalities are worst for minority patients, low-income patients, patients with limited English language proficiency, and patients in rural areas.A number of solutions have emerged to improve health outcomes for minority and medically underserved patients. These solutions fall into three main categories: increasing specialty care availability, ensuring high-quality care, and helping patients engage in care.As these inequities are also significant drivers of health costs, payers, health care provider organizations, and policy makers have a strong incentive to invest in solutions that will both improve outcomes and reduce unnecessary costs. These actors play a critical role in ensuring that equity is embedded into core care delivery at scale.
A Market for Success: How a Robust Service Provider Market Can Help Community Colleges Improve Student Completion
Outlines how external service providers can help community colleges enhance institutional redesign, use of data, student services and supports, and faculty development in order to remove barriers to completion, increase efficiency, and improve outcomes
Middleware-based Database Replication: The Gaps between Theory and Practice
The need for high availability and performance in data management systems has
been fueling a long running interest in database replication from both academia
and industry. However, academic groups often attack replication problems in
isolation, overlooking the need for completeness in their solutions, while
commercial teams take a holistic approach that often misses opportunities for
fundamental innovation. This has created over time a gap between academic
research and industrial practice.
This paper aims to characterize the gap along three axes: performance,
availability, and administration. We build on our own experience developing and
deploying replication systems in commercial and academic settings, as well as
on a large body of prior related work. We sift through representative examples
from the last decade of open-source, academic, and commercial database
replication systems and combine this material with case studies from real
systems deployed at Fortune 500 customers. We propose two agendas, one for
academic research and one for industrial R&D, which we believe can bridge the
gap within 5-10 years. This way, we hope to both motivate and help researchers
in making the theory and practice of middleware-based database replication more
relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on
Management of Data, Vancouver, Canada, June 200
CHORUS Deliverable 3.4: Vision Document
The goal of the CHORUS Vision Document is to create a high level vision on audio-visual search engines in order to give guidance to the future R&D work in this area and to highlight trends and challenges in this domain. The vision of CHORUS is strongly connected to the CHORUS Roadmap Document (D2.3). A concise document integrating the outcomes of the two deliverables will be prepared for the end of the project (NEM Summit)
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