12,077 research outputs found
Sketch of Big Data Real-Time Analytics Model
Big Data has drawn huge attention from researchers in information sciences, decision makers in governments and enterprises. However, there is a lot of potential and highly useful value hidden in the huge volume of data. Data is the new oil, but unlike oil data can be refined further to create even more value. Therefore, a new scientific paradigm is born as data-intensive scientific discovery, also known as Big Data. The growth volume of real-time data requires new techniques and technologies to discover insight value. In this paper we introduce the Big Data real-time analytics model as a new technique. We discuss and compare several Big Data technologies for real-time processing along with various challenges and issues in adapting Big Data. Real-time Big Data analysis based on cloud computing approach is our future research direction
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
BigDataBench: a Big Data Benchmark Suite from Internet Services
As architecture, systems, and data management communities pay greater
attention to innovative big data systems and architectures, the pressure of
benchmarking and evaluating these systems rises. Considering the broad use of
big data systems, big data benchmarks must include diversity of data and
workloads. Most of the state-of-the-art big data benchmarking efforts target
evaluating specific types of applications or system software stacks, and hence
they are not qualified for serving the purposes mentioned above. This paper
presents our joint research efforts on this issue with several industrial
partners. Our big data benchmark suite BigDataBench not only covers broad
application scenarios, but also includes diverse and representative data sets.
BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench .
Also, we comprehensively characterize 19 big data workloads included in
BigDataBench with varying data inputs. On a typical state-of-practice
processor, Intel Xeon E5645, we have the following observations: First, in
comparison with the traditional benchmarks: including PARSEC, HPCC, and
SPECCPU, big data applications have very low operation intensity; Second, the
volume of data input has non-negligible impact on micro-architecture
characteristics, which may impose challenges for simulation-based big data
architecture research; Last but not least, corroborating the observations in
CloudSuite and DCBench (which use smaller data inputs), we find that the
numbers of L1 instruction cache misses per 1000 instructions of the big data
applications are higher than in the traditional benchmarks; also, we find that
L3 caches are effective for the big data applications, corroborating the
observation in DCBench.Comment: 12 pages, 6 figures, The 20th IEEE International Symposium On High
Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando,
Florida, US
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
Crisis Analytics: Big Data Driven Crisis Response
Disasters have long been a scourge for humanity. With the advances in
technology (in terms of computing, communications, and the ability to process
and analyze big data), our ability to respond to disasters is at an inflection
point. There is great optimism that big data tools can be leveraged to process
the large amounts of crisis-related data (in the form of user generated data in
addition to the traditional humanitarian data) to provide an insight into the
fast-changing situation and help drive an effective disaster response. This
article introduces the history and the future of big crisis data analytics,
along with a discussion on its promise, challenges, and pitfalls
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
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