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In the era of big data, new scientific applications such as those used in astronomy [1] are emerging and challenging High Performance Computing (HPC) systems and software. Traditionally, HPC applications were compute-bounded, with a light use of the I/O capabilites at the start and end of the execution. In contrast, emergent applications present data- intensive behaviors arising several new challenges...
Stateful data analytics framework have emerged to provide fresh and low-latency results for big data processing. At present, it is desired to achieve the fine-grained data model in mainstream data processing framework, e.g. Spark. However, Spark adopts coarse-grained data model in order to facilitate parallization, it makes the fine-grained data access in stateful data analytics very challenging....
Computational and statistical assessment of data has become the most valuable resource in every field including geoscience for making accurate decision. Analysis of soil and earth structures requires complex mathematical and numerical simulation which demands significant expertise and resources inciting costlier and tedious tasks. The unavailability of proper open-source tools and technical resources...
Data analytics has become not only an essential part of day-to-day decision making, but also reinforces long-term strategic decisions. Whether it is real-time fraud detection, resource management, tracking and prevention of disease outbreak, natural disaster management or intelligent traffic management, the extraction and exploitation of insightful information from unparalleled quantities of data...
Distributed Applications from different domains like Health care, E-Commerce, science, social networks etc., tend to generate large volumes of heterogeneous data that grow exponentially over a period of time leading to big data sets. Descriptive Analytics, on big data sets, pose a great challenge for traditional data analytical tools, since it is to be performed on the full data set, unlike predictive...
Big Data can be defined as large data sets which are being generated from different sources like social media, audios, imaging, logging online websites etc. A need exists to process and analyze this huge amount of data to extract meaningful information. This can be a challenging task. Big data exceeds the processing capability of traditional databases to capture, manage, and process the voluminous...
Data analytics becomes increasingly important in big data applications. Adaptively subsetting large amounts of data to extract the interesting events such as the centers of hurricane or thunderstorm, statistically analyzing and visualizing the subset data, is an effective way to analyze ever-growing data. This is particularly crucial for analyzing Earth Science data, such as extreme weather. The Hadoop...
More varied data channels, increasingly diverse analytic methods, and new deployment models--along with some fundamental technology shifts--will significantly impact the next generation of big data systems.
Beehive is a parallel programming framework designed for cluster-based computing environments in cloud data centers. It is specifically targeted for graph data analysis problems. The Beehive framework provides the abstraction of key-value based global object storage, which is maintained in memory of the cluster nodes. Its computation model is based on optimistic concurrency control in executing concurrent...
According to data volumes in scientific applications have grown exponentially, new scientific methods to analyze and organize the data are required. MapReduce programming is driving Internet services and those services operation in a cloud environment. Hence it is required to efficiently provide resources for handling diverse MapReduce applications. In this paper we show the Hadoop application with...
The current workflow nets models of WS-BPEL are feature completed, but almost all of them lack data information, so they cannot be used to detect the data access exception in WS-BPEL. In order to overcome this shortcoming, this paper presents a new type of Petri nets-DWFN(Data workflow nets), which makes the data flow modeling and exceptions detection of WS-BPEL possible. Then the DWFN model of corresponding...
Requirements modeling is a crucial step in the software development process. It takes an important role in requirements engineering. Requirements models are used to discover and clarify the functional and data requirements for software systems. It is the basis for understanding user requirement and designing information system. This paper describes an entire process of building a software requirements...
This paper emphasizes particularly on introduction of the application of non-Redundant Rules Algorithm on Data Analyses of Forest Inventory. By establishing the data mining model, MVNR Algorithm is applied to analyzing the relation of species, origin, age, chest, circumference, height and canopy density of trees. The results provide the best valuable information for forestation programming management...
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