Software tools and techniques for big data computing in healthcare clouds – call for papers – elsevier

CiteScore measures the average citations received per document published in this title. Database building CiteScore values are based on citation counts in a given year (e.g. Data recovery top 10 2015) to documents published in three previous calendar years (e.g. Database hosting 2012 – 14), divided by the number of documents in these three previous years (e.g. Data recovery best 2012 – 14).

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As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. 7 data recovery 94fbr For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis. Database languages The data originated from multiple types of sources including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, health data etc. Database ranking Such ‘Data Explosions’ has led to one of the most challenging research issues of the current Information and Communication Technology (ICT) era: how to effectively and optimally manage such large amount of data and identify new ways to analyze large amounts of data for unlocking information. Data recovery youtube The issue is also known as the ‘Big Data’ problem, which is defined as the practice of collecting complex data sets so large that it becomes difficult to analyze and interpret manually or using on-hand data management applications. 911 database From the perspective of real-world applications, the Big Data problem has also become a common phenomenon in domain of science, medicine, engineering, and commerce. Data recovery download Representative applications include clinical decision support systems, digital agriculture, social media analytics, high energy physics, earth observation, genomics, automobile simulations, medical imaging, body area networks, translational medicine, and the like.

An important class of Big Data application exists in the healthcare domain. Data recovery after factory reset There are wide varieties of health related datasets that play a critical role in the health information systems (HIS) and clinical decision support systems (CDSS). Database schema design These datasets differ widely in their volume, variety, and velocity, from patient focused sets such as electronic medical records to population focused sets such as public health data, and knowledge focused sets such as drug-to-drug, drug-to-disease, disease to disease interaction registries. Database uml While decision makers’ (healthcare practitioner, government decision makers) ability to understand and process the health data dictates the accuracy of the final decision, the exponential growth in the size of the aforementioned health related raw data sets has widened this integration gap. Data recovery ntfs This further makes the timely information aggregation, retrieval, and analysis a challenge. Database error This is severely limiting the potential benefits of having large datasets and HIS/CDSS for medical decision-making processes.

Another important class of Big Data application in the healthcare domain includes the Medical Body Area Networks (MBANs). Database functions According to the market intelligence company ABI research (http://www.abiresearch.com/), over the next five years, close to five million disposable wireless MBAN sensors will be shipped. Top 10 data recovery MBANs enable a continuous monitoring of patient’s condition by sensing and transmitting measurements such as heart rate, electrocardiogram (ECG), body temperature, respiratory rate, chest sounds, and blood pressure etc. Database job titles MBANs will allow: (i) real-time and historical monitoring of patient’s health; (ii) infection control; (iii) patient identification and tracking; and (iv) geo-fencing and vertical alarming. Data recovery linux live cd However, to manage and analyze such massive MBAN data from millions of patients in real-time, healthcare providers will need access to an intelligent and highly secure ICT infrastructure.

In all of the aforementioned health application scenarios, hundreds of petabytes of heterogeneous data (images, text, video, raw sensor data, and the like) will be generated and required to be efficiently processed (stored, distributed, and indexed with an ontology and semantics) in a way that does not compromise end-users’ Quality of Service (QoS) in terms of data availability, data search delay, data analysis delay, and the like. S pombe database Many of the existing ICT systems that store, process, distribute, and index hundreds of petabytes of heterogeneous data fall shortly of this challenge or do not exist. Database usa We need to develop new techniques that aims to optimize all these in less than 10 milliseconds and to achieve this without any cloud configuration knowledge (i.e., by automatically detecting cloud storage proximity and the QoS of network links between storage alternatives).

We believe that Cloud computing infrastructures (e.g., Amazon, Microsoft Azure, etc.) in conjunction with fast communication networks, data-intensive programming paradigms (MapReduce, distributed storage system, etc.), semantic web, and machine learning algorithms will form the basis of designing and developing Big Data Analytics based innovation framework in health domain . Data recovery pro license key We need to develop software tools and techniques that allow for fast query processing and speeds-up data analytics in a global cloud computing based Big Data network that exploits such data provide awareness and knowledge in real-time.

The progress in this area will be made by applying and extending well-founded formal models and techniques from multiple domains of computer science. Data recovery on android In semantic web, we will need to develop application/domain-specific data modelling and representation techniques for the integration and analyses of information coming from multiple, heterogeneous sources. Icare data recovery 94fbr In operations research, we will need to develop cloud-deployable combinatorial techniques for optimising multiple (often-conflicting) selection and deployment QoS targets associated with BigData applications. Image database In theoretical computer science, we will need to apply computational statistics for developing Big Data application workload prediction models. Database web application In data-intensive computing, we will need to extend the existing massive data processing paradigm (e.g., MapReduce) with ability to process application data across multiple cloud data centres.

*Please select Special Issue Paper as your manuscript type, and enter “Big Data Analytics Health Cloud” as both the Special Issue title and as your Preferred Editor*

Papers submitted to this special issue for possible publication must be original and must not be under consideration for publication in any other journal or conference.

Submitted papers should be formatted according to the journal style. Database graphic For more detailed information concerning the requirements for submission, please refer to the journal homepage at: http://www.elsevier.com/journals/future-generation-computer-systems/0167-739X/guide-for-authors

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