Leukemia, a prevalent childhood cancer affecting the blood and bone marrow, necessitates a proactive approach to risk mitigation through lifestyle adaptations. Despite previous investigations into similar aspects across different cancer types, a comprehensive inquiry focusing on all leukemia subtypes within Pakistan remains a significant research gap. Acknowledging the influence of regional variations on individuals' lifestyles, this study aims to identify lifestyle and demographic factors associated with leukemia development and predict specific leukemia subtypes using clinical data. Our data collection included 364 leukemia cases and 896 control subjects, gathered from different cancer or tertiary care hospitals in Islamabad and Peshawar, Pakistan. The data was meticulously categorized into laboratory results, demographic characteristics, and lifestyle parameters. For demographic and lifestyle analysis, we employed advanced techniques of Machine Learning, and statistical and graph-based methodologies to assess leukemia development risk. This study highlights factors associated with a higher risk of leukemia, including passive smoking, rural residence, and poor nutrition. These insights emphasize the promotion of healthier lifestyle choices to potentially reduce leukemia incidences. Additionally, we transformed the clinical dataset into graph data, which was utilized for leukemia classification and subtype prediction. We conducted classification on both graph and structured (tabular) data, with the structured data, achieving a 96% accuracy rate, notably on oversampled data. To enhance the interoperability of our classification outcomes, we employed the SHapley Additive exPlanations (SHAP) algorithm to explain the classification, offering comprehensive insights into the rationale behind leukemia classification.

The number of applications based on Apache Hadoop is dramatically increasing due to the robustness and dynamic features of this system. At the heart of Apache Hadoop, the Hadoop Distributed File System (HDFS) provides the reliability and high availability for computation by applying a static replication by default. However, because of the characteristics of parallel operations on the application layer, the access rate for each data file in HDFS is completely different. Consequently, maintaining the same replication mechanism for every data file leads to detrimental effects on the performance. By rigorously considering the drawbacks of the HDFS replication, this paper proposes an approach to dynamically replicate the data file based on the predictive analysis. With the help of probability theory, the utilization of each data file can be predicted to create a corresponding replication strategy. Eventually, the popular files can be subsequently replicated according to their own access potentials. For the remaining low potential files, an erasure code is applied to maintain the reliability. Hence, our approach simultaneously improves the availability while keeping the reliability in comparison to the default scheme. Furthermore, the complexity reduction is applied to enhance the effectiveness of the prediction when dealing with Big Data.

Personalized healthcare envisions providing customized treatment and management plans to individuals at their doorstep. Key factors to ensure personalized healthcare is to involve with the individual in their daily life activities and process the gathered information to provide recommendations. We identified the mostly exposed domains for gathering chronic disease patients information that includes: clinical, social media, and daily life activities. Clinical data is related to the health-care of the patients while social media, sensory, and wearables data is related to the wellness data of the patients. A framework is required to monitor the health and wellness information of the patients for health and wellness analytics provisioning to the physicians for better decision making. We propose Personalized, Ubiquitous Life-care Decision Support System (PULSE); a state of the art decision support system that helps physicians and patients in life-style management of chronic disease patients such as Diabetes. The proposed approach not only utilizes clinical information but also personalized information by correlation to find hidden information using big data health analytic for improvement of life-care. PULSE provides health analytics by utilizing and processing clinical information of the patient. In the same way, it provides wellness analytics to the patients by using their social, activities, emotions and daily life information. The co-relation between clinical and personalized analytics is performed for better recommendations to the patients. This eventually results in improved life-care and healthy living of the individuals.

Summary It is a fact that the attention of research community in computer science, business executives, and decision makers is drastically drawn by big data. As the volume of data becomes bigger, it needs performance‐oriented data‐intensive processing frameworks such as MapReduce, which can scale computation on large commodity clusters. Hadoop MapReduce processes data in Hadoop Distributed File System as jobs scheduled according to YARN fair scheduler and capacity scheduler. However, with advancement and dynamic changes in hardware and operating environments, the performance of clusters is greatly affected. Various efforts in literature have been made to address the issues of heterogeneity (i.e., clusters consisting of virtual machines and machines with different hardware), network communication, data locality, better resource utilization, and run‐time scheduling. In this paper, we present a survey to discuss various research efforts made so far to improve Hadoop MapReduce scheduling. We classify scheduling algorithms and techniques proposed in the literature so far based on their addressing areas and present a taxonomy. Furthermore, we also discuss various aspects of open issues and challenges in the scheduling of MapReduce to improve its performance. Copyright © 2015 John Wiley & Sons, Ltd.

Hadoop based Map Reduce (MR) has emerged as big data processing mechanism in terms of its data intensive applications. In data intensive systems, analysis and visualizations as a result of various algorithms can lead to differentiable and comparable results. Current implementations of MR facilitates to reuse the results of MR jobs in other MR jobs and to distribute the cloud resources among jobs. However, very little work is done in terms of using same data for multiple algorithms at the same time in a single job using either shared resources or dynamic resource allocation based on the data and scheduling of Map Reduce jobs. In this paper we propose a method to execute multiple algorithms on same data in HDFS concurrently and to use the same available resources by dynamically managing the task assignment and results aggregation. Our proposed approach reduces the execution time and supports multiple algorithms execution in parallel. In-Map/In-Reduce shows 200% decrease in execution time.

MPI provides high performance parallel file access API called MPI-IO. ROMIO library implements MPI-IO specifications thus providing this facility to C and Fortran programmers. Similarly, object-oriented languages such as Java and C# have adapted MPI specifications and their implementations provide HPC facility to its programmers. These implementations, however, lack parallel file access capability which is very important for large-scale parallel applications. In this paper, we propose a Java based parallel file access API called MPJ-IO and describe its reference implementation. We describe design details and performance evaluation of this implementation. We use JNI calls in our code to utilize functions from ROMIO library. In addition, we highlight the reasons for using JNI calls in our code.

Mobile cloud computing aims at improving user experience through enhancing the ability of mobile applications by doing intensive tasks in the cloud. In this paper we consider an environment similar to a hybrid cloud in which the mobile device works as a private cloud. Given that the mobile phone has both limited processing resources and battery time, the proposed mobile application architecture has been designed with the capability of sending specified data/parameters to the cloud. This data is subsequently used for further processing/mining and visualization to assist in inferring further information through mapreduce. This information gives details about resource and battery consumption which will help in optimizing the relationship between the mobile device and cloud. It will also be beneficial to the optimization of the mobile application through the trends visualized in the cloud. In this paper we created an activity recognition health application as an example and helped the user about his health along with giving an insight into abnormal behavior and lifestyle trends.

Cloud Computing has been adopted by health-care industry for their data's storage, manipulation, and secured sharing needs. However, cloud's distributed nature can be exploited to the use of scientific applications that are designed for health-care data evaluation. These scientific applications, such as, Medical Imaging, Gene and Protein annotation, Mapdrug therapy and Clinical Decision Support Systems (CDSS), require high-performance messaging libraries with minimum computational and communication overhead, and efficient resource utilization. The proposed High Performance Java Sockets (HPJS) encapsulates the needs of high-performance messaging of scientific applications for cloud platforms. HPJS effectively uses Java's socket implementation for high-performance inter-process communication. With single-copy protocol, thread re-usability and reduced communication overhead, HPJS can perform messaging twice as fast to conventional buffered-base communication libraries.