An Effective Classification Approach for Big Data Security Based on GMPLS/MPLS Networks

The need for effective approaches to handle big data that is characterized by its large volume, different types, and high velocity is vital and hence has recently attracted the attention of several research groups. This is especially the case when traditional data processing techniques and capabilities proved to be insufficient in that regard. Another aspect that is equally important while processing big data is its security, as emphasized in this paper. Accordingly, we propose to process big data in two different tiers.The first tier classifies the data based on its structure and on whether security is required or not. In contrast, the second tier analyzes and processes the data based on volume, variety, and velocity factors. Simulation results demonstrated that using classification feedback from a MPLS/GMPLS core network proved to be key in reducing the data evaluation and processing time

Big Data Security

All-Schemes (.TCL) and Labeling-Tier (.c) files should be incorporated into the MPLS library files in NS2 and then run them for the intended parameters in table 1 to get the generated simulation data

Hypertension and treatment outcomes in Palestine refugees in United Nations Relief and Works Agency primary health care clinics in Jordan.

OBJECTIVE: In six United Nations Relief and Works Agency (UNRWA) primary health care clinics in Jordan serving Palestine refugees diagnosed with hypertension, to determine the number, characteristics, programme outcomes and measures of disease control for those registered up to 30 June, 2013, and in those who attended clinic in the second quarter of 2013, the prevalence of disease-related complications between those with hypertension only and hypertension combined with diabetes mellitus. METHOD: Retrospective cohort study with programme and outcome data collected and analysed using E-Health. RESULTS: There were 18 881 patients registered with hypertension with females (64%) and persons aged ≥ 40 years (87%) predominating. At baseline, cigarette smoking was recorded in 17%, physical inactivity in 48% and obesity in 71% of patients. 77% of all registered patients attended clinic in the second quarter of 2013; of these, 50% had hypertension and diabetes and 50% had hypertension alone; 9% did not attend the clinics and 10% were lost to follow-up. Amongst those attending clinic, 92% had their blood pressure measured, of whom 83% had blood pressure <140/90 mm Hg. There were significantly more patients with hypertension and diabetes (N = 966, 13%) who had disease-related complications than patients who had hypertension alone (N = 472, 6%) [OR 2.2, 95% CI 2.0-2.5], and these differences were found for both males [18% vs. 10%, OR 1.9, 95% CI 1.6-2.2] and females [11% vs. 5%, OR 2.4, 95% CI 2.1-2.9]. CONCLUSION: Large numbers of Palestine refugees are being registered and treated for hypertension in UNRWA primary health care clinics in Jordan. Cohort analysis and E-Health can be used to regularly assess caseload, programme outcomes, clinic performance, blood pressure control and cumulative prevalence of disease-related complications. Current challenges include the need to increase clinic attendance and attain better control of blood pressure

A Global Paradigm for Designing Parallel Relational Data Warehouses in Distributed Environments

Designing a Parallel Relational Data Warehouse (PRDW) consists of a set of tasks: (i) choosing the hardware architecture; (ii) fragmenting the data warehouse schema; (iii) allocating the generated fragments; (iv) replicating fragments in order to ensure high performance; (v) defining the strategies for load balancing and query processing. The major drawback of this life-cycle is the fact that it does not consider the inter-dependency among sub-problems related to the design of PRDW, and it makes use of heterogeneous metrics to evaluate the \u201cquality\u201d of the final design. In previous research efforts, we introduced an analytical cost model for parallel OLAP query processing in cluster environments. In a second experience, we have taken into account the inter-dependency existing between fragmentation and allocation. In this paper, we propose a novel methodology, called F&A&R, which further extends previous results, and defines an approach where the main PRDW design phases (i.e., fragmentation, allocation, and replication) are performed simultaneously, in a global fashion. In particular, our approach determines whether the fragmentation pattern currently generated is relevant to the allocation process or not. An original method of supporting data replication, based on fuzzy k-means clustering, is also proposed and successfully integrated within the whole design framework. Finally, we experimentally assessed the performance of F&A&R against a well-known data warehouse benchmark, with very promising results