Layered performance modelling and evaluation for cloud topic detection and tracking based big data applications

“Big Data” best characterized by its three features namely “Variety”, “Volume” and “Velocity” is revolutionizing nearly every aspect of our lives ranging from enterprises to consumers, from science to government. A fourth characteristic namely “value” is delivered via the use of smart data analytics over Big Data. One such Big Data Analytics application considered in this thesis is Topic Detection and Tracking (TDT). The characteristics of Big Data brings with it unprecedented challenges such as too large for traditional devices to process and store (volume), too fast for traditional methods to scale (velocity), and heterogeneous data (variety). In recent times, cloud computing has emerged as a practical and technical solution for processing big data. However, while deploying Big data analytics applications such as TDT in cloud (called cloud-based TDT), the challenge is to cost-effectively orchestrate and provision Cloud resources to meet performance Service Level Agreements (SLAs). Although there exist limited work on performance modeling of cloud-based TDT applications none of these methods can be directly applied to guarantee the performance SLA of cloud-based TDT applications. For instance, current literature lacks a systematic, reliable and accurate methodology to measure, predict and finally guarantee performances of TDT applications. Furthermore, existing performance models fail to consider the end-to-end complexity of TDT applications and focus only on the individual processing components (e.g. map reduce). To tackle this challenge, in this thesis, we develop a layered performance model of cloud-based TDT applications that take into account big data characteristics, the data and event flow across myriad cloud software and hardware resources and diverse SLA considerations. In particular, we propose and develop models to capture in detail with great accuracy, the factors having a pivotal role in performances of cloud-based TDT applications and identify ways in which these factors affect the performance and determine the dependencies between the factors. Further, we have developed models to predict the performance of cloud-based TDT applications under uncertainty conditions imposed by Big Data characteristics. The model developed in this thesis is aimed to be generic allowing its application to other cloud-based data analytics applications. We have demonstrated the feasibility, efficiency, validity and prediction accuracy of the proposed models via experimental evaluations using a real-world Flu detection use-case on Apache Hadoop Map Reduce, HDFS and Mahout Frameworks

City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed Systems and Technologies (IJDST), 201

Visible emission and energy transfer in Tb³⁺/Dy³⁺ co-doped phosphate glasses

In this work, we systematically study the spectroscopic properties of Tb3+/Dy3+ co-doped phosphate glasses in the visible spectral region and explore the sensitization role of Dy3+ in the enhancement of visible fluorescence of Tb3+ ions. Judd-Ofelt parameters Ω2 and Ω4/Ω6 of the phosphate glass as host for Tb3+ are calculated as 21.60 × 10-20 cm2 and 0.73, respectively, based on the measured spectral absorption. Multiple energy transfer (ET) routes from Dy3+ to Tb3+ and their efficiencies are characterized, and the enhanced fluorescence properties of Tb3+ are investigated, including the emission spectral strength and the spontaneous emission lifetime as functions of Dy3+ doping concentration. The efficient nonradiative ET processes between Dy3+ and Tb3+ allow a moderate concentration level of Tb3+ to achieve favorably stronger spectral absorption at blue and ultraviolet wavelengths. Tb3+/Dy3+ co-doped phosphate glass shows promising potential for phosphors and lasing operation at visible wavelengths.</p

One-step synthesis of Pt@three-dimensional graphene composite hydrogel: an efficient recyclable catalyst for reduction of 4-nitrophenol

A Pt@three-dimensional graphene (Pt@3DG) composite hydrogel with a unique porous nanostructure was prepared and used as an efficient, recyclable and robust catalyst for the reduction of 4-nitrophenol to 4-aminophenol under mild conditions. The influence of graphene architecture on catalytic activities was comparatively investigated by loading the same amount of Pt on reduced graphene oxide. Pt@3DG exhibits a very high catalytic activity owing to the three-dimensional macroporous framework with high specific surface area, numerous activation sites and efficient transport pathways. Moreover, catalyst separation can be easily achieved by simple filtration, and the catalyst can be reused for at least five runs, maintaining its high catalytic activity

Long Noncoding RNA HOTTIP Serves as an Independent Predictive Biomarker for the Prognosis of Patients with Clear Cell Renal Cell Carcinoma

Several studies have indicated that HOXA transcript at the distal tip (HOTTIP) play important roles in the tumorigenesis and development of various cancers. We aim to investigate the expression and prognostic value of HOTTIP in clear cell renal cell carcinoma (ccRCC). A systematic review of PubMed, Embase, Medline, and Web of Science databases was performed to select eligible literatures relevant to the correlation between HOTTIP expression and clinical outcome of different cancers. The association between the HOTTIP level and overall survival (OS), lymph node metastasis (LNM), or clinical stage was subsequently analyzed. Survival analyses were performed in a large cohort of more than 500 patients with ccRCC from The Cancer Genome Atlas (TCGA) using bioinformatic methods. Seventeen studies with a total of 1594 patients with thirteen kinds of carcinomas were included in this analysis. The result showed that high HOTTIP expression could predict worse outcome in cancer patients, with the pooled hazard ratio (HR) of 2.34 (95% confidence interval (CI) 1.96–2.79, p<0.0001). The result also showed that elevated HOTTIP expression was correlated with more LNM (OR=2.61, 95% CI 1.91-3.58, p<0.0001) and advanced clinical stage (OR=3.57, 95% CI 2.58-4.93, p<0.0001). We further validated that ccRCC patients with higher HOTTIP expression tend to have unsatisfactory outcomes both in the entire TCGA dataset and different clinical stratums, like age, grade, and stage. The tumor of those patients was associated with a larger size, easier to metastasis, advanced clinical stage, and a higher pathological grade. These findings suggested that increased HOTTIP expression might act as a novel prognostic marker for ccRCC patients

Thermal Sensitivity of Birefringence in Polarization-Maintaining Hollow-Core Photonic Bandgap Fibers

Polarization-maintaining (PM) fiber is the core sensitive component of a fiber optic gyroscope (FOG); its birefringence temperature stability is crucial for maintaining accuracy. Here, we systematically investigated the structural thermal deformation and the resulting birefringence variation in typical PM hollow-core photonic bandgap fibers (HC-PBGFs) for FOG according to varying fiber structure parameters. To verify the application potential of PM HC-PBGFs in FOG, we compared the thermal sensitivity of birefringence (TSB) with that of the commonly used Panda PM fiber, which was tested to 5.07 × 10−5/100 °C. For rhombic-core fibers, the TSB was determined by the structure of the cladding and could be tuned as low as low as 10−7/100 °C, two orders of magnitude smaller than that of the panda PM fibers. For hexagonal-core fibers, the birefringence variation depended mainly on the drift of the surface modes (SMs) caused by the deformation of the core. A slight drift in SMs could cause a dramatic birefringence variation in hexagonal-core fiber, and the TSB could be as high as 10−4/100 °C, much higher than that of panda PM fiber. This study lays the foundation for the development of high birefringence temperature-stable HC-PBGFs and their applications in FOG

Thermal Sensitivity of Birefringence in Polarization-Maintaining Hollow-Core Photonic Bandgap Fibers

Polarization-maintaining (PM) fiber is the core sensitive component of a fiber optic gyroscope (FOG); its birefringence temperature stability is crucial for maintaining accuracy. Here, we systematically investigated the structural thermal deformation and the resulting birefringence variation in typical PM hollow-core photonic bandgap fibers (HC-PBGFs) for FOG according to varying fiber structure parameters. To verify the application potential of PM HC-PBGFs in FOG, we compared the thermal sensitivity of birefringence (TSB) with that of the commonly used Panda PM fiber, which was tested to 5.07 &times; 10&minus;5/100 &deg;C. For rhombic-core fibers, the TSB was determined by the structure of the cladding and could be tuned as low as low as 10&minus;7/100 &deg;C, two orders of magnitude smaller than that of the panda PM fibers. For hexagonal-core fibers, the birefringence variation depended mainly on the drift of the surface modes (SMs) caused by the deformation of the core. A slight drift in SMs could cause a dramatic birefringence variation in hexagonal-core fiber, and the TSB could be as high as 10&minus;4/100 &deg;C, much higher than that of panda PM fiber. This study lays the foundation for the development of high birefringence temperature-stable HC-PBGFs and their applications in FOG

A multi-layered performance analysis for cloud-based topic detection and tracking in Big Data applications

In the era of the Internet of Things and social media; communities, governments, and corporations are increasingly eager to exploit new technological innovations in order to track and keep up to date with important new events. Examples of such events include the news, health related incidents, and other major occurrences such as earthquakes and landslides. This area of research commonly referred to as Topic Detection and Tracking (TDT) is proving to be an important component of the current generation of Internet-based applications, where it is of critical importance to have early detection and timely response to important incidents such as those mentioned above. The advent of Big data though beneficial to TDT applications also brings about the enormous challenge of dealing with data variety, velocity and volume (3Vs). A promising solution is to employ Cloud Computing, which enables users to access powerful and scalable computational and storage resources in a "pay-as-you-go" fashion. However, the efficient use of Cloud resources to boost the performance of mission critical applications employing TDT is still an open topic that has not been fully and effectively investigated. An important prerequisite is to build a performance analysis capable of capturing and explaining specific factors (for example; CPU, Memory, I/O, Network, Cloud Platform Service, and Workload) that influence the performances of TDT applications in the cloud. Within this paper, our main contribution, is that we present a multi-layered performance analysis for big data TDT applications deployed in a cloud environment. Our analysis captures factors that have an important effect on the performance of TDT applications. The novelty of our work is that it is a first kind of vertical analysis on infrastructure, platform and software layers. We identify key parameters and metrics in each cloud layer (including Infrastructure, Software, and Platform layers), and establish the dependencies between these metrics across the layers. We demonstrate the effectiveness of the proposed analysis via experimental evaluations using real-world datasets obtained from Twitter