Big Data Testing Techniques: Taxonomy,Challenges and Future Trends

Big Data is reforming many industrial domains by providing decision support through analyzing large data volumes. Big Data testing aims to ensure that Big Data systems run smoothly and error-free while maintaining the performance and quality of data. However, because of the diversity and complexity of data, testing Big Data is challenging. Though numerous research efforts deal with Big Data testing, a comprehensive review to address testing techniques and challenges of Big Data is not available as yet. Therefore, we have systematically reviewed the Big Data testing techniques’ evidence occurring in the period 2010–2021. This paper discusses testing data processing by highlighting the techniques used in every processing phase. Furthermore, we discuss the challenges and future directions. Our findings show that diverse functional, non-functional and combined (functional and non-functional) testing techniques have been used to solve specific problems related to Big Data. At the same time, most of the testing challenges have been faced during the MapReduce validation phase. In addition, the combinatorial testing technique is one of the most applied techniques in combination with other techniques (i.e., random testing, mutation testing, input space partitioning and equivalence testing) to find various functional faults through Big Data testing.     

Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers. In this paper, we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance. Research have mostly focused the problem of human detection in thin crowd, overall behavior of the crowd and actions of individuals in video sequences. Vision based Human behavior modeling is a complex task as it involves human detection, tracking, classifying normal and abnormal behavior. The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e., fill hole inside objects algorithm. Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm. The classification task is achieved using binary and multi class support vector machines. The proposed technique is validated through accuracy, precision, recall and F-measure metrics.     

Evaluation of <Emphasis Type="Italic">h</Emphasis>-index,its variants and extensions based on publication age &amp; citation intensity in civil engineering

The scientific community has proposed diversified set of parameters to rank researchers, including publications, citations, h-index, different variants and extensions of h-index. However, there is a debate in the scientific Community which index ranks authors in a better way. Current state-of-the-art depicts that these indices are evaluated on imaginary case scenarios and small datasets. Furthermore, these indices are evaluated on different datasets, making it difficult to grasp the contribution and importance of each index over the others. To analyze the individual behavior of each index, these indices should comprehensively be evaluated on some extensive data set. This study emphasizes on the scrutiny of h-index, some of its variants and extensions to rank authors. These indices are evaluated using a comprehensive data set of Civil Engineering field. For the evaluation of results obtained from these indices, first correlation was calculated among indices. There exists weak correlation between various indices, which demonstrates that the author’s rankings acquired from these indices are not identical. Secondly, occurrences of awardees are checked in all ranked lists. The prestigious award winners of four Civil Engineering societies are considered as a benchmark. In top 10% of ranked list, maximum 47% of the awardees were brought by Wu-index. Overall, none of the index succeeded in bringing 100% awardees to the top rankings. Highest number of awardees on top of all ranked lists are found to be from ACI (American Concrete Institute), which shows ACI might be dependent on these indices for its criterion to honor awards.     

Mars Observer Laser Altimeter: laser transmitter

The Mars Observer Laser Altimeter utilizes a space-qualified diode-laser-pumped Q-switched Nd:YAG laser transmitter. A simple numerical model of the laser energetics is presented, which predicts the pulse energy and pulse width. Comparisons with the measured data available are made. The temperature dependence of the laser transmitter is also predicted. This dependence prediction is particularly important in determining the operational temperature range of the transmitter. Knowing the operational temperature range is especially important for a passive, thermally controlled laser operating in space.     

Application of SEUMRE global optimization algorithm in automotive magnetorheological brake design

In this paper, the superior performance of a novel space exploration and unimodal region elimination global optimization algorithm, SEUMRE, is demonstrated through comparisons with other well known global optimization techniques, including genetic algorithm (GA), simulated annealing (SA), and a highly nonlinear design problem—the optimal design of automotive magnetorheological brake (MRB). Unlike the conventional brakes, an MRB employs the interaction between a magnetorheological fluid and an applied magnetic field to generate the retarding braking torque. The SEUMRE design optimization algorithm was used to maximize the braking torque and minimize the weight of the brake structure. The computation time and optimized design parameters illustrated SEUMRE’s capability to converge to an accurate result faster than the conventional global optimization methods. However, SA provided significantly better optimization results than GA and SEUMRE in terms of the cost function.     

Advanced signal processing of magnetic flux leakage data obtained from seamless gas pipeline

Natural gas is normally transported through a vast network of pipelines. A major segment of this network employs seamless pipes. The manufacturing processes associated with the production of seamless pipes contribute to a helical variation in the grain properties of the pipe. This introduces an artifact, known as the seamless pipe noise (SPN), in the data obtained from magnetic flux leakage (MFL) inspection of these pipelines. SPN can overwhelm the signals generated by defects and other elements in pipelines, and can therefore, mask their indications in the MFL data. This paper presents a new technique for detecting signals in MFL data obtained from seamless pipes. The overall approach employs an adaptive filter and a wavelet based de-noising technique. The algorithm is computationally efficient and data independent. Results from application of the approach to data from field tests are presented.     

Targeting Interleukin-10 Restores Graft Microvascular Supply and Airway Epithelium in Rejecting Allografts

Interleukin-10 (IL-10) is a vital regulatory cytokine, which plays a constructive role in maintaining immune tolerance during an alloimmune inflammation. Our previous study highlighted that IL-10 mediated immunosuppression established the immune tolerance phase and thereby modulated both microvascular and epithelial integrity, which affected inflammation-associated graft malfunctioning and sub-epithelial fibrosis in rejecting allografts. Here, we further investigated the reparative effects of IL-10 on microvasculature and epithelium in a mouse model of airway transplantation. To investigate the IL-10 mediated microvascular and epithelial repair, we depleted and reconstituted IL-10, and monitored graft microvasculature, airway epithelium, and associated repair proteins. Our data demonstrated that both untreated control allografts and IL-10 (−) allografts showed a significant early (d6) increase in microvascular leakiness, drop-in tissue oxygenation, blood perfusion, and denuded airway epithelium, which is associated with loss of adhesion protein Fascin-1 and β-catenin on vascular endothelial cells at d10 post-transplantation. However, IL-10 (+) promotes early microvascular and airway epithelial repair, and a proportional increase in endothelial Fascin-1, and β-catenin at d10 post-transplantation. Moreover, airway epithelial cells also express a significantly higher expression of FOXJ1 and β-catenin in syngrafts and IL-10 (+) allografts as compared to IL-10 (−) and untreated controls at d10 post-transplantation. Collectively, these findings demonstrated that IL-10 mediated microvascular and epithelial changes are associated with the expression of FOXJ1, β-catenin, and Fascin-1 proteins on the airway epithelial and vascular endothelial cells, respectively. These findings establish a potential reparative modulation of IL-10 associated microvascular and epithelial repair, which could provide a vital therapeutic strategy to facilitate graft repair in clinical settings.     

MPEGTool: An X window-based MPEG encoder and statistics tool

In this paper, we describe MPEGTool, an X window-based tool that can be used to generate a motion picture expert group (MPEG) encoded bit stream for video sequences and to study the statistical properties of the encoded data. The tool was designed to study the characteristics of variable bit rate video sources for transmission over asynchronous transfer mode (ATM) based broadband integrated services digital network (B-ISDN). The tool, which has a window-based graphic user interface, allows a user to specify several of the MPEG parameters such as the intraframe-to-interframe ratio and the quantizer scale. The tool also includes a statistical package that allows the user to plot graphs of various statistics including bit distributions, ATM cell distributions, time se ries, autocorrelation functions and cell interarrival times.This work was supported in part by grant NCR 90-16165 from the National Science Foundation     

Biodegradable microparticulates of beta-estradiol: preparation and in vitro characterization

Beta-estradiol has been recommended for the long-term therapy of osteoporosis and its oral formulations are subjected to intensive first pass metabolism. The present investigation was aimed at preparing and characterizing biodegradable microparticles of beta-estradiol with polymers such as PLA, PLGA 85/15, PLGA 75/25, and their mixtures. The microparticles were prepared by solvent evaporation method using methylene chloride as a solvent and polyvinyl alcohol as a surfactant. The drug-polymer ratios were 1:3, 1:5, and 1:7. The prepared microparticles (twelve formulations) were tested for encapsulation efficiency and in vitro drug release in 50% methyl alcohol/phosphate buffer pH 7.4. The results showed that the encapsulation efficiency varied from 81 to 100% and the formulation fabricated from PLGA 85/15 (1:3) showed less burst and consistent long time release. This formulation when further characterized displayed irregular spherical shape with an average particle size of 72 µm. The crystallinity of the drug was reduced when investigated using X-ray diffractometry. No chemical interaction between the drug and the polymer was observed as evidenced by FT-IR analysis. The results indicated that beta-estradiol biodegradable microparticles with PLGA 85/15 (1:3) could be a suitable approach for long term therapy of osteoporosis.