Secure Hardware Performance Analysis in Virtualized Cloud Environment

The main obstacle in mass adoption of cloud computing for database operations is the data security issue. In this paper, it is shown that IT services particularly in hardware performance evaluation in virtual machine can be accomplished effectively without IT personnel gaining access to real data for diagnostic and remediation purposes. The proposed mechanisms utilized TPC-H benchmark to achieve 2 objectives. First, the underlying hardware performance and consistency is supervised via a control system, which is constructed using a combination of TPC-H queries, linear regression, and machine learning techniques. Second, linear programming techniques are employed to provide input to the algorithms that construct stress-testing scenarios in the virtual machine, using the combination of TPC-H queries. These stress-testing scenarios serve 2 purposes. They provide the boundary resource threshold verification to the first control system, so that periodic training of the synthetic data sets for performance evaluation is not constrained by hardware inadequacy, particularly when the resources in the virtual machine are scaled up or down which results in the change of the utilization threshold. Secondly, they provide a platform for response time verification on critical transactions, so that the expected Quality of Service (QoS) from these transactions is assured

A Review of Subsequence Time Series Clustering

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies

Big data reduction framework for value creation in sustainable enterprises

Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as a) lowering the service utilization cost, b) enhancing the trust between customers and enterprises, c) preserving privacy of customers, d) enabling secure data sharing, and e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-to-end data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications

A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets

Corrosion behaviour and morphological analysis of Ni/Cu nanolayer coating in salt solution

The electrochemical corrosion studies of Nickel/Copper (Ni/Cu) compositionally modulated multilayer nanolayer (CMM) in 3.5 wt% of Sodium Chloride (NaCl) solution at room temperature were investigated using potentiodynamic polarization (PDP) method. A multinanolayer of Ni/Cu with the total thicknesses of 3 μm was successfully produced on Cu substrate via electrodeposition process through dual bath technique (DBT). The electrodeposition with 3 different sublayer thicknesses (40 nm, 80 nm and 100 nm) was produced by varying the deposition time. The results of electrochemical experiment indicate that Ni/Cu multi-nanolayer coating have superior corrosion resistance in 3.5 wt % of NaCl solution than the uncoated Cu substrate. The corrosion resistance is increased when the sublayer thicknesses decrease. The morphological analysis of Ni/Cumulti-nanolayer after corrosion testing was examined. The results shows that the uncoated Cu substrate corrode faster if compared to that of Cu substrate coated with Ni/Cu multinanolayer coating

Vibration-Controlled Transient Elastography Scores to Predict Liver-Related Events in Steatotic Liver Disease

Importance Metabolic dysfunction–associated steatotic liver disease (MASLD) is currently the most common chronic liver disease worldwide. It is important to develop noninvasive tests to assess the disease severity and prognosis.Objective To study the prognostic implications of baseline levels and dynamic changes of the vibration-controlled transient elastography (VCTE)–based scores developed for the diagnosis of advanced fibrosis (Agile 3+) and cirrhosis (Agile 4) in patients with MASLD.Design, Setting, and Participants This cohort study included data from a natural history cohort of patients with MASLD who underwent VCTE examination at 16 tertiary referral centers in the US, Europe, and Asia from February 2004 to January 2023, of which the data were collected prospectively at 14 centers. Eligible patients were adults aged at least 18 years with hepatic steatosis diagnosed by histologic methods (steatosis in ≥5% of hepatocytes) or imaging studies (ultrasonography, computed tomography or magnetic resonance imaging, or controlled attenuation parameter ≥248 dB/m by VCTE).Main Outcomes and Measures The primary outcome was liver-related events (LREs), defined as hepatocellular carcinoma or hepatic decompensation (ascites, variceal hemorrhage, hepatic encephalopathy, or hepatorenal syndrome), liver transplant, and liver-related deaths. The Agile scores were compared with histologic and 8 other noninvasive tests.Results A total of 16 603 patients underwent VCTE examination at baseline (mean [SD] age, 52.5 [13.7] years; 9600 [57.8%] were male). At a median follow-up of 51.7 (IQR, 25.2-85.2) months, 316 patients (1.9%) developed LREs. Both Agile 3+ and Agile 4 scores classified fewer patients between the low and high cutoffs than most fibrosis scores and achieved the highest discriminatory power in predicting LREs (integrated area under the time-dependent receiver-operating characteristic curve, 0.89). A total of 10 920 patients (65.8%) had repeated VCTE examination at a median interval of 15 (IQR, 11.3-27.7) months and were included in the serial analysis. A total of 81.9% of patients (7208 of 8810) had stable Agile 3+ scores and 92.6% of patients (8163 of 8810) had stable Agile 4 scores (same risk categories at both assessments). The incidence of LREs was 0.6 per 1000 person-years in patients with persistently low Agile 3+ scores and 30.1 per 1000 person-years in patients with persistently high Agile 3+ scores. In patients with high Agile 3+ score at baseline, a decrease in the score by more than 20% was associated with substantial reduction in the risk of LREs. A similar trend was observed for the Agile 4 score, although it missed more LREs in the low-risk group.Conclusions and Relevance Findings of this study suggest that single or serial Agile scores are highly accurate in predicting LREs in patients with MASLD, making them suitable alternatives to liver biopsy in routine clinical practice and in phase 2b and 3 clinical trials for steatohepatitis

A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data.

Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. The performance of similarity measures is mostly addressed in two or three-dimensional spaces, beyond which, to the best of our knowledge, there is no empirical study that has revealed the behavior of similarity measures when dealing with high-dimensional datasets. To fill this gap, a technical framework is proposed in this study to analyze, compare and benchmark the influence of different similarity measures on the results of distance-based clustering algorithms. For reproducibility purposes, fifteen publicly available datasets were used for this study, and consequently, future distance measures can be evaluated and compared with the results of the measures discussed in this work. These datasets were classified as low and high-dimensional categories to study the performance of each measure against each category. This research should help the research community to identify suitable distance measures for datasets and also to facilitate a comparison and evaluation of the newly proposed similarity or distance measures with traditional ones