Enhancing Big Data Conversion Validation with Alpha-Lightweight Coreset

Lean Big Data integration in systems biology and systems pharmacology

The hype that big data will improve healthcare delivery is extraordinary.1 However, the reality of actually translating big data insights into meaningful healthcare improvements is far more limited. Substantial challenges exist. Variability in data standards and interoperatability prevent efficient linkage of health data. Regulatory and privacy concerns regarding the widespread dissemination and use of health data remain. Healthcare data are largely observational, meaning that any relationships identified in analysis of healthcare data are subject to specious correlations, bias, and unmeasured confounding.2 Even if the insights from big data analysis prove valid, optimal methods to integrate them effectively into healthcare delivery are unknown.3 Clearly, the conversion of big data hype to reality will require significant work. Article, see p 532 So, assuming that one has not lost resolve in the face of such challenges, where to begin? A logical place is identifying the best data. Administrative data from medical billing codes, also known as claims data, are an attractive option. Because claims data are generated by virtually all healthcare systems, they provide a broader view of healthcare delivery than clinical registry data or abstracted medical chart reviews. In addition, claims data are standardized across healthcare systems, generally using the International Classification of Disease or the Systemized Nomenclature of Medicine terminology. As a result, claims data should, in theory, generate broad and generalizable insights into care delivery patterns and inform quality and value improvements. Unfortunately, claims data often fall short of this potential because they often lack sufficient clinical granularity to effectively characterize healthcare delivery. For example, billing codes can identify both a hospitalization for a coronary artery stent thrombosis and its preceding percutaneous coronary intervention procedure. However, they cannot distinguish the underlying causes behind these events, such as poor procedural technique or insufficient antiplatelet therapy. This lack of …

The paper studies the processing of large information data arrays (Big Data) in construction. The issues of the applicability of the big data concept (Big Data) at various stages of the life cycle of buildings and structures are considered. Methods for data conversion for their further processing are proposed. The methods used in the analysis of "big data" allow working with unstructured data sets (Data Mining). An approach is considered, in which the analysis of arbitrary data can be reduced to text analysis, similar to the analysis of ordinary text messages. At the moment, it is important and interesting to isolate non-obvious links present in the analysed data. The advantage of using big data is that it is not necessary to advance hypotheses for testing. Hypotheses appear during data analysis. Dependence analysis is a basic approach when working with big data. The concept of an automatic big data analysis system is proposed. For data mining, text analysis algorithms should be used, and discriminant functions should be used for the main problem to be solved (data classification).

This paper designs a cloud platform for agricultural products e-commerce information service based on big data, relying on the Internet of Things, big data, cloud computing and other technologies. The data acquisition layer mainly uses IoT perception to collect data. The system completes data conversion, processing, and analysis in the big data center, and realizes the application through the visual display of the application layer. The development system uses ASP.Net technology and uses C# as the programming language. This article uses Silverer database as the background database to build an agricultural product e-commerce platform. The research shows that this paper applies big data technology to the field of agricultural product information service to realize real-time sharing of agricultural product big data. The system is conducive to the effective and full utilization of agricultural product data resources.

The rise of GPS-equipped mobile devices has led to the emergence of big trajectory data. The collected raw data usually contain errors and anomalies information caused by device failure, sensor error, and environment influence. Low-quality data fails to support application requirements and therefore raw data will be comprehensively cleaned before usage. Existing methods are suboptimal to detect GPS data errors and do the repairing. To solve the problem, we propose a framework called GPSClean to analyze the anomalies data and develop effective methods to repair the data. There are primarily four modules in GPSClean : (i) data preprocessing, (ii) data filling, (iii) data repairing, and (iv) data conversion. For (i), we propose an approach named MDSort (Maximum Disorder Sorting) to efficiently solve the issue of data disorder. For (ii), we propose a method named NNF (Nearest Neighbor Filling) to fill missing data. For (iii), we design an approach named RCSWS (Range Constraints and Sliding Window Statistics) to repair anomalies and also improve the accuracy of data repairing by mak7ing use of driving direction. We use 45 million real trajectory data to evaluate our proposal in a prototype database system SECONDO. Experimental results show that the accuracy of RCSWS is three times higher than an alternative method SCREEN and nearly an order of magnitude higher than an alternative method EWMA.

With the rapid development of big data technology, accounting information systems are facing unprecedented challenges in processing and analyzing massive financial data. In order to improve the efficiency and accuracy of data processing, this article deeply explores the application of big data mining algorithms in optimizing accounting information systems. By introducing advanced big data mining algorithms, accounting information systems have achieved automation in data cleaning, transformation, and analysis, significantly reducing manual intervention and improving data processing efficiency. This article compares the performance of different big data mining algorithms in the analysis of accounting informationization risk transactions. Through practical verification, we found that the selected algorithm performs well in terms of accuracy, reaching over 95%, which is a significant improvement compared to traditional methods. Meanwhile, in terms of computation time, the algorithm has also demonstrated significant advantages, reducing computation time by over 30% when processing datasets of the same size. These performance improvements not only improve the operational efficiency of accounting information systems, but also provide enterprises with more accurate and timely financial information. In addition, this article also conducted a survey on the intelligent management of accounting information systems. We collected valuable opinions on the current status of accounting information intelligent system management by distributing survey questionnaires to in-service MBA and MPAcc students. The survey results show that over 76% of accounting personnel and almost all management personnel (91.18%) agree with the intelligent features of accounting information systems and believe that establishing a separate accounting knowledge base or knowledge management system is necessary. This discovery further emphasizes the importance of optimizing accounting information systems and provides direction for future research.

Spectrum awareness (SA) stretches the performance bounds of spectrum-sensing-based dynamic spectrum access by intelligently exploiting the big spectrum data (BSD) generated by a network. Hence, in order to analyze the performance and scalability of large scale cognitive radio networks (CRNs), spectrum awareness capacity would take preeminence over spectrum sensing capacity. Although, conventional methods use techniques such as the receiver operating characteristic (ROC) curve and the root mean square error (RMSE) technique to quantify the performance of CRN SA, they do not consider the impact of BSD velocity, variety and volume. Therefore, this research work proposes a novel knowledge-centric method for quantifying, analyzing and comparing the performance and scalability of CRNs based on spectrum awareness. The proposed method considers key performance indices including reliability, computational complexity and latency of the network parameters that are generated by spectrum data acquisition, conversion and dissemination. The steps and applicability of the proposed method in user and network-level performance measurement are also analyzed.

Solar energy has gained attention in the past two decades, since it is an effective renewable energy source that causes no harm to the environment. Solar Irradiation Prediction (SIP) is essential to plan, schedule, and manage photovoltaic power plants and grid-based power generation systems. Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time. In this scenario, commonly available Artificial Intelligence (AI) technique can be trained over past values of irradiance as well as weather-related parameters such as temperature, humidity, wind speed, pressure, and precipitation. Therefore, in current study, the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models (BDAAI- SIP) using weather forecasting data. In order to perform long-term collection of weather data, Hadoop MapReduce tool is employed. The proposed solar irradiance prediction model operates on different stages. Primarily, data preprocessing take place using various sub processes such as data conversion, missing value replacement, and data normalization. Besides, Elman Neural Network (ENN), a type of feedforward neural network is also applied for predictive analysis. It is divided into input layer, hidden layer, load-bearing layer, and output layer. To overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count, Mayfly Optimization (MFO) algorithm is applied. In order to validate the performance of the proposed model, a series of experiments was conducted. The experimental values infer that the proposed model outperformed other methods used for comparison.

Background A computerized 12-lead electrocardiogram (ECG) can automatically generate diagnostic statements, which are helpful for clinical purposes. Standardization is required for big data analysis when using ECG data generated by different interpretation algorithms. The common data model (CDM) is a standard schema designed to overcome heterogeneity between medical data. Diagnostic statements usually contain multiple CDM concepts and also include non-essential noise information, which should be removed during CDM conversion. Existing CDM conversion tools have several limitations, such as the requirement for manual validation, inability to extract multiple CDM concepts, and inadequate noise removal.Objectives We aim to develop a fully automated text data conversion algorithm that overcomes limitations of existing tools and manual conversion.Methods We used interpretations printed by 12-lead resting ECG tests from three different vendors: GE Medical Systems, Philips Medical Systems, and Nihon Kohden. For automatic mapping, we first constructed an ontology-lexicon of ECG interpretations. After clinical coding, an optimized tool for converting ECG interpretation to CDM terminology is developed using term-based text processing.Results Using the ontology-lexicon, the cosine similarity-based algorithm and rule-based hierarchical algorithm showed comparable conversion accuracy (97.8 and 99.6%, respectively), while an integrated algorithm based on a heuristic approach, ECG2CDM, demonstrated superior performance (99.9%) for datasets from three major vendors.Conclusion We developed a user-friendly software that runs the ECG2CDM algorithm that is easy to use even if the user is not familiar with CDM or medical terminology. We propose that automated algorithms can be helpful for further big data analysis with an integrated and standardized ECG dataset.

The resilience condition of the Indonesian people in facing threats, disturbances, obstacles, and challenges (AGHT) can be identified through conversations on social media. The conversational data of social media users is important data for understanding the national resilience of the Indonesian people. The method developed is more explorative, descriptive, and quantitative by describing the variable: volume of social media users, user profiles, reach, conversation trends, types of issues, top tweets, emotion, sentiment, people who influence (top influencer), intermediary (bridge), and robot analysis (bot analysis). The research sample is from March 1, 2022, to May 1, 2022. Consideration of the timing is due to many public reactions to the “three periods” issue. The results of this study indicate that the three-period issue is the most dominant compared to other topics. The issue of “three periods” spread throughout Indonesia, and the most dominant was in DKI Jakarta Province. The social media users’ profile shows that the issue of three periods is mostly discussed by users between the ages of 19 and 29. Men are more dominant in discussing the “three periods” issue than women. Most Indonesian people reject the three-period issue. It shows that the resilience of the Indonesian people is exceptional because they can confront negative issues.

While data from Supervisory Control And Data Acquisition (SCADA) systems is sent upstream, it is both the length of pulses as well as their frequency present an excellent opportunity to incor-porate statistical fingerprinting. This is so, because datagrams in SCADA traffic follow a poison distribution. Although wrapping the SCADA traffic in a protective IPsec stream is an obvious choice, thin clients and unreliable communication channels make is less than ideal to use crypto-graphic solutions for security SCADA traffic. In this paper, we propose a smart alternative of data obfuscation in the form of Impulsive Statistical Fingerprinting (ISF). We provide important insights into our research in healthcare SCADA data security and the use of ISF. We substantiate the conversion of sensor data through the ISF into HL7 format and define policies of a seamless switch to a non HL7-based non-secure HIS to a secure HIS.

The daily traffic volume of the State Grid 95598 Customer Service Center exceeds 800,000. In order to manage and store these massive data, we must carefully handle it. The call from electric customers implement the business philosophy of “Quality service is the lifeline of electric power enterprise”, and carry out related data aggregation, processing, statistics, conversion and analysis based on data warehouse, and fully explore and analyze these with big data analysis technology. The potential value in the data guides the optimization of business processes and improves the service quality and efficiency of the business center.

Nowadays time series are generated relatively more easily and in larger quantity than ever, by the advances of IoT and sensor applications. Training a prediction model effectively using such big data streams poses certain challenges in machine learning. Data sampling has been an important technique in handling over-sized data in pre-processing which converts the huge data streams into a manageable and representative subset before loading them into a model induction process. In this paper a novel data conversion method, namely Kennard-Stone Balance (KSB) Algorithm is proposed. In the past decades, KS has been used by researchers for partitioning a bounded dataset into appropriate portions of training and testing data in cross-validation. In this new proposal, we extend KS into balancing the sub-sampled data in consideration of the class distribution by round-robin. It is also the first time KS is applied on time-series for the purpose of extracting a meaningful representation of big data streams, for improving the performance of a machine learning model. Preliminary simulation results show the advantages of KBS. Analysis, discussion and future works are reported in this short paper. It is anticipated that KBS brings a new alternative of data sampling to data stream mining with lots of potentials.

Cross-modality data processing is critical for the Internet-of-Things (IoT) deployment in healthcare. It can convert the innumerable raw day-to-day medical big data from massive IoT-based medical devices to diagnostic valuable data so that they can be feed to clinical routine. In this article, we propose a novel spatiotemporal two-streams generative adversarial network (SpGAN) as a cross-modality data processing approach to deploy the medical IoT in infarction learning. Our SpGAN remotely converts diagnostic valuable contrast-enhanced images (the “gold standard” for infarction learning, but it requires the injection of contrast agents) directly from raw nonenhanced cine MR images. This converting allows physicians to remotely perform infarction observation and analysis to break through the limitations of time and space by building a cloud computing platform of IoT-based MRI devices. Importantly, this converting offers a low-risk IoT-based manner to eliminate the potential fatal risk caused by contrast agent injection in the current infarction learning workflow. Specifically, SpGAN consists of: 1) a spatiotemporal two-stream framework as an encoding–decoding model to achieve data converting and 2) a spatiotemporal pyramid network enhances those features that are responsible to the infarction learning during encoding to improve decoding performance. Real IoT-based remote diagnosis experiments performed on 230 patients demonstrate that SpGAN provides high-quality converted images for infarction learning and promotes the in-depth application and deployment of IoT in the medical field.