An Integrated Indexing Approach to Improve Query Processing

Nowadays, with the advancement of database information technology, databases has led to large-scale distributed databases. According to this study, database management systems are improved and optimized so that they provide responses to customer questions with lower cost. Query processing in database management systems is one of the important topics that grabs attentions. Until now, many techniques have been implemented for query processing in database system. The purpose of these methods is to optimize query processing in the database. The main topics that is interested in query processing in the database makes run-time adjustments of processing or summarizing topics by using the new approaches. The aim of this research is to optimize processing in the database by using adaptive methods. Ant Colony Algorithm (ACO) is used for solving optimization problems. ACO relies on the created pheromone to select the optimal solution. In this article, in order to make adaptive hybrid query processing. The proposed algorithm is fundamentally divided into three parts: separator, replacement policy, and query similarity detector. In order to improve the optimization and frequent adaption and correct selection in queries, the Ant Colony Algorithm has been applied in this research. In this algorithm, based on Versatility (adaptability) scheduling, Queries sent to the database have been attempted be collected. The simulation results of this method demonstrate that reduce spending time in the database. According to the proposed algorithm, one of the advantages of this method is to identify frequent queries in high traffic times and minimize the time and the execution time. This optimization method reduces the system load during high traffic load times for adaptive query Processing and generally reduces the execution runtime and aiming to minimize cost. The rate of reduction of query cost in the database with this method is 2.7%. Due to the versatility of high-cost queries, this improvement is manifested in high traffic times. In the future Studies, by adapting new system development methods, distributed databases can be optimized.

Over the past several years, digital mammography systems have been installed clinically across North America in small but growing numbers. A photostimulable phosphor-based full-field digital mammography image was evaluated in this investigation. Commonly known as computed radiography (CR), its use closely mimics the screen-film mammography paradigm. System performance using modulation transfer function (MTF) and detective quantum efficiency (DQE) metrics show MTF(2.5 mm(-1)) = 0.5, DQE(2.5 mm(-1)) = 0.3, and MTF(5.0 mm(-1)) = 0.2, DQE(5.0 mm(-1)) = 0.05, for a 26 kVp beam, 0.03 mm molybdenum tube filtration, 4.5 cm tissue attenuation, and 15 mR incident exposure to the detector. Slightly higher DQE values were measured at 32 kVp with 0.025 mm rhodium tube filtration. CR mammography advantages include the ability to use existing mammography machines, where multiple rooms can be converted to "digital" operation, which allows overall cost savings compared to integrated digital mammography systems. Chief disadvantages include the labor-intensive handling of the cassettes prior to and after the imaging exam, lack of a direct interface to the x-ray system for recording technique parameters, and relatively slow processing time. Clinical experience in an IRB-approved research trial has suggested that digital mammography with photostimulable storage phosphors and a dedicated CR reader is a viable alternative to conventional screen-film mammography.

Research background: Higher education institutions are generating multiple formats of data from diverse sources across the globe. The data ingestion layer is responsible for collecting data and transform for analysis. Learning analytics plays a vital role in providing decision-making support and selection of suitable timely intervention. The lack of tailored big-data ingestion architectures for academics led to several implementation challenges. Purpose of the article: The purpose of this article is to propose data ingestion architecture enabled for big data learning analytics. Methods: The study reviews existing literature to examine big-data ingestion tools and frameworks; and identify big-data ingestion challenges. An optimized framework for the real world learning analytics application was not yet in place at global higher educations. Consequently, the big-data ingestion pipeline is experiencing challenges of inefficient and complex data access, slow processing time, and security issues associated with transferring data to the system. The proposed data ingestion architecture is based on review of recent literature and adapts best international practices, guidelines, and techniques to meet the demand of current big-data ingestion issues. Findings & value added: This study identifies the current global challenges in implementing learning analytics projects. Review of recent big data ingestion techniques has been done based on defined metrics tuned for learning analytics purposes. The proposed data ingestion framework would increase the effectiveness of collecting, importing, processing and storing of learning data. Besides, the proposed architecture contributes to the construction of full-fledged big-data learning analytics ecosystem of higher educations.

The similarity ainong elemcnts emerges naturally as the most adequate to ask about complex data (such as, multimdia and genomic sequenees among others). There are two basie similarity queries: Range Query and k-Nearest Neighbor Query. The increasing volume of complex data stored in Database Management Systems (DBMS), makes it neeessary to provide support for these data tvpes. One way to support complex data types in current DBMS is to include similarity queries in its query processor, and consequently, in the relational algebra. This fact leacls to produce ways to express such queries in the DBMS language as predicates in select operations. As a consequence, the two basic similarity queries cari be combined in more complex expressions involving boolean conjunctions and disjunctions among them, i.e., complex similarity queries. However, for complex similarity queries to be processed efhciently in a DBMS, it is neeessary to provide support in the optimization and runtime laycrs of the; query proeessing. There are many works involving the development, of algorithms to ariswer specific and simple similarity query whereas there is not a generic algorithm efficiently able to handle complex similarity queries. Furthermore, the similarity query optimization is a topic not frequently explored in the literature. This work establishes a structured rnethod 011 how to analyze complex similarity queries. This method is used to extend the relational algebra through algebraic rules and to determine a small set of algorithms that can be used to answer any complex similarity query. In addition, the proposed method makes it possible to formalize rules for selectivity estimai,ion of these. queries thus assisting cost estimation. To validate the concepts presented, exjjeriments are being performed on real and synthetic data sets that highlight meaningful results. The algebraic rules. algorithms and metrics to est-imate the selectivity can be employed in the optimization process of a DBMS in order to derive efficient complex similarity query execution plans. Therefore, this work deals with essential poiuts that enable the practical use of similarity (jueries in Relational Database Management Systems.

Low renewal rate is a key challenge facing the sustainability of community-based health insurance (CBHI) schemes. While there is a large literature on initial enrolment into such schemes, there is limited evidence on the factors that impede renewal. This article uses longitudinal data to analyse what determines renewal, both 1year and 2years after the introduction of three CBHI schemes, which have been operating in rural Bihar and Uttar Pradesh since 2011. We find that initial scheme uptake is ∼23-24% and that 2years after scheme operation, only ∼20% of the initial enrolees maintain their membership. A household's socio-economic status does not seem to play a large role in impeding renewal. In some instances, a greater understanding of the scheme boosts renewal. The link between health status and use of health care in maintaining renewal is mixed. The clearest effect is that individuals living in households that have received benefits from the scheme are substantially more likely to renew their contracts. We conclude that the low retention rates may be attributed to limited benefit packages, slow claims processing times and the gap between the amounts claimed and amounts paid out by insurance.

In order to optimize the composition ofrecording mediasuitable forfieldmodulation, the dynamic analysis of recordingprocess was simulated. It was madeclearthattherecording medium whose Ms is small andHc is high from theroom temperatureto Curiepomt is suitableforfield modulation method. A high speed(2.2MB/s), large capacity(600 MB/side) overwritable 5.25 magneto-optical disk drive has been developed using magnetic field modulation by optimizing the recording media 1. INTRODUCTION Recently, first-generation magneto-optical (MO) disk drives have been commercialized for use as an external storagein personal computer. The application of these drives is limited by their slow data processing time including data transfer rateand cess time. Moreover, these drives don't have the function of overwriting. Solution ofthe overwriting problem would goa short way toward making MO drives a direct repl&'ement for rigid magnetic disk drives. There are many kinds of approaches for direct overwriting's. One of the simplest approaches is magnetic field

Ion traps are routinely directly coupled to mass analyzers, where they serve to suitably cool and shape an ion population prior to pulsed extraction into the analyzer proper. Such devices benefit from high duty cycle and transmission but suffer slow ion processing times caused by a compromise in the buffer gas pressure range that suitably dampens the ion kinetic energy without causing excessive scatter during extraction or within the analyzer. A rectilinear RF quadrupole ion trap has been characterized, conjoining a pressurized collision region with a pumped extraction region, and an unbroken RF interface for seamless ion transfer between them. Auxiliary electrodes mounted between the RF electrodes provide DC voltage gradients that serve to both guide ions through the device and position them at the extraction slot. The influence of the auxiliary DC upon the trapping RF field was measured, and suitable parameters were defined. A mode of operation was developed that allowed parallel processing of ions in both regions, enabling a repetition rate of 200 Hz when the device was coupled to a high-resolution accurate-mass analyzer.

Parallel processing is a typical approach to answer analytical queries on large database. As the size of the database increases, we often try to increase the parallelism by incorporating more processing nodes. However, this approach increases the possibility of node failure as well. According to the conventional practice, if a failure occurs during query processing, the database system restarts the query processing from the beginning. Such temporal cost may be unacceptable to the user. This paper proposes a fault-tolerant query processing mechanism, named PhoeniQ, for analytical parallel database systems. PhoeniQ continuously takes a checkpoint for every operator pipeline and replicates the output of each stateful operator among different processing nodes. If a single processing node fails during query processing, another can promptly take over the processing. Hence, PhoneniQ allows the database system to efficiently resume query processing after a partial failure event. This paper presents a key design of PhoeniQ and prototype-based experiments to demonstrate that PhoeniQ imposes negligible performance overhead and efficiently continues query processing in the face of node failure.

Distributed data processing is becoming a reality. Businesses want to do it for many reasons, and they often must do it in order to stay competitive. While much of the infrastructure for distributed data processing is already there (e.g., modern network technology), a number of issues make distributed data processing still a complex undertaking: (1) distributed systems can become very large, involving thousands of heterogeneous sites including PCs and mainframe server machines; (2) the state of a distributed system changes rapidly because the load of sites varies over time and new sites are added to the system; (3) legacy systems need to be integrated—such legacy systems usually have not been designed for distributed data processing and now need to interact with other (modern) systems in a distributed environment. This paper presents the state of the art of query processing for distributed database and information systems. The paper presents the “textbook” architecture for distributed query processing and a series of techniques that are particularly useful for distributed database systems. These techniques include special join techniques, techniques to exploit intraquery paralleli sm, techniques to reduce communication costs, and techniques to exploit caching and replication of data. Furthermore, the paper discusses different kinds of distributed systems such as client-server, middleware (multitier), and heterogeneous database systems, and shows how query processing works in these systems.

Insufficient sleep affects circadian hormonal profiles and inflammatory markers and may modulate attention, executive functioning and decision-making. Medical professionals and specifically resident physicians, who are involved in long-term nightshift schedules during their post-graduate training, are prone to acute and chronic sleep deprivation and disruption, putting them at risk for making medical errors. The aim of the study was to evaluate the impact of chronic and acute-on-chronic sleep deprivation and disruption among residents on selected physiological and cognitive measures. Thirty-three medical and surgical residents were evaluated twice - at baseline and after a 26-hour shift. Eighteen young attending physicians who did not engage in nightshift schedules served as controls and were evaluated once. Measures included morning cortisol and high-sensitivity C-reactive protein (hs-CRP), computerised tests of attention and behaviour, the Behaviour Rating Inventory of Executive Function, a risk-taking questionnaire and the Pittsburgh Sleep Quality Index. Residents, but not attendings, reported chronic sleep disruption and deprivation. Residents at baseline exhibited reduced morning cortisol levels and elevated hs-CRP levels, compared to attendings. Residents at baseline had impaired global executive function compared to attendings. A nightshift with acute sleep deprivation further reduced residents' executive function. Residents at baseline and after a nightshift demonstrated increased impulsivity and slower processing time than attendings. Residents and attendings did not differ in risk-taking tendencies which were assessed in a separate cohort. In a real-life setting, resident physicians exhibit increased low-grade systemic inflammation (hs-CRP) and impaired HPA-axis function. Their chronic sleep curtailment is associated with greater impulsivity, slower cognitive processing, and impaired executive function. Future research is warranted to understand how improving working schedule by increasing sleep duration may minimise the short-term and potential long-term risks to physicians in training.

The advancements in quantum computing and the potential for polynomial-time solutions to traditional public key cryptography (i.e., Rivest–Shamir–Adleman (RSA) and elliptic-curve cryptography (ECC)) using Shor’s algorithm pose a serious threat to the security of pre-quantum blockchain technologies. This paper proposes an efficient quantum-safe blockchain that incorporates new quantum-safe consensus algorithms. We integrate post-quantum signature schemes into the blockchain’s transaction signing and verification processes to enhance resistance against quantum attacks. Specifically, we employ the Falcon signature scheme, which was selected during the NIST post-quantum cryptography (PQC) standardization process. Although the integration of the post-quantum signature scheme results in a reduction in the blockchain’s transactions per second (TPSs), we introduce efficient approaches to mitigate this performance degradation. Our proposed post-quantum delegated proof of luck (PQ-DPoL) combines a proof of luck (PoL) mechanism with a delegated approach, ensuring quantum resistance, energy efficiency, and fairness in block generation. Experimental results demonstrate that while post-quantum cryptographic algorithms like Falcon introduce larger signature sizes and slower processing times, the PQ-DPoL algorithm effectively balances security and performance, providing a viable solution for secure blockchain operations in a post-quantum era.

Positive or negative mentions on social media sites such as Reddit help drive stock trading, and when joined with historical data, it may be possible to forecast stock price changes. Existing approaches to produce accurate predictive models utilize clustering analytics, linear data modeling, and convolutional neural networks. However, in order to capture the human element of stock volatility, in this work, we utilize a recently proposed technique of graph neural networks and contrast its performance with other analytics methods. Graph neural network implementation required significant time and system resources. The scale of this dataset results in more than a thousand graph representations of daily market data over the span of eight years, which has resulted in slower processing times between iterations. Narrowing the length of time studied and thus the number of samples analyzed results in an increase of speed at the expense of predictive accuracy. A recurrent neural network utilizing the full eight-year dataset was successfully implemented and maintained significant precision in predicting stock market closing values, with reduced system resource utilization and computational time reduction when compared to graph neural networks. Support vector machine regression, and multiple linear regression results were not as conclusively accurate.

Partial shading is an unavoidable condition which significantly reduces the efficiency and stability of a photovoltaic (PV) system. When partial shading occurs the system has multiple-peak output power characteristics. In order to track the global maximum power point (GMPP) within an appropriate period a reliable technique is required. Conventional techniques such as hill climbing and perturbation and observation (P&O) are inadequate in tracking the GMPP subject to this condition resulting in a dramatic reduction in the efficiency of the PV system. Recent artificial intelligence methods have been proposed, however they have a higher computational cost, slower processing time and increased oscillations which results in further instability at the output of the PV system. This paper proposes a fast and efficient technique based on Radial Movement Optimization (RMO) for detecting the GMPP under partial shading conditions. The paper begins with a brief description of the behavior of PV systems under partial shading conditions followed by the introduction of the new RMO-based technique for GMPP tracking. Finally, results are presented to demonstration the performance of the proposed technique under different partial shading conditions. The results are compared with those of the PSO method, one of the most widely used methods in the literature. Four factors, namely convergence speed, efficiency (power loss reduction), stability (oscillation reduction) and computational cost, are considered in the comparison with the PSO technique.

In this paper, we present a novel approach for multimedia data indexing and retrieval that is machine independent and highly flexible for sharing multimedia data across applications. Traditional multimedia data indexing and retrieval problems have been attacked using the central data server as the main focus, and most of the indexing and query-processing for retrieval are highly application dependent. This precludes the use of created indices and query processing mechanisms for multimedia data which, in general, have a wide variety of uses across applications. The approach proposed in this paper addresses three issues: 1. multimedia data indexing; 2. inference or query processing; and 3. combining indices and inference or query mechanism with the data to facilitate machine independence in retrieval and query processing. We emphasize the third issue, as typically multimedia data are huge in size and requires intra-data indexing. We describe how the proposed approach addresses various problems faced by the application developers in indexing and retrieval of multimedia data. Finally, we present two applications developed based on the proposed approach: video indexing; and video content authorization for presentation.

In this paper, the problem of attitude estimation of a quad-copter system equipped with a multi-rate camera and gyroscope sensors is addressed through extension of a sampling importance re-sampling (SIR) particle filter (PF). Attitude measurement sensors, such as cameras, usually suffer from a slow sampling rate and processing time delay compared to inertial sensors, such as gyroscopes. A discretized attitude kinematics in Euler angles is employed where the gyroscope noisy measurements are considered the model input, leading to a stochastic uncertain system model. Then, a multi-rate delayed PF is proposed so that when no camera measurement is available, the sampling part is performed only. In this case, the delayed camera measurements are used for weight computation and re-sampling. Finally, the efficiency of the proposed method is demonstrated through both numerical simulation and experimental work on the DJI Tello quad-copter system. The images captured by the camera are processed using the ORB feature extraction method and the homography method in Python-OpenCV, which is used to calculate the rotation matrix from the Tello’s image frames.