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In this article, we consider the resilience problem in the presence of communication faults encountered in distributed secondary voltage and frequency control of an islanded alternating current microgrid. Such faults include the partial failure of communication links and some classes of data manipulation attacks. This practical and important yet challenging issue has been taken into limited consideration by existing approaches, which commonly assume that the measurement or communication between the distributed generations (DGs) is ideal or satisfies some restrictive assumptions. To achieve communication resilience, a novel adaptive observer is first proposed for each individual DG to estimate the desired reference voltage and frequency under unknown communication faults. Then, to guarantee the stability of the closed-loop system, voltage and frequency restoration, and accurate power sharing regardless of unknown communication faults, sufficient conditions are derived. Some simulation results are presented to verify the effectiveness of the proposed secondary control approach.

Made available in DSpace on 2022-04-29T08:46:05Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Structural health monitoring techniques are widely used in industry applications to guarantee the integrity of several types of components. This article proposes a baseline-free damage imaging algorithm based on Spatial Frequency Domain Virtual Time Reversal (SFD-VTR). Virtual time reversal (VTR) is an alternative to traditional time reversal as it reduces the burden of physically back-propagating re-emitted signals by applying signal operations between the transmitted and received waveforms. However, VTR rely on the reconstruction of the emitted signal in the time domain, which involves significant data manipulation causing sampling errors of reconstructed signals. Novel SFD-VTR damage indices were here proposed to enhance defect detection as they do not require the time domain reconstruction of re-emitted signals. Experimental results showed that the proposed algorithm was able to localise material flaws and can be use as Human Machine Interface (HMI). Universidade Estadual Paulista, 28108 Sao Paulo, Brazil, 01049-010 (e-mail: bruno.castro@unesp.br) Electrical Engineering, Universidade Estadual Paulista, 28108 Bauru, SP, Brazil, 17033360 (e-mail: f.baptista@unesp.br) Electrical Engineering, UTFSM, 28090 Santiago, Chile, 8940000 (e-mail: jorge.ardila@usm.cl) University of Surrey, 3660 Guildford, Surrey, United Kingdom of Great Britain and Northern Ireland, GU2 7XH (e-mail: f.ciampa@surrey.ac.uk)

A considerable number of recent analyses report statistical evidence indicating that governments manipulate official macroeconomic indicators. Employing creative strategies to identify systematic biases in statistical outputs, these studies have shown that political manipulation of economic statistics does occur. But they have paid less attention to the question how official statistics are being manipulated. To shed light on the processes behind data manipulation, this article examines three recent high-profile cases in depth: Greece’s public deficit figures, controversies about Argentina’s inflation statistics, and the Brazilian “fiscal pedaling” scandal. We make two main contributions: first, macroeconomic indicators are much more ambiguous than it is commonly realized. Therefore, the line between accurate and manipulated data is more blurry than typical narratives about manipulation acknowledge. Second, in recognition of this ambiguity we introduce a typology distinguishing four types of manipulation: outright manipulation (type 1), politically motivated guesstimating (type 2), the opportunistic use of methodology space (type 3), and indicators-management through indirect means (type 4). The findings from our cases highlight that the politics of statistics do not revolve around “right” and “wrong” numbers. They are better understood as contestations about different shades of wrong.

Although multi-temporal XML databases supporting schema versioning are used in several domains, like e-commerce, e-health, and e-government, existing database management systems and XML tools do not provide any support for managing (inserting, updating, and deleting) temporal XML data or temporal XML schema versioning. Besides, whereas much research work has focused in the last decade on schema versioning in temporal XML databases, any attention has been devoted to manipulating data in such databases. To fill this theoretical and practical gap, we propose in this paper a generic approach, named TempoX (Temporal XML), for data manipulation in multi-temporal and multi-schema-version XML databases. Indeed, we (i) define a new multi-temporal XML data model supporting temporal schema versioning, named TempoXDM (Temporal XML Data Model), (ii) introduce the principles on which our approach is based, and (iii) provide the specifications of the basic data manipulation operations: “insert”, “replace”, “evolve”, and “delete”. Moreover, to show the feasibility of TempoX, we use it to propose a temporal XML update language, named TempoXUF (Temporal XQuery Update Facility), as an extension of the W3C XQuery Update Facility language to temporal and versioning aspects. Furthermore, to validate our language proposal, we develop a system prototype, named TempoXUF-Manager, that supports TempoXUF.

Purpose: Currently, many fake certificates or diplomas are used to apply for jobs to get a better paying job. With that said, currently certificates cannot prove a person's expertise or skills. However, some students have implemented a Blockchain system in securing their certificates so that data manipulation is minimized, but only partially. Therefore, this research aims to motivate anyone, especially students in this Blockchain system and students will get incentives from all activities that are followed by racing in Blockchain gamification concept 4.0. Study design: This study uses the Pieces method to classify a problem (problem), opportunities (opportunities), and existing directions. Data collection techniques used primary data obtained by distributing questionnaires in the form of google form to respondents by involving students (n = 1129). Conclusions were analyzed with the SUS trial using a Likert scale with the cut and SUS method. Result: The results of this study are expected to Gamification in the system Blockchain can run optimally in implementation. The use of Gamification on the Alphabet Blockchain is included in the acceptable category. Value: The platform can check certificates that are not genuine.

The emergence of large amounts of hesitant fuzzy data brings more opportunities and challenges for optimal decision-making results. The granularity of the hesitant fuzzy set has been significantly improved, but the potential for more inconsistent data also increases. Due to high computational complexity and low efficiency, the existing data aggregation methods cannot handle the intensive hesitant fuzzy data. To solve this problem, we first propose the optimization strategy for the intensive hesitant fuzzy data. Then the data redundancy elimination method and the data aggregation method are proposed. The extensions of hesitant fuzzy set and normal-type hesitant fuzzy set are proposed for intensive hesitant fuzzy data manipulations. The aggregation methods and the data structure estimation methods of the extensions are also discussed. Finally, a practical application to the wine quality determination is provided, and some comparative analyses are conducted to demonstrate the effectiveness of the proposed methods.

Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and pathology images. The emerging field of Computational Pathology targets the high-throughput extraction and analysis of the spatial distribution of cells from digital histopathology images. The associated morphological and architectural features allow researchers to quantify and characterize new imaging biomarkers for cancer diagnosis, prognosis, and treatment decisions. However, while the image feature space grows, exploration and analysis become more difficult and ineffective. There is a need for dedicated interfaces for interactive data manipulation and visual analysis of computational pathology and clinical data. For this purpose, we present IIComPath , a visual analytics approach that enables clinical researchers to formulate hypotheses and create computational pathology pipelines involving cohort construction, spatial analysis of image-derived features, and cohort analysis. We demonstrate our approach through use cases that investigate the prognostic value of current diagnostic features and new computational pathology biomarkers.

Now that we learned how to write advanced SQL statements to retrieve data, it is time to learn how to write advanced SQL statements to change data. Oracle Data Manipulation Language (DML) lets us insert, update, and delete data.

The growth of IoT technology, increasing prevalence of embedded devices, and advancements in biomedical technology have led to the emergence of numerous wearable health monitoring devices (WHMDs) in clinical settings and in the community. The majority of these devices are Bluetooth Low Energy (BLE) enabled. Though the advantages offered by BLE-enabled WHMDs in tracking, diagnosing, and intervening with patients are substantial, the risk of cyberattacks on these devices is likely to increase with device complexity and new communication protocols. Furthermore, vendors face risk and financial tradeoffs between speed to market and ensuring device security in all situations. Previous research has explored the security and privacy of such devices by manually testing popular BLE-enabled WHMDs in the market and generally discussed categories of possible attacks, while mostly focused on IP devices. In this work, we propose a new semi-automated framework that can be used to identify and discover both known and unknown vulnerabilities in WHMDs. To demonstrate its implementation, we validate it with a number of commercially available BLE-enabled enabled wearable devices. Our results show that the devices are vulnerable to a number of attacks, including eavesdropping, data manipulation, and denial of service attacks. The proposed framework could therefore be used to evaluate potential devices before adoption into a secure network or, ideally, during the design and implementation of new devices.

Urban platforms are becoming a vital role player in city resources management for achieving the right balance between social and economic services and their impact on the environment. More and more cities are starting to benefit from an urban platform to state the city conditions and re-coin the shape of life depending on data gathered from different city systems. However, urban platforms need further support of data analytics in respect of reaching a smart city platform helping city planners with better decision making. Besides, the majority of operational urban platforms consider 2D data only, missing out on the possible information that could be obtained from 3D city models. This work proposes a concept for an urban platform that supports data manipulation and visualization plus interactive analytic functionalities. As a prototype, an urban platform is implemented for integrating and processing 3D city models and 2D traffic data to derive air pollutants emission rates with analytic visualization, leading urban planning to concentrate on the most affected areas. In addition, interactive features are implemented, including filtering, querying, and classifying data to support the analytic visualization in the developed urban platform.

This article tackles the issue of data manipulation in pandemics wherein often the data is manipulated or underreported for various reasons by certain government and public institutions. This problem came to light during the first few months of the COVID-19 pandemic and had a major effect on the public health response in countries around the globe. To solve this issue, a blockchain-based system and implementation is proposed to allow hospitals and testing centres to transmit their data while also minimising the risk of manipulation from government bodies. The article makes use of the United States CDC framework for data reporting by testing labs and hospitals in order to develop this proposal for a blockchain system. Under this framework, certain pieces of data are shared with the other parties in the healthcare system based on their role and position. To accomplish this, the article then delineates the implementation of private data collections and other features of the Hyperledger software. A potential web–app interface is also presented and the transaction logic has been described in detail. Along with this, an evaluation of the risks, limitations and potential extension of the system has been performed.

It is not a secret that Internet of Things (IoT) devices often come with not so realistic processing power (i.e. processing power and storage requirements) that would provide a basis for strong security and encryption algorithms. This work proposes an approach to adding a simple interface as a security gateway architecture for IoT devices. The security interface provides mapping for the IoT device remote services as long as support for stronger cryptographic algorithms. The solution improves the security of the data that IoT devices send to remote services by performing compatible cryptographic algorithms on the data before it sends to remote services. The result of this work is the development of a security interface that provides support for any cryptographic algorithm, uses Internet Protocol (IP) mapping to prevent access to the devices behind the interface from non-authorized IP addresses. As such it provides robust protection against attacks and data manipulation. The work is tested for memory usage and the strength of the security it provides.

Malicious data manipulation reduces the effectiveness of machine learning techniques, which rely on accurate knowledge of the input data. Motivated by real-world applications in network flow classification, we address the problem of robust online learning with delayed feedback in the presence of malicious data generators that attempt to gain favorable classification outcome by manipulating the data features. When the feedback delay is static, we propose online algorithms termed ROLC-NC and ROLC-C when the malicious data generators are non-clairvoyant and clairvoyant, respectively. We then consider the dynamic delay case, for which we propose online algorithms termed ROLC-NC-D and ROLC-C-D when the malicious data generators are non-clairvoyant and clairvoyant, respectively. We derive regret bounds for these four algorithms and show that they are sub-linear under mild conditions. We further evaluate the proposed algorithms in network flow classification via extensive experiments using real-world data traces. Our experimental results demonstrate that the proposed algorithms can approach the performance of an optimal static offline classifier that is not under attack, while outperforming the same offline classifier when tested with a mixture of normal and manipulated data.

Background: Phasor Measurement Unit (PMU) Data Manipulation Attacks (PDMA) can change the state estimates of power systems and cause significant damage to the smart grid. So it is vital to research a method to detect it. Objective: In this paper, we propose a detection mechanism and model for PDMA. Methods: Firstly, the distribution's characteristics of Phasor Data Concentrator (PDC) and PMU are analyzed, and we use these characteristics to detect a PDMA detection mechanism that could help us reduce the number of detection samples. Secondly, we use the Sliced Recurrent Neural Network (SRNN) to extract the time series data's temporal characteristics of PMU data. Thirdly, based on the temporal characteristics, the Convolutional Neural Networks (CNN) and Attention mechanisms are used to extract the spatial features of these data. Finally, we sent the spatial features to the Fully Layer and used the softmax function to classify. Results: The proposed SRCAM in this paper has two advantages. One is that it implements the parallel computation on data by using the segmentation concept of SRNN, which reduces the computation time. The other is that using the Attention mechanism on CNN can make the spatial features more prominent. At the end of the paper, we do many comparative experiments between SRCAM and other models, such as some algorithms of Machine learning and soft computing. We take IEEE node data as experimental data and TensorFlow as an experimental platform. Experimental results show that the SRCAM model has an excellent performance of the detection of PDMA with high precision and accuracy. Conclusion: The superiority of SRCAM is theoretically and experimentally proved in this paper. As we expected, SRCAM showed great results in the application of PDMA detection.

The increase in the number of motor vehicles is difficult to anticipate, the roads used are not in accordance with capacity. To prevent congestion, the government imposes a progressive tax on motor vehicles that have moved owners but are still on behalf of the previous owner. Manunggal Administration System under One Roof (SAMSAT) conducts Door To Door activities to request direct confirmation to taxpayers whose vehicles are subject to progressive taxes. It is difficult for taxpayers to find out what makes data manipulation happen. Created Android-based Door to Door Application System using waterfall method to facilitate Door to Door activities and avoid manipulation of taxpayer data. By using waterfall method this application is made starting from the analysis of needs, design of system design, implementation, to the application of the program. Application system design is built using UML (Uniferd Modeling Language) system design and system design implementation using Android Studio and RestAPI.

As data volume and complexity grow at an unprecedented rate, the performance of data manipulation programs is becoming a major concern for developers. In this article, we study how alternative API choices could improve data manipulation performance while preserving task-specific input/output equivalence. We propose a lightweight approach that leverages the comparative structures in Q&A sites to extracting alternative implementations. On a large dataset of Stack Overflow posts, our approach extracts 5,080 pairs of alternative implementations that invoke different data manipulation APIs to solve the same tasks, with an accuracy of 86%. Experiments show that for 15% of the extracted pairs, the faster implementation achieved >10x speedup over its slower alternative. We also characterize 68 recurring alternative API pairs from the extraction results to understand the type of APIs that can be used alternatively. To put these findings into practice, we implement a tool, AlterApi7 , to automatically optimize real-world data manipulation programs. In the 1,267 optimization attempts on the Kaggle dataset, 76% achieved desirable performance improvements with up to orders-of-magnitude speedup. Finally, we discuss notable challenges of using alternative APIs for optimizing data manipulation programs. We hope that our study offers a new perspective on API recommendation and automatic performance optimization.

Despite recent advancements in deep learning methods for protein structure prediction and representation, little focus has been directed at the simultaneous inclusion and prediction of protein backbone and sidechain structure information. We present SidechainNet, a new dataset that directly extends the ProteinNet dataset. SidechainNet includes angle and atomic coordinate information capable of describing all heavy atoms of each protein structure and can be extended by users to include new protein structures as they are released. In this article, we provide background information on the availability of protein structure data and the significance of ProteinNet. Thereafter, we argue for the potentially beneficial inclusion of sidechain information through SidechainNet, describe the process by which we organize SidechainNet, and provide a software package (https://github.com/jonathanking/sidechainnet) for data manipulation and training with machine learning models.

Generators are critical components of electrical grids and have to operate synchronously. Unfortunately, faults in power grids can cause significant electro-mechanical oscillations. A number of methods to estimate these electro-mechanical oscillation modes have been proposed in the literature. With the advent of synchrophasors which enable real-time measurements over a wide-area, and in keeping with a trend towards more distributed monitoring and control applications (e.g., WAMPAC), measurement-based methods to estimate oscillation modes in a distributed manner have been proposed. Like other power system applications these distributed mode estimation approaches also face the prospect of false data injection attacks. In this paper, we introduce a mechanism, inspired by byzantine fault tolerance, for making distributed alternating direction of multipliers method of mode estimation resilient against data manipulation attacks. We evaluate our approach using IEEE 68-bus and 145-bus test systems under different attack scenarios and show that in all the scenarios considered our approach allows distributed mode estimation to converge well.

While research on the dysfunctional uses of performance data is growing, we are still in search of theories that go beyond system-specific explanations and address data manipulation behavior at the level of the employee. In this article, we conceptualize different gaming responses to performance systems and test a model of performance cheating that emphasizes the critical role of employees’ prosocial impact, their job stress, and organizations’ red tape. We screen through a sample of almost 10,000 potential subjects and identify 964 public employees who work with performance data. Conducting a list experiment, a technique known to yield unbiased ratings of sensitive behaviors, we find that all three factors tend to reinforce performance cheating among public employees. The article contributes to the extension of causal chains in performance gaming theory via the inclusion of factors that have proven to be influential in behavioral research.

There are four core DML statements in SQL, namely INSERT, UPDATE, DELETE, and SELECT. These statements apply to base tables and views (views will be discussed in the next chapter). The chapter proceeds under the following subheadings

In the challenge of Artificial Intelligence in processing semantically evaluable information, the application of deep learning techniques depends not only on the algorithms, but also on the principles that explain how they work. The malfunction of a machine learning system, ML, can occur due lack of knowledge of the algorithm intended behavior. The difficulty in debugging ML can be overcome by using strategies based on the universal structure of language that overlaps in the cognitive architecture of biological and intelligent systems. The appropriate choice of an algorithm inspired by the functioning of human language offers the computational scientist methodological strategies to clarify its performance analysis to optimize the interpretative activity under the good instrumentation of the system and to reach the performance level of an application considered safe. Neurolinguistic principles that link interpretation to language and cognition; the semantic dimension that arises not only from the linguistic system, but also from the context in which the information is produced; and the theoretical bases for understanding language as a 'form' (process) and not as a substance (set of signs) provide the groundwork for the intelligent systems’ improvement so that they have universal consistency and lessen the effects of the ‘curse of dimensionality’ or of the bias in the interpretation by the system. Semantics and statistics are considered to understand universal consistency as opposed to ideal consistency when evaluating a data set, since training alone is not sufficient to avoid data manipulation. We conclude that the 'key' for a good information classifier to achieve an acceptable performance of neural networks is in the dynamic aspect of language (language as a form / process) that: Guides the apprehension of how neural networks have access to weights (values); replicates this for intelligent systems making them invariant to many input transformations and guarantees an infinite amount of finite sample information, avoiding semantic distortion.

Forecasting is a challenging task that typically requires making assumptions about the observed data but also the future conditions. Inevitably, any forecasting process will result in some degree of inaccuracy. The forecasting performance will further deteriorate as the uncertainty increases. In this article, we focus on univariate time series forecasting and we review five approaches that one can use to enhance the performance of standard extrapolation methods. Much has been written about the “wisdom of the crowds” and how collective opinions will outperform individual ones. We present the concept of the “wisdom of the data” and how data manipulation can result in information extraction which, in turn, translates to improved forecast accuracy by aggregating (combining) forecasts computed on different perspectives of the same data. We describe and discuss approaches that are based on the manipulation of local curvatures (theta method), temporal aggregation, bootstrapping, sub-seasonal and incomplete time series. We compare these approaches with regards to how they extract information from the data, their computational cost, and their performance.

With the development of big data technology, the data management of complex applications has become more and more resource intensive. In this paper, we propose an automatic approach (DRLISA) to achieve NoSQL database index selection. For different workloads, we automatically select its corresponding indexes and parameters which can totally improve the database performance. Our DRLISA establishes an optimal index by building a deep reinforcement learning model which is able to adapt the dynamic change of workloads. We conducted our experiments in five aspects (the impact of data manipulation, the impact of operation count, comparison with random selection, comparison with existing method and the robustness of DRLISA) using the open source benchmark, YCSB. The experimental results showed that DRLISA has a high efficient index recommendation under the dynamic workloads.

In the domain of data parallel computation, most works focus on data flow optimization inside the PE array and favorable memory hierarchy to pursue the maximum parallelism and efficiency, while the importance of data contents has been overlooked for a long time. As we observe, for structured data, insights on the contents (i.e., their values and locations within a structured form) can greatly benefit the computation performance, as fine-grained data manipulation can be performed. In this paper, we claim that by providing a flexible and adaptive data path, an efficient architecture with capability of fine-grained data manipulation can be built. Specifically, we design SOM, a portable and highly-adaptive data transmission network, with the capability of operand sorting, non-blocking self-route ordering and multicasting. Based on SOM, we propose the processing-in-transmission architecture (PITA), which extends the traditional SIMD architecture to perform some fundamental data processing during its transmission, by embedding multiple levels of SOM networks on the data path. We evaluate the performance of PITA in two irregular computation problems. We first map the matrix inversion task onto PITA and show considerable performance gain can be achieved, resulting in $3\times$ 3 × - $20\times$ 20 × speedup against Intel MKL, and $20\times$ 20 × - $40\times$ 40 × against cuBLAS. Then we evaluate our PITA on sparse CNNs. The results indicate that PITA can greatly improve computation efficiency and reduce memory bandwidth pressure. We achieved $2\times$ 2 × - $9\times$ 9 × speedup against several state-of-art accelerators on sparse CNN, where nearly 100 percent PE efficiency is maintained under high sparsity. We believe the concept of PIT is a promising computing paradigm that can enlarge the capability of traditional parallel architecture.

Motivation Seurat is one of the most popular software suites for the analysis of single-cell RNA sequencing data. Considering the popularity of the tidyverse ecosystem, which offers a large set of data display, query, manipulation, integration and visualization utilities, a great opportunity exists to interface the Seurat object with the tidyverse. This interface gives the large data science community of tidyverse users the possibility to operate with familiar grammar. Results To provide Seurat with a tidyverse-oriented interface without compromising efficiency, we developed tidyseurat, a lightweight adapter to the tidyverse. Tidyseurat displays cell information as a tibble abstraction, allowing intuitively interfacing Seurat with dplyr, tidyr, ggplot2 and plotly packages powering efficient data manipulation, integration and visualization. Iterative analyses on data subsets are enabled by interfacing with the popular nest-map framework. Availability and implementation The software is freely available at cran.r-project.org/web/packages/tidyseurat and github.com/stemangiola/tidyseurat. Supplementary information Supplementary data are available at Bioinformatics online.

This article presents a framework, Genesis (genome analysis), to efficiently and flexibly accelerate generic data manipulation operations that have become performance bottlenecks in the genomic data processing pipeline utilizing FPGAs-as-a-service. Genesis conceptualizes genomic data as a very large relational database and uses extended SQL as a domain-specific language to construct data manipulation queries. To accelerate the queries, we designed a Genesis hardware library of efficient coarse-grained primitives that can be composed into a specialized dataflow architecture. This approach explores a systematic and scalable methodology to expedite domain-specific end-to-end accelerated system development and deployment.

Data security in the cloud has become a dominant topic being discussed in recent times as the security of data in the cloud has been focused on by several researchers. However, the data security was enforced at the attribute level, the adversaries are capable of learning the method of data encryption even there are access restrictions are enforced at an attribute level. To challenge the adversaries with more sophisticated security measures, an efficient real-time service-centric feature sensitivity analysis (RSFSA) model is proposed in this paper. The RSFSA model analyses the sensitivity of different features being accessed by any service and at multiple levels. At each level, the method checks the set of features being accessed and the number of features the user has access grant to compute the FLAG value for the user according to the profile given. Based on the value of FLAG, the user has been granted or denied service access. On the other side, the method maintains different encryption schemes and keys for each level of features. As the features are organized in multiple levels, the method maintains a set of schemes and keys for each level dedicative. Based on the service level and data, the method selects an encryption scheme and key to perform data encryption. According to that, the service access data has been encrypted at the attribute level with a specific scheme and key. Data encrypted has been uploaded to the blockchain and the method modifies the reference part of the chain to connect only the blocks to which the user has access. The chain given to the user would do not contain any reference from a specific block to which the user has no access. The proposed method improves the performance of data security and access restriction greatly.

Blockchain and Internet of Things (IoT) technologies are used in many domains, predominantly for electronic-healthcare. Here, IoT devices has the ability to provide real-time sensor data from patients to get processed and analyzed. As a single point of failure, mistrust, data manipulation and tampering, and privacy avoidance may all occur as a result of such a method. Through offering shared computing and storage for IoT data, blockchain can help solve such issues.Maintaining and sharing Medical data is necessary here.If there occurs loss of confidence means it threatens the medical data and loss of integrity creates impact on the life of patient. So, the first objective is to protect the medical records. Also, a central server to the records will pretend the hackers to attack and continuous fetching is difficult.Therefore, combining Blockchain and IoT will be a threat breaker for computerized medical records.

Compression technique is basically used to compress the size of table or reduce the storage area. Oracle already gives this feature for the table compression as well as for the index compression. when index is created on particular column of a table then it contain some space, which require some storage or disk space by this technique we can save our disk space because in industry the company have to purchase the disk space according to the size of the their data and pay according to their disk space. To utilize this disk space for useful records data rather than wasting it. In this paper used the data pump utility for the compression of Bitmap index and table. Data pump utility performed for the logical backups in database.in this paper implemented data pump for compression, to release the space and change the index pointing location. It will not release the space even after deletion of records. This is of special interest for the case to compress the bitmap index and table space along with the’S (Data Manipulation Language).

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Adverse effects can be seen in the entire body due to the major disorders known as Diabetes. The risk of dangers like diabetic nephropathy, cardiac stroke and other disorders can increase severally because of the undiagnosed diabetes. Around the globe the people are suffering from this disease. For a healthy life early detection of this disease is very curtail. As the causes of the diabetes is increasing rapidly this disease might turn up as a reason for worldwide concern. Increasing the chances for a more accurate predictions and form experiences automatic learning by computational method may be provided by Machine Learning (ML). With the help of R data manipulation tool for trends development and with risk factor patterns detection in Pima Indian diabetes technique of machine learning is been used in the current researches. With the use of R data manipulation tool analysis and development five different predictive models is done for the categorization of patients into diabetic and non- diabetic. supervised machine learning algorithms namely multifactor dimensionality reduction (MDR), k-nearest neighbor (k-NN), artificial neural network (ANN) radial basis function (RBF) kernel support vector machine and linear kernel support vector machine (SVM-linear) are used for this purpose.

Database is a collection of data that forming an information that useful for managers in an organization to facilitate data management process to become more effective and safely guarantee. A good database has fast and accurate response time performance in data execution processing. To manage the database, it requires a special language or query known as Structure Query Language (SQL). Increase need for fast information, will need a proper SQL as well. Therefore, through this research, a test analysis will be used to optimize the query response time in database management system commonly used today, namely the My Structured Query Language (MySQL) using PHP version of 7.2.27 relational database which will be tested on the Oebufu Village Aspiration and Information Service System (SELMA) that acessed from localhost panels with a total of 1000 test data records. The test is devoted to the effectiveness of using SELECT queries in calling and displaying data through restructuring the use of clauses. This study aims to help improve data management by providing the results of the comparative analysis of response times between the general query model and the optimized query results in order to determine the correct and efficient query structure. The result is proven by doing query optimization, it is found that the presentation of time optimization is significant, which is 78% much faster than the initial query, through this test it can be concluded that optimization plays an important role in streamlining the response time of database queries.

This research analyzes the effectiveness of the list experiment and crosswise model in measuring self-plagiarism and data manipulation. Both methods were implemented in a large-scale survey of academics on social norms and academic misconduct. As the results lend little confidence about the effectiveness of the methods, researchers are best advised to avoid them or, at best, to handle them with care.

Advanced Metering Infrastructure (AMI) is a component of electrical networks that combines the energy and telecommunication infrastructure to collect, measure and analyze consumer energy consumptions. One of the main elements of AMI is a smart meter that used to manage electricity generation and distribution to end-user. The rapid implementation of AMI raises the need to deliver better maintenance performance and monitoring more efficiently while keeping consumers informed on their consumption habits. The convergence from analog to digital has made AMI tend to inherit the current vulnerabilities of digital devices that prone to cyber-attack, where attackers can manipulate the consumer energy consumption for their benefit. A huge amount of data generated in AMI allows attackers to manipulate the consumer energy consumption to their benefit once they manage to hack into the AMI environment. Anomalies detection is a technique can be used to identify any rare event such as data manipulation that happens in AMI based on the data collected from the smart meter. The purpose of this study is to review existing studies on anomalies techniques used to detect data manipulation in AMI and smart grid systems. Furthermore, several measurement methods and approaches used by existing studies will be addressed.

Control and management in smart grids are facing many challenges such as scalability, heterogeneity and technology innovation. This requires a transformation from the traditional centralised paradigm into a distributed one. In this article, a new distributed programming methodology, called Centralised Programming and Distributed Execution (CPDE), is proposed. CPDE relies on (i) the abstraction of the whole system as a distributed database; (ii) the use of the Smartlog declarative and reactive rule based language for expressing data manipulation; and (iii) the automatic Smartlog rule distribution according to data distribution. It thus provides a simple and straightforward mean for distributed programming. A centralised algorithm of fair over-voltage regulation of PV systems is used as a typical smart grids study case to validate the methodology and to compare it with centralized implementations. The experiments are implemented in a real-time simulation platform with a network of Raspberry Pis. In addition to showing its correctnes and ease of use, the performance of the CPDE implementation is studied, as well as its sensitivity to the increasing number of computing units and the data distribution. Results are promising and show the clear benefits of this methodology compared to more classical implementations.

Information retrieval system is a system that is widely used to retrieve information. This research will discuss how the system finds back the information stored in database tables. Tables in the database are arranged to store all forms of data entered by database users so that later the stored data can be used again. Re-accessing the database's information must go through a mechanism known as a database management system (DBMS). One of the most widely used DBMS is MySQL. By using a DBMS, information and data can be manipulated according to user needs. Data manipulation in the database is done in a special language, namely SQL (Structure Query Language). Mastery of SQL commands is an obligation for database users so that the manipulated data can produce the required information. However, many database users still do not understand how the actual SQL command syntax manages and manipulates data into information. This is, of course, very risky if the solution is not immediately sought because it will hinder the process of retrieving information from the data stored in the database. For this problem to be resolved, it is necessary to design a system that can help database users translate their wishes into SQL command syntax. This paper will discuss how a command in Indonesian can be translated into SQL command syntax. The method used to solve this research problem is rule-based. There are two stages in the main process: the pre-processing stage, which consists of a word tokenization process, and a translation stage, including a keyword grouping process. This keyword grouping process consists of the keyword group analysis phase, table and column analysis, identification of SQL commands, and mapping of SQL commands. From all stages that have been passed and testing of 7 scenarios with ten (10) commands for each scenario, the accuracy is 81.42%. The inaccuracy in the testing process is more a problem of displaying data from two or more tables, for example, using the join table command. This problem can be addressed by adding new rules for the use of table joins.

DNA computing has a rich history of computing paradigms with great expressive power. However, far less expressive power is needed for data manipulation. Indeed, the relational algebra, the yardstick of database systems, is expressible in first-order logic, and thus less powerful than Turing-complete models. Turing-complete DNA computing models have to account for many and varied scenarios. A DNA implementation of data manipulations might be nimbler and perform its operation faster than a Turing-complete DNA computing model. Hence, we propose a restrictive model for implementing data manipulation operations, focused on implementability in DNA. We call this model the sticker complex model. A forte of the sticker complex model, is its ability to detect when hybridization becomes an uncontrolled chain reaction. Such chain reactions make hybridization less predictable and thus less attractive for deterministic computations. Next, we define a query language on sticker complexes, called DNAQL. DNAQL is a typed, applicative functional programming language, powerful enough to simulate the relational algebra on sticker complexes. The type system enjoys a number of desirable properties such as soundness, maximality, and tightness. Jan Van den Bussche is partially supported by the National Natural Science Foundation of China (61972455). van den Bussche, J (corresponding author), Hasselt Univ, Hasselt, Belgium. jan.vandenbussche@uhasselt.be

Skyline queries have been widely used as an effective query tool in many contemporary database applications. The main concept of skyline queries relies on retrieving the non-dominated tuples in the database which are known skylines. In most database applications, the contents of the databases are dynamic due to the continuous changes made towards the database. Typically, the changes in the contents of the database occur through data manipulation operations (INSERT and/or UPDATE). Performing these operations on the database results in invalidating the most recent skylines before changes are made on the database. Furthermore, the presence of incomplete data in databases becomes frequent phenomena in recent database applications. Data incompleteness causes several challenges on skyline queries such as losing the transitivity property of the skyline technique and the test dominance process between tuples being cyclic. Reapplying skyline technique on the entire updated incomplete database to determine the new skylines is unwise due to the exhaustive pairwise comparisons. Thus, this paper proposes an approach, named Incomplete Dynamic Skyline Algorithm (IDSA) which attempts to determine the skylines on dynamic and incomplete databases. Two optimization techniques have been incorporated in IDSA, namely: pruning and selecting superior local skylines. The pruning process attempts to exploit the derived skylines before the INSERT/UPDATE operation made on the database to identify the new skylines. Moreover, selecting superior local skylines process assists in further eliminating the remaining non-skylines from further processing. These two optimization techniques lead to a large reduction in the number of domination tests due to avoiding re-computing of skylines over the entire updated database to derive the new skylines. Extensive experiments have been accomplished on both real and synthetic datasets, and the results demonstrate that IDSA outperforms the existing solutions in terms of the number of domination tests and the processing time of the skyline operation.

The use of Machine Learning (ML) solutions for decision automation in manufacturing environments is critical if operators trust ML-predictions without critically questioning them. The vulnerability of ML-applications to data manipulation, data-poisoning and adversarial examples raise concerns about its reliability and security. This paper evaluates an on-edge predictive maintenance solution through an IT security perspective, showing how the model’s forecasting can be affected by intentional data manipulation and thus identifying the system’s vulnerabilities for this particular use case. It concludes with suggestions on how to mitigate threats and manage risks.

The command line is a standard way of using the Linux operating system. It contains many features essential for efficiently handling data editing and analysis processes. Therefore, it is very useful in bioinformatics applications. Commands allow for rapid manipulation of large ASCII files or very numerous files, making basic command line programming skills a critical component in modern life science research. The following article is not a guide to Linux commands. In this manuscript, in contrast to many various Linux manuals, we aim to present basic command line tools helpful in handling biological sequence data. This manuscript provides a collection of simple and popular hacks dedicated to users with very basic experience in the area of the Linux command line. It includes a description of data formats and examples of editing of four types of data formats popular in bioinformatics applications.

The structure and performance of content management systems are analyzes. Over the past three decades, sites have evolved from a conventional way of transmitting hypertext to multi-component software that serves thousands of users simultaneously and allows different types of Internet resources to exist on the same platform. For easy data manipulation in this case use content management systems content management systems. The four most popular in many countries content management systems: WordPress, OpenCart, Joomla, Drupal are compared in the article using ten key features : platform; technology; database management systems used in the design; developer; request processing speed; cost; reliability; target audience; ease of use; availability of translation of the administrative panel into Ukrainian. As research has shown, there is no completely universal content management system. Every of the existing software for creating a web resource is designed to solve a specific range of tasks. The method of building a content management systems is based on classical methods of Web-site development, namely it is a hybrid of manual HTML development, site development software, tool systems, frameworks. Free content management systems are open source. Free systems that have appeared on the market for more than 10 years ago are not inferior to paid, to newer, options in terms of security and volume of functional content. This state of the problem is due to the fact that the development of plug-ins and add-ons can be engaged not only in system developers, but also third-party developers. The structure of the content management system is based on the use of the modular principle. This makes the content management system flexible, easy for both the user and the developer to interact with, and allows you to expand the range of one module by installing another. WordPress is a content management system that has a more sophisticated architecture that reflects performance, intuitively clear administrative panel and, finally, has more advantages over analogues

The development of the technological world provides for humans in carrying out business transactions. With the existence of business process information systems or can be more efficient and effective. Soto Kudus still uses manual paper records in its business processes, so that data requests are less effective which causes data manipulation, and there are no web-based monitoring charts. By implementing a web-based information system, it is expected to be able to provide convenience in running business processes. The programming language used is using the PHP programming language with a codeigniter framework. For storing data, the author uses Mysqli. The results of this application to facilitate the admin in processing data and facilitating management in monitoring sales data charts, as well as saving paper usage and sales data security