Data Access Functions Research Articles

СИСТЕМЫ ПОДДЕРЖКИ ПРИНЯТИЯ РЕШЕНИЙ КАК ОСНОВА РАЗВИТИЯ И ЦИФРОВОЙ ТРАНСФОРМАЦИИ МАЛОГО И СРЕДНЕГО БИЗНЕСА

Advances in information technology in recent years have led to the emergence of a wide range of systems that managers currently use to make and implement decisions. Managers are responsible for making decisions on all issues and such decisions have to be made every day. Decision support systems (DSS) play an important role in decision-making, which combines the use of models and analytical methods with traditional data access and extraction functions. The main purpose of the DSS is to improve the work of an individual decision maker - to improve its quality or decision-making by increasing their efficiency and effectiveness. The same managers are constantly making decisions both in the short term and in the long term, regardless of this, the decisions made in any case will have significant impact and consequences for both the organization and its stakeholders.

Framework Design of an Edge Gateway System Supporting Multi-Protocol Standardized Access Detection

Recently, intelligent city construction has been promoted with the development of the Internet of things (IoT). The edge IoT gateway plays a critical role as the data aggregation core and processing center. Most existing gateways mainly solve heavy data storage and processing loads in cloud computing centers. There is less attention paid to multi-protocol data transmission and fusion. However, multiple products with different protocols in an IoT system require a flexible gateway compatible with multiple protocols. This paper proposes a multi-protocol edge gateway. The frame design was based on the actual demand for edge data acquisition. The gateway hardware platform used an RK3399 chip transplanted from the embedded operating system. It could support simultaneous multi-protocol access to ZigBee, LoRa, Bluetooth, and Wi-Fi. We combined the plug-and-play (PnP) hardware device access detection scheme with the system onboard interface driver to realize dynamic access detection and unified device management. In addition, the gateway also integrated data storage and access functions and partial edge computing functions. Finally, the experiment results verified that the multi-protocol edge gateway could meet the demand for data access and device control.

Optimization of Network Furniture Management System Based on Big Data

In order to make the traditional household equipment can be remotely controlled, wireless networking technology is introduced into the traditional furniture equipment to achieve the effect of access and remote control. In view of the problem that the traditional cloud storage system lacks flexibility, opacity, weak robustness and cannot effectively store, manage, and maintain big data in data storage, a big data oriented cloud storage system is designed and implemented to intelligently process the business requests of the furniture system through the logic control module. The user access module is used to provide users with various functions realized at the bottom, the storage module is used to provide transparent data access function for the furniture system, and the file read/write module is used to isolate the upper logic processing from the lower storage, so as to make the designed system more robust. In the process of software design, the program code of data cloud storage is given to realize big data cloud storage. The experimental results show that the new generation of network furniture cloud storage systems has high feasibility and practicability.

WebStart WEPS: WEPS with Remote Data Access and Cloud-Computing Functionality

HighlightsWebStart WEPS is a Java WebStart enabled browser-accessible version of WEPS.This model-as-a-service cloud-based system provides remote access to model inputs and remote model execution.Default QuickPlot reports are provided to quickly visualize model results.MapViewer revisions are provided to improve map-based site selections.Abstract. The Wind Erosion Prediction System (WEPS) is a physical process-based wind erosion model developed by the USDA Agricultural Research Service (USDA-ARS). WEPS simulates daily climate and management-driven changes to the surface, vegetation, and soil. It estimates erosion losses when surface conditions and wind speeds are sufficient for soil movement. WEPS was released in 2010 to the USDA Natural Resources Conservation Service (USDA-NRCS) for assisting land managers in controlling wind erosion, establishing field-level plans for soil conservation, and determining wind erosion susceptibility as a part of national conservation programs to conserve the soil resource. The WEPS interface allows easy user access to national-scale input data. Four primary inputs are required to set up and execute a WEPS run: (1) field dimensions; (2) field location, which automatically determines applicable local climate data; (3) a representative soil; and (4) time series of management practices. These inputs require access to a significant amount of data, which are typically stored locally; however, maintaining these data on every computer running WEPS and keeping them current were problematic for the NRCS. To address these concerns, remote access functionality was incorporated into WEPS to deliver climate and wind data, as well as soil, operation, crop/residue, and management (a date-sequenced list of management operations) records. In addition, WebStart WEPS also features the option of running WEPS components remotely “in the cloud” as web services. The Java WebStart framework provides a simplified delivery mechanism to users’ computers and offers automatic updating capability. Keywords: Cloud services, CSIP, Erosion, Java WebStart, Modeling, WEPS, Wind erosion.

EMPAIA App Interface: An open and vendor-neutral interface for AI applications in pathology

Background and objectiveArtificial intelligence (AI) apps hold great potential to make pathological diagnoses more accurate and time efficient. Widespread use of AI in pathology is hampered by interface incompatibilities between pathology software. We studied the existing interfaces in order to develop the EMPAIA App Interface, an open standard for the integration of pathology AI apps. MethodsThe EMPAIA App Interface relies on widely-used web communication protocols and containerization. It consists of three parts: A standardized format to describe the semantics of an app, a mechanism to deploy and execute apps in computing environments, and a web API through which apps can exchange data with a host application. ResultsFive commercial AI app manufacturers successfully adapted their products to the EMPAIA App Interface and helped improve it with their feedback. Open source tools facilitate the adoption of the interface by providing reusable data access and scheduling functionality and enabling automatic validation of app compliance. ConclusionsExisting AI apps and pathology software can be adapted to the EMPAIA App Interface with little effort. It is a viable alternative to the proprietary interfaces of current software. If enough vendors join in, the EMPAIA App Interface can help to advance the use of AI in pathology.

Opportunities for the use of health information technology to support research.

In the last decade, expanding use of health information technology (IT) across the United States has created opportunities for use of electronic health data for health services and biomedical research, but efforts may be hampered by limited data access, data quality, and system functionality. We identify five opportunities to advance the use of health IT for health services and biomedical research, which informed a federal government-led, collaborative effort to develop a relevant policy and development agenda. In particular, the health IT infrastructure should more effectively support the use of electronic health data for research; provide adaptable technologies; incorporate relevant research-related functionality; support patient and caregiver engagement in research; and support effective integration of knowledge into practice. While not exhaustive, these represent important opportunities that the biomedical and health informatics communities can pursue to better leverage health IT and electronic health data for research.

Smart decimation method for fusion research data

New fusion research experiments will generate massive experimental data. In this context, fusion research appears in the scope of the big data where both search and access functions require new approaches and optimizations. One common data access functionality is decimation. It allows to retrieve a limited number of points of the total available. One classical mechanism of decimation is downsampling by an integer factor called step. It works by selecting a value every ‘step’ number of values. The main characteristic of classical decimation is that the selected values are uniformly distributed along the total. However, in case of time evolution experimental signals, the relevancy of data is not uniform. There are some intervals where the provided information is more complex and richer, and usually more interesting from the user point of view. This contribution presents a new data decimation technology for unidimensional time evolution signals where the limited number of accessed points are distributed following the criterion of data interest level. The new method implements, on one hand, a heuristic function which is able to determine the level of interest of an interval based on its data characteristics, and on the other hand, a selection algorithm where points are distributed based on weighted intervals.

Virtual European Solar & Planetary Access (VESPA): A Planetary Science Virtual Observatory Cornerstone

The Europlanet-2020 programme, which ended on Aug 31st, 2019, included an activity called VESPA (Virtual European Solar and Planetary Access), which focused on adapting Virtual Observatory (VO) techniques to handle Planetary Science data. This paper describes some aspects of VESPA at the end of this 4-years development phase and at the onset of the newly selected Europlanet-2024 programme starting in 2020. The main objectives of VESPA are to facilitate searches both in big archives and in small databases, to enable data analysis by providing simple data access and online visualization functions, and to allow research teams to publish derived data in an interoperable environment as easily as possible. VESPA encompasses a wide scope, including surfaces, atmospheres, magnetospheres and planetary plasmas, small bodies, helio-physics, exoplanets, and spectroscopy in solid phase. This system relies in particular on standards and tools developed for the Astronomy VO (IVOA) and extends them where required to handle specificities of Solar System studies. It also aims at making the VO compatible with tools and protocols developed in different contexts, for instance GIS for planetary surfaces, or time series tools for plasma-related measurements. An essential part of the activity is to publish a significant amount of high-quality data in this system, with a focus on derived products resulting from data analysis or simulations.

HadoopTrajectory: a Hadoop spatiotemporal data processing extension

The recent advances in location tracking technologies and the widespread use of location-aware applications have resulted in big datasets of moving object trajectories. While there exists a couple of research prototypes for moving object databases, there is a lack of systems that can process big spatiotemporal data. This work proposes HadoopTrajectory, a Hadoop extension for spatiotemporal data processing. The extension adds spatiotemporal types and operators to the Hadoop core. These types and operators can be directly used in MapReduce programs, which gives the Hadoop user the possibility to write spatiotemporal data analytics programs. The storage layer of Hadoop, the HDFS, is extended by types to represent trajectory data and their corresponding input and output functions. It is also extended by file splitters and record readers. This enables Hadoop to read big files of moving object trajectories such as vehicle GPS tracks and split them over worker nodes for distributed processing. The storage layer is also extended by spatiotemporal indexes that help filtering the data before splitting it over the worker nodes. Several data access functions are provided so that the MapReduce layer can deal with this data. The MapReduce layer is extended with trajectory processing operators, to compute for instance the length of a trajectory in meters. This paper describes the extension and evaluates it using a synthetic dataset and a real dataset. Comparisons with non-Hadoop systems and with standard Hadoop are given. The extension accounts for about 11,601 lines of Java code.

A collaborative analysis framework for distributed gridded environmental data

As the amount of environmental data expands exponentially worldwide, researchers are challenged to efficiently analyze data maintained in multiple data centers. Because distributed data access, server-side analysis, multinode collaboration, and extensible analytic functions are still research gaps in this field, this paper introduces a collaborative analysis framework for gridded environmental data, i.e. CAFE. Multiple CAFE nodes can collaborate to perform complex data analysis. Analytic functions are performed near where data are stored. A web-based user interface allows researchers to search for data of interest, submit analytic tasks, check the status of tasks, visualize the analysis results, and download the resulting data files. CAFE facilitates overall research efficiency by dramatically lowering the amount of data that must be transmitted from data centers to researchers for analysis. The results of this study may lead to the further development of collaborative computing paradigm for environmental data analysis.

Coalitional Game for the Creation of Efficient Virtual Core Network Slices in 5G Mobile Systems

Many ongoing research activities relevant to 5G mobile systems concern the virtualization of the mobile core network, including the evolved packet core (EPC) elements, aiming for system scalability, elasticity, flexibility, and cost-efficiency. Virtual EPC (vEPC)/5G core will principally rely on some key technologies, such as network function virtualization, software defined networking, and cloud computing, enabling the concept of mobile carrier cloud. The key idea beneath this concept, also known as core network as a service, consists in deploying virtual instances (i.e., virtual machines or containers) of key core network functions [i.e., virtual network functions (VNF) of 4G or 5G], such as the mobility management entity (MME), Serving GateWay (SGW), Packet Data network gateWay (PGW), access and mobility management function (AMF), session management function (SMF), authentication server function (AUSF), and user plane functions, over a federated cloud. In this vein, an efficient VNF placement algorithm is highly needed to sustain the quality of service (QoS) while reducing the deployment cost. Our contribution in this paper is twofold. First, we devise an algorithm that derives the optimal number of virtual instances of 4G (MME, SGW, and PGW) or 5G (AMF, SMF, and AUSF) core network elements to meet the requirements of a specific mobile traffic. Second, we propose an algorithm for the placement of these virtual instances over a federated cloud. While the first algorithm is based on mixed integer linear programming, the second is based on coalition formation game, wherein the aim is to build coalitions of cloud networks to host the virtual instances of the vEPC/5G core elements. The obtained results clearly indicate the advantages of the proposed algorithms in ensuring QoS given a fixed cost for vEPC/5G core deployment, while maximizing the profits of cloud operators.

QUIS

Existing data integration frameworks are poorly suited for the special requirements of scientists. To answer a specific research question, often, excerpts of data from different sources need to be integrated. The relevant parts and the set of underlying sources may differ from query to query. The analyses also oftentimes involve frequently changing data and exploratory querying. Additionally, The data sources not only store data in different formats, but also provide inconsistent data access functionality. The classic Extract-Transform-Load (ETL) approach seems too complex and time-consuming and does not fit well with interest and expertise of the scientists. With QUIS (QUery In-Situ), we provide a solution for this problem. QUIS is an open source heterogeneous in-situ data querying system. It utilizes a federated query virtualization approach that is built upon plugged-in adapters. QUIS takes a user query and transforms appropriate portions of it into the corresponding computation model on individual data sources and executes it. It complements the segments of the query that the target data sources can not execute. Hence, it guarantees full syntax and semantic support for its language on all data sources. QUIS's in-situ querying facility almost eliminates the time to prepare the data while maintaining a competitive performance and steady scalability. The present demonstration illustrates interesting features of the system: virtual Schemas, heterogeneous joins, and visual query results. We provide a realistic data processing scenario to examine the system's features. Users can interact with QUIS using its desktop workbench, command line interface, or from any R client including RStudio Server.

A bedr way of genomic interval processing.

BackgroundNext-generation sequencing is making it critical to robustly and rapidly handle genomic ranges within standard pipelines. Standard use-cases include annotating sequence ranges with gene or other genomic annotation, merging multiple experiments together and subsequently quantifying and visualizing the overlap. The most widely-used tools for these tasks work at the command-line (e.g. BEDTools) and the small number of available R packages are either slow or have distinct semantics and features from command-line interfaces.ResultsTo provide a robust R-based interface to standard command-line tools for genomic coordinate manipulation, we created bedr. This open-source R package can use either BEDTools or BEDOPS as a back-end and performs data-manipulation extremely quickly, creating R data structures that can be readily interfaced with existing computational pipelines. It includes data-visualization capabilities and a number of data-access functions that interface with standard databases like UCSC and COSMIC.Conclusionsbedr package provides an open source solution to enable genomic interval data manipulation and restructuring in R programming language which is commonly used in bioinformatics, and therefore would be useful to bioinformaticians and genomic researchers.Electronic supplementary materialThe online version of this article (doi:10.1186/s13029-016-0059-5) contains supplementary material, which is available to authorized users.

Secure searching on cloud storage enhanced by homomorphic indexing

Enterprise cloud tenants would store their outsourced cloud data in encrypted form for data privacy and security. However, flexible data access functions such as data searching is usually sacrificed as a result. Thus, enterprise tenants demand secure data retrieval and computation solution from the cloud provider, which will allow them to utilize cloud services without the risks of leaking private data to outsiders and even service providers.In this paper, we propose an exclusive-or (XOR) homomorphism encryption scheme to support secure keyword searching on encrypted data for cloud storage. First, this scheme specifies a new data protection method by encrypting the keyword and randomizing it by performing XOR operation with a random bit-string for each session to protect access pattern leakage; Secondly, the homomorphic evaluation key enables the searching evaluation to be on-demand calculated, thus it removes the dependency of key storage on cloud and enhance protection against cloud’s violability; Thirdly, this scheme can effectively protect data-in-transit against passive attack such as access pattern analysis due to the randomization. This scheme also can reduce data leakage to service provider because the homomorphism-key solution instead of key storage on cloud. The above three features have been proved by the experiments and further tested out at Email service which can support secure subject searching. The execution time of one searching process is just in the order of milliseconds. We could get 2–3 times speedup compared to default utility grep with the concern of expensive one-time indexing which can be built off-line in advance.

National rural drinking water monitoring: progress and challenges with India's IMIS database

National drinking water programs seek to address monitoring challenges that include self-reporting, data sampling, data consistency and quality, and sufficient frequency to assess the sustainability of water systems. India stands out for its comprehensive rural water database known as Integrated Management Information System (IMIS), which conducts annual monitoring of drinking water coverage, water quality, and related program components from the habitation level to the district, state, and national levels. The objective of this paper is to evaluate IMIS as a national rural water supply monitoring platform. This is important because IMIS is the official government database for rural water in India, and it is used to allocate resources and track the results of government policies. After putting India's IMIS database in an international context, the paper describes its detailed structure and content. It then illustrates the geographic patterns of water supply and water quality that IMIS can present, as well as data analysis issues that were identified. In particular, the fifth section of the paper identifies limitations on the use of state-level data for explanatory regression analysis. These limitations lead to recommendations for improving data analysis to support national rural water monitoring and evaluation, along with strategic approaches to data quality assurance, data access, and database functionality.

Vector data flow analysis for SIMD optimizations on OpenCL programs

SummaryMulti‐core systems equipped with micro processing units and accelerators such as digital signal processors (DSPs) and graphics processing units (GPUs) have become a major trend in processor design in recent years in attempts to meet ever‐increasing application performance requirements. Open Computing Language (OpenCL) is one of the programming languages that include new extensions proposed to exploit the computing power of these kinds of processors. Among the newly extended language features, the single‐instruction multiple‐data (SIMD) linguistics and vector types are added to OpenCL to exploit hardware features of the accelerators. The addition makes it necessary to consider how traditional compiler data flow analysis can be adopted to meet the optimization requirements of vector linguistics. In this paper, we propose a calculus framework to support the data flow analysis of vector constructs for OpenCL programs that compilers can use to perform SIMD optimizations. We model OpenCL vector operations as data access functions in the style of mathematical functions. We then show that the data flow analysis for OpenCL vector linguistics can be performed based on the data access functions. Based on the information gathered from data flow analysis, we illustrate a set of SIMD optimizations on OpenCL programs. The experimental results incorporating our calculus and our proposed compiler optimizations show that the proposed SIMD optimizations can provide average performance improvements of 22% on x86 CPUs and 4% on advanced micro devices GPUs. For the selected 15 benchmarks, 11 of them are improved on x86 CPUs, and six of them are improved on advanced micro devices GPUs. The proposed framework has the potential to be used to construct other SIMD optimizations on OpenCL programs. Copyright © 2015 John Wiley & Sons, Ltd.

On-Demand Snapshot: An Efficient Versioning File System for Phase-Change Memory

Versioning file systems are widely used in modern computer systems as they provide system recovery and old data access functions by retaining previous file system snapshots. However, existing versioning file systems do not perform well with the emerging PCM (phase-change memory) storage, because they are optimized for hard disks. Specifically, a large amount of additional writes incurred by maintaining snapshot degrades the performance of PCM seriously as write operations are the performance bottleneck of PCM. This paper presents a novel versioning file system, designed for PCM, that reduces the writing overhead of a snapshot significantly. Unlike existing versioning file systems that incur cascade writes up to the file system root, our scheme breaks the recursive update chain at the immediate parent level. The proposed file system is implemented on Linux 2.6 as a prototype. Measurement studies with various I/O benchmarks show that the proposed file system improves the I/O throughput by 144 percent on average, compared to ZFS, a representative versioning file system.

EpHLA: An innovative and user-friendly software automating the HLAMatchmaker algorithm for antibody analysis

The global challenge for solid organ transplantation programs is to distribute organs to the highly sensitized recipients. The purpose of this work is to describe and test the functionality of the EpHLA software, a program that automates the analysis of acceptable and unacceptable HLA epitopes on the basis of the HLAMatchmaker algorithm. HLAMatchmaker considers small configurations of polymorphic residues referred to as eplets as essential components of HLA-epitopes. Currently, the analyses require the creation of temporary files and the manual cut and paste of laboratory tests results between electronic spreadsheets, which is time-consuming and prone to administrative errors. ResultsThe EpHLA software was developed in Object Pascal programming language and uses the HLAMatchmaker algorithm to generate histocompatibility reports. The automated generation of reports requires the integration of files containing the results of laboratory tests (HLA typing, anti-HLA antibody signature) and public data banks (NMDP, IMGT). The integration and the access to this data were accomplished by means of the framework called eDAFramework. The eDAFramework was developed in Object Pascal and PHP and it provides data access functionalities for software developed in these languages. The tool functionality was successfully tested in comparison to actual, manually derived reports of patients from a renal transplantation program with related donors. ConclusionsWe successfully developed software, which enables the automated definition of the epitope specificities of HLA antibodies. This new tool will benefit the management of recipient/donor pairs selection for highly sensitized patients.

A New Proxy Server of CHIS Based on Caché

The data accessing technology is the core and foundation technology of the community health information systems. In the process of designing the system, the relational database compared with the Caché database from the data model, extensibility, scalability, etc. Study the associated technology about the heterogeneous data sharing with the Caché database. After analyzed the common solution of heterogeneous data sharing then the paper proposed a new data sharing solution, which design a middle proxy server. With the middle proxy, the implementation class completed the uploading and sharing of data access and other functions.

OWLIM: A family of scalable semantic repositories

An explosion in the use of RDF for representing information about resources has driven the requirements for Web-scale server systems that can store and process huge quantities of data, and furthermore provide powerful data access and mining functionalities. This paper describes OWLIM, a family of semantic repositories that provide storage, inference and novel data-access features delivered in a scalable, resilient, industrial-strength platform.