PDF Report Research Articles

Elite Sequence Mining of Big Data using Hadoop Mapreduce

Text mining can deal with unstructured information. The proposed work extricates content from a PDF report is changed over to plain content configuration; at that point record is tokenized and serialized. Record grouping and classification is finished by discovering similarities between reports put away in cloud. Comparable archives are distinguished utilizing Singular Value Decomposition (SVD) strategy in Latent Semantic Indexing (LSI). At that point comparative records are assembled together as a group. A similar report is done between LFS (Local File System) and HDFS (HADOOP DISTRIBUTED FILE SYSTEM) as for rate and dimensionality. The System has been assessed on genuine records and the outcomes are classified.

The data warehouse for down payment administration in the Constitutional Court of Republic of Indonesia

The Constitutional Court of The Republic of Indonesia is an institution which has authority to judge and examine constitutions. In this institution, each component has responsibility to manage their own finance and budgeting. Based on this case, they need a solution to summarize the amount and absorption of the budget in every component. This paper proposes a solution to design a data warehouse model to process and analyse the down payment administration data. The nine steps design methodology for developing data warehouse is implemented in this work. This paper aims to bring some benefit for The Constitutional Court, especially in form of information about down payment absorption for every component each year. The result is presented in form of pdf report and informative dashboard using data warehouse tools.

Archiving and exchange of digital ECGs: A review of existing data formats

Digital ECG is today a common practice but a universal format for its storage and exchange has never been widely implemented. The reason is linked on one side to the need of the manufacturing industry to (rightly) protect intellectual propriety and technology, but on the other to an inadequate effort of the research community to sufficiently enforce the use of digital ECG data. To some degree, and at least from a practical point of view, the problem is also linked to other factors, such as the need in some instances to protect patient-sensitive information, and whether digital exchanged data should also include annotations and measurements from an algorithm or by human intervention.As a result, after more than 30 years it is still common that the full ECG acquired information is not preserved, but only partially stored or saved as a PDF report. Paradoxically, the modern era of hospital information technology and the advent and large diffusion of electronic health record systems did not bring expected improvements: the process of digital ECG retrieval and management remains extremely complicated and cumbersome. The ultimate risk is that the ECG may end up being considered “just” an image rather than a voltage-versus-time signal as it has always been.A critical review of the most commonly used formats for digital ECG will be given, focusing in particular to those linked with DICOM, HL7 and SCP-ECG standards, and highlighting the respective advantages and limitations with special emphasis to the needs typically encountered by the research community. The goal is to provide a snapshot of the present, and to discuss mid- and long-term potential directions and changes, emphasizing what digital ECG organizations could do to “save” ECG information and facilitate its widespread exchange.

Automatic characterization of iron ore by digital microscopy and image analysis

This paper presents an automatic system for mineralogical and textural characterization of iron ores based on digital microscopy and image analysis. It employs a motorized and computer-controlled reflected light microscope in a correlative approach that combines bright field and circular polarization modes. Mosaic images covering large areas of polished sections are acquired to image thousands of particles. Different classifiers discriminate compact and non-compact hematite, polycrystalline and monocrystalline particles, and identify particles as granular, lamellar, and lobular. The entire process is automatic and produces a full pdf report containing typical images and the quantification of mineral and textural phases.

Epi Archive: Automated Synthesis of Global Notifiable Disease Data

ObjectiveLANL has built software that automatically collects global notifiable disease data, synthesizes the data, and makes it available to humans and computers within the Biosurveillance Ecosystem (BSVE) as a novel data stream. These data have many applications including improving the prediction and early warning of disease events.IntroductionMost countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health.While most nations likely store incident data in a machine-readable format, governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues1.A survey conducted by LANL of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. The majority (>70%) produce reports as PDF files on a regular basis. The bulk of the PDF reports present data in a structured tabular format, while some report in natural language.The structure and format of PDF reports change often; this adds to the complexity of identifying and parsing the desired data. Not all websites publish in English, and it is common to find typos and clerical errors.LANL has developed a tool, Epi Archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible.MethodsWe conducted a survey of the national notifiable disease reporting systems notating how the data are reported and in what formats. We determined the minimal metadata that is required to contextualize incident counts properly, as well as optional metadata that is commonly found.The development of software to regularly ingest notifiable disease data and make it available involves three or four main steps: scraping, detecting, parsing and persisting.Scraping: we examine website design and determine reporting mechanisms for each country/website, as well as what varies across the reporting mechanisms. We then designed and wrote code to automate the downloading of the data for each country. We store all artifacts presented as files (PDF, XLSX, etc.) in their original form, along with appropriate metadata for parsing and data provenance.Detecting: This step is required when parsing structured non-machine-readable data such as tabular data in PDF files. We combined the Nurminen methodology of PDF table detection with in-house heuristics to find the desired data within PDF reports2.Parsing: We determined what to extract from each dataset and parsed these data into uniform data structures, correctly accommodating the variations in metadata (e.g., time interval definitions) and the various human languages.Persisting: We store the data in the Epi Archive database and make it available on the internet and through the BSVE. The data is persisted into a structured and normalized SQL database.ResultsThe Epi Archive tool currently contains national and/or subnational notifiable disease data from twenty nations. When a user accesses the Epi Archive site, they are prompted with four fields: country, subregion, disease of interest, and date duration. Upon form submission, a time series is generated from the users’ specifications. The generated graph can then be downloaded into a CSV file if a user is interested in performing personal analysis. Additionally, the data from Epi Archive can be reached through a REST API (Representational State Transfer Application Programming Interface).ConclusionsLANL, as part of a currently funded DTRA effort, is automatically and continually collecting global notifiable disease data. While 20 nations are in production, more are being brought online in the near future. These data are already being utilized and will have many applications including improving the prediction and early warning of disease events.

RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting.

RNA-Seq is increasingly being used to measure human RNA expression on a genome-wide scale. Expression profiles can be interrogated to identify and functionally characterize treatment-responsive genes. Ultimately, such controlled studies promise to reveal insights into molecular mechanisms of treatment effects, identify biomarkers, and realize personalized medicine. RNA-Seq Reports (RSEQREP) is a new open-source cloud-enabled framework that allows users to execute start-to-end gene-level RNA-Seq analysis on a preconfigured RSEQREP Amazon Virtual Machine Image (AMI) hosted by AWS or on their own Ubuntu Linux machine via a Docker container or installation script. The framework works with unstranded, stranded, and paired-end sequence FASTQ files stored locally, on Amazon Simple Storage Service (S3), or at the Sequence Read Archive (SRA). RSEQREP automatically executes a series of customizable steps including reference alignment, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially expressed genes, heatmaps, co-expressed gene clusters, enriched pathways, and a series of custom visualizations. The framework outputs a file collection that includes a dynamically generated PDF report using R, knitr, and LaTeX, as well as publication-ready table and figure files. A user-friendly configuration file handles sample metadata entry, processing, analysis, and reporting options. The configuration supports time series RNA-Seq experimental designs with at least one pre- and one post-treatment sample for each subject, as well as multiple treatment groups and specimen types. All RSEQREP analyses components are built using open-source R code and R/Bioconductor packages allowing for further customization. As a use case, we provide RSEQREP results for a trivalent influenza vaccine (TIV) RNA-Seq study that collected 1 pre-TIV and 10 post-TIV vaccination samples (days 1-10) for 5 subjects and two specimen types (peripheral blood mononuclear cells and B-cells).

RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting

RNA-Seq is increasingly being used to measure human RNA expression on a genome-wide scale. Expression profiles can be interrogated to identify and functionally characterize treatment-responsive genes. Ultimately, such controlled studies promise to reveal insights into molecular mechanisms of treatment effects, identify biomarkers, and realize personalized medicine. RNA-Seq Reports (RSEQREP) is a new open-source cloud-enabled framework that allows users to execute start-to-end gene-level RNA-Seq analysis on a preconfigured RSEQREP Amazon Virtual Machine Image (AMI) hosted by AWS or on their own Ubuntu Linux machine via a Docker container or installation script. The framework works with unstranded, stranded, and paired-end sequence FASTQ files stored locally, on Amazon Simple Storage Service (S3), or at the Sequence Read Archive (SRA). RSEQREP automatically executes a series of customizable steps including reference alignment, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially expressed genes, heatmaps, co-expressed gene clusters, enriched pathways, and a series of custom visualizations. The framework outputs a file collection that includes a dynamically generated PDF report using R, knitr, and LaTeX, as well as publication-ready table and figure files. A user-friendly configuration file handles sample metadata entry, processing, analysis, and reporting options. The configuration supports time series RNA-Seq experimental designs with at least one pre- and one post-treatment sample for each subject, as well as multiple treatment groups and specimen types. All RSEQREP analyses components are built using open-source R code and R/Bioconductor packages allowing for further customization. As a use case, we provide RSEQREP results for a trivalent influenza vaccine (TIV) RNA-Seq study that collected 1 pre-TIV and 10 post-TIV vaccination samples (days 1-10) for 5 subjects and two specimen types (peripheral blood mononuclear cells and B-cells).

Sociotechnical Obstacles to Archaeological Data Reuse

ABSTRACTThe ease of digital data capture and the proliferation of concepts such as the “data deluge” suggest that modern researchers are drowning in datasets. Yet citations of archaeological datasets are few and far between, pointing to low rates of data reuse. This article explores the difficulties that surround data reuse in large-scale regional research, including the cost and coordination necessary to extract useful data from digitized PDF reports. The amount of correction and enhancement matches the effort needed to undertake a small field survey project and can only be circumvented with a thoughtful application of computer-assisted text analysis. Missing data in excavation report PDFs are not only intractable but also insidious due to their concealed nature, leading to poor outcomes in terms of (re)use. Consequently, the degree of data reuse in archaeology has been overestimated.

Post-operative deep brain stimulation assessment: Automatic data integration and report generation

BackgroundThe gold standard for post-operative deep brain stimulation (DBS) parameter tuning is a monopolar review of all stimulation contacts, a strategy being challenged by recent developments of more complex electrode leads. ObjectiveProviding a method to guide clinicians on DBS assessment and parameter tuning by automatically integrating patient individual data. MethodsWe present a fully automatic method for visualization of individual deep brain structures in relation to a DBS lead by combining precise electrode recovery from post-operative imaging with individual estimates of deep brain morphology utilizing a 7T-MRI deep brain atlas. ResultsThe method was evaluated on 20 STN DBS cases. It demonstrated robust automatic creation of 3D-enabled PDF reports visualizing electrode to brain structure relations and proved valuable in detecting miss placed electrodes. DiscussionAutomatic DBS assessment is feasible and can conveniently provide clinicians with relevant information on DBS contact positions in relation to important anatomical structures.

Automatic Attendance Using Face Recognition

Human face detection and recognition is an important technology used in various applications such as video monitor system. Traditional method for taking attendance is Roll Number of student and record the attendance in sheet which takes a lot of time. Because of that systems like automatic attendance is used. To overcome the problems like wastage of time, incorrect attendance, the proposed system gives a method like when he enters the class room , system marks the attendance by extracting the image using Principal Component Analysis algorithm. The system will record the attendance of the student automatically. The student database is collected, it includes name of the students, there images and roll number. It carries an entry in log report of every student of each subject and generates a pdf report of the attendance of the student.

RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting

RNA-Seq is increasingly being used to measure human RNA expression on a genome-wide scale. Expression profiles can be interrogated to identify and functionally characterize treatment-responsive genes. Ultimately, such controlled studies promise to reveal insights into molecular mechanisms of treatment effects, identify biomarkers, and realize personalized medicine. RNA-Seq Reports (RSEQREP) is a new open-source cloud-enabled framework that allows users to execute start-to-end gene-level RNA-Seq analysis on a preconfigured RSEQREP Amazon Virtual Machine Image (AMI) hosted by AWS or on their own Ubuntu Linux machine via a Docker container or installation script. The framework works with unstranded, stranded, and paired-end sequence FASTQ files stored locally, on Amazon Simple Storage Service (S3), or at the Sequence Read Archive (SRA). RSEQREP automatically executes a series of customizable steps including reference alignment, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially expressed genes, heatmaps, co-expressed gene clusters, enriched pathways, and a series of custom visualizations. The framework outputs a file collection that includes a dynamically generated PDF report using R, knitr, and LaTeX, as well as publication-ready table and figure files. A user-friendly configuration file handles sample metadata entry, processing, analysis, and reporting options. The configuration supports time series RNA-Seq experimental designs with at least one pre- and one post-treatment sample for each subject, as well as multiple treatment groups and specimen types. All RSEQREP analyses components are built using open-source R code and R/Bioconductor packages allowing for further customization. As a use case, we provide RSEQREP results for a trivalent influenza vaccine (TIV) RNA-Seq study that collected 1 pre-TIV and 10 post-TIV vaccination samples (days 1-10) for 5 subjects and two specimen types (peripheral blood mononuclear cells and B-cells).

PDF–ECG in clinical practice: A model for long–term preservation of digital 12–lead ECG data

BackgroundIn clinical practice, data archiving of resting 12–lead electrocardiograms (ECGs) is mainly achieved by storing a PDF report in the hospital electronic health record (EHR). When available, digital ECG source data (raw samples) are only retained within the ECG management system. ObjectiveThe widespread availability of the ECG source data would undoubtedly permit successive analysis and facilitate longitudinal studies, with both scientific and diagnostic benefits. Methods & resultsPDF-ECG is a hybrid archival format which allows to store in the same file both the standard graphical report of an ECG together with its source ECG data (waveforms). Using PDF-ECG as a model to address the challenge of ECG data portability, long-term archiving and documentation, a real-world proof-of-concept test was conducted in a northern Italy hospital. A set of volunteers undertook a basic ECG using routine hospital equipment and the source data captured. Using dedicated web services, PDF-ECG documents were then generated and seamlessly uploaded in the hospital EHR, replacing the standard PDF reports automatically generated at the time of acquisition. Finally, the PDF-ECG files could be successfully retrieved and re-analyzed. ConclusionAdding PDF-ECG to an existing EHR had a minimal impact on the hospital's workflow, while preserving the ECG digital data.

The ACORN-SAT linked climate dataset

In 2012 the Australian Bureau of Meteorology published a dataset, ACORN-SAT, containing the homogenised daily temperature observations of 112 locations throughout Australia for the last 100 years. The dataset employs the latest analysis techniques and takes advantage of newly digitised observational data to monitor climate variability and change in Australia. The observations in ACORN-SAT were initially published only as comma separated values, whereas the metadata was published in a PDF report. In 2013 we converted the metadata and the observation data into RDF and published the result as Linked Open Data, accessible online via a pilot government linked data service built on the Linked Data API. In this article we describe the process of transforming the original tabular data into a Linked Sensor Data Cube (in: Proc. of the 5th International Workshop on Semantic Sensor Networks, SSN12, CEUR-WS.org, 2012, pp. 1-16) based on the W3C Semantic Sensor Network ontology (Web Semantics: Science, Services and Agents on the World Wide Web 17 (2012), 25-32) and the W3C RDF Data Cube vocabulary (The RDF Data Cube Vocabulary, W3C Recommendation, 16 January 2014). We further discuss how the dataset has since been used and interlinked with near-real time weather observations for the 112 sensing locations of the ACORN-SAT that are published by the Bureau of Meteorology. Both the original ACORN-SAT dataset and the weather observation data are accessible online at lab.environment.data.gov.au.

Creating reproducible dynamic reports using data from the Secure Anonymised Information Linkage (SAIL) Databank

ABSTRACT
 Introduction/BackgroundR, an open source analytical software has gained wide popularity for its powerful statistical and graphical techniques, little known is that it also proves to be a fantastic tool for generating automated, content specific documentation using a combination of extensible packages (i.e. R markdown, Pandoc, MiKTex, Knitr). One such example of using these techniques is to create content specific PDF reports for the 300 plus general practices in Wales who are contributing data to the Secure Anonymised Information Linkage (SAIL) Databank.
 Objectives1). Create large number of automated PDF reports 2). Content specific to individual practice
 ApproachThis project aimed to facilitate the production of a large number of similar reports (tailored to specific recipients) by reducing the repetition and manual effort required. This was done by using a combination of packages in R to create scripts to automate the production of large numbers of tailored reports that capture the characteristics of each individual practice.
 ResultsThe output of this piece of work was a set of tailored reports for more than 300 Walsh general practices contributing data to the Secure Anonymised Information Linkage (SAIL) Databank up to 2015. The production of these reports was automated using a combination of R packages; the source code is flexible and can be applied to a range of contexts.
 ConclusionMethods described in this talk is highly efficient and easy to adapt to different context (e.g. automate documentation for metadata)

The Use of Residual Income Valuation Methods by U.S. Sell-Side Equity Analysts

ABSTRACTWe investigate the use and performance of residual income (RI) valuation methods by U.S. sell-side equity analysts in a small-sample manner wherein we extract the rich details of analysts' RI valuations from their PDF reports over the period 1998–2013. We observe that RI valuations are much rarer than discounted cash flow (DCF) valuations, and that different RI and DCF valuations are sometimes provided by the same analyst for the same firm in the same report. We find that while some analysts build RI models around net operating income (RNOA-RI) and others around net income (ROE-RI), RNOA-RI valuations are optimistic to the same degree as DCF valuations and contain RNOAs that increase to an economically unlikely terminal year median of 27 percent. In contrast, analysts' ROE-RI valuations are unbiased when done in tandem with a DCF valuation, as are the DCFs that accompany them, and contain ROEs that decline over the forecast horizon to a more plausible terminal year median of 17 percent. We conclude that analysts' use of ROE-RI methods can lead to more sophisticated forecasts of economic fundamentals and equity valuations, especially when used in tandem with DCF.JEL Classifications: G12; G17; G32.

P016 Overcoming electronic medical record interoperability challenges with a combined discrete data and encapsulated binary test results interface

Aim Our aim is to deeply integrate University of Nebraska Medical Center’s institutional electronic medical record (EMR), EPIC, and HLA Laboratory Management System, mTilda, in order to achieve: streamlined clinician order entry and resulting; a higher level of fidelity of discrete HLA data within the EMR in preparation for data intensive test types including Next-gen Sequencing; the automation of manual and error-prone processes by means of a data interface capable of receiving orders and sending test results programmatically. Methods Process maps of paper-based HLA laboratory functions and workflows were prepared and optimized by deploying a laboratory management information systems and enterprise interface platform (MIRTH) that is Health Level 7 (HL7) compliant. The HLA lab identified forty-nine (49) unique HLA test types to send via HL7 data interchange. Results The HL7 data format was extended to allow clinician-readable test results to be sent to the EMR as binary 64-bit encapsulated PDFs through the HL7 Order Message protocol. The HL7 Reference Information Model (RIM) was extended to include discrete HLA laboratory meta-data from test results. Conclusions Extending the HL7 data model allowed us to encode higher volume and fidelity of discrete HLA data into an HL7 results message interface for integration into the EMR database. As EMR vendors expand their data model to include HLA-specific database fields, it is increasingly important to send a combination of discrete HLA test results data and traditional well-formatted PDF reports to ensure that a patient’s longitudinal medical records include immunogenetic-relevant discrete data.

DeepDive

The dark data extraction or knowledge base construction (KBC) problem is to populate a SQL database with information from unstructured data sources including emails, webpages, and pdf reports. KBC is a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems. The key idea in DeepDive is that statistical inference and machine learning are key tools to attack classical data problems in extraction, cleaning, and integration in a unified and more effective manner. DeepDive programs are declarative in that one cannot write probabilistic inference algorithms; instead, one interacts by defining features or rules about the domain. A key reason for this design choice is to enable domain experts to build their own KBC systems. We present the applications, abstractions, and techniques of DeepDive employed to accelerate construction of KBC systems.

SU‐F‐T‐94: Plan2pdf ‐ a Software Tool for Automatic Plan Report for Philips Pinnacle TPS

Purpose:To implement an automatic electronic PDF plan reporting tool for Philips Pinnacle treatment planning system (TPS)Methods:An electronic treatment plan reporting software is developed by us to enable fully automatic PDF report from Pinnacle TPS to external EMR programs such as MOSAIQ. The tool is named “plan2pdf”. plan2pdf is implemented using Pinnacle scripts, Java and UNIX shell scripts, without any external program needed. plan2pdf supports full auto‐mode and manual mode reporting. In full auto‐mode, with a single mouse click, plan2pdf will generate a detailed Pinnacle plan report in PDF format, which includes customizable cover page, Pinnacle plan summary, orthogonal views through each plan POI and maximum dose point, DRR for each beam, serial transverse views captured throughout the dose grid at a user specified interval, DVH and scorecard windows. The final PDF report is also automatically bookmarked for each section above for convenient plan review. The final PDF report can either be saved on a user specified folder on Pinnacle, or it can be automatically exported to an EMR import folder via a user configured FTP service. In manual capture mode, plan2pdf allows users to capture any Pinnacle plan by full screen, individual window or rectangular ROI drawn on screen. Furthermore, to avoid possible patients’ plan mix‐up during auto‐mode reporting, a user conflict check feature is included in plan2pdf: it prompts user to wait if another patient is being exported by plan2pdf by another user.Results:plan2pdf is tested extensively and successfully at our institution consists of 5 centers, 15 dosimetrists and 10 physicists, running Pinnacle version 9.10 on Enterprise servers.Conclusion:plan2pdf provides a highly efficient, user friendly and clinical proven platform for all Philips Pinnacle users, to generate a detailed plan report in PDF format for external EMR systems.

Ecg-kit: a Matlab Toolbox for Cardiovascular Signal Processing

The electrocardiogram kit ( ecg-kit ) for Matlab is an application-programming interface (API) developed to provide users a common interface to access and process cardiovascular signals. In the current version, the toolbox supports several ECG recording formats, most of them used by the most popular databases, which allows access to more than 7 TB of information, stored in public databases such as those included in Physionet or the THEW project. The toolbox includes several algorithms frequently used in cardiovascular signal processing, such as heartbeat detectors and classifiers, pulse detectors for pulsatile signals and an ECG delineator. In addition, it provides a tool for manually reviewing and correcting the results produced by the automatic algorithms. The results obtained can be stored in a Matlab (.MAT) file for backup or subsequent processing, or used to create a PDF report.

The application of IQWorks image analysis software to quality assurance in MR imaging

Recent developments have made available free software tools that would expedite local quality assurance (QA) in magnetic resonance imaging (MRI), incorporating established methodology. IQWorks is a software package for digital Imaging and communication in medicine image analysis, which was found to have highly appropriate functionality. Key advantages are provided by the definition of an analysis process via an ‘analysis tree’, which facilitates user-implementation (without the need for further programming) of automated or partially automated analysis schemes incorporating testing methodology. Aspects of the analysis tree include automated region of interest positioning relative to the position of a detected edge, and production of results as PDF reports. In this study analysis trees were built to analyse the Eurospin test objects in order to assess signal to noise ratio, uniformity, ghosting, slice width, geometric linearity and spatial resolution. We aimed to examine the robustness and general applicability of IQWorks for MRI QA, by obtaining results using images from multiple scanners (various models) in routine use, and compared with results obtained from propriety manufacturers’ software and other image analysis software. The use of analysis trees in IQWorks was found to provide automated implementation of specified analysis protocols without reliance on manual placement of sampling regions of interest. Results obtained were highly congruent with those obtained using scanner console analysis tools. We conclude that IQWorks expedites development of local MRI QA incorporating nationally established methodology, and facilitates the implementation of QA procedures by staff without appropriate programming expertise. The analysis tree methodology would expedite implementation of revised protocols in new guidance documents.