Automated Data Management Research Articles

Data quality enhancement and knowledge discovery from relevant signals in acoustic emission

The increasing popularity of structural health monitoring has brought with it a growing need for automated data management and data analysis tools. Of great importance are filters that can systematically detect unwanted signals in acoustic emission datasets. This study presents a semi-supervised data mining scheme that detects data belonging to unfamiliar distributions. This type of outlier detection scheme is useful detecting the presence of new acoustic emission sources, given a training dataset of unwanted signals. In addition to classifying new observations (herein referred to as “outliers”) within a dataset, the scheme generates a decision tree that classifies sub-clusters within the outlier context set. The obtained tree can be interpreted as a series of characterization rules for newly-observed data, and they can potentially describe the basic structure of different modes within the outlier distribution. The data mining scheme is first validated on a synthetic dataset, and an attempt is made to confirm the algorithms’ ability to discriminate outlier acoustic emission sources from a controlled pencil-lead-break experiment. Finally, the scheme is applied to data from two fatigue crack-growth steel specimens, where it is shown that extracted rules can adequately describe crack-growth related acoustic emission sources while filtering out background “noise.” Results show promising performance in filter generation, thereby allowing analysts to extract, characterize, and focus only on meaningful signals.

Actionable Persistent Identifier Collections

Persistent Identifiers (PIDs) have lately received a lot of attention from scientific infrastructure projects and communities that aim to employ them for management of massive amounts of research data and metadata objects. Such usage scenarios, however, require additional facilities to enable automated data management with PIDs. In this article, we present a conceptual framework that is based on the idea of using common abstract data types (ADTs) in combination with PIDs. This provides a well-defined interface layer that abstracts from both underlying PID systems and higher-level applications. Our practical implementation is based on the Handle System, yet the fundamental concept of PID-based ADTs is transferable to other infrastructures, and it is well suited to achieve interoperability between them.

Development of an Automated Measurement System for Dilution Process and Spraying Amount of Disinfectant

Purpose: The objectives of this study were to develop an automated disinfectant dilution system, and an automated data management system for spraying amount for resolving uncertainty problem. Methods: Proper diluting rate was made by a controlled volume pump for liquid disinfectant and a screw conveyer pump for solid disinfectant. The water capacity of disinfecting system of 400 L was controlled by two water level sensors. The water quantity of water tank was controlled by the signals which were produced by the water level sensors. Signals were processed by Labview Programming, and ON/OFF of solenoid valve that was used for controlling water supplying to water tank, was controlled by SSR. The operating time of pumps for disinfectant was controlled quantitatively. A turbine flowmeter was used for development of automated measurement system for spraying amount of disinfectant. In order to save the flowmeter data and to control the spraying system, a multi-function data logger was used, and it was processed and saved in Excel file by a program developed in this study. Results: Labview 2010 was used for programming to control the automated measurement system for spraying amount of disinfectant. Results showed that the relationship between flowmeter value and time had a significant linear relationship such as 0.99 of <TEX>$R^2$</TEX>. Generally, 6.74 L/s of diluted disinfectant is sprayed for a vehicle passing through the disinfection system (about 15 seconds). Test results showed that average error between the measured spraying amount and the flowmeter data was 50 mL, and the range of error was 1.3%. Since the amount and time of spraying could be saved in real-time by using the spreadsheet files which could not be modified arbitrarily, it made possible to judge objectively whether the disinfection spraying was performed or not. Test results of spraying liquid and solid disinfectant showed that the errors between the measured discharge rate and the theoretical one were ranged within 3-4% for various dilution rates. Conclusions: The disinfection system developed would be working accurately. The automated spraying data base management system satisfied the purpose of this study. The automated dilution process system developed in this study could discharge liquid and solid disinfectant with accurate dilution rate, relatively.

MapReduce with communication overlap (MaRCO)

MapReduce is a programming model from Google for cluster-based computing in domains such as search engines, machine learning, and data mining. MapReduce provides automatic data management and fault tolerance to improve programmability of clusters. MapReduce’s execution model includes an all-map-to-all-reduce communication, called the shuffle, across the network bisection. Some MapReductions move large amounts of data (e.g., as much as the input data), stressing the bisection bandwidth and introducing significant runtime overhead. Optimizing such shuffle-heavy MapReductions is important because (1) they include key applications (e.g., inverted indexing for search engines and data clustering for machine learning) and (2) they run longer than shuffle-light MapReductions (e.g., 5x longer). In MapReduce, the asynchronous nature of the shuffle results in some overlap between the shuffle and map. Unfortunately, this overlap is insufficient in shuffle-heavy MapReductions. We propose MapReduce with communication overlap (MaRCO) to achieve nearly full overlap via the novel idea of including reduce in the overlap. While MapReduce lazily performs reduce computation only after receiving all the map data, MaRCO employs eager reduce to process partial data from some map tasks while overlapping with other map tasks’ communication. MaRCO’s approach of hiding the latency of the inevitably high shuffle volume of shuffle-heavy MapReductions is fundamental for achieving performance. We implement MaRCO in Hadoop’s MapReduce and show that on a 128-node Amazon EC2 cluster, MaRCO achieves 23% average speed-up over Hadoop for shuffle-heavy MapReductions.

ADAM: automated data management for research datasets

Existing repositories for experimental datasets typically capture snapshots of data acquired using a single experimental technique and often require manual population and continual curation. We present a storage system for heterogeneous research data that performs dynamic automated indexing to provide powerful search, discovery and collaboration features without the restrictions of a structured repository. ADAM is able to index many commonly used file formats generated by laboratory assays and therefore offers specific advantages to the experimental biology community. However, it is not domain specific and can promote sharing and re-use of working data across scientific disciplines.Availability and implementation: ADAM is implemented using Java and supported on Linux. It is open source under the GNU General Public License v3.0. Installation instructions, binary code, a demo system and virtual machine image and are available at http://www.imperial.ac.uk/bioinfsupport/resources/software/adam. Contact: m.woodbridge@imperial.ac.uk

PyXNAT: XNAT in Python

As neuroimaging databases grow in size and complexity, the time researchers spend investigating and managing the data increases to the expense of data analysis. As a result, investigators rely more and more heavily on scripting using high-level languages to automate data management and processing tasks. For this, a structured and programmatic access to the data store is necessary. Web services are a first step toward this goal. They however lack in functionality and ease of use because they provide only low-level interfaces to databases. We introduce here PyXNAT, a Python module that interacts with The Extensible Neuroimaging Archive Toolkit (XNAT) through native Python calls across multiple operating systems. The choice of Python enables PyXNAT to expose the XNAT Web Services and unify their features with a higher level and more expressive language. PyXNAT provides XNAT users direct access to all the scientific packages in Python. Finally PyXNAT aims to be efficient and easy to use, both as a back-end library to build XNAT clients and as an alternative front-end from the command line.

Platform for structural health monitoring of buildings utilizing smart sensors and advanced diagnosis tools

A structural health monitoring (SHM) system monitors the condition of structures such as tall buildings and promptly and quantitatively evaluates their deterioration and damage due to natural hazards and aging. The system is under keen attention for prolonging the structures' lives and increasing their durability while keeping them in healthy condition. Several SHM systems have been proposed and tested for experiments or for real structures. However, most of these systems are customized to particular structures, so that they are not sufficiently scalable. Moreover, there is no standard data format for the data they gather. Thus, it is difficult to treat the data stochastically. We have been developing smart sensor units with data acquisition and data management capability. Many data analysis tools utilizing system identification and an easy-to-use user interface for SHM have been also studied. The SHM platform consists of these tools to evaluate the soundness of building structures, the server system to publish the diagnosis results to the users, and the smart sensor units for automatic data management. Moreover, as it has a flexible data model and an automatic data entry system for acquired data, the system can be applied to any building structures for treating them from a stochastic viewpoint. In addition, the smart sensor unit we developed shortens the configuration process and enables us to control the measurement configuration through a web interface. This study presents the key features of the SHM platform. Copyright © 2010 John Wiley & Sons, Ltd.

DFBIdb: A Software Package for Neuroimaging Data Management

We present DFBIdb: a suite of tools for efficient management of neuroimaging project data. Specifically, DFBIdb was designed to allow users to quickly perform routine management tasks of sorting, archiving, exploring, exporting and organising raw data. DFBIdb was implemented as a collection of Python scripts that maintain a project-based, centralised database that is based on the XCEDE 2 data model. Project data is imported from a filesystem hierarchy of raw files, which is an often-used convention of imaging devices, using a single script that catalogues meta-data into a modified XCEDE 2 data model. During the import process data are reversibly anonymised, archived and compressed. The import script was designed to support multiple file formats and features an extensible framework that can be adapted to novel file formats. An ACL-based security model, with accompanying graphical management tools, was implemented to provide a straightforward method to restrict access to raw and meta-data. Graphical user interfaces are provided for data exploration. DFBIdb includes facilities to export, convert and organise customisable subsets of project data according to user-specified criteria. The command-line interface was implemented to allow users to incorporate database commands into more complex scripts that may be utilised to automate data management tasks. By using DFBIdb, neuroimaging laboratories will be able to perform routine data management tasks in an efficient manner.

Ms_lims, a simple yet powerful open source laboratory information management system for MS‐driven proteomics

MS-based proteomics produces large amounts of mass spectra that require processing, identification and possibly quantification before interpretation can be undertaken. High-throughput studies require automation of these various steps, and management of the data in association with the results obtained. We here present ms_lims (http://genesis.UGent.be/ms_lims), a freely available, open-source system based on a central database to automate data management and processing in MS-driven proteomics analyses.

Preservation Archiving Storage Technologies

This poster presentation is intended to provide a recommendation for standards implementation and best practices for a viable, cost effective, and reliable long term archiving storage system. This solution is based on a combination of storage technologies to provide long-term viability.We will examine operating system platforms and filesystems for archive storage systems. We will examine the performance and archive capabilities of the filesystems including ZFS, Lustre, and SAM/QFS. We will also discuss the Hierarchical Storage System, or HSM, as a key software element of the archive. The HSM provides one of the key components that contributes to reliability by through data integrity checks and automated file migration. The HSM provides the ability to automate making multiples copies of files, auditing files for errors based on checksum, rejecting bad copies of files and making new copies based on the results of those audits. The HSM also provides the ability to read in an older file format and write-out a new file format thus migrating the format and application information required to ensure archival integrity of the stored content. The automation of these functions provides for improved performance and reduced operating costs.Oracle's Sun StorageTek Storage Archive Manager (SAM) software provides the core functionality of the recommended Archive Storage Architecture. SAM provides policy based data classification and placement across a multitude of storage devices from high speed disk, low cost disk, or tape. SAM also simplifies data management by providing centralized meta-data. SAM is a self-protecting file system with continuous file integrity checks.The digital content archive provides the content repository (or digital vault) within Sun's award winning Digital Asset Management Reference Architecture (DAM RA). DAM RA enables digital workflow and the content archive provides permanent access to digital content files. With SAM software, the files are stored, tracked, and retrieved based on the archival requirements. Files are seamlessly and transparently available to other services. SAM software creates virtually limitless capacity. Its scalability allows for continual growth throughout the archive with support for all data types. The policy based SAM software stores and manages data for compliance and noncompliance archives using a tiered storage approach with integrated disk and tape into a seamless storage solution, SAM software simplifies the archive storage. Allows you to automate data management policies based on file attributes. You can manage data according to the storage and access requirements of each user on the system and decide how data is grouped, copied, and accessed based on the needs of the application and the users. Helps you maximize return on investments by storing data on the media type appropriate for the life cycle of the data and simplifying system administration.

Inpatient glucose control: a glycemic survey of 126 U.S. hospitals

Despite increased awareness of the value of treating inpatient hyperglycemia, little is known about glucose control in U.S. hospitals. The Remote Automated Laboratory System-Plus (RALS-Plus Medical Automation Systems, Charlottesville, VA) was used to extract inpatient point-of-care bedside glucose (POC-BG) tests from 126 hospitals for the period January to December 2007. Patient-day-weighted mean POC-BG and hypoglycemia/hyperglycemia rates were calculated for intensive care unit (ICU) and non-ICU areas. The relationship of POC-BG levels with hospital characteristics was determined. A total of 12,559,305 POC-BG measurements were analyzed: 2,935,167 from the ICU and 9,624,138 from the non-ICU. Patient-day-weighted mean POC-BG was 165 mg/dL for ICU and 166 mg/dL for non-ICU. Hospital hyperglycemia (>180 mg/dL) prevalence was 46.0% for ICU and 31.7% for non-ICU. Hospital hypoglycemia (<70 mg/dL) prevalence was low at 10.1% for ICU and 3.5% for non-ICU. For ICU and non-ICU there was a significant relationship between number of beds and patient-day-weighted mean POC-BG levels, with larger hospitals (> or = 400 beds) having lower patient-day weighted mean POC-BG per patient day than smaller hospitals (<200 beds, P < 0.001). Rural hospitals had higher POC-BG levels compared to urban and academic hospitals (P < 0.05), and hospitals in the West had the lowest values. POC-BG data captured through automated data management software can support hospital efforts to monitor the status of inpatient glycemic control. From these data, hospital hyperglycemia is common, hypoglycemia prevalence is low, and POC-BG levels vary by hospital characteristics. Increased hospital participation in data collection and reporting may facilitate the creation of a national benchmarking process for the development of best practices and improved inpatient hyperglycemia management.

Automation in an addiction treatment research clinic: Computerised contingency management, ecological momentary assessment and a protocol workflow system

A challenge in treatment research is the necessity of adhering to protocol and regulatory strictures while maintaining flexibility to meet patients' treatment needs and to accommodate variations among protocols. Another challenge is the acquisition of large amounts of data in an occasionally hectic environment, along with the provision of seamless methods for exporting, mining and querying the data. We have automated several major functions of our outpatient treatment research clinic for studies in drug abuse and dependence. Here we describe three such specialised applications: the Automated Contingency Management (ACM) system for the delivery of behavioural interventions, the transactional electronic diary (TED) system for the management of behavioural assessments and the Protocol Workflow System (PWS) for computerised workflow automation and guidance of each participant's daily clinic activities. These modules are integrated into our larger information system to enable data sharing in real time among authorised staff. ACM and the TED have each permitted us to conduct research that was not previously possible. In addition, the time to data analysis at the end of each study is substantially shorter. With the implementation of the PWS, we have been able to manage a research clinic with an 80 patient capacity, having an annual average of 18,000 patient visits and 7300 urine collections with a research staff of five. Finally, automated data management has considerably enhanced our ability to monitor and summarise participant safety data for research oversight. When developed in consultation with end users, automation in treatment research clinics can enable more efficient operations, better communication among staff and expansions in research methods.

Inpatient Point-of-Care Bedside Glucose Testing: Preliminary Data on Use of Connectivity Informatics to Measure Hospital Glycemic Control

Point-of-care (POC) bedside glucose (BG) testing and timely evaluation of its effectiveness are important components of hospital inpatient glycemic control programs. We describe a new technology to evaluate inpatient POC-BG testing and report preliminary results of inpatient glycemic control from 10 U.S. hospitals. We used the Remote Automated Laboratory System RALS-Tight Glycemic Control Module (TGCM) (Medical Automation Systems, Charlottesville, VA) connected to the RALS-Plus to extract and analyze inpatient POC-BG tests from 10 U.S. hospitals for a 3-month period. POC-BG measurements were evaluated in aggregate from all 10 facilities for intensive care unit (ICU), non-ICU, and ICU + non-ICU combined. A total of 742,154 POC-BGs were analyzed. The combined (ICU + non-ICU) mean POC-BG was 159 mg/dL, compared with 146 mg/dL for the ICU and 164 mg/dL for non-ICU. The proportion of hypoglycemic values (<70 mg/dL) was low at 4%, but the percentage of measurements that would be considered hyperglycemic (>180 mg/dL) was high, with more than 30% of values in the non-ICU and 20% in the ICU being elevated. POC-BG data can be captured through automated data management software and can support hospital efforts to evaluate and monitor the status of inpatient glycemic control. These preliminary data suggest that there is a need to conduct broad-based efforts to improve inpatient glucose management. Increasing hospital participation in data collection has the potential to create a national benchmarking process for the development of best practices and improved inpatient hyperglycemia management.

Algorithms and tools for analysis and management of mass spectrometry data

Mass spectrometry (MS) is a technique that is used for biological studies. It consists in associating a spectrum to a biological sample. A spectrum consists of couples of values (intensity, m/z), where intensity measures the abundance of biomolecules (as proteins) with a mass-to-charge ratio (m/z) present in the originating sample. In proteomics experiments, MS spectra are used to identify pattern expressions in clinical samples that may be responsible of diseases. Recently, to improve the identification of peptides/proteins related to patterns, MS/MS process is used, consisting in performing cascade of mass spectrometric analysis on selected peaks. Latter technique has been demonstrated to improve the identification and quantification of proteins/peptide in samples. Nevertheless, MS analysis deals with a huge amount of data, often affected by noises, thus requiring automatic data management systems. Tools have been developed and most of the time furnished with the instruments allowing: (i) spectra analysis and visualization, (ii) pattern recognition, (iii) protein databases querying, (iv) peptides/proteins quantification and identification. Currently most of the tools supporting such phases need to be optimized to improve the protein (and their functionalities) identification processes. In this article we survey on applications supporting spectrometrists and biologists in obtaining information from biological samples, analyzing available software for different phases. We consider different mass spectrometry techniques, and thus different requirements. We focus on tools for (i) data preprocessing, allowing to prepare results obtained from spectrometers to be analyzed; (ii) spectra analysis, representation and mining, aimed to identify common and/or hidden patterns in spectra sets or in classifying data; (iii) databases querying to identify peptides; and (iv) improving and boosting the identification and quantification of selected peaks. We trace some open problems and report on requirements that represent new challenges for bioinformatics.

OmniStore: Automating data management in a personal system comprising several portable devices

The ever growing amount of data generated and consumed on the move using portable devices gives rise to serious data management issues. The fact that each person may own and is likely to carry several such devices further aggravates this problem. On the other hand, new opportunities for achieving cleverer device interaction emerge due to the increased wireless networking capabilities of wearable and portable devices. This paper introduces OmniStore, a system that combines portable devices and infrastructure-based services to relieve the user from explicit and time consuming file management tasks. Our approach is to let devices communicate with each other as well as with a repository service, in an opportunistic and asynchronous fashion, to perform a variety of file movement and copying actions behind the scenes, which would have typically required considerable explicit user interaction.

Validation of Stroke Work and Ventricular Arterial Coupling as Markers of Cardiovascular Performance during Resuscitation

Resuscitation regimens based on stroke work index (SWI) and ventricular-arterial coupling (VAC) are controversial. The Signal Interpretation and Monitoring (SIMON) system continuously collects and stores physiologic intensive care unit (ICU) bedside data at 3- to 5-second intervals. The purpose of this study was to demonstrate the capabilities of a completely automated data management system by further evaluating SWI-based resuscitation. This study was a retrospective review of all severely injured patients requiring a pulmonary artery catheter (PAC) for acute postinjury resuscitation. Patients with a severe head injury were excluded. Hemodynamic (HD) data (21 million datapoints) were densely acquired and archived by SIMON. Mean values of HD variables were compared between survivors and nonsurvivors. Receiver operator characteristic (ROC) curves were constructed for HD variables. Threshold values which maximized sensitivity and specificity were determined. Eighty-eight patients over a 19-month time period met criteria and were included in the analysis. SWI was significantly greater in survivors versus nonsurvivors (4421 +/- 1278 versus 3163 +/- 1066 mm Hg . mL/m, p = 0.0008). VAC was quantified by the ratio (RATIO) of afterload (Ea) to contractility (Ees). RATIO (Ea/Ees) in survivors was significantly better than in nonsurvivors (1.9 +/- 1.1 vs. 2.9 +/- 1.0, p = 0.002). ROC curves identified threshold values of 3250 mm Hg x mL/m for SWI and 2.1 for RATIO (AUC = 0.78 and 0.82, respectively). Previous work demonstrating the use of SWI and VAC as resuscitation guidelines was supported through the use of a powerful ICU data management system (SIMON). The emergence of these "new vital signs" may change the way injured patients are evaluated and resuscitated in the ICU.

Using Omaha System Data to Improve the Clinical Education Experiences of Nursing Students: The University of Cincinnati Project

Changes in the health care delivery system suggest that graduates must be capable of working in community settings and using automated data management tools that include standardized assessments, diagnoses, interventions, and outcomes. Faculty at the College of Nursing, University of Cincinnati (UC) responded to this need by including the Omaha System in an automated format (Nightingale Tracker) in the community clinical experiences of their RN to bachelor of science of nursing students. The Omaha System was used in the traditional manner to plan and document the care for homebound senior clients. Visit data gathered from the documentation of these students were also used in a secondary manner to compare faculty clinical education instruction with the care needs of this vulnerable population. This article describes how UC faculty used the Omaha System in a highly focused way to analyze the best pedagogical practices needed for community-focused nursing education.

A FREEWARE PACKAGE FOR THE ANALYSIS AND INTERPRETATION OF COMMON-OFFSET GROUND PROBING RADAR DATA, BASED ON GENERAL PURPOSE COMPUTING ENGINES

The Ground Probing Radar (GPR) has become an invaluable means of exploring shallow structures for geoscientific, engineering environmental and archaeological work. At the same time, GPR analysis software is mostly proprietary and expensive. The academic freeware community has been slow to react the limited free software is usually highly focused and generally unorganized. Herein we report the beginning of an attempt to remedy this situation with a cross-platform freeware analysis and interpretation package, which can also be expandable and customizable to the requirements of a particular user with relatively little programming effort. Almost ideal platforms for the development of such a kind of software are general purpose computing engines, such as MATLAB ™ and/or OCTAVE. These provide two very similar, cross-platform and complete environments for the development of advanced analysis software. We have designed a two-layered software system. The Bottom Layer comprises a set of self-contained and self-documented functions to handle, visualize, process and interpret GPR data and is expandable with addition of a user's own functions. The Top Layer organizes these functions, automating data management and streamlining the flow of work by means of a GU Interface. At the present stage of development, this GPR analysis offers a decent and in many respects advanced means of treating and interpreting common-offset data; future releases may even become competitive, if maintained and developed by collective effort along the spirit of the GNU project.

Information in medical decision making: how consistent is our management?

The use of outcomes data in clinical environments requires a correspondingly greater variety of information used in decision making, the measurement of quality, and clinical performance. As information becomes integral in the decision-making process, trustworthy decision support data are required. Using data from a national census of certified health information managers, variation in automated data quality management practices was examined. Relatively low overall adoption of automated data management exists in health care organizations, with significant geographic and practice setting variation. Nonuniform regional adoption of computerized data management exists, despite national mandates that promote and in some cases require uniform adoption. Overall, a significant number of respondents (42.7%) indicated that they had not adopted policies and procedures to direct the timeliness of data capture, with 57.3% having adopted such practices. The inconsistency of patient data policy suggests that provider organizations do not use uniform information management methods, despite growing federal mandates to do so.

Managing the Volume and Maximising its Value - The Benefits of an Automated Seismic Data Management System

Over the past 10 years there has been an exponential growth in the amount of seismic data being acquired, processed and interpreted within the Australian region. The data management techniques used to handle this growing volume however, continue to use technology from the 1970?s. Woodside identified this as a major stumbling block in its drive to remain a world class exploration company. The manual system in use was close to breaking point and it was concluded that an external automated data management system was needed. The system we have implemented is called SeisLIB and we hope to integrate it into a national on-line data repository. The major benefits of this system are that it reduces the overall management costs, improves work practices, greatly improves the quality of the archived data and helps to unlock its interpretative potential. SeisLIB is a comprehensive data management system where all navigation, poststack seismic data and seismic support data is stored online, while the prestack data is catalogued and stored offline. There is direct access from the workstation to SeisLIB via a fibre optic link. GIS maps can be built and data delivered directly to the workstation in a number of formats ready for loading. It is cost effective, flexible, quick and accurate.