Environmental Data Management Research Articles

Update on the ASDEX Upgrade data acquisition and data management environment

It has been a while since it had been reported on the status of ASDEX Upgrade data acquisition (DAQ) and data management environment. An update on changes, expansions, and enhancements applied in the last years will be given.The acquired amount of data per shot increased from 4GiB to 40GiB in eight years.Network, storage, and archive challenges have been managed by stepwise improvements.New DAQ techniques have been introduced to replace outdated technologies.Real-time diagnostics speed-up data provisioning and contribute to feedback control.Information technology applied to ASDEX Upgrade is under permanent change. Recent and future steps are outlined.

Developing a Data Identifier Taxonomy

As the amount of research data management is growing, the use of identity metadata for discovering, linking, and citing research data is growing too. To support the awareness of different identifier systems and the comparison and selection of an identifier for a particular data management environment, there is need for a knowledge base. This article contributes to that goal and analyzes the data management and related literatures to develop a data identifier taxonomy. The taxonomy includes four categories (domain, entity types, activities, and quality dimensions). In addition, the article describes 14 identifiers referenced in the literature and analyzes them along the taxonomy.

Towards a Service-Based Framework for Environmental Data Processing

Scientists are confronted with significant data management problems due to the huge volume and high complexity of environmental data. An important aspect of environmental data management is that data, needed for a process, are not always in the adequate format. In this contribution, we analyze environmental data structure, and model this data using a semantic-based method. Using this model, we design and implement a framework based on Web services for transformation between massive environmental text-based data and relational databases. We present a mapping model for environmental data transformation to be used in the scenario devoted to the methodology for development of stochastic models for prediction of environmental parameters by application of Gaussian processes.

Data visualization and analysis within a Hydrologic Information System: Integrating with the R statistical computing environment

This paper presents a prototype software system for visualization and analysis of hydrologic data that provides interoperability between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) and the R statistical computing environment. By linking these two systems within a single desktop software application, an integrated hydrologic data management and analysis environment has been created that simplifies the process used by scientists and engineers to find, access, organize, and analyze the hydrologic data needed in modeling and managing hydrologic and environmental systems. The implementation of this work is a software plug-in for the CUAHSI HIS HydroDesktop software system called HydroR. We describe the design, graphical user interface, and implementation of the HydroR plug-in. An example application of HydroR is presented in which total suspended solids concentrations are modeled for the Little Bear River using a regression developed from turbidity and total suspended solids observations downloaded from the CUAHSI HIS using HydroDesktop. Finally, we conclude with a summary of our experience in developing interoperability between HIS and R and suggest future developments that can extend the capabilities we have developed.

RegaDB: community-driven data management and analysis for infectious diseases

Summary: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface.Availability and implementation: Source code, binaries and documentation are available on http://rega.kuleuven.be/cev/regadb. RegaDB is written in the Java programming language, using a web-service-oriented architecture.Contact: pieter.libin@rega.kuleuven.be

빅 데이터를 이용한 소셜 미디어 분석 기법의 활용

The analysis method using Big Data has evolved based on the Big data Management Technology. There are quite a few researching institutions anticipating new era in data analysis using Big Data and IT vendors has been sided with them launching standardized technologies for Big Data management technologies. Big Data is also affected by improvements of IT gadgets IT environment. Foreran by social media, analyzing method of unstructured data is being developed focusing on diversity of analyzing method, anticipation and optimization. In the past, data analyzing methods were confined to the optimization of structured data through data mining, OLAP, statics analysis. This data analysis was solely used for decision making for Chief Officers. In the new era of data analysis, however, are evolutions in various aspects of technologies; the diversity in analyzing method using new paradigm and the new data analysis experts and so forth. In addition, new patterns of data analysis will be found with the development of high performance computing environment and Big Data management techniques. Accordingly, this paper is dedicated to define the possible analyzing method of social media using Big Data. this paper is proposed practical use analysis for social media analysis through data mining analysis methodology.

Exploiting compression and approximation paradigms for effective and efficient online analytical processing over sensor network readings in data grid environments

SUMMARYAggregate queries are useful tools in the context of sensor network‐based systems as they retrieve knowledge from huge amounts of summarized readings to be exploited for knowledge discovery purposes. Actually, data representation and query models are problematic issues for managing sensor network data, because streams produced by sensors are theoretically unbounded. In this paper, we present a Grid framework, called SensorGrid, on the basis of data compression and approximation paradigms, which allows us to provide approximate answers to aggregate queries on summarized sensor network data. These queries are the basis for achieving Online Analytical Processing (OLAP) over sensor network readings in Data Grid environments, with both effectiveness and efficiency. We also present our experience in the context of a real‐life system focused on the management of environmental sensor network data. Another contribution of our research is represented by the extensive experimental evaluation and analysis of SensorGrid, which, in more details, focuses on two main classes of aggregate range queries over sensor readings, namely, (i) the window queries, which apply an SQL aggregation operator over a fixed window over the reading stream produced by the sensor network, and (ii) the continuous queries, which instead consider a ‘moving’ window and produce as output a stream of answers. Both classes of queries are extremely useful to extract summarized knowledge to be exploited by OLAP‐like analysis tools over sensor network data. The experimental results, conducted on both synthetic and real‐life data sets, clearly confirm the benefits deriving from embedding data compression and approximation paradigms into Grid‐based sensor network data‐intensive management systems.Copyright © 2013 John Wiley & Sons, Ltd.

OMT-G Modeling and Cloud Implementation of a Reference Database of Addressing in Morocco

A reference database of addressing is an important resource for urban applications. The efficiency of an addressing system depends on both data quality and technical architecture. Data must respect a standard model that is flexible to meet different cases in the field. The technical architecture should be service oriented to offer a shared resource for multiple users and applications. This paper is to develop an addressing model for Morocco that extends Davis’s and Fonseca’s model presented in their work on the certainty of locations produced by an address geocoding system. We discuss the addressing data dictionary and acquisition plan in Morocco, revealing a diversified data management environment, characterized by multiple sources and actors. As a novelty in the field of GIS, we establish our technical architecture around cloud computing, according Service Oriented Application (SOA) standards. Our approach is based on the three pillars of cloud computing which are Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a service (IaaS).

ICT Methodologies and Spatial Data Infrastructure for Air Quality Information Management

Applications for atmospheric pollution monitoring and modelling are fundamental tools to develop environmental policies oriented to control and possibly reduce the impact of pollution on ecosystems and human health. However, differences among monitoring systems and data availability are an important limitation in environmental management. In order to standardize protocols and procedures, the EU directives (e.g., INSPIRE, Air Quality Directive) and international programs (i.e., GEOSS) have oriented the community towards developing interoperable standardized systems that assure real time data analysis and dissemination. To coordinate national earth and cross-disciplinary systems for promoting GEOSS and to support the INSPIRE implementation, the GIIDA (Integrated and Interoperable Management of Environmental Data) project was launched. Within GIIDA the working group on air quality was instructed to develop an interoperable system for air quality information management. The system is based on open-source tools compliant with standards and designed to develop a Spatial Data Infrastructure (SDI), which will contain information collected at ground-based as well as at off-shore monitoring sites. This infrastructure aims to store, mine and visualize information. The SDI will support modelling activities and environmental assessments for different scenarios in order to evaluate the impact of atmospheric pollution ecosystems and human health. In this paper we discuss the application of the IT framework and methodologies for the SDI development, which was built on open-source components: Postgis for data storage, Geoserver to export services and GeoNetwork for metadata generation. Finally, we describe the ICT pluggable framework GeoInt, which was developed to simplify the SDI component's complexity for end users by supporting data input from different sources, as well as metadata management.

Bioinformatics resource manager v2.3: an integrated software environment for systems biology with microRNA and cross-species analysis tools

BackgroundMicroRNAs (miRNAs) are noncoding RNAs that direct post-transcriptional regulation of protein coding genes. Recent studies have shown miRNAs are important for controlling many biological processes, including nervous system development, and are highly conserved across species. Given their importance, computational tools are necessary for analysis, interpretation and integration of high-throughput (HTP) miRNA data in an increasing number of model species. The Bioinformatics Resource Manager (BRM) v2.3 is a software environment for data management, mining, integration and functional annotation of HTP biological data. In this study, we report recent updates to BRM for miRNA data analysis and cross-species comparisons across datasets.ResultsBRM v2.3 has the capability to query predicted miRNA targets from multiple databases, retrieve potential regulatory miRNAs for known genes, integrate experimentally derived miRNA and mRNA datasets, perform ortholog mapping across species, and retrieve annotation and cross-reference identifiers for an expanded number of species. Here we use BRM to show that developmental exposure of zebrafish to 30 uM nicotine from 6–48 hours post fertilization (hpf) results in behavioral hyperactivity in larval zebrafish and alteration of putative miRNA gene targets in whole embryos at developmental stages that encompass early neurogenesis. We show typical workflows for using BRM to integrate experimental zebrafish miRNA and mRNA microarray datasets with example retrievals for zebrafish, including pathway annotation and mapping to human ortholog. Functional analysis of differentially regulated (p<0.05) gene targets in BRM indicates that nicotine exposure disrupts genes involved in neurogenesis, possibly through misregulation of nicotine-sensitive miRNAs.ConclusionsBRM provides the ability to mine complex data for identification of candidate miRNAs or pathways that drive phenotypic outcome and, therefore, is a useful hypothesis generation tool for systems biology. The miRNA workflow in BRM allows for efficient processing of multiple miRNA and mRNA datasets in a single software environment with the added capability to interact with public data sources and visual analytic tools for HTP data analysis at a systems level. BRM is developed using Java™ and other open-source technologies for free distribution (http://www.sysbio.org/dataresources/brm.stm).

Design of Warehouse Environmental Monitoring System Based on WSN and Fuzzy-PID Strategy

In order to solve the issues existing in the current warehouse environmental monitoring systems, such as the complex wiring, the node power consumption, not flexible deployment and inconvenience management and so on, a warehouse environment parameters monitoring system based on fuzzy-PID strategy is designed. The system uses CC2430 as the core to develop wireless sensor nodes which completing real-time monitoring of warehouse environment temperature and humidity, uses ZigBee technology to achieve networking of wireless sensors and the automatic aggregation of monitoring data, uses fuzzy PID control technology to achieve the regulation of warehouse temperature and humidity and uses embedded database management model to achieve the sensor nodes management, environmental data management and early warning capabilities. The practical monitoring results have shown that this system has the feature of low power consumption, network flexibility, strong extensibility, friendly interface etc., which can better meet the application demands of warehouse environment parameters monitoring.

TOLKIN--Tree of Life Knowledge and Information Network: filling a gap for collaborative research in biological systematics.

The development of biological informatics infrastructure capable of supporting growing data management and analysis environments is an increasing need within the systematics biology community. Although significant progress has been made in recent years on developing new algorithms and tools for analyzing and visualizing large phylogenetic data and trees, implementation of these resources is often carried out by bioinformatics experts, using one-off scripts. Therefore, a gap exists in providing data management support for a large set of non-technical users. The TOLKIN project (Tree of Life Knowledge and Information Network) addresses this need by supporting capabilities to manage, integrate, and provide public access to molecular, morphological, and biocollections data and research outcomes through a collaborative, web application. This data management framework allows aggregation and import of sequences, underlying documentation about their source, including vouchers, tissues, and DNA extraction. It combines features of LIMS and workflow environments by supporting management at the level of individual observations, sequences, and specimens, as well as assembly and versioning of data sets used in phylogenetic inference. As a web application, the system provides multi-user support that obviates current practices of sharing data sets as files or spreadsheets via email.

Harmonization and Interoperability of EU Environmental Information and Services

Sharing resources across multiple information communities requires new technologies. This article highlights activities and best practices of some EU projects as they relate to linked open data. This paper also provides a better understanding of the issues related to semantic interoperability in the context of environmental data management in the EU, and of the efforts to address these problems. The paper also provide the EU policy context and highlight solutions adopted in the Infrastructure for Spatial Information in the European Community (INSPIRE) for enabling semantic interoperability, as well as their relevance to linked and open data.

Database Management and Mapping of Secondary Education Infrastructure in Sabon-Gari and Zaria Local Governments, Kaduna State, NigERIA

The learning conditions in schools are alarming with lack of teaching materials, overcrowded classrooms and generally run-down condition of many of the school buildings. In spite of the relative availability of data, educational data in Nigeria still suffers from the following issues relating to the quality and completeness: erroneously recorded and reported data, inconsistent coding systems for information on schools and teachers, prolonged periods between data collection and data release, poor school record keeping. The aim of this research was to develop a GIS database for private and public secondary schools in the study area so as to enable effective and efficient planning and management of these schools. The aim of the research was achieved through identification of private and public secondary schools in the study area, mapping the secondary schools in the study area, with some basic facilities, generating the attribute data of the public and private sec- ondary schools and finally, creation of GIS database for schools. The attribute data was obtained through the administration of questionnaires to the schools. A satellite image of the study area was obtained using Google earth to derive the base map through the digitizing process. The coordinates of each school were obtained using a hand-held GPS receiver to geo code the schools on the base map. Finally, a GIS database was therefore created and the spatial and attribute data encoded and analysis carried out using Arc GIS 9.2 software. The result of the database provides the users with a working environment for data management and also allows efficiency query of information needed for school management.

Based on the Condition of the Equipment Management

With the edge of the application and key technologies, the management ideas that are the integration of equipment development and production stage of technology status management put forward and combed this project, providing comprehensive data management environment, providing rapid decision-making, timely information and scientific basis

Research and Design of Embedded Wireless Meal Ordering System Based on SQLite

The paper describes features and internal architecture and developing method of SQLite. And then it gives a design and program of meal ordering system. The system realizes the information interaction among the users and embedded devices with SQLite as database system. The embedded database SQLite manages the data and achieves wireless communication by using Bluetooth. A system program based on Qt/Embedded and Linux drivers realizes the local management of environmental data.

Implementation of a WebGIS-Based Geological Environment Data Management Information System for Guangyuan City

Geological hazard is one of the major natural hazards world facing now, In order to prevent and predict geological hazard, techniques for geological hazard management, assessment and prediction must be developed for the prevention of geological hazards. In this paper, a geological environment data management information system (GEMIS) was implemented based on WebGIS. GEMIS is designed for management geological environment data, as well as geological hazard point. GEMIS has achieved the functions for geological environmental data displaying, querying and analyzing, and many users can share the same geological environmental data due to the B/S frame work. The use of GEMIS can improve and enhance the rapid identification of receptor sites and fate of geological hazards, improving response time and mitigation strategies in the event of a geological hazard. GEMIS also provides each incident responder with access to the same maps and geological hazard information that other responders possess.

Towards a Generic Security Framework for Cloud Data Management Environments

Providing an adequate security level in Cloud Environments is currently an extremely active research area. More specifically, malicious behaviors targeting large-scale Cloud data repositories (e.g., Denial of Service attacks) may drastically degrade the overall performance of such systems and cannot be detected by typical authentication mechanisms. This article proposes a generic security management framework allowing providers of Cloud data management systems to define and enforce complex security policies. This security framework is designed to detect and stop a large array of attacks defined through an expressive policy description language and to be easily interfaced with various data management systems. The authors show that they can efficiently protect a data storage system by evaluating the security framework on top of the BlobSeer data management platform. The authors evaluate the benefits of preventing a DoS attack targeted towards BlobSeer through experiments performed on the Grid’5000 testbed.

Effort Tracking Metrics Provide Data for Optimal Budgeting and Workload Management in Therapeutic Cancer Clinical Trials

Clinical trials operations struggle to achieve optimal distribution of workload in a dynamic data management and regulatory environment, and to achieve adequate cost recovery for personnel costs. The University of Michigan Comprehensive Cancer Center developed and implemented an effort tracking application to quantify data management and regulatory workload to more effectively assess and allocate work while improving charge capture. Staff recorded how much time they spend each day performing specific study-related and general office tasks. Aggregated data on staff use of the application from 2006 through 2009 were analyzed to gain a better understanding of what trial characteristics require the most data management and regulatory effort. Analysis revealed 4 major determinants of staff effort: 1) study volume (actual accrual), 2) study accrual rate, 3) study enrollment status, and 4) study sponsor type. Effort tracking also confirms that trials that accrued at a faster rate used fewer resources on a per-patient basis than slow-accruing trials. In general, industry-sponsored trials required the most data management and regulatory support, outweighing other sponsor types. Although it is widely assumed that most data management efforts are expended while a trial is actively accruing, the authors learned that 25% to 30% of a data manager's effort is expended while the study is either not yet open or closed to enrollment. Through the use of a data-driven effort tracking tool, clinical research operations can more efficiently allocate workload and ensure that study budgets are negotiated to adequately cover study-related expenses.

Service-Oriented Collaborative Framework for High-Performance Data Transfer in Grids

As the number of data-intensive applications increases in various domains, scientists need to save, retrieve, and analyze increasingly large datasets. The huge volume of data and the long latency of data transfer on the Internet make it very difficult to ensure high-performance access to Data Grids. Thus, data replication techniques have been widely adopted to solve the latency problem. In this paper, we propose an efficient data replication algorithm for multi-source data transfer, whereby a data replica can be assembled in parallel from multiple distributed data sources and adapted to the fluctuation of network bandwidths. The experimental results show that the proposed algorithm can obtain more aggregated bandwidth, reduce connection overheads, and achieve superior load balance. We also develop a Service-oriented Collaborative Framework that not only implements the proposed data replication algorithm in a real Grid system, but also provides a virtual data management environment that integrates distributed and heterogeneous storage systems and enables users to share their data with group members for collaborative work.