Data Management Facilities Research Articles

Building Equitable Neuroscience Research Collaborations in Resource-limited Settings.

The burden of noncommunicable neurological disorders, such as stroke, dementia, and headache disorders, are on the rise in low- to middle-income countries (LMICs), while neuroinfectious diseases remain a major concern. The development of neuroscience research aimed at defining the burden of neurological diseases across the lifespan, as well as optimizing diagnosis and treatment strategies, is fundamental to improving neurological health in resource-limited settings. One of the key factors to advancing neuroscience research in LMICs is the establishment of effective collaborations based on responsible and trustworthy partnerships between local scientists in LMICs and international collaborators. LMIC researchers face many logistical, institutional, and individual level challenges as they embark on their neuroscience research journey. Despite these challenges, there are opportunities for improving LMIC investigator-led research that should focus on human and institutional infrastructure development. With regard to human capacity building, potential areas for offering support include enhancing research methodology training, offering instruction in manuscript and grant-writing, institutionalizing mentorship programs, and providing opportunities to conduct funded, mentored research to disseminate in high-impact journals. The foundational elements required for implementing and optimizing neuroscience research within an institution include an institutional review board, mentorship programs, data management, research administration, and laboratory facilities. This institutional capacity varies significantly across and within countries, and many rely on collaborations with better-resourced institutions to initiate research. Successful equitable collaborations ensure the engagement of all local and international stakeholders, as well as implementation of a self-sustaining long-term program. Building research capacity in LMICs is an essential endeavor that requires ongoing commitment to training independent scientists. As research capacity increases, LMIC institutions and governments should consider developing competitive research grant programs to support innovative studies led by local researchers, foster regional collaborations, and hence create a sustainable and independent neuroscience research environment.

IMPLEMENTASI TEKNOLOGI INFORMASI RESOURCE UPAYA PENINGKATAN LAYANAN DAN MANAJEMEN DATA PERKUBURAN KEPADA MASYARAKAT

The Badan Pengelola Perkuburan Islam (BPPI) is an organization that manages cemeteries and provides burial service facilities to the community. BPPI in managing cemetery data experiences problems in data management and cemetery information service facilities are not updated and are slow. The facts found by the PKM team when visiting BPPI were the lack of data management knowledge and skills in using information technology to manage data and service facilities for the community. These findings prove that BPPI has not implemented information technology in managing cemetery data. Based on these findings, the implementing team has prepared a series of activities using structured training and mentoring methods through Community Based Empowerment (PBM) activities. PBM activities are expected to optimize the application of resource information technology and data management training with information technology as an effort to increase the knowledge and skills of BPPI administrators in managing cemetery data. By implementing hybrid-based resource information technology, administrators are helped in managing cemetery data management and the public can use hybrid-based applications to search and find information on cemetery locations online so that cemetery information services are provided more quickly and accurately. The results that have been achieved from PBM activities are an increase in knowledge by 81.93% and an increase in skills for BPPI administrators by 74.37%.

Integrated Framework for Virtual Tours and 3D Visualization of Cultural Tourism in Pattani, Thailand Based on WebGIS Platform

This paper presents a framework for developing a lightweight WebGIS platform as a web map service for publishing and servicing cultural tourism information. The archaeological sites, history, and cultural arts in Pattani Province were used as case studies in this article. It incorporates various digital technologies to create digital archives in providing web-based information services, including geospatial databases, virtual tours and virtual reality, 3D models, and online cultural maps for tourism. The framework describes the necessary infrastructure based on open-source code that is specially developed and provides libraries with free software interoperability, which can be considered as a real-time data management facility and simple completion. It is a relatively low-cost but a reasonably efficient system that is very useful and suitable for local authorities and small organizations with budget constraints to be able to implement practical cultural heritage management for a digital tourism experience that can be displayed on computer equipment, smartphones, and VR headgear efficiently. Furthermore, it also offers a conceptual framework in disclosing future research directions.

Short-Term (6 Weeks) Experience of a Modular Workcell for Hemostasis Testing Including an Intelligent Data Manager at a Tertiary Care Hospital.

Modular workcells could be a better solution than total laboratory automation (TLA) in hemostasis laboratories. Here, we evaluated the impact of implementing a modular workcell (HemoCell) with an intelligent data management facility (HemoHub). We compared the turnaround times (TATs), numbers of rerun samples, and rerun times pre- and postimplementation of the HemoCell at Gil Medical Center. Prothrombin time (PT), activated partial thromboplastin time (aPTT), D-dimer, and fibrinogen were evaluated. The TAT standard deviations (SDs) and maximum TAT values decreased after HemoCell implementation, although the mean TATs for PT, aPTT, and D-dimer were increased. Numbers of rerun samples were increased (18.1/day vs 44.7/day). However, rerun times were reduced, and SDs were decreased during the post-HemoCell period compared with pre-HemoCell. Additionally, technologists needed smaller working space and less labor. The modular workcell could improve quality and efficiency by providing more consistent TATs and shorter rerun times in the hemostasis laboratory.

A Model for Unauthorized Person access in a Particular Area

Abstract: A method of unauthorized person identification in a particular area of an entity includes maintaining a database of identification data specific to the person and condition of entries and time period, providing a unique description for each person enabling access to the person identification data in the database. The automated face recognition system uses a new face-finding and an automated eye- finding approach combined with an intelligent metric. This allows templates to be placed on any size chip or into two-dimensional (2-D) barcodes for selfauthenticating documents, as well as for quick easy transmission over the internet, wireless devices, or Ethernet (i.e., LAN, WAN, etc.). A biometric and identity enrollment camera for collecting personal data includes a slidable main module and at least one modifiable section removable coupled to the main module. The main module includes a processor and one or more biometric sensing devices coupled to the processor. Methods of detecting authorized person identity, differentiating between users of a computerized service, and detecting an unauthorized person. The data management facility may be geographically distributed at a plurality of data management sites and the data storage nodes may exist inside and outside of a particular area of the first person.

Design and operation of a long-term monitoring system for spectral electrical impedance tomography (sEIT)

Abstract. Spectral electrical impedance tomography (sEIT) is increasingly used to characterise the structure of subsurface systems using measurements in the megahertz to kilohertz range. Additionally, hydrogeophysical and biogeophysical processes are characterised and monitored using sEIT. The method combines multiple, spatially distributed, spectroscopic measurements with tomographic inversion algorithms to obtain images of the complex electrical resistivity distribution in the subsurface at various frequencies. Spectral polarisation measurements provide additional information about the systems under investigation and can be used to reduce ambiguities that occur if only the in-phase resistivity values are analysed. However, spectral impedance measurements are very sensitive to details of the measurement setup as well as to external noise and error components. Despite promising technical progress in improving measurement quality as well as progress in the characterisation and understanding of static polarisation signatures of the subsurface, long-term (i.e. multi-month to multi-year) monitoring attempts with fixed setups are still rare. Yet, measurement targets often show inherent non-stationarity that would require monitoring for a proper system characterisation. With the aim of improving operating foundations for similar endeavours, we here report on the design and field deployment of a permanently installed monitoring system for sEIT data. The specific aim of this monitoring installation is the characterisation of crop root evolution over a full growing season, requiring multiple measurements per day over multiple months to capture relevant system dynamics. In this contribution, we discuss the general layout and design of the monitoring setup, including the data acquisition system, additional on-site equipment, required corrections to improve data quality for high frequencies, data management and remote-processing facilities used to analyse the measured data. The choice and installation of electrodes, cables and measurement configurations are discussed and quality parameters are used for the continuous assessment of system functioning and data quality. Exemplary analysis results of the first season of operation highlight the importance of continuous quality control. It is also found that proper cable elevation decreased capacitive leakage currents and in combination with the correction of inductive effects led to consistent tomographic results up to 1 kHz measurement frequency. Overall, the successful operation of an sEIT monitoring system over multiple months with multiple daily tomographic measurements was achieved.

PEMILIHAN SEKOLAH DASAR PENERIMA BANTUAN SARANA DAN PRASARANA PEMBELAJARAN TATAP MUKA PADA ERA NEW NORMAL DENGAN METODE WEIGHTED PRODUCT DI KECAMATAN JIWAN KABUPATEN MADIUN

This study aims to design, build, and implement a decision support system for selecting Elementary School recipients of face-to-face learning facilities and infrastructure in the New Normal Era with the Weighted Product (WP) method in Jiwan District, Madiun Regency, East Java. The data used is secondary data in the form of public data obtained from the site http://school.data.kemdikbud.go.id/. and the number of elementary school data obtained with the boundaries of Jiwan District, Madiun Regency, amounted to 23 schools. At the stage of designing the system for this research, it is divided into three parts, namely the UML System, User Interface System and Database with ERD. The research findings show that the decision support system for selecting primary schools that receive face-to-face learning facilities and infrastructure in the New Normal Era with the WP method has several user interfaces, namely (1) User interface Login is used for security features of the Elementary School election system. (2) The Home Page user interface contains facilities for criteria data management, criteria weight data management, alternative data management, SPK value data management, user data management, and logout. (3) Criteria Data User Interface can perform criteria data management by adding and saving, changing, and deleting criteria data. (4) The Criteria Weighting Data User Interface can fill in the value of the weight of each criterion. (5) Alternative Data User Interface can add and save, change, and delete alternative data. (6) The Value Data User Interface can view and save by adding values to each alternative according to the criteria. (7) The DSS Management User Interface can make decisions by calculating the weight correction, calculating the S vector value, and calculating the V vector value. (8) User Interface Viewing Results can see the results of decision making after calculating the SPK. (9) User Management Interface Users can add, edit, and delete data from users containing usernames and passwords. Then by using the 57 item test scenarios of the blackbox method, the decision support system that was built had 100% success according to the expectations of the test scenario.

PolyFrame

In the last few years, the field of data science has been growing rapidly as various businesses have adopted statistical and machine learning techniques to empower their decision-making and applications. Scaling data analyses to large volumes of data requires the utilization of distributed frameworks. This can lead to serious technical challenges for data analysts and reduce their productivity. AFrame, a data analytics library, is implemented as a layer on top of Apache AsterixDB, addressing these issues by providing the data scientists' familiar interface, Pandas Dataframe, and transparently scaling out the evaluation of analytical operations through a Big Data management system. While AFrame is able to leverage data management facilities (e.g., indexes and query optimization) and allows users to interact with a large volume of data, the initial version only generated SQL++ queries and only operated against AsterixDB. In this work, we describe a new design that retargets AFrame's incremental query formation to other query-based database systems, making it more flexible for deployment against other data management systems with composable query languages.

Breast Cancer Diagnostics, Therapy, and Outcomes in Sub-Saharan Africa: A Population-Based Registry Study.

Breast cancer (BC) is the most common cancer in sub-Saharan Africa (SSA). However, little is known about the actual therapy received by women with BC and their survival outcome at the population level in SSA. This study aims to describe the cancer-directed therapy received by patients with BC at the population level in SSA, compare these results with the NCCN Harmonized Guidelines for SSA (NCCN Harmonized Guidelines), and evaluate the impact on survival. Random samples of patients with BC (≥40 patients per registry), diagnosed from 2009 through 2015, were drawn from 11 urban population-based cancer registries from 10 countries (Benin, Congo, Cote d'Ivoire, Ethiopia, Kenya, Mali, Mozambique, Namibia, Uganda, and Zimbabwe). Active methods were used to update the therapy and outcome data of diagnosed patients ("traced patients"). Excess hazards of death by therapy use were modeled in a relative survival context. A total of 809 patients were included. Additional information was traced for 517 patients (63.8%), and this proportion varied by registry. One in 5 traced patients met the minimum diagnostic criteria (cancer stage and hormone receptor status known) for use of the NCCN Harmonized Guidelines. The hormone receptor status was unknown for 72.5% of patients. Of the traced patients with stage I-III BC (n=320), 50.9% received inadequate or no cancer-directed therapy. Access to therapy differed by registry area. Initiation of adequate therapy and early-stage diagnosis were the most important determinants of survival. Downstaging BC and improving access to diagnostics and care are necessary steps to increase guideline adherence and improve survival for women in SSA. It will also be important to strengthen health systems and facilities for data management in SSA to facilitate patient follow-up and disease surveillance.

Prediction of odor complaints at a large composite reservoir in a highly urbanized area: A machine learning approach.

Odorous compound emissions and odor complaints from the public are rising concerns for agricultural, industrial, and water resource recovery facilities (WRRFs) near urban areas. Many facilities are deploying sensors that measure malodorous compounds and other factors related to odor creation and dispersion. Focusing on the Metropolitan Water Reclamation District of Greater Chicago's (MWRDGCs) Thornton Composite Reservoir (7.9 billion gallon capacity), we used meteorological, operational, and H2S sensor data to train a 3-day advance-warning predictor of local odor complaints, so as to implement targeted odor prevention measures. Using a machine learning approach, we bypassed difficulties in modeling both physical dispersion and human perception of odors. Utilizing random forest algorithms with varied settings and input attributes, we find that a small network of H2S sensors, meteorological data, and operational data are able to predict odor complaints three days in advance with greater than 60% accuracy and less than 25% false-positive rates, exceeding MWRDGC's standards required for full-scale deployment. PRACTITIONER POINTS: A random forest algorithm trained on H2 S, weather, and operations data successfully predicted odor complaints surrounding a large composite reservoir. Thirty-two data attribute combinations were tested. It was found that H2 S sensor data alone are insufficient for predicting odor complaints. The best predictor was a Random Forest Classifier trained on weather, operational, and H2 S readings from the reservoir corner locations. This study demonstrates odor complaint prediction capability utilizing a limited set of data sources and open-source machine learning techniques. Given a small network of H2 S sensors and organized data management, WRRFs and similar facilities can conduct advance-warning odor complaint prediction.

West-Life: A Virtual Research Environment for structural biology

The West-Life project (https://about.west-life.eu/) is a Horizon 2020 project funded by the European Commission to provide data processing and data management services for the international community of structural biologists, and in particular to support integrative experimental approaches within the field of structural biology. It has developed enhancements to existing web services for structure solution and analysis, created new pipelines to link these services into more complex higher-level workflows, and added new data management facilities. Through this work it has striven to make the benefits of European e-Infrastructures more accessible to life-science researchers in general and structural biologists in particular.

Ensuring Rigor in Qualitative Data Analysis

Deep and insightful interactions with the data are a prerequisite for qualitative data interpretation, in particular, in the generation of grounded theory. The researcher must also employ imaginative insight as they attempt to make sense of the data and generate understanding and theory. Design research is also dependent upon the researchers’ creative interpretation of the data. To support the research process, designers surround themselves with data, both as a source of empirical information and inspiration to trigger imaginative insights. Constant interaction with the data is integral to design research methodology. This article explores a design researchers approach to qualitative data analysis, in particular, the use of traditional tools such as colored pens, paper, and sticky notes with the CAQDAS software, NVivo for analysis, and the associated implications for rigor. A design researchers’ approach which is grounded in a practice which maximizes researcher data interaction in a variety of learning modalities ensures the analysis process is rigorous and productive. Reflection on the authors’ research analysis process, combined with consultation with the literature, would suggest digital analysis software packages such as NVivo do not fully scaffold the analysis process. They do, however, provide excellent data management and retrieval facilities that support analysis and write-up. This research finds that coding using traditional tools such as colored pens, paper, and sticky notes supporting data analysis combined with digital software packages such as NVivo supporting data management offer a valid and tested analysis method for grounded theory generation. Insights developed from exploring a design researchers approach may benefit researchers from other disciplines engaged in qualitative analysis.

FROG: A Robust and Green Wireless Sensor Node for Fog Computing Platforms

Over the past few years, we have witnessed the widespread deployment of wireless sensor networks and distributed data management facilities: two main building blocks of the Internet of things (IoT) technology. Due to the spectacular increase on the demand for novel information services, the IoT-based infrastructures are more and more characterized by their geographical sparsity and increasing demands giving rise to the need of moving from a cloud to a fog model: a novel deployment paradigm characterized by the provisioning of elastic resources geographically located as close as possible to the end user. Despite the large number of wireless sensor networks already available in the market, there are still many issues to be addressed on the design and deployment of robust network platforms capable of meeting the demand and quality of fog-based systems. In this paper, we undertake the design and development of a wireless sensor node for fog computing platforms addressing two of the main issues towards the development and deployment of robust communication services, namely, energy consumption and network resilience provisioning. Our design is guided by examining the relevant macroarchitecture features and operational constraints to be faced by the network platform. We based our solution on the integration of network hardware platforms already available on the market supplemented by smart power management and network resilience mechanisms.

Abstract 378: The Cancer Genome Collaboratory

Abstract The Cancer Genome Collaboratory is an academic compute cloud designed to enable computational research on the world’s largest and most comprehensive cancer genome dataset, the International Cancer Genome Consortium (ICGC). The ICGC is on target to categorize the genomes of 25,000 tumors by 2018. A subproject of ICGC, the PanCancer Analysis of Whole Genomes (PCAWG) alone has generated over 800TB of harmonized sequence alignments, variants and interpreted data from over 2,800 cancer patients. A dataset of this size requires months to download and significant resources to store and process. By making the ICGC data available in cloud compute form in the Collaboratory, researchers can bring their analysis methods to the cloud, yielding benefits from the high availability, scalability and economy offered by cloud services, avoiding a large investment in static compute resources and essentially eliminating the time needed to download the data. To facilitate the computational analysis on the ICGC data, the Collaboratory has developed software solutions that are optimized for typical cancer genomics workloads, including well tested and accurate genome aligners and somatic variant calling pipelines. We have developed a simple to use, but fast and secure, data transfer tool that imports genomic data from cloud object storage into the user’s compute instances. Because a growing number of cancer datasets have restrictions on their storage locations, it is important to have software solutions that are interoperable across multiple cloud environments. We have successfully demonstrated interoperability across The Cancer Genome Atlas (TCGA) dataset hosted at University of Chicago’s Bionimbus Protected Data Cloud, the ICGC dataset hosted at the Collaboratory, and ICGC datasets stored in the Amazon Web Services (AWS) S3 storage. Lastly, we have developed a non-intrusive user authorization system that allows the Collaboratory to authenticate against the ICGC Data Access Compliance Office (DACO) when researchers require access to controlled tier data. We anticipate that our software solutions will be implemented on additional commercial and academic clouds. The Collaboratory is actively growing, with a target hardware infrastructure of over 3000 CPU cores and 15 petabytes of raw storage. As of November 2016, the Collaboratory holds information on 2,000 ICGC PCAWG donors (500TB total). We anticipate expanding the Collaboratory to host the entire ICGC dataset of 25,000 donors (approximately 5PB) and to extend its data management and analysis facilities across multiple clouds. During the current closed beta phase, the Collaboratory has been successfully utilized by multiple research groups, most notably PCAWG project researchers who analyzed thousands of genomes at scale over a few weeks’ time. The Collaboratory will open to the public during the second quarter of 2017. We invite cancer researchers to learn more about our cloud resources at cancercollaboratory.org, and apply for access to the Collaboratory. Citation Format: Christina K. Yung, George L. Mihaiescu, Bob Tiernay, Junjun Zhang, Francois Gerthoffert, Andy Yang, Jared Baker, Guillaume Bourque, Paul C. Boutros, Bartha M. Knoppers, BF Francis Ouellette, Cenk Sahinalp, Sohrab P. Shah, Vincent Ferretti, Lincoln D. Stein. The Cancer Genome Collaboratory [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 378. doi:10.1158/1538-7445.AM2017-378

A new radiation infrastructure for the Modular Earth Submodel System (MESSy, based on version 2.51)

Abstract. The Modular Earth Submodel System (MESSy) provides an interface to couple submodels to a base model via a highly flexible data management facility (Jöckel et al., 2010). In the present paper we present the four new radiation related submodels RAD, AEROPT, CLOUDOPT, and ORBIT. The submodel RAD (including the shortwave radiation scheme RAD_FUBRAD) simulates the radiative transfer, the submodel AEROPT calculates the aerosol optical properties, the submodel CLOUDOPT calculates the cloud optical properties, and the submodel ORBIT is responsible for Earth orbit calculations. These submodels are coupled via the standard MESSy infrastructure and are largely based on the original radiation scheme of the general circulation model ECHAM5, however, expanded with additional features. These features comprise, among others, user-friendly and flexibly controllable (by namelists) online radiative forcing calculations by multiple diagnostic calls of the radiation routines. With this, it is now possible to calculate radiative forcing (instantaneous as well as stratosphere adjusted) of various greenhouse gases simultaneously in only one simulation, as well as the radiative forcing of cloud perturbations. Examples of online radiative forcing calculations in the ECHAM/MESSy Atmospheric Chemistry (EMAC) model are presented.

An Efficient Privacy Preserving Search Scheme with Access Control for Cloud Data Centers

The internet and the emergence of social networks produce terabytes of data every day. In this big data scenario, the ability to outsource the data to a cloud storage facility saves the data management and storage facility cost. Some major challenges with this scheme are providing security and ensuring the privacy of the outsourced data. Although data security can be achieved through encryption, searching on encrypted data become a complex task. The proposed work suggests an efficient searching scheme for encrypted cloud data based on hierarchical clustering of documents. The hierarchical clustering method preserves the semantic relationship between the documents in the encrypted domain to speed up the search process. Consequently, the proposed system has linear computational complexity during the search phase in response to an exponential increase in the number of documents. The system also ensures data privacy by providing only limited access of the documents to the different types of users by implementing access control mechanisms resulting in more secured data storage in the cloud.

AusPlots Rangelands field data collection and publication: Infrastructure for ecological monitoring

The TERN AusPlots Rangelands field data collection system has been developed to facilitate simple and efficient data collection by ecologists operating in the Australian outback. The infrastructure provides tooling for ‘clean’ data collection on mobile (tablet) devices, associated data storage in a cloud-based server infrastructure, facilities for data curation and management and interfaces with the Australian Ecological Knowledge and Observation System (ÆKOS) data repository for long-term data management and semantic enrichment. In this paper, we introduce the AusPlots Rangelands field data collection solution, providing a systems-level view and motivating its development through the discussion of key functional requirements. We provide an outline of the ÆKOS data repository and demonstrate that the combined system provides a unique end-to-end data collection, curation, archiving and publishing mechanism for ecological data.

Semantic catalog of things, services, and data to support a wind data management facility

Transparency and data integrity are crucial to any scientific study wanting to garner impact and credibility in the scientific community. The purpose of this paper is to discuss how this can be achieved using what we define as the Semantic Catalog. The catalog exploits community vocabularies as well as linked open data best practices to seamlessly describe and link things, data, and off-the-shelf (OTS) services to support scientific offshore wind energy research for the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) Wind and Water Power Program. This is largely made possible by leveraging collaborative advances in the Internet of Things (IoT), Semantic Web, Linked Services, Linked Open Data (LOD), and Resource Description Framework (RDF) vocabulary communities, which provides the foundation for our design. By adapting these linked community best practices, we designed a wind characterization Data Management Facility (DMF) capable of continuous data collection, processing, and preservation of in situ and remote sensing instrument measurements. The design incorporates the aforementioned Semantic Catalog which provides a transparent and ubiquitous interface for its user community to the things, data, and services for which the DMF is composed.

IGI grid services for the bioinformatics community

Motivations. In the last decade many projects related to grids have been carried out in Europe at both national and international levels with an important economic contribution by the European Commission.The grid middleware developed within these projects has been deployed into the European grid infrastructure (EGI) made by more than 350 sites all over Europe. The Italian Grid Infrastructure (IGI) is part of EGI and is one of the most important and widest national grid infrastructures in Europe since it provides about 33000 CPU cores, 17 PB of disk space and 9 PB of tape capacity spread over more than 50 sites. Although the grid infrastructure has been initially built according to the needs of a few scientific communities (high energy physics, earth observation and Bioinformatics among the others), it has been gradually evolving in order to provide grid services to a wider and wider user base. Several scientific communities are observing that as the instruments become more and more powerful the need for storage and computing is increasing day by day. This will also increase the number of users that could benefit from a geographical distributed computing grid infrastructure. Methods. In order to support new communities (users and resource providers), various activities have been started. Training has been considered very important, so tutorial for users and grid administrators are regularly organized. In addition great effort has been devoted to understanding the user needs, by defining appropriate use cases, and to supporting the user communities to port their applications on the grid environment (the so-called "gridification"). An important effort is also spent in developing new tools that could make the interaction between the final users and the grid as easy as possible. In particular web tools to submit jobs to the grid infrastructure have been deployed and used by some bioinformatics communities. In order to address the needs of users relying on high-level tools like Workflow managers (e.g. Taverna and other similar tools), a front-end web service has been developed. This web interface could be used as a bridge towards the EGI/IGI grid infrastructure. The IGI community is also providing a service that allows the exploitation of Relational Databases over the grid infrastructure, assuring a high level of security and privacy. Results. The usage of the standard EGI/IGI resources and services, together with the high level services that IGI is providing on top of the grid, has provided the end users with the capability of carrying out their high demanding computing activities in an easy and reliable way. In the past years, indeed, IGI has supported several bioinformatics communities to "gridify" many different applications such as: ASPic, PAML, MrBayes, CSTGrid, DNAfan, BLAST, BayeSSC, FT-Comar, Muscle, Gene Ontology DB analysis, ABCtoolbox, EMBOSS, Bowtie, SAMtools, Illumina Solexa data processing, AmpliconNoise, BioPython, HMMER. As a result the CPU consumed by various Italian bioinformatics groups on the IGI grid infrastructure has exceeded the 10 years in a few days of activity, thus hugely reducing the overall time needed for the execution of the jobs. Thanks to IGI the bioinformatics users have carried out their analysis in an easy and transparent way, both through simple web interfaces and through complex WorkFlow managers, reducing the time needed to get the results of about 2-3 orders of magnitude. The IGI infrastructure has also been exploited by the Computational Biology group of the Bologna University, in the frame of DUCK (Distributed Unified Computing for Knowledge, a collaboration between multidisciplinary Academic and Research Institutions located in the Emilia Romagna region), to run protein annotation application based on the Bologna Annotation Resource (BAR) method, and to perform massively parallel genome sequencing including about 18 millions of protein sequences. More than 150 computing nodes in the IGI grid infrastructure have been used, successfully dropping the computational times if compared to the computing resources of the local cluster available to the group. The protein annotation application reached a dropping factor of 120, i. e. the computation was performed in few weeks instead of years. Data management facilities offered by the Grid were also exploited to easily handle input and output files. To provide an easy-to-use service to the user communities IGI is developing a web portal that will hide the complexity of the authentication/authorization mechanisms and will also integrate the computing frameworks needed by the different user communities. A prototype of this portal can be easily set up for the bioinformatics community. Availability http://www.italiangrid.it/

A two-folded impact analysis of schema changes on database applications

Database applications are becoming increasingly popular, mainly due to the advanced data management facilities that the underlying database management system offers compared against traditional legacy software applications. The interaction, however, of such applications with the database system introduces a number of issues, among which, this paper addresses the impact analysis of the changes performed at the database schema level. Our motivation is to provide the software engineers of database applications with automated methods that facilitate major maintenance tasks, such as source code corrections and regression testing, which should be triggered by the occurrence of such changes. The presented impact analysis is thus two-folded: the impact is analysed in terms of both the affected source code statements and the affected test suites concerning the testing of these applications. To achieve the former objective, a program slicing technique is employed, which is based on an extended version of the program dependency graph. The latter objective requires the analysis of test suites generated for database applications, which is accomplished by employing testing techniques tailored for this type of applications. Utilising both the slicing and the testing techniques enhances program comprehension of database applications, while also supporting the development of a number of practical metrics regarding their maintainability against schema changes. To evaluate the feasibility and effectiveness of the presented techniques and metrics, a software tool, called DATA, has been implemented. The experimental results from its usage on the TPC-C case study are reported and analysed.