12,443 research outputs found

    Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

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    Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

    ImageJ2: ImageJ for the next generation of scientific image data

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    ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs

    Evolution and Biogeography of Haemonchus contortus: Linking Faunal Dynamics in Space and Time

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    History is the foundation that informs about the nuances of faunal assembly that are essential in understanding the dynamic nature of the host-parasite interface. All of our knowledge begins and ends with evolution, ecology and biogeography, as these interacting facets determine the history of biodiverse systems. These components, relating to Haemonchus, can inform about the complex history of geographical distribution, host association and the intricacies of host-parasite associations that are played out in physiological and behavioural processes that influence the potential for disease and our capacity for effective control in a rapidly changing world. Origins and evolutionary diversification among species of the genus Haemonchus and Hae- monchus contortus occurred in a complex crucible defined by shifts in environmental structure emerging from cycles of climate change and ecological perturbation during the late Tertiary and through the Quaternary. A history of sequential host colonization associated with waves of dispersal bringing assemblages of ungulates from Eurasia into Africa and processes emerging from ecosystems in collision and faunal turnover defined the arena for radiation among 12 recognized species of Haemonchus. Among congeners, the host range for H. contortus is exceptionally broad, including species among artiodactyls of 40 genera representing 5 families (and within 12 tribes of Bovi- dae). Broad host range is dramatically reflected in the degree to which translocation, introduction and invasion with host switching, has characterized an expanding distribution over time in North America, South America, southern Eurasia, Australia and New Zealand, coincidental with agriculture, husbandry and global colonization by human populations driven particularly by European exploration after the 1500s. African origins in xeric to mesic habitats of the African savannah suggest that historical constraints linked to ecological adaptations (tolerances and developmental thresholds defined by temperature and humidity for larval stages) will be substantial determinants in the potential outcomes for widespread geographical and host colonization which are predicted to unfold over the coming century. Insights about deeper evolutionary events, ecology and biogeography are critical as understanding history informs us about the possible range of responses in complex systems under new regimes of environmental forcing, especially, in this case, ecological perturbation linked to climate change. A deeper history of perturbation is relevant in understanding contemporary systems that are now strongly structured by events of invasion and colonization. The relaxation of abiotic and biotic controls on the occurrence of H. contortus, coincidental with inception and dissemination of anthelmintic resistance may be synergistic, serving to exacerbate challenges to control parasites or to limit the socioeconomic impacts of infection that can influence food security and availability. Studies of haemonchine nematodes contribute directly to an expanding model about the nature of diversity and the evolutionary trajectories for faunal assembly among complex hosteparasite systems across considerable spatial and temporal scales

    Designing a Controlled Medical Vocabulary Server: The VOSER Project

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    journal articleBiomedical Informatic

    Addendum to Informatics for Health 2017: Advancing both science and practice

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    This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

    AI and OR in management of operations: history and trends

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    The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

    Bioconductor: open software development for computational biology and bioinformatics.

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    The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples

    Protein-Protein Docking with F2Dock 2.0 and GB-Rerank

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    Rezaul Chowdhury is with UT Austin; Muhibur Rasheed is with UT Austin; Maysam Moussalem is with UT Austin; Donald Keidel is with The Scripps Research Institute; Arthur Olson is with The Scripps Research Institute; Michel Sanner is with The Scripps Research Institute; Chandrajit Bajaj is with The Scripps Research Institute.Motivation -- Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F2 Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error. Results -- The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F2 Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F2 Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F2 Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F2 Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other. Availability -- The docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http://www.cs.utexas.edu/~bajaj/cvc/soft​ware/f2dock.shtml. Client: http://www.cs.utexas.edu/~bajaj/cvc/soft​ware/f2dockclient.shtml.The research of C.B., R.C., M.M., and M.R. of University of Texas, was supported in part by National Science Foundation (NSF) grant CNS-0540033, and grants from the National Institutes of Health (NIH) R01-GM074258, R01-GM073087, R01-EB004873. The research of M.M. was additionally supported by an NSF Graduate Research Fellowship. The research of M.S. and A.O. of TSRI was supported in part by a subcontract on NIH grant R01-GM073087. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Computer Science
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