A scientific workflow framework for scientific data querying and processing

We are at the beginning of the new era of ``e-science\u27\u27. Researchers in many areas of science, especially in astrophysics, physics, climatology and biology, are now facing tremendous increases in data volumes, as well as corresponding data analysis tools. These increased data and tools demand a better framework to manage the new generation scientific research cycle from data capture, data curation to data analysis, data query and data visualization. Scientific workflows are proving to be one of the key technologies for scientists to formalize and structure complex scientific processes to enable and accelerate many significant scientific discoveries. Although several scientific workflow management systems (SWFMSs) are developed, a formal scientific workflow composition framework, in which workflows and constructs can be composed arbitrarily to process and query collectional scientific data sets, is still to be proposed. In this thesis, I make several contributions towards formalizing a scientific workflow composition framework. First, We proposed a dataflow-based scientific workflow composition model including a scientific workflow model that separates the declaration of the workflow interface from the definition of its functional body; and a set of workflow constructs, including Map, Reduce, Tree, Loop, Conditional, and Curry, which are fully compositional one with another. Our workflow composition framework is unique in that workflows are the only operands for composition; in this way, our approach elegantly solves the two-world problem in existing composition frameworks, in which composition needs to deal with both the world of tasks and the world of workflows. Second, We formalized a collection-oriented data model, called collectional data model, to model hierarchical collection-oriented scientific data, and a set of well-defined operators to manipulate and query such data. To our best knowledge, this is the first algebraic approach to modeling collection-oriented scientific data. Finally, we developed a prototype scientific workflow management system, called View. The View system implemented the above techniques in its subsystems and integrated them within a service-oriented architecture

Distribution Law of Principal Stress Difference of Deep Surrounding Rock of Gob-side Entry and Optimum Design of Coal Pillar Width

The optimum design of deep-coal pillar width at the gob-side entry is the key to control surrounding rocks. Existing studies on this issue are based on the distribution law of mining abutment pressure without considering the principal stress difference distribution, which is related to the shear failure of surrounding rock. This study aimed to optimize the design of deep-coal pillar width at gob-side entry based on the distribution law of the principal stress difference. With FLAC 3D numerical simulation, the distribution characteristics of principal stress difference in the side mining stress field of the deep mining face and the distribution laws of the principal stress difference of the surrounding rock at the gob-side entry under different coal pillar width were explored. According to the distribution characteristics of the principal stress difference, the coal and rock mass on the goaf side in the deep mining face could be divided into three zones, i.e., decreasing, increasing and stable zones. Results show that the principal stress differences of the deep surrounding rock on the roof, floor, and coal wall rib at the gob-side entry present a single-peak curve distribution under different coal pillar width. However, the principal stress difference of the surrounding rock on the coal pillar rib displays a single-peak curve when the coal pillar width is less than or equal to 8 m, but a double-peak curve appears when the coal pillar width is greater than 8 m. The peak value of the shallow surrounding rock is obviously smaller than that of the deep surrounding rock. The width of the coal pillar of the gob-side entry in 11030-tunnel was optimized according to the theoretical calculation. This work provides a novel idea and method for the arrangement of deep coal pillars of gob-side entry

Service-Oriented Architecture for VIEW: A Visual Scientific Workflow Management System

Scientific workflows have recently emerged as a new paradigm for scientists to formalize and structure complex and distributed scientific processes to enable and accelerate many scientific discoveries. In contrast to business workflows, which are typically controlflow oriented, scientific workflows tend to be dataflow oriented, introducing a new set of requirements for system development. These requirements demand a new architectural design for scientific workflow management systems (SWFMSs). Although several SWFMSs have been developed that provide much experience for future research and development, a study from an architectural perspective is still missing. The main contributions of this paper are: i) based on a comprehensive survey of the literature and identification of key requirements for SWFMSs, we propose the first reference architecture for SWFMSs, ii) in compliance with the reference architecture, we further propose a service-oriented architecture for VIEW (a VIsual sciEntific Workflow management system), iii) we implement VIEW to validate the feasibility of the proposed architectures, and iv) we present two case studies to showcase the applications of our VIEW system

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Opportunities and Challenges in Running Scientific Workflows on the Cloud

Abstract — Cloud computing is gaining tremendous momentum in both academia and industry. The application of Cloud computing, however, has mostly focused on Web applications and business applications; while the recognition of using Cloud computing to support large-scale workflows, especially data-intensive scientific workflows on the Cloud is still largely overlooked. We coin the term “Cloud Workflow”, to refer to the specification, execution, provenance tracking of large-scale scientific workflows, as well as the management of data and computing resources to enable the execution of scientific workflows on the Cloud. In this paper, we analyze why there has been such a gap between the two technologies, and what it means to bring Cloud and workflow together; we then present the key challenges in running Cloud workflow, and discuss the research opportunities in realizing workflows on the Cloud. Cloud computing; Scientific Workflow; Cloud workflow; Data Intensive Computin