dataesr/scientific-paragraphs-categorization huggingface dataset:
Workflow integration and MPI task management Coordination among the ML and ABM modules in this workflow requires deep integration of components written in Python, R, and C++/MPI. The overall workflow shares hardware resources for these systems and rapidly switches among them. The workflow generates parameters in R-based optimizers, distributes these parameters to C++/ MPI simulations, and registers progress via database operations called via Python-all on the same resources. Thus, building this complex application requires a workflow system with the capability to manage in-memory user libraries and third-party packages such as scripting language interpreters. Other workflow solutions are generally constructed to orchestrate Unix-like program invocations  (Zhao et al. , 2007)  or whole-job scheduler submissions  (Thain et al.
Innovations realized The innovations within this work are in two areas: 1. distributed simulation with CityCOVID and 2. application and workflow integration of large-scale machine learning. A schematic for the CityCOVID Hypervolume Refinement (HVR) workflow, our most complex workflow for which we provide full machine performance results ( § 5), is shown in Figure  1. Empirical data from hospital systems and public health departments are used to parameterize CityCOVID and to provide targets for calibration. Each iteration of the HVR algorithm (described in full in § 3. 2. 2) consists of three ML phases, each using an ensemble of single-node, many-core R methods distributed across the machine. The first phase is for fitting Gaussian process, or kriging, metamodels to the evaluated simulation runs, where evaluations are errors in fitting both hospitalization and death time series model outputs to empirical data.
The second phase calculates the Expected Hypervolume Improvement of new candidate parameter points, and the third phase optimizes the best new candidate points. The calculations from the three ML phases produce simulation allocations, which are assigned across the existing Pareto front and new candidate points for evaluation. These simulations are instantiated and distributed across the system as multinode C++/MPI programs. The HVR algorithm proceeds through multiple iterations as it refines the Pareto front (i. e. , jointly optimizes multiple objectives). Simulation parameters are written to a Postgres database as the algorithm progresses for real-time feedback and final reporting.
There are a number of ABC methods and libraries implementing them  (Jabot et al. , 2013; Maerzluft et al. , 2021; Dutta et al. , 2017; Lintusaari et al. , 2018). Sequential ABC methods  (Beaumont et al. , 2009; Moral et al. , 2011; Rutter et al. , 2019)  improve the yield of parameter space sampling compared with one-shot rejection sampling approaches. In addition, batch sequential ABC methods can exploit the concurrency available on HPC resources, providing the ability to tune the batch sampling size to balance sample yield and time to solution. We utilized a batch sequential ABC algorithm  Lenormand et al. (2013)  implemented in the EasyABC library  Jabot et al. (2013) , and we directly integrated it into an EMEWS workflow for our initial model calibration. The calibration was preceded by parameter prioritization analyses using an efficient global sensitivity method  Morris (1991) , identifying the nine parameters θ = θ 1 ,…, θ 9 most relevant to the empirical data that we were attempting to match, time series of COVID-19 attributable hospitalizations T H (t) and deaths T D (t) for residents of Chicago, with corresponding simulation outputs T H ðt,θ,ξÞ and T D ðt,θ,ξÞ, where ξ is a random variable encapsulating the random stream for each simulation replicate.
Based on empirical means and variance of initial evaluations-provided here by the ABC computations-the first ML phase (ML Phase 1 in Figure  1 ) is to build one GP model per objective, with the DiceKriging package  Roustant et al. (2012). The optimization of the corresponding hyperparameters is performed in parallel. Note that instead of global GP models that can predict everywhere, local GP models can be constructed to predict at specific locations, for example, with  Gramacy (2016). The advantage of this second option is to be more decentralized. Then the expected hypervolume improvement is optimized with a multistart approach, since the EHI optimization landscape is expected to be highly multimodal. Specifically, in ML Phase 2, a large set of uniformly sampled designs is evaluated, from which the best candidates constitute starting points for local optimization in ML Phase 3.
The database access tasks call into Python libraries using a single core at a time. Thus, Swift/T faces a significant work distribution challenge. Swift/T translates workflows into ADLB  (Lusk et al. , 2010)  programs. In the implementation used by Swift/T, ADLB divides the available ranks into workers and servers. Any number of servers and workers may be used, down to a singleton of each. The workers simply perform work requested from their server. Servers exchange work using the work stealing approach from Scioto  Dinan et al. (2008) , in which empty servers steal half of the work queue from another server selected at random. This explains the exponential ramp-up commonly seen in Swift/T performance plots. For the CityCOVID workload, we used increasing numbers of servers for increasing run sizes.
Supporting public health We have been using the CityCOVID model to forecast the spread of COVID-19 in Chicago and have been providing model results to support city and state public health officials and decision-makers. From the beginning, the decisionmakers we work with have had the philosophy that their "decisions would be guided by data, science, and public health experts. " To meet these requirements, decisionmakers recruited data analytics support for understanding the current COVID-19 situation and epidemiological modeling support for forecasting what might happen in the future based on possible NPIs, and they paired these groups with public health departments. We have supported the Chicago Department of Public Health (CDPH) since the early stages of the pandemic, March 2020  (Thomas (2021) ).
What applications were used to measure performance All software was built with GCC 7. 3. 0 with the Cray MPI wrappers for Theta as appropriate. The CityCOVID model is based on the RepastHPC 2. 3. 1 agent-based modeling system and the ChiSIM Chicago model 0. 4. 2. The workflow system was Swift/T 1. 4. 3, which was linked to interpreters in Python 3. 8. 2. 1, R 3. 6. 0, Tcl 8. 6. 6, and Cray MPI. All Python-and R-libraries used were the latest compatible with these interpreters as of October 1, 2020. The Rlibrary size to perform these optimizations contains 112 packages totaling 321 MB. In order to improve the performance associated with numerical operations in the machine learning tasks, an external R with Cray LibSci v. 20. 03. 1 providing vendor-optimized BLAS/LAPACK libraries was also used.
How performance was measured The technical details of our workflow solution are as follows. The workflow runs as a single job allocation in the Cobalt scheduler on Theta. The Swift/T runtime runs as a single MPI program across all nodes and evaluates the logic of the Swift workflow. Tasks in the workflow such as the invocation of R-code snippets (the ML algorithms) or MPI-enabled libraries (CityCOVID/C++) are placed in a distributed task queue in the ADLB library underlying Swift/T. Even database accesses are represented as Python code snippets (via Psycopg2); thus, they do not block the progress of the overall workflow. MPI tasks are launched by Swift/T using MPI 3. 0 features developed previously  (Wozniak et al. , 2013b). Performance was simply measured by using the clock libraries available in the programming models used and reported in log files.
Performance results In this section, we present five types of performance results: an illustration of a full-scale run on Theta ( § 5. 1), strongscaling results for full-system scale ( § 5. 2), time to solution results for the learning tasks ( § 5. 3), communication and messaging rates ( § 5. 4), and ABM specific performance results ( § 5. 5). A key performance challenge of this workflow is juggling the 2040 MPI simulation tasks generated by the workflow, each a 256-core execution. As parameters for these tasks emerge from the HVR algorithm, this work must be rapidly distributed. Additionally, there are constraints on the task placement, since the MPI task should occupy contiguous cores across whole nodes to support fast communication and a data caching strategy used by CityCOVID. Nevertheless, the worker utilization across the two optimization iterations, including the learning and simulation tasks, is 84%.
Convergence of data science, learning, and simulation for HPC This application demonstrates the capability and need to bring HPC closer to diverse scientific communities, including the public health sector. By using detailed local data as a starting point to parameterize and calibrate simulations and produce actionable analyses (i. e. , from observational data to scenario outputs documented in an integrated Postgres database), we applied Theta as the numerical engine at the center of a data pipeline. This made the overall workflow more relevant to our public health collaborators and demonstrated the capability of the system to be an integral part of the decision-making process. From a computer systems perspective, the complex integrations and high performance achieved here demonstrate that applications written in high-level workflow languages and mathematical systems can be executed at large scale.
Additionally, they can be closely integrated with previously developed MPI-based scientific applications that are scalable in themselves. We also demonstrated that very large (thousands) ensembles of MPI program executions can be run on the system without negatively impacting the scheduler or other users by tapping into the capabilities of MPI, via the high-level model offered by Swift/T. We expect that data+learning+simulation workflows will become more tightly integrated in the near future. As shown in Figure  4 , learning is already half of the job. The capabilities shown here are initial steps to more general-purpose HPC-enabled decision-support platforms that can rapidly assess and forecast the course of disease outbreaks and other crises. We expect that incorporating real-time data streams and weather-forecast-like automated steering will drive further enhancements and increase the performance and robustness of the results obtained.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the  DOE Office of Science  through the  National Virtual Biotechnology Laboratory , a consortium of DOE national laboratories focused on response to COVID-19, with funding provided by the  Coronavirus CARES Act. This work was also funded by an award from the c3. aiDigital  Transformation Institute  and the  National Institute of Allergy and Infectious Diseases  grant  R01AI136056. This research used resources of the  Argonne Leadership Computing Facility , which is a  DOE Office of Science User Facility  supported under Contract  DE-AC02-06CH11357. We would like to thank the  Argonne Leadership Computing Facility  staff for their timely and critical support.
Using data warehouses to analyse multidimensional data is a significant task in company decision-making. The need for analyzing data stored in different data warehouses generates the requirement of merging them into one integrated data warehouse. The data warehouse merging process is composed of two steps: matching multidimensional components and then merging them. Current approaches do not take all the particularities of multidimensional data warehouses into account, e. g. , only merging schemata, but not instances; or not exploiting hierarchies nor fact tables. Thus, in this paper, we propose an automatic merging approach for star schema-modeled data warehouses that works at both the schema and instance levels. We also provide algorithms for merging hierarchies, dimensions and facts. Eventually, we implement our merging algorithms and validate them with the use of both synthetic and benchmark datasets.
EXPERIMENTAL ASSESSMENTS To validate the effectiveness of our approach, we applied our algorithms on benchmark data. Unfortunately, we did not find a suitable benchmark for our problem. So, we adapted the datasets of the TPC-H benchmark to generate different DWs. Originally, the TPC-H benchmark serves for benchmarking decision support systems by examining the execution of queries on large volumes of data. Because of space limit, we put the test results in github 1. 1 https://github. com/Implementation111/Multidimensional-DW-merging.
Technical environment and Datasets The algorithms were implemented by Python 3. 7 and were executed on a processor of Intel(R) Core(TM) i5-8265U CPU@ 1. 60GHz with a 16G RAM. The data are implemented in R-OLAP format through the Oracle 11g DBMS. The TPC-H benchmark provides a pre-defined relational schema 2 with 8 tables and a generator of massive data. First, we generated 100M of data files, there are respectively 600572, 15000, 25, 150000, 20000, 80000, 5, 1000 tuples in the table of ,  ,  , , , ,  and. Second, to have more deeper hierarchies, we included the data of   and  into  and  , and those of  into. Third, we transformed these files to generate two use cases by creating 2 DWs for each case. To make sure that there are both common and different instances in different DWs, for each dimension, instead of selecting all the corresponding data, we selected randomly 3/4 of them.
Star schema generation After executing our algorithms, we obtain one star schema as shown in Figure  11  which is consistent with the expectations. The parameters of the hierarchies satisfy the relationships of functional dependency. The run time is 30. 70s. The 3 dimensions  , ,  of the original DWs are merged. Between the different dimensions  1. and  2. , there is a matched attribute  , so they are also merged such that  1. provides  2. with the attribute. Then the  in the merged DW also has the attribute. We can also observe that normally, in the merged schema, there should be the original hierarchy  → ℎ →  of  2. but which is deleted. By looking up in the table, we find that there is no tuple which belongs to this hierarchy but not to  → ℎ →  →  , that's why it is removed. At the instance level, the result is shown in github.
Numerical performance one-sided VSI EWMA MCV control charts without the presence of measurement errors In this Section, we present the numerical results to evaluate the performance of both upward and downward VSI EWMA-γ 2 control charts. The problems  (12) ,  (13)  are solved to find the optimal parameters that minimise AT S subjecting to the corresponding constraints. For the sake of comparison, the AT S 0 is set to 370. 4 and E 0 (h) = 1. The optimisation solving is implemented with Matlab using Levenberg-Marquardt algorithm  [25]  based on a number of following scenarios of parameters: γ 0 ∈ {0. 1, 0. 2, 0. 3, 0. 4, 0. 5}; -p ∈ {2, 3, 4}; -n ∈ {5, 15}; -τ ∈ {0. 5, 0. 75, 0. 9, 1. 1, 1. 25, 1. 5}; -W U , W D ∈ {0. 1, 0. 3, 0. 6, 0. 9}; -h S ∈ {0. 1, 0. 5}. These parameters are chosen to cover a large scale of possible scenarios.
Illustrative example In this Section, an illustrative example is presented, in order to help practitioners to apply the upward VSI EWMA chart for the MCV. The data and the context are taken from  [16]. In this example, the rate of return of some investments in p = 3 different industrial sectors (namely, S 1 = automotive, S 2 = aeronautic and S 3 = electronic) and in n = 5 geographical regions (R 1 = Africa, R 2 = North America, R 3 = South America, R 4 = Asia and R 5 = Europe) is considered. So, for each instant of time being considered, for each sector S j and each region R i , the observed value X i,j represents the rate of return of the investment in sector S j in region R i at that moment. The MCV of the rate of return is regarded as a measure of the relative volatility or risk of the investment, and this is why it makes sense to monitor it over time.
e. ,controlflow)byconsumingandproducing resourcessuchaswater,steam,mechanicalenergyorelectricalenergy(i. e. ,data-flow). Thisexample conformstothephysicallimitsdefinedintheterritory,systemandphysicalviews(seeFigure2,5, 6and7). evolutionofacriterion,i. e. ,thechangeinvalueovertime. Theaggregationfunction,asitsname suggests,aggregatesthevaluesofatleasttwocriteria. Theinfluencefunctiondetermineshowagiven criterion/system changes another one under specified conditions. The criteria, influences and aggregationsaredefinedmathematicallyformingthefunctionalityevaluationmodelusingtheObeo Designerplatform(https://www. obeodesigner. com). ThismodelisfurthertransformedintotheGAMA simulationplatform(https://gama-platform. github. io/)foracontinuousresilienceassessment. The resilienceassessmentapproachisbasedontenrulesillustratedinTable2.
ACKNowLeDGMeNT This paper shows a result of the RESIIST project (Résilience des infrastructures et systèmes interconnectés-ResilienceofInterconnectedInfrastructuresandSystemshttps://research-gi. minesalbi. fr/display/resiist/RESIIST+Home[inFrench]). TheRESIISTprojectisfundedjointlybythe FrenchNationalResearchAgency(ANR)andtheGeneralSecretaryofDefenseandNationalSecurity (SGDSN). TheGenMyModelmodelingplatform(https://www. genmymodel. com/)wasusedtodevelop themodelingdiagramsandthecodegenerationprocess. Thenewmodelingplatformandtheresults willbepubliclyavailableontheRESIISTprojectwebsite. Theauthorsacknowledgetheseorganizations fortheirsupport,andparticularly,theindustrialpartnersforthedefinitionoftheapplicationcases.
The data science tool set gives the chance to gain knowledge that supports the strategic and operative decision-making within the enterprise. The various systems-either internal or external-can be used as source systems. Data can be extracted, prepared, ingested, and then stored in a data lake, data warehouse, and data mart in a combination of available technologies. The data are transformed step-by-step so that they are ready for further analysis to acquire information and knowledge through the application of sophisticated data science algorithms according to the specific requirements for innovative business ideas  [16]. Data science and modern information architectures such as data lakes and data warehouses make it possible to very quickly analyze large datasets with heterogeneous structures.
The data scientist deals with interpreting the data that the data engineer has cleaned and made ready. The data scientist is dedicated to data analysis, and the data engineer concentrates on programming and developing databases-either relational or Not Only SQL style (NoSQL)  [26, 27] -to support data analytics. The data engineer's primary task is to understand and specify the requirements of the users. The task of data scientists is principally to define such models of data analysis that users can understand and perceive  [28, 29].
Methodology We focused on the business information systems and enterprise engineering trends in the domain of digital financial services. We followed a two-step qualitative process to determine the underlying enterprise architecture, collection, and use of customer data, of the generic data science financial ecosystem. First, we conducted desk research, obtained relevant documentation and related developments in the use of data science methods and tools in financial companies, and came up with the toolbox of data science methods for future researchers or practitioners to use in the domain. Second, we collected data regarding 4 case studies. We relied on the data sources as specified in Table  1. A descriptive review approach was employed to review relevant documentation on the enterprise architecture and to identify the typical patterns in financial e-services, process models, and enterprise architecture.
Data regrading the case studies were collected in Spring 2020. Multiple data sources were consulted. First, we collected general information about the companies through their websites, external documents, and sources in our possession. Some participants also provided us with further materials, in the form of business plans or initiative overviews. We also conducted interviews, giving us access to responders' interpretations of the processes happening inside their respective companies  [30]. Then, we moved on to "data triangulation" for the data coming from the case studies, essential for qualitative research such as ours, as this ensures trustworthy and accurate results  [31]. We followed an iterative process, initially gathering and considering only one set of secondary sources, before conducting interviews with the founders.
In data management and capture, there has been an evolution in the data-handling systems' overarching network and hierarchical, relational, and NoSQL database management systems, then data warehouses, data marts, and data lakes. This progress makes it possible for an enormous volume of data to be collected in databases and accessed on the web by companies. Decision support has been an interdisciplinary area between management, computer sciences, and business administration. It utilizes the methods of operational research in the mathematical background. Table  2. Relationships between methods of data analytics and disciplines of informatics  [41]. Business intelligence is perceived as data modeling and retrieval that are based on well-organized data warehouses storing high-quality data so that business intelligence creates standard reports.
The business requirements for forecasting and in-depth analysis stepped over the services of reporting. The exploratory data analysis along with the multidimensional databases offered approaches that provided techniques for such complex analysis as univariate, bivariate, and multivariate statistical investigation  [42, 43] , i. e. , a tool set for multidimensional analysis. The development of interrelated fields such as computational intelligence, artificial intelligence, and data science produced algorithms that combined with the exploratory data analysis laid the foundation for methods that can tackle complex business problems to forecast, predict, and prescribe the deeds of companies  [19].
For this section, we used the findings from our case studies. We analyzed and grouped the different data collected from the case studies, and we drew what would be the generic architecture of a financial company, taking into account and satisfying the different functional needs of the case studies. This architecture should provide the capability to cooperate securely from the consumers' desktops, business partners, and "suppliers" (business agents, brokers, etc. ) on the following lines: 1. Business policies: The policies incorporate the strategic management, vision, and the derivation of subpolicies such as security, data protection, etc. This set of policies defines the overarching objectives and the realization of the company vision; 2. Business processes at operation: These business processes and workflows are inherently collaborative and operate in a controlled, secure environment in which the acquired knowledge and information assets can be shared securely across diverse business units and, in some cases, silos that are created by necessity because of specific security and data protection constraints; 3.