Data Placement And Task Mapping Optimization For Big Data Workflows In The Cloud

Data-centric workflows naturally process and analyze a huge volume of datasets. In this new era of Big Data there is a growing need to enable data-centric workflows to perform computations at a scale far exceeding a single workstation\u27s capabilities. Therefore, this type of applications can benefit from distributed high performance computing (HPC) infrastructures like cluster, grid or cloud computing. Although data-centric workflows have been applied extensively to structure complex scientific data analysis processes, they fail to address the big data challenges as well as leverage the capability of dynamic resource provisioning in the Cloud. The concept of “big data workflows” is proposed by our research group as the next generation of data-centric workflow technologies to address the limitations of exist-ing workflows technologies in addressing big data challenges. Executing big data workflows in the Cloud is a challenging problem as work-flow tasks and data are required to be partitioned, distributed and assigned to the cloud execution sites (multiple virtual machines). In running such big data work-flows in the cloud distributed across several physical locations, the workflow execution time and the cloud resource utilization efficiency highly depends on the initial placement and distribution of the workflow tasks and datasets across the multiple virtual machines in the Cloud. Several workflow management systems have been developed for scientists to facilitate the use of workflows; however, data and work-flow task placement issue has not been sufficiently addressed yet. In this dissertation, I propose BDAP strategy (Big Data Placement strategy) for data placement and TPS (Task Placement Strategy) for task placement, which improve workflow performance by minimizing data movement across multiple virtual machines in the Cloud during the workflow execution. In addition, I propose CATS (Cultural Algorithm Task Scheduling) for workflow scheduling, which improve workflow performance by minimizing workflow execution cost. In this dissertation, I 1) formalize data and task placement problems in workflows, 2) propose a data placement algorithm that considers both initial input dataset and intermediate datasets obtained during workflow run, 3) propose a task placement algorithm that considers placement of workflow tasks before workflow run, 4) propose a workflow scheduling strategy to minimize the workflow execution cost once the deadline is provided by user and 5)perform extensive experiments in the distributed environment to validate that our proposed strategies provide an effective data and task placement solution to distribute and place big datasets and tasks into the appropriate virtual machines in the Cloud within reasonable time

(Neg)Entropic scenarios affecting the wicked design spaces of knowledge management systems

CITATION: Schmitt, U. 2020. (Neg)Entropic scenarios affecting the wicked design spaces of knowledge management systems. Entropy, 22(2):169, doi:10.3390/e22020169.The original publication is available at https://www.mdpi.comThe envisioned embracing of thriving knowledge societies is increasingly compromised by threatening perceptions of information overload, attention poverty, opportunity divides, and career fears. This paper traces the roots of these symptoms back to causes of information entropy and structural holes, invisible private and undiscoverable public knowledge which characterize the sad state of our current knowledge management and creation practices. As part of an ongoing design science research and prototyping project, the article’s (neg)entropic perspectives complement a succession of prior multi-disciplinary publications. Looking forward, it proposes a novel decentralized generative knowledge management approach that prioritizes the capacity development of autonomous individual knowledge workers not at the expense of traditional organizational knowledge management systems but as a viable means to foster their fruitful co-evolution. The article, thus, informs relevant stakeholders about the current unsustainable status quo inhibiting knowledge workers; it presents viable remedial options (as a prerequisite for creating the respective future generative Knowledge Management (KM) reality) to afford a sustainable solution with the generative potential to evolve into a prospective general-purpose technology.https://www.mdpi.com/1099-4300/22/2/169Publisher's versio

Bridging Nano and Micro-scale X-ray Tomography for Battery Research by Leveraging Artificial Intelligence

X-ray Computed Tomography (X-ray CT) is a well-known non-destructive imaging technique where contrast originates from the materials' absorption coefficients. Novel battery characterization studies on increasingly challenging samples have been enabled by the rapid development of both synchrotron and laboratory-scale imaging systems as well as innovative analysis techniques. Furthermore, the recent development of laboratory nano-scale CT (NanoCT) systems has pushed the limits of battery material imaging towards voxel sizes previously achievable only using synchrotron facilities. Such systems are now able to reach spatial resolutions down to 50 nm. Given the non-destructive nature of CT, in-situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area, and volume expansion during battery operation or cycling. Combined with powerful Artificial Intelligence (AI)/Machine Learning (ML) analysis techniques, extracted 3D tomograms and battery-specific morphological parameters enable the development of predictive physics-based models that can provide valuable insights for battery engineering. These models can predict the impact of the electrode microstructure on cell performances or analyze the influence of material heterogeneities on electrochemical responses. In this work, we review the increasing role of X-ray CT experimentation in the battery field, discuss the incorporation of AI/ML in analysis, and provide a perspective on how the combination of multi-scale CT imaging techniques can expand the development of predictive multiscale battery behavioral models.Comment: 33 pages, 5 figure

Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities

Reframing a novel decentralized knowledge management concept as a desirable vision: As we may realize the Memex

CITATION: Schmitt, Ulrich. 2021. "Reframing a Novel Decentralized Knowledge Management Concept as a Desirable Vision: As We May Realize the Memex" Sustainability 13(7): 4038. doi.10.3390/su13074038The original publication is available at https://www.mdpi.com/journal/sustainabilityProposing a major (though envisaged synergetic) shift in the knowledge management (KM) paradigm needs to convince a skeptical audience. This article attempts such a feat and motivates its conceptual considerations by fusing a wide scope of theoretical KM-related foundations in response to current KM unsustainabilities and emerging enabling technologies. The envisioned workflows, infrastructure, affordances, and impact resulting from the progressing design science research and prototyping efforts are consolidated and reframed, guided by a five-step visioneering process and twelve triple-criteria-clusters combining innovative, technological, and vision-related qualities. Inspired by Bush’s “Memex”, a desirable vision never realized since its suggestion three quarters of a century ago, the novel KM system (KMS) pursues the scenario of a mutually beneficial co-evolution between individual and institutional KM activities. This article follows up on the unsatisfactory and unsustainable state of current KM affairs suffering from accelerating information abundance, invisible work, structural interdisciplinary holes, lacking personal tools, and widening opportunity divides. By portraying a potentially transformative and game-changing technology, the crafting and drafting of a desirable, sustainable, and viable KMS vision assures transparency and can be more easily shared with a critical mass of stakeholders as a prerequisite for creating the respective future KM reality. The drafting of the “Desirable Sustainability Vision” is envisaged to assist a currently accepted KMS start-up project and investment.Publisher’s versio

Toward a Unified Description of Battery Data

Battery research initiatives and giga-scale production generate an abundance of diverse data spanning myriad fields of science and engineering. Modern battery development is driven by the confluence of traditional domains of natural science with emerging fields like artificial intelligence and the vast engineering and logistical knowledge needed to sustain the global reach of battery Gigafactories. Despite the unprecedented volume of dedicated research targeting affordable, high-performance, and sustainable battery designs, these endeavours are held back by the lack of common battery data and vocabulary standards, as well as, machine readable tools to support interoperability. An ontology is a data model that represents domain knowledge as a map of concepts and the relations between them. A battery ontology offers an effective means to unify battery-related activities across different fields, accelerate the flow of knowledge in both human- and machine-readable formats, and support the integration of artificial intelligence in battery development. Furthermore, a logically consistent and expansive ontology is essential to support battery digitalization and standardization efforts, such as, the battery passport. This review summarizes the current state of ontology development, the needs for an ontology in the battery field, and current activities to meet this need.publishedVersio

Discovering teacher and student needs in online courses for improving the Learning Management System (LMS) of universities

The COVID-19 pandemic in 2020 caused a shift from traditional classroom learning to online learning in higher education institutions. This rapid environmental change confused teachers and students, due to their inadequate readiness and past experience with online learning. As synchronous learning had been the primary approach for universities, teachers encountered difficulties increasing asynchronous learning experiences for students, which occur in a Learning Management System (LMS). Therefore, it was apparent that LMSs should be further developed to help teachers ensure a high quality of education asynchronously. This thesis investigates challenges that teachers and students faced in online courses, particularly during the pandemic. Thus, an improved workflow with user-interfaces is proposed that could support teachers to enhance work efficiency and asynchronous interactions with students. Ultimately, teacher and student needs are discovered to help with the development of the LMS that could incorporate digital technologies into teaching practices in an asynchronous learning environment. The research adopts service design and user-centred approaches to collect and analyse qualitative data. The qualitative research methods include interviews and observations, and data analysis is conducted by affinity diagram. Moreover, the concept proposal is validated through a focus group with teachers. Hence, three gaps between teachers and students are identified, reflecting thirteen challenges of online learning. Thus, a workflow is designed based on a teaching process which follows the journey of online courses, and four features that need to be improved are suggested including efficiency, flexibility, compatibility, and learnability. The research is expected to impact on future studies about the development of an LMS that could provide students with high quality of asynchronous learning experiences in universities