Data‐Driven Optimization of Seismicity Models Using Diverse Data Sets: Generation, Evaluation, and Ranking Using Inlabru

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

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Data‐Driven simulation of inelastic materials using structured data sets and tangential transition rules

PAMM ◽

10.1002/pamm.202000241 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Kerem Ciftci ◽

Klaus Hackl

Keyword(s):

Structured Data ◽

Data Driven ◽

Data Sets ◽

Transition Rules

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Nonparametric reconstruction of the dark energy equation of state from diverse data sets

Physical Review D ◽

10.1103/physrevd.84.083501 ◽

2011 ◽

Vol 84 (8) ◽

Cited By ~ 43

Author(s):

Tracy Holsclaw ◽

Ujjaini Alam ◽

Bruno Sansó ◽

Herbie Lee ◽

Katrin Heitmann ◽

...

Keyword(s):

Dark Energy ◽

Equation Of State ◽

Energy Equation ◽

Data Sets ◽

Diverse Data

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Data-driven campaigns in public sensemaking: Discursive positions, contextualization, and maneuvers in American, British, and German debates around computational politics

Communications ◽

10.1515/commun-2019-0125 ◽

2020 ◽

Vol 45 (s1) ◽

pp. 535-559

Author(s):

Christian Pentzold ◽

Lena Fölsche

Keyword(s):

United States ◽

United Kingdom ◽

Presidential Election ◽

Data Analytics ◽

Large Data ◽

Large Data Sets ◽

Data Driven ◽

Data Sets ◽

Federal Election ◽

Online Comments

AbstractOur article examines how journalistic reports and online comments have made sense of computational politics. It treats the discourse around data-driven campaigns as its object of analysis and codifies four main perspectives that have structured the debates about the use of large data sets and data analytics in elections. We study American, British, and German sources on the 2016 United States presidential election, the 2017 United Kingdom general election, and the 2017 German federal election. There, groups of speakers maneuvered between enthusiastic, skeptical, agnostic, or admonitory stances and so cannot be clearly mapped onto these four discursive positions. Coming along with the inconsistent accounts, public sensemaking was marked by an atmosphere of speculation about the substance and effects of computational politics. We conclude that this equivocality helped journalists and commentators to sideline prior reporting on the issue in order to repeatedly rediscover the practices they had already covered.

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Stamping Plant 4.0 – Basics for the Application of Data Mining Methods in Manufacturing Car Body Parts

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.639.21 ◽

2015 ◽

Vol 639 ◽

pp. 21-30 ◽

Cited By ~ 7

Author(s):

Stephan Purr ◽

Josef Meinhardt ◽

Arnulf Lipp ◽

Axel Werner ◽

Martin Ostermair ◽

...

Keyword(s):

Data Mining ◽

Data Acquisition ◽

Data Driven ◽

Quality Analysis ◽

Process Conditions ◽

Data Sets ◽

Body Parts ◽

Car Body ◽

Sample Data ◽

Mining Methods

Data-driven quality evaluation in the stamping process of car body parts is quite promising because dependencies in the process have not yet been sufficiently researched. However, the application of data mining methods for the process in stamping plants would require a large number of sample data sets. Today, acquiring these data represents a major challenge, because the necessary data are inadequately measured, recorded or stored. Thus, the preconditions for the sample data acquisition must first be created before being able to investigate any correlations. In addition, the process conditions change over time due to wear mechanisms. Therefore, the results do not remain valid and a constant data acquisition is required. In this publication, the current situation in stamping plants regarding the process robustness will be first discussed and the need for data-driven methods will be shown. Subsequently, the state of technology regarding the possibility of collecting the sample data sets for quality analysis in producing car body parts will be researched. At the end of this work, an overview will be provided concerning how this data collection was implemented at BMW as well as what kind of potential can be expected.

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Future of Medical Research in Rare Diseases and Cancers: Shift from Pharma to Biotech and the Golden Age of Medical Advancement

Cancer and Clinical Oncology ◽

10.5539/cco.v6n2p12 ◽

2017 ◽

Vol 6 (2) ◽

pp. 12

Author(s):

Abhith Pallegar

Keyword(s):

Big Data ◽

Medical Research ◽

Rare Diseases ◽

Network Effects ◽

Medical Knowledge ◽

Economic Cost ◽

Medical Data ◽

Data Sets ◽

Leading Role ◽

Diverse Data

The objective of the paper is to elucidate how interconnected biological systems can be better mapped and understood using the rapidly growing area of Big Data. We can harness network efficiencies by analyzing diverse medical data and probe how we can effectively lower the economic cost of finding cures for rare diseases. Most rare diseases are due to genetic abnormalities, many forms of cancers develop due to genetic mutations. Finding cures for rare diseases requires us to understand the biology and biological processes of the human body. In this paper, we explore what the historical shift of focus from pharmacology to biotechnology means for accelerating biomedical solutions. With biotechnology playing a leading role in the field of medical research, we explore how network efficiencies can be harnessed by strengthening the existing knowledge base. Studying rare or orphan diseases provides rich observable statistical data that can be leveraged for finding solutions. Network effects can be squeezed from working with diverse data sets that enables us to generate the highest quality medical knowledge with the fewest resources. This paper examines gene manipulation technologies like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) that can prevent diseases of genetic variety. We further explore the role of the emerging field of Big Data in analyzing large quantities of medical data with the rapid growth of computing power and some of the network efficiencies gained from this endeavor.

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Linking Norms, Ratings, and Relations of Words and Concepts Across Multiple Language Varieties

10.31234/osf.io/tgw3z ◽

2020 ◽

Author(s):

Annika Tjuka ◽

Robert Forkel ◽

Johann-Mattis List

Keyword(s):

Web Application ◽

Age Of Acquisition ◽

Data Curation ◽

Data Sets ◽

Data Types ◽

Word Meanings ◽

Language Varieties ◽

Diverse Data ◽

Multiple Languages ◽

Word Frequencies

Psychologists and linguists have collected a great diversity of data for word and concept properties. In psychology, many studies accumulate norms and ratings such as word frequencies or age-of-acquisition often for a large number of words. Linguistics, on the other hand, provides valuable insights into relations of word meanings. We present a collection of those data sets for norms, ratings, and relations that cover different languages: ‘NoRaRe.’ To enable a comparison between the diverse data types, we established workflows that facilitate the expansion of the database. A web application allows convenient access to the data (https://digling.org/norare/). Furthermore, a software API ensures consistent data curation by providing tests to validate the data sets. The NoRaRe collection is linked to the database curated by the Concepticon project (https://concepticon.clld.org) which offers a reference catalog of unified concept sets. The link between words in the data sets and the Concepticon concept sets makes a cross-linguistic comparison possible. In three case studies, we test the validity of our approach, the accuracy of our workflow, and the applicability of our database. The results indicate that the NoRaRe database can be applied for the study of word properties across multiple languages. The data can be used by psychologists and linguists to benefit from the knowledge rooted in both research disciplines.

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