Bitcoins sind derzeit auch bei uns am CERN ein brandheißes Thema. Innerhalb weniger Wochen stieg der Wert eines Bitcoins (BTC) von 20 Cent im Dezember 2010 auf Größenordnungen von bis zu 30 Dollar. Dennoch lohnt sich das Mining kaum, zumindest nicht zu den aktuellen Strompreisen.
Die Bitcoin-Börse bitcoin.de schafft hier nun Abhilfe! Ein gutes halbes Jahr später, am 26. August 2011, hat der erste deutsche Marktplatz zum Kaufen und Verkaufen von Bitcoins den Handel aufgenommen. Auf bitcoin.de können User auf einfache Art und Weise Bitcoins an andere User verkaufen oder von diesen kaufen.
Dafür ist es erforderlich, dass sich die User bei bitcoin.de registrieren und, insofern sie als Verkäufer auftreten wollen, auf ihr Benutzerkonto ein Bitcoin-Guthaben übertragen. Sobald für die eigenen Bitcoins ein Käufer gefunden wurde, werden automatisch alle Informationen zur Bezahlung an den Käufer übermittelt.
Die Bezahlung der Bitcoins erfolgt direkt zwischen Käufer und Verkäufer. Erst wenn die Zahlung beim Verkäufer eingegangen ist, werden die Bitcoins abzüglich einer geringen Gebühr aus dem Guthaben des Verkäufers in das Guthaben des Käufers übertragen.
On this day 20 years ago the world’s first website went live. The website, created by Tim Berners-Lee at CERN, was a basic text page with hyperlinks and went live on August 6, 1991.
The website was hosted on Berners-Lees‘ NeXT computer, the first web server ever, which had a note taped to the front that said: „This machine is a server. DO NOT POWER DOWN“.
NeXT computer used as first World Wide Web server
Today this computer is displayed in the CERN Computer Center, which is just located next to my office.
Time really flies when you immerse yourself in the world of data science research and unravel the mysteries of the universe! It’s been an incredible journey over the past year as I’ve immersed myself in the world of data science at CERN. For those unfamiliar, CERN — set against a stunning backdrop of snow-capped mountains and tranquil Lake Geneva — is home to the Large Hadron Collider (LHC), the world’s most powerful particle accelerator. But what often goes unnoticed is the critical role that data science plays in powering this colossal machine and its quest for groundbreaking discoveries like the elusive Higgs boson.
The Data Tsunami: A Behind-The-Scenes Look
Imagine having to sift through one petabyte (PB) of data every second — yes, you read that right. That’s the amount of data generated by the LHC’s detectors. To make it manageable, high-level triggers act as an advanced filtering system, reducing this torrent of data to a more digestible gigabyte per second. This filtered data then finds its way to the LHC Computing Grid.
High-Level Trigger data flow, crucial for data science research in the ALICE experiment at CERN.
About 50PB of this data is stored on tape, and another 20PB is stored on disk, managed by a Hadoop-based cloud service. This platform runs up to two million tasks per day, making it a beehive of computational activity.
The Role of Data Science Research at CERN
Data scientists and software engineers are the unsung heroes at CERN, ensuring the smooth operation of the LHC and subsequent data analysis. Machine learning algorithms are used to discover new correlations between variables, including both LHC data and external data sets. This is critical for real-time analysis, where speed and accuracy are of the essence.
While managing the exponential growth of data is an ongoing challenge, the role of data scientists at CERN goes far beyond that. We are at the forefront of fostering a data-driven culture within the organization, transferring knowledge, and implementing best practices. In addition, as technology continues to evolve, part of our role is to identify and integrate new, cutting-edge tools that meet our specific data analysis needs.
The Road Ahead: A Data-Driven Journey
Looking ahead, scalability will remain a key focus as CERN’s data continues to grow. But the horizon of possibilities is vast. From exploring quantum computing to implementing advanced AI models, the role of data science in accelerating CERN’s research goals will only grow.
As I celebrate my one-year anniversary at CERN, I’m filled with gratitude and awe for what has been an incredible journey. From delving into petabytes of data to pushing the boundaries of machine learning in research, it’s been a year of immense learning and contribution.
Gestern haben wir Angels & Demons (deutscher Titel: Illuminati) im Kino gesehen. Die Verfilmung des gleichnamigen Bestsellers von Dan Brown war vor allem visuell sehr ansprechend. Tom Hanks hat wie schon in Da Vinci Code souverän die Rolle des Protagonisten Robert Langdon verkörpert.
Ein Teil der Handlung des Films spielt am CERN. Tatsächlich wurden einige Einstellungen am ATLAS-Detektor des LHC gedreht. Regisseur Ron Howard sah sich ebenfalls das CERN-Gelände an, um den Film authentischer zu gestalten. Die Herstellung einer Bombe aus Antimaterie ist hingegen ebenso Fiktion wie die „Schöpfung aus dem Nichts“, welche im Film lediglich dazu dient den Konflikt zwischen Religion und Naturwissenschaft zu entfachen.
CERN hat Angels & Demons eigens eine Ausstellung (siehe Foto oben) und eine Website gewidmet, um „the science behind the story“ zu erläutern und auf häufig gestellte Fragen („Does CERN create black holes?“, etc.) einzugehen.
Screenshot of Tableau 4.0 analyzing High Energy Physics Data at CERN
About a year ago, I had a first try with Tableau and some survey data for a university project. Last week, I finally found time to test Tableau with High Energy Physics (HEP) data from CERN’s Proton Synchrotron (PS). Tableau enjoys a stellar reputation among the data visualization community, while the HEP community heavily uses Gnuplot and Python.
Tableau 4.0: Connect to Data
I was using an ordinary CSV file as data source for this quick visualization. Furthermore, Tableau can connect to other file types such as Excel, as well as to databases like Microsoft SQL Server, Oracle, and Postgres.
I’m also quite impressed by the ease and speed with which insightful analysis seems to appear out of bland data. Even though your analysis toolchain is script-based (as usual at CERN where batch processing is mandatory), I highly recommend using Tableau for prototyping and for ad-hoc data exploration.
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