Software has become integral part of any scientific project or experiment. It is used to operate and monitor hardware devices, accumulate, store and analyze data, and visualize results. A part of the experiment software, as well as the hardware, is highly specialized and can only be developed and maintained by the real domain experts. But usually, the larger part of the software is a common one that is used in any other businesses worldwide. This includes databases, web applications, work-flow management, access management, geographic information, etc. Scientific people are very smart thus after taking care of the specialized hardware and software they start to develop and maintain the other parts of the project. It is not uncommon that the common software part that provides data storage and management, visualization, etc. becomes even bigger in the number of code lines than the specialized project or experiment itself. This takes ever more and more efforts and knowledge to develop and maintain. And eventually, projects find themselves with the swiveling towers of custom, badly written and unmaintainable software with no support. This happens because, though junior scientists that are assigned to take care of the software are smart and know how to 'do things' they do not know (or do not want to know) a word about design patterns, coding standards, testing, project iteration cycles, etc. it is common, that they use outdated or too novice software tools and packages or misuse them this eventually leads to disaster. To solve this problem scientific projects and experiments have to take software professionals on board. Just like in other businesses, scientists have to inject their projects with the experienced software people that could drive their software projects as manager(s), analyst(s) and/or programmer(s). It is a win-win situation because while working closely with software professionals junior scientists will learn and eventually become better in the software themselves. It is necessary to mention that a rare software company would go with scientific project because those usually relate with lots of uncertainties in the scope, budget and requirements. This is there computer scientists that have an extensive project management and analysis experience in businesses can help. They are agile, willing to learn and take uncertainty as the must. Important thing is that their budget and management model is just as yours.
Vilnius University started the collaboration with CERN (namely with CMS experiment) in 2005. Within these years VU accumulated a lot of experience designing/producing/giving IT and programming services to CMS scientists and specialists. In this period of time there were 58 students, who accomplished internship at CERN, now there are 7 former students of VU working within long-lasting (1, 2, 3 years of duration) projects of CMS. Current competences of Vilnius University (developed within collaboration with CMS, not to mention other topics) include:
The team of Vilnius university students and researches are working on the detector and data monitoring and error detection tools for the CMS detector at LHC. Below is the list of current projects.
CERN, the European Organization for Nuclear Research, is one of the world's largest and most respected centres for scientific research. Its business is fundamental physics, finding out what the Universe is made of and how it works. At CERN, the world's largest and most complex scientific instruments are used to study the basic constituents of matter - the fundamental particles. By studying what happens when these particles collide, physicists learn about the laws of Nature. The instruments used at CERN are particle accelerators and detectors. Accelerators boost beams of particles to high energies before they are made to collide with each other or with stationary targets. Detectors observe and record the results of these collisions. Founded in 1954, the CERN Laboratory sits astride the Franco-Swiss border near Geneva. It was one of Europe's first joint ventures and now has 20 Member States.More about CERN
The Large Hadron Collider (LHC) at CERN smashes protons together at close to the speed of light with seven times the energy of the most powerful accelerators built up to now. Some of the collision energy is turned into mass, creating new particles which are observed in the Compact Muon Solenoid (CMS) particle detector. CMS data is analyzed by scientists around the world to build up a picture of what happened at the heart of the collision. This will help us answer questions such as: "what is the Universe really made of and what forces act within it?" and "what gives everything substance?" Such research increases our basic understanding and may also spark new technologies that change the world we live in.More about CMS
The online and offline Web-Based Monitoring (WBM) system of the CMS experiment consists of a web services framework based on Jakarta/Tomcat and the ROOT data display package. The primary source of data used by WBM is the online Oracle database; the WBM tools provide browsing and transformation functions to convert database entries into HTML tables, graphical plot representations, XML, text and ROOT-based object output.
The monitoring and certification of the quality of the CMS data consists of a multi-step procedure, spanning from online data taking to the offline reprocessing of data recorded earlier. The quality assessment is based on both visual inspection of data distributions by monitoring shift persons as well as algorithmic tests of the distributions against references. The Run Registry (RR), reported here, is the central workflow management and tracking tool used to certify collected data, to keep track of the certification results and to expose them to the whole CMS collaboration. The RR consists of the suite of 3 applications (online, offline and user) with a web-based user interface frontend and a dedicated database and includes facilities for the manual input of data quality decisions and the automatic collection of detector and beam conditions, as well as the querying of the data and the export of selected information onto the screen and/or into flat output files of various formats. Among other purposes, is regularly used by the CMS collaboration for the creation of official 'good-run list' files which are used as input to downstream selection of the data for reprocessings and for physics analyses. Automation of the data input and querying is possible through an API. The web based service is protected using the CERN Single-Sign-On service. Locally, on the Run Registry, an SSL-certificate system is used to authorize users for different roles: read-only, online shift, offline shift or expert.
Cathode strip chambers (CSC) compose the endcap muon system of the CMS experiment at the LHC. Two years of data taking have proven that various online systems like Detector Control System (DCS), Data Quality Monitoring (DQM), Trigger, Data Acquisition (DAQ) and other specialized applications driven by the experts are doing their task very well. But the need for better integration between these systems is starting to emerge. Automatic and fast problem identification and resolution, tracking detector performance trend, maintenance of known problems, current and past detector status and alike tasks are still hard to handle and require a lot of efforts from many experts. Moreover, this valuable expert knowledge is rarely well documented. CSC Expert system prototype is aiming to fill in these gaps and provides a solution for online systems integration and automation. Its design is based on solid industry standards - Service Bus and Application Integration, Data Warehouse and Online analytical processing (OLAP), Complex Event Processing (CEP, i.e. Rule Engine) and ontology based Knowledge Base. CSC Expert system receives and accumulates Facts (i.e. detector status, conditions, shifter/expert actions), manages Conclusions (i.e. hot device, masked chamber, weak HV segment, high radiation background), stores detector inventory - Assets (i.e. hardware, software, links) and outputs Facts, Conclusions and Assets for other applications and users. CEP engine allows experts to describe their valuable knowledge in SQL-like language and to execute it taking subsequent action in real time (e.g. sends emails, SMS'es, commands and fact requests to other applications, raise alarms). A year of running the CSC Expert system has proven the correctness of the solution and displays its applicability in detector control automation.