Data file

Summary

A data file is a which stores data to be used by a computer application or system, including input and output data. A data file usually does not contain instructions or code to be executed (that is, a computer program). Most of the computer programs work with data files. Types of data files Data files can be stored in two ways:

Text files;
Binary files.

Text files A text file (also called ASCII files) stores information in ASCII characters. A text file contains human-readable characters. A user can read the contents of a text file or edit it using a text editor. In text files, each line of text is terminated, (delimited) with a special character known as EOL (End of Line) character. In text files some internal translations take place when this EOL character is read or written. Examples of text files *A text document (often .txt) Binary files A binary file is a file that contains information in the same format in which the information is held i

Official source

https://en.wikipedia.org/wiki/Data_file

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (3)

Related publications

Related people

Related units

Related concepts (1)

Related concepts

Related courses (6)

Related courses

Related lectures (14)

Related lectures

Practical algorithms for gathering stored correlated data in a network

Ramin Khalili

Many sensing systems remotely monitor/measure an environment at several sites, and then report these observations to a central site. We propose and investigate several practical algorithms for joint routing and compression of data files as they are forward from remote nodes to a central site, with the goal of minimizing the communication cost incurred. Our algorithms are practical in that they do not assume that nodes have a priori information about the correlation structure (and resulting compression gains) of the individual measurements at a given sensor or among multiple sensors. Instead, this correlation structure is learned as pieces of the files are routed and jointly compressed on their way to the sink, and routes are adaptively changed as the nodes learn more about the correlation structure of the data.

2008

Sensitivity of the SHiP experiment to Heavy Neutral Leptons

Aurelio Bay, Alexey Boyarsky, Nikolaos Charitonidis, Hans Dijkstra, Elena Graverini, Guido Haefeli, Sun Hee Kim, Ekaterina Kuznetsova, Anton Petrov, Federico Leo Redi, Oleg Ruchayskiy, Lesya Shchutska, Sergei Smirnov, Maria Elena Stramaglia, Jean-Loup Tastet, Inar Timiryasov

Heavy Neutral Leptons (HNLs) are hypothetical particles predicted by many extensions of the Standard Model. These particles can, among other things, explain the origin of neutrino masses, generate the observed matter-antimatter asymmetry in the Universe and provide a dark matter candidate. The SHiP experiment will be able to search for HNLs produced in decays of heavy mesons and travelling distances ranging between O(50 m) and tens of kilometers before decaying. We present the sensitivity of the SHiP experiment to a number of HNL's benchmark models and provide a way to calculate the SHiP's sensitivity to HNLs for arbitrary patterns of flavour mixings. The corresponding tools and data files are also made publicly available.

Springer2019

, ,

Database management systems (DBMS) provide incredible flexibility and performance when it comes to query processing, scalability and accuracy. To fully exploit DBMS features, however, the user must define a schema, load the data, tune the system for the expected workload, and answer several questions. Should the database use a column-store, a row-store or some hybrid format? What indices should be created? All these questions make for a formidable and time-consuming hurdle, often deterring new applications or imposing high cost to existing ones. A characteristic example is that of scientific databases with huge data sets. The prohibitive initialization cost and complexity still forces scientists to rely on "ancient" tools for their data management tasks, delaying scientific understanding and progress. Users and applications collect their data in flat files, which have traditionally been considered to be "outside" a DBMS. A DBMS wants control: always bring all data "inside", replicate it and format it in its own "secret" way. The problem has been recognized and current efforts extend existing systems with abilities such as reading information from flat files and gracefully incorporating it into the processing engine. This paper proposes a new generation of systems where the only requirement from the user is a link to the raw data files. Queries can then immediately be fired without preparation steps in between. Internally and in an abstract way, the system takes care of selectively, adaptively and incrementally providing the proper environment given the queries at hand. Only part of the data is loaded at any given time and it is being stored and accessed in the format suitable for the current workload.

2011