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Data collection, storage, analytics, processing and AI

Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing. The components of data acquisition systems include:

  • Sensors, to convert physical parameters to electrical signals.
  • Signal conditioning circuitry, to convert sensor signals into a form that can be converted to digital values.
  • Analog-to-digital converters, to convert conditioned sensor signals to digital values.

Data acquisition applications are usually controlled by software programs developed using various general purpose programming languages
So, as a summary, Data acquisition is in itself a vast group of protocols, technologies, sensors, hardware and software…


Data storage is the recording (storing) of information (data) in a storage medium. DNA and RNA, handwriting, phonographic recording, magnetic tape, and optical discs are all examples of storage media. (from

  • ICT solutions for next generation data storage and information mining
  • Dataspaces

    Dataspaces are an abstraction in data management that aim to overcome some of the problems encountered in data integration system. The aim is to reduce the effort required to set up a data integration system by relying on existing matching and mapping generation techniques, and to improve the system in "pay-as-you-go" fashion as it is used. (From

  • Relational databases

Data processing is, generally, the collection and manipulation of items of data to produce meaningful information (

  • Cloud computing, edge computing

Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques. (from

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is used to describe machines that mimic "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving"  (from

  • Fuzzy logic

    Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 inclusive. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false (from

  • Neural networks

    Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks and astrocytes that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs.[4] Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. (from

  • Genetic algorithms

    In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. (

  • Heuristics

    A heuristic function, also called simply a heuristic, is a function that ranks alternatives in search algorithms at each branching step based on available information to decide which branch to follow. (from