|
Drift (data Science)
In data science and related fields, drift is an evolution of data that invalidates the data model. Common areas where identification of data drift is important are machine learning and data mining, as well as maintenance of large software systems. Drift detection and drift adaptation are of paramount importance in the fields that involve dynamically changing data and data models. Predictive model decay In machine learning and predictive analytics this drift phenomenon is called concept drift. In machine learning, a common element of a data model are the statistical properties, such as probability distribution of the actual data. If they deviate from the statistical properties of the training data set, then the learned predictions may become invalid, if the drift is not addressed. Data configuration decay Another important area is software engineering, where three types of data drift affecting data fidelity may be recognized. Changes in the software environment ("infrastructur ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data, and apply knowledge from data across a broad range of application domains. Data science is related to data mining, machine learning, big data, computational statistics and analytics. Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" in order to "understand and analyse actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science ( empirical, theoretical, computational, and now data-driven) and asserted that "everything about sc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Software Engineering
Software engineering is a systematic engineering approach to software development. A software engineer is a person who applies the principles of software engineering to design, develop, maintain, test, and evaluate computer software. The term '' programmer'' is sometimes used as a synonym, but may also lack connotations of engineering education or skills. Engineering techniques are used to inform the software development process which involves the definition, implementation, assessment, measurement, management, change, and improvement of the software life cycle process itself. It heavily uses software configuration management which is about systematically controlling changes to the configuration, and maintaining the integrity and traceability of the configuration and code throughout the system life cycle. Modern processes use software versioning. History Beginning in the 1960s, software engineering was seen as its own type of engineering. Additionally, the development of soft ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Snyk
Snyk is a cybersecurity company specializing in cloud computing. It was founded in 2015 out of London and Tel Aviv with headquarters in Boston. History Snyk was founded in 2015 by Guy Podjarny (Hebrew: גיא פודחרני), Assaf Hefetz (אסף חפץ), and Danny Grander, (דני גרנדר) coming from Unit 8200, a SIGINT unit of the Israel Defense Forces."THE ISRAELI CYBER COMPANY SNYK IS PREPARING FOR AN INITIAL PUBLIC OFFERING" ''Israel Financial Insider'', January 1, 2022"Israeli cybersecurity co Snyk raises $150m" '' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Checksum
A checksum is a small-sized block of data derived from another block of digital data for the purpose of detecting errors that may have been introduced during its transmission or storage. By themselves, checksums are often used to verify data integrity but are not relied upon to verify data authenticity. The procedure which generates this checksum is called a checksum function or checksum algorithm. Depending on its design goals, a good checksum algorithm usually outputs a significantly different value, even for small changes made to the input. This is especially true of cryptographic hash functions, which may be used to detect many data corruption errors and verify overall data integrity; if the computed checksum for the current data input matches the stored value of a previously computed checksum, there is a very high probability the data has not been accidentally altered or corrupted. Checksum functions are related to hash functions, fingerprints, randomization functions ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
InformationWeek
''InformationWeek'' is a digital magazine which conducts corresponding face-to-face events, virtual events, and research. It is headquartered in San Francisco, California and was first published in 1985 by CMP Media, later called Informa. The print edition of the magazine has ceased, with the last issue published on June 24, 2013. History The print edition began in 1985 using the name ''Information Week''. * April 1999 - Information Week began its 14th international edition: Brazil. * May 1997 through 2000 – The worldwide regional publications of '' LAN Magazine'' were renamed to the already existing ''Network Magazine''. Networkmagazine.com and lanmag.com now redirect to informationweek.com * September 2005 – ''Network Magazine'' (networkmagazine.com) was renamed ''IT Architect'' (itarchitect.com). The offline publication was shut down after the March 2006 issue. itarchitect.com now redirects to InformationWeek. * June 2006 – The company announced that offline publicati ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cloud Computing
Cloud computing is the on-demand availability of computer system resources, especially data storage ( cloud storage) and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each of which is a data center. Cloud computing relies on sharing of resources to achieve coherence and typically uses a "pay as you go" model, which can help in reducing capital expenses but may also lead to unexpected operating expenses for users. Value proposition Advocates of public and hybrid clouds claim that cloud computing allows companies to avoid or minimize up-front IT infrastructure costs. Proponents also claim that cloud computing allows enterprises to get their applications up and running faster, with improved manageability and less maintenance, and that it enables IT teams to more rapidly adjust resources to meet fluctuating and unpredictable demand, providing burst computing capability: high computing p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Database Schema
The database schema is the structure of a database described in a formal language supported by the database management system (DBMS). The term "schema" refers to the organization of data as a blueprint of how the database is constructed (divided into database tables in the case of relational databases). The formal definition of a database schema is a set of formulas (sentences) called integrity constraints imposed on a database. These integrity constraints ensure compatibility between parts of the schema. All constraints are expressible in the same language. A database can be considered a structure in realization of the database language. The states of a created conceptual schema are transformed into an explicit mapping, the database schema. This describes how real-world entities are modeled in the database. "A database schema specifies, based on the database administrator's knowledge of possible applications, the facts that can enter the database, or those of interest to the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Fidelity
Data integrity is the maintenance of, and the assurance of, data accuracy and consistency over its entire life-cycle and is a critical aspect to the design, implementation, and usage of any system that stores, processes, or retrieves data. The term is broad in scope and may have widely different meanings depending on the specific context even under the same general umbrella of computing. It is at times used as a proxy term for data quality, while data validation is a prerequisite for data integrity. Data integrity is the opposite of data corruption. The overall intent of any data integrity technique is the same: ensure data is recorded exactly as intended (such as a database correctly rejecting mutually exclusive possibilities). Moreover, upon later retrieval, ensure the data is the same as when it was originally recorded. In short, data integrity aims to prevent unintentional changes to information. Data integrity is not to be confused with data security, the discipline of protect ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Training Data Set
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided in multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation and test sets. The model is initially fit on a training data set, which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. The model (e.g. a naive Bayes classifier) is trained on the training data set using a supervised learning method, for example using optimization methods such as gradient descent or stochastic gradient descent. In practice, the training data set often consists of pairs of an input vector (or scalar) and the corresponding ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Model
A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner. The term data model can refer to two distinct but closely related concepts. Sometimes it refers to an abstract formalization of the objects and relationships found in a particular application domain: for example the customers, products, and orders found in a manufacturing organization. At other times it refers to the set of concepts used in defining such formalizations: for example concepts such as entities, attributes, relations, or tables. So the "data model" of a banking application may be defined using the entity-relationship "data model". This article uses the term in both senses. A data model explicitly determines the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Probability Distribution
In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of would take the value 0.5 (1 in 2 or 1/2) for , and 0.5 for (assuming that the coin is fair). Examples of random phenomena include the weather conditions at some future date, the height of a randomly selected person, the fraction of male students in a school, the results of a survey to be conducted, etc. Introduction A probability distribution is a mathematical description of the probabilities of events, subsets of the sample space. The sample space, often denoted by \Omega, is the set of all possible outcomes of a random phe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Concept Drift
In predictive analytics and machine learning, concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes. The term ''concept'' refers to the quantity to be predicted. More generally, it can also refer to other phenomena of interest besides the target concept, such as an input, but, in the context of concept drift, the term commonly refers to the target variable. Examples In a fraud detection application the target concept may be a binary attribute fraudulent with values "yes" or "no" that indicates whether a given transaction is fraudulent. Or, in a weather prediction application, there may be several target concepts such as temperature, pressure, and humidity. The behavior of the customers in an online shop may change over time. For example, if weekly merchandise sales are to be predicted, and a predictive mod ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
.jpg "Click for more on -> Data Science")
.svg "Click for more on -> Probability Distribution")