88 skills found · Page 3 of 3
bodil / Purescript KanrenRelational programming for PureScript
dont-rely-on-nulls / KarutaA compiler for a relational programming language for the BEAM written in OCaml
tuya / Tuya Connector GoThe tuya-connector-go helps you quickly set up projects regarding the open API and message subscription capabilities of Tuya Cloud Development. You can put all the focus on application development without taking care of server-side programming nor relational databases.
celinehocquette / Numsynth Aaai23C. Hocquette and A. Cropper, Relational program synthesis with numerical reasoning, AAAI23.
stewSquared / UkanrenRelational Programming DSL in Scala. Yet another minikanren port!
RAbraham / MercylogDatalog based relational programming in Python.
mmzeeman / EsqlESQL provides an abstraction layer between Erlang programs and SQL relational databases. This lets you write database code once, in Erlang, and have it work with any number of backend SQL databases (Sqlite, MySQL, Oracle, PostgreSQL, ODBC-compliant databases, etc.)
2jun0 / Async SqlmodelAsync-SQLModel is an extension module of SQLModel, making it compatible with asynchronous programming, especially useful when lazy-loading relational fields asynchronously.
terohuttunen / Proto VulcanA relational logic programming language embedded in Rust.
dabrady / LittleLogicLangsProject files for my paper, "Little Logic Languages for Relational Programming"
nkeranova / Databases:open_file_folder: Databases is a collection of information that is organized so that it can easily be accessed, managed, and updated. In one view, databases can be classified according to types of content: bibliographic, full-text, numeric, and images. DEFINITION database Posted by: Margaret Rouse WhatIs.com Contributor(s): Allan Leake Sponsored News Using Automation to Solve Data Management Challenges –Veritas Avoid the Pain of Cloud Silos With Unified Management and Visibility –Splunk See More Vendor Resources Guide to Consolidating SQL Server 2000 and SQL Server 2005 Databases to SQL ... –Dell and Microsoft SQL Zero-Time Upgrades to Oracle Database 11g Using Oracle GoldenGate –Oracle Corporation A database is a collection of information that is organized so that it can easily be accessed, managed, and updated. In one view, databases can be classified according to types of content: bibliographic, full-text, numeric, and images. Download this free guide Download Our Exclusive Big Data Analytics Guide An unbiased look at real-life analytics success stories, including a Time Warner Cable case study, and tips on how to evaluate big data tools. This guide will benefit BI and analytics pros, data scientists, business execs and project managers. Start Download In computing, databases are sometimes classified according to their organizational approach. The most prevalent approach is the relational database, a tabular database in which data is defined so that it can be reorganized and accessed in a number of different ways. A distributed database is one that can be dispersed or replicated among different points in a network. An object-oriented programming database is one that is congruent with the data defined in object classes and subclasses. Computer databases typically contain aggregations of data records or files, such as sales transactions, product catalogs and inventories, and customer profiles. Typically, a database manager provides users the capabilities of controlling read/write access, specifying report generation, and analyzing usage. Databases and database managers are prevalent in large mainframe systems, but are also present in smaller distributed workstation and mid-range systems such as the AS/400 and on personal computers. SQL (Structured Query Language) is a standard language for making interactive queries from and updating a database such as IBM's DB2, Microsoft's SQL Server, and database products from Oracle, Sybase, and Computer Associates.
vodolaz095 / GosshaCross-platform ssh-server based chat program, with data persisted into relational databases of MySQL, PostgreSQL or Sqlite3.
yeguixin / POEMDeep Program Structure Modeling ThroughMulti-Relational Graph-based Learning
tangentstorm / Maclennan Rplpapers on relational programming by bruce j maclennan
namin / Relational VirologySynthesis of simple virus-like programs via relational interpreter.
muldis / Muldis Data LanguageMuldis Data Language (MDL) - Relational database application programming language
dwayne / Elm Trs2The Reasoned Schemer (2nd Edition) in Elm.
ethframe / MicrokanrenmicroKanren in python
Aryia-Behroziuan / Robot LearningIn developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68]
trivio / CoddRelational Algebra for Functional Programming in Python
