Data modelA 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 corresponding professional activity is called generally data modeling or, more specifically, database design.
Data modelingData modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques. It may be applied as part of broader Model-driven engineering (MDD) concept. Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the information system.
Semantic data modelA semantic data model (SDM) is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. An SDM specification describes a database in terms of the kinds of entities that exist in the application environment, the classifications and groupings of those entities, and the structural interconnections among them.
DataIn common usage and statistics, data (USˈdætə; UKˈdeɪtə) is a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures.
Data analysisData analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.
Sanitary sewerA sanitary sewer is an underground pipe or tunnel system for transporting sewage from houses and commercial buildings (but not stormwater) to a sewage treatment plant or disposal. Sanitary sewers are a type of gravity sewer and are part of an overall system called a "sewage system" or sewerage. Sanitary sewers serving industrial areas may also carry industrial wastewater. In municipalities served by sanitary sewers, separate storm drains may convey surface runoff directly to surface waters.
SewerageSewerage (or sewage system) is the infrastructure that conveys sewage or surface runoff (stormwater, meltwater, rainwater) using sewers. It encompasses components such as receiving drains, manholes, pumping stations, storm overflows, and screening chambers of the combined sewer or sanitary sewer. Sewerage ends at the entry to a sewage treatment plant or at the point of discharge into the environment. It is the system of pipes, chambers, manholes, etc. that conveys the sewage or storm water.
Combined sewerA combined sewer is a type of gravity sewer with a system of pipes, tunnels, pump stations etc. to transport sewage and urban runoff together to a sewage treatment plant or disposal site. This means that during rain events, the sewage gets diluted, resulting in higher flowrates at the treatment site. Uncontaminated stormwater simply dilutes sewage, but runoff may dissolve or suspend virtually anything it contacts on roofs, streets, and storage yards.
Support vector machineIn machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).
Big dataBig data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.
Gravity sewerA gravity sewer is a conduit utilizing the energy resulting from a difference in elevation to remove unwanted water. The term sewer implies removal of sewage or surface runoff rather than water intended for use; and the term gravity excludes water movement induced through force mains or vacuum sewers. Most sewers are gravity sewers; because gravity offers reliable water movement with no energy costs wherever grades are favorable.
Philosophical methodologyIn its most common sense, philosophical methodology is the field of inquiry studying the methods used to do philosophy. But the term can also refer to the methods themselves. It may be understood in a wide sense as the general study of principles used for theory selection, or in a more narrow sense as the study of ways of conducting one's research and theorizing with the goal of acquiring philosophical knowledge.
Sanitary sewer overflowSanitary sewer overflow (SSO) is a condition in which untreated sewage is discharged from a sanitary sewer into the environment prior to reaching sewage treatment facilities. When caused by rainfall it is also known as wet weather overflow. Causes of sanitary sewer overflows include: Blockage of sewer lines, infiltration/Inflow of excessive stormwater into sewer lines during heavy rainfall, malfunction of pumping station lifts or electrical power failure, broken sewer lines.
SanitationSanitation refers to public health conditions related to clean drinking water and treatment and disposal of human excreta and sewage. Preventing human contact with feces is part of sanitation, as is hand washing with soap. Sanitation systems aim to protect human health by providing a clean environment that will stop the transmission of disease, especially through the fecal–oral route. For example, diarrhea, a main cause of malnutrition and stunted growth in children, can be reduced through adequate sanitation.
Data warehouseIn computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence. Data warehouses are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise. This is beneficial for companies as it enables them to interrogate and draw insights from their data and make decisions.
Data scienceData science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.
Bootstrap aggregatingBootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.
Vacuum sewerA vacuum sewer or pneumatic sewer system is a method of transporting sewage from its source to a sewage treatment plant. It maintains a partial vacuum, with an air pressure below atmospheric pressure inside the pipe network and vacuum station collection vessel. Valves open and reseal automatically when the system is used, so differential pressure can be maintained without expending much energy pumping. A single central vacuum station can collect the wastewater of several thousand individual homes, depending on terrain and the local situation.
Data managementData management comprises all disciplines related to handling data as a valuable resource. The concept of data management arose in the 1980s as technology moved from sequential processing (first punched cards, then magnetic tape) to random access storage. Since it was now possible to store a discrete fact and quickly access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems.
Statistical hypothesis testingA statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters. While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see .
