Difference between revisions of "Terms and Concepts"

Revision as of 16:30, 4 October 2017

MOD-CO (meta-omics data of collection objects) developed and elaborated a comprehensive namespace schema and assigned integrative controlled vocabularies to describe the full spectrum of observation and measurement elements as well as the procedural steps in the frame of the metagenomic, metatranscriptomic, and metametabolomic characterization of environmental collection samples.

These sets of terms and vocabularies include descriptors for meta-omics analysis targets, environmental collection objects, metagenomics data generation devices and protocols, data types, formats and storage, and is able to describe representing primary and secondary meta-omics data analysis.

Existing ontologies and concepts, distributed in literature, published methods and thesauri were compared and evaluated for their possible use in the MOD-CO context.

Term definitions in the context of the MOD-CO schema and representation

Conceptual schema https://en.wikipedia.org/wiki/Conceptual_schema A conceptual schema is a high-level description of a business's informational needs. It typically includes only the main concepts and the main relationships among them. Typically this is a first-cut model, with insufficient detail to build an actual database. This level describes the structure of the whole database for a group of users. The conceptual model is also known as the data model as data model can be used to describe the conceptual schema when a database system is implemented. It hides the internal details of physical storage and targets on describing entities, datatype, relationships and constraints.

===> It has been suggested that this article be merged with conceptual model (computer science). (Discuss) Proposed since June 2016.

Conceptual model https://en.wikipedia.org/wiki/Conceptual_model_(computer_science) A conceptual model in the field of computer science is a special case of a general conceptual model. To distinguish from other types of models, it is also known as a domain model. Conceptual modeling should not be confused with other modeling disciplines such as data modelling, logical modelling and physical modelling. The conceptual model is explicitly chosen to be independent of design or implementation concerns, for example, concurrency or data storage. The aim of a conceptual model is to express the meaning of terms and concepts used by domain experts to discuss the problem, and to find the correct relationships between different concepts. The conceptual model attempts to clarify the meaning of various, usually ambiguous terms, and ensure that problems with different interpretations of the terms and concepts cannot occur. Such differing interpretations could easily cause confusion amongst stakeholders, especially those responsible for designing and implementing a solution, where the conceptual model provides a key artifact of business understanding and clarity. Once the domain concepts have been modeled, the model becomes a stable basis for subsequent development of applications in the domain. The concepts of the conceptual model can be mapped into physical design or implementation constructs using either manual or automated code generation approaches. The realization of conceptual models of many domains can be combined to a coherent platform.

Database schema https://en.wikipedia.org/wiki/Database_schema The database schema of a database system is its structure 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.[citation needed] 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.[1] 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.

Scheme https://en.wikipedia.org/wiki/Scheme Not to be confused with Schema.

Schemata vs. Standards

- for data exchange - for data mapping - for process description

===> I would say, standards are based on schemata

Schema https://en.wikipedia.org/wiki/Schema Computer science Database schema --> no other type mentioned here

Database model https://en.wikipedia.org/wiki/Database_model A database model is a type of data model that determines the logical structure of a database and fundamentally determines in which manner data can be stored, organized and manipulated. The most popular example of a database model is the relational model, which uses a table-based format. Data model Examples: Hierarchical database model; Network model; Relational model; Entity�relationship model; Enhanced entity�relationship model; Object model; Document model: Entity�attribute�value model; Star schema

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

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

for interoperability and data exchange

https://en.wikipedia.org/wiki/Data_mapping#Standards Standards X12 standards are generic Electronic Data Interchange (EDI) standards designed to allow a company to exchange data with any other company, regardless of industry. The standards are maintained by the Accredited Standards Committee X12 (ASC X12), with the American National Standards Institute (ANSI) accredited to set standards for EDI. The X12 standards are often called ANSI ASC X12 standards. In the future, tools based on semantic web languages such as Resource Description Framework (RDF), the Web Ontology Language (OWL) and standardized metadata registry will make data mapping a more automatic process. This process will be accelerated if each application performed metadata publishing. Full automated data mapping is a very difficult problem (see Semantic translation).

Data exchange https://en.wikipedia.org/wiki/Data_exchange Data exchange is the process of taking data structured under a source schema and transforming it into data structured under a target schema, so that the target data is an accurate representation of the source data.[1] Data exchange allows data to be shared between different computer programs. It is similar to the related concept of data integration except that data is actually restructured (with possible loss of content) in data exchange. There may be no way to transform an instance given all of the constraints. Conversely, there may be numerous ways to transform the instance (possibly infinitely many), in which case a "best" choice of solutions has to be identified and justified.

Data exchange languages https://en.wikipedia.org/wiki/Data_exchange#Data_exchange_languages Examples: RDF, XML, Atom, JSON, YAML, REBOL, Gellish, etc.

Data mapping https://en.wikipedia.org/wiki/Data_mapping In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks including: Data transformation or data mediation between a data source and a destination Identification of data relationships as part of data lineage analysis Discovery of hidden sensitive data such as the last four digits of a social security number hidden in another user id as part of a data masking or de-identification project Consolidation of multiple databases into a single data base and identifying redundant columns of data for consolidation or elimination

Semantic mapping https://en.wikipedia.org/wiki/Data_mapping#Standards Semantic mapping is similar to the auto-connect feature of data mappers with the exception that a metadata registry can be consulted to look up data element synonyms. For example, if the source system lists FirstName but the destination lists PersonGivenName, the mappings will still be made if these data elements are listed as synonyms in the metadata registry. Semantic mapping is only able to discover exact matches between columns of data and will not discover any transformation logic or exceptions between columns. Data Lineage is a track of the life cycle of each piece of data as it is ingested, processed and output by the analytics system. This provides visibility into the analytics pipeline and simplifies tracing errors back to their sources. It also enables replaying specific portions or inputs of the dataflow for step-wise debugging or regenerating lost output. In fact, database systems have used such information, called data provenance, to address similar validation and debugging challenges already.