query: what stores metadata instead of actual data in dbms

pos: Metadata repository or registries that are expected to provide all of the necessary functionality to access the data stored within. Vendors do not want other companies to be capable of easily migrating data away from their products and into competitors products, so they are proprietary with the way they handle metadata. CASE tools, DBMS dictionaries, ETL tools, data cleansing tools, OLAP tools, and data mining tools all handle and store metadata differently. Only a metadata repository can be designed to store the metadata components from all of these tools. Metadata repositories should store metadata in four classifications: ownership, descriptive characteristics, rules and

pos: Metadata repository Metadata repository A metadata repository is a database created to store metadata. Metadata is information about the structures that contain the actual data. Metadata is often said to be "data about data", but this is misleading. Data profiles are an example of actual "data about data". Metadata is one layer of abstraction removed from this – it is data about the structures that contain data. Metadata may describe the structure of any data, of any subject, stored in any format. A well-designed metadata repository typically contains data far beyond simple definitions of the various data structures. Typical repositories store dozens

pos: Metadata repository would include ISBN, Binding_Type, Page_Count, Author, etc. Within Binding_Type, metadata would include possible bindings, material, etc. This contextual information of business data include meaning and content, policies that govern, technical attributes, specifications that transform, and programs that manipulate. The metadata repository is responsible for physically storing and cataloging metadata. Data in a metadata repository should be generic, integrated, current, and historical. Generic : meta model should store the metadata by generic terms instead of storing it by an applications-specific defined way, so that if your data base standard changes from one product to another the physical meta model of the

pos: Synergy International Systems is Commercial Off-the-Shelf (COTS) software technology platform that enables the creation of database-driven business applications. The core of IDM’s metadata-driven architecture – knowledgebase, is a metadata repository that stores the logical representation of application features and functionality. This knowledgebase and the resulting application data are animated by a modular suite of capabilities for data management, visualization, reporting, and business process automation and user management. The business requirements of a client are being tailored to the knowledgebase to create a wide array of data-driven business applications. Gartner included Synergy International Systems as a representative vendor in its 2015 Market Guide for

neg: Data dictionary DBA, but it is mainly accessed by the various software modules of the DBMS itself, such as DDL and DML compilers, the query optimiser, the transaction processor, report generators, and the constraint enforcer. On the other hand, a "data dictionary" is a data structure that stores metadata, i.e., (structured) data about information. The software package for a stand-alone data dictionary or data repository may interact with the software modules of the DBMS, but it is mainly used by the designers, users and administrators of a computer system for information resource management. These systems maintain information on system hardware and software

neg: Data dictionary Data dictionary A data dictionary, or metadata repository, as defined in the "IBM Dictionary of Computing", is a "centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format". "Oracle" defines it as a collection of tables with metadata. The term can have one of several closely related meanings pertaining to databases and database management systems (DBMS): The terms "data dictionary" and "data repository" indicate a more general software utility than a catalogue. A "catalogue" is closely coupled with the DBMS software. It provides the information stored in it to the user and the

neg: Database It also contains all the information needed (e.g., metadata, "data about the data", and internal data structures) to reconstruct the "conceptual level" and "external level" from the internal level when needed. Putting data into permanent storage is generally the responsibility of the database engine a.k.a. "storage engine". Though typically accessed by a DBMS through the underlying operating system (and often using the operating systems' file systems as intermediates for storage layout), storage properties and configuration setting are extremely important for the efficient operation of the DBMS, and thus are closely maintained by database administrators. A DBMS, while in operation, always

neg: Metadata repository to hundreds of separate pieces of information about each data structure. Comparing the metadata of a couple data items - one digital and one physical - will help one understand what metadata really is: First, digital: for data stored in a database one may have a table called "Patient" with many columns, each containing data which describes a different attribute of each patient. One of these columns may be named "Patient_Last_Name". What is some of the metadata about the column that contains the actual surnames of patients in the database? We have already used two items: the name of the

neg: Column-oriented DBMS Column-oriented DBMS A column-oriented DBMS (or columnar database management system) is a database management system (DBMS) that stores data tables by column rather than by row. Practical use of a column store versus a row store differs little in the relational DBMS world. Both columnar and row databases can use traditional database query languages like SQL to load data and perform queries. Both row and columnar databases can become the backbone in a system to serve data for common extract, transform, load (ETL) and data visualization tools. However, by storing data in columns rather than rows, the database can more

neg: Tablespace in a filesystem file, a single file must be part of a single tablespace. Some database management systems allow tablespaces to be configured directly over operating-system device entries, called raw devices, providing better performance by avoiding the OS filesystem overheads. Oracle stores data logically in tablespaces and physically in datafiles associated with the corresponding tablespace. Tablespace A tablespace is a storage location where the actual data underlying database objects can be kept. It provides a layer of abstraction between physical and logical data, and serves to allocate storage for all DBMS managed segments. (A database segment is a database object

neg: Metadata repository on the changes, and can do impact analysis of the tool. Usually business spends lots of time and money to make decisions based on discovery and research on impacts to make changes or to add new data structures or remove structures in data management of the organization. With a structured and well maintained repository, moving the product from ideation to delivery takes the least amount of time (considering other variables are constant). To sum it up: Each database management system (DBMS) and database tools have their own language for the metadata components within. Database applications already have their own repositories

neg: Blue Whale Clustered file system equals to another layer of RAID structured between multiple disk arrays, which maximizes the performance of disk arrays. Another factor performance factor should be considered is the location of metadata. A file consists of actual data and metadata. Actual data is the content of a file, while metadata includes file attributes, permissions and so on. When a file is created, modified, or deleted, metadata information shall be modified, which means a file is processed by reading both file data and metadata. Usually, large file is read and written continuously, while metadata shall be read by moving magnetic-disc head to other

neg: Column-oriented DBMS open source and commercial implementations. MonetDB was released under an open-source license on September 30, 2004, followed closely by the now defunct C-Store. C-store was a university project that eventually, with team member Michael Stonebraker staying on, led to Vertica, which he co-founded in 2005. The MonetDB-related X100 project evolved into VectorWise. Druid is a column-oriented data store that was open-sourced in late 2012 and now used by numerous organizations. Column-oriented DBMS A column-oriented DBMS (or columnar database management system) is a database management system (DBMS) that stores data tables by column rather than by row. Practical use of a

neg: Metadata engine Metadata engines virtualize the view of data. Combined with client access protocols such as NFS v4.2 and SMB 2.1 or SMB 3.0 (Samba), a metadata engine can create a global namespace to allow applications to see a logical view of their data, and can orchestrate data movement between different types or tiers of storage without disrupting the application’s access to its data. Metadata engine A metadata engine collects, stores and analyzes information about data and metadata (data about data) in use within a domain. It virtualizes the view of data for an application by separating the data (physical) path from

neg: Metadata engine Metadata engine A metadata engine collects, stores and analyzes information about data and metadata (data about data) in use within a domain. It virtualizes the view of data for an application by separating the data (physical) path from the metadata (logical) path so that data management can be performed independently of where the data physically resides. This expands the domain beyond a single storage device to span all devices within its namespace. The main use of a metadata engine is to enable flexible IT infrastructure or environments, by making applications more storage aware and conversely, a storage device application—or data—aware.

neg: JBOB it. With fixed length records, it is possible to calculate the exact position of a particular record. Even with variable length records, the length of each record is given so navigation is easier. Since records are determined by the content of the data, metadata is required, such as what is/are the record termination character(s), and is usually stored externally to the actual data or file. The processing of JBOB data is usually more difficult and may require special knowledge by the computer program. Metadata might also be required for structured data, such as the fixed record length or the largest

neg: Sparse file Sparse file In computer science, a sparse file is a type of computer file that attempts to use file system space more efficiently when the file itself is partially empty. This is achieved by writing brief information (metadata) "representing" the empty blocks to disk instead of the actual "empty" space which makes up the block, using less disk space. The full block size is written to disk as the actual size only when the block contains "real" (non-empty) data. When reading sparse files, the file system transparently converts metadata representing empty blocks into "real" blocks filled with zero bytes at

neg: SAP IQ HANA. SAP BusinessObjects BI can be used to achieve visibility across both platforms. To a user, SAP IQ looks just like any relational DBMS with a SQL-based language layer accessible via ODBC/JDBC drivers. However, inside, Sybase IQ is a column-oriented DBMS, which stores data tables as sections of columns of data rather than as rows of data like most transactional databases. Column-orientation has a number of advantages. If a search is being done for items matching a particular value in a column of data, only the storage objects corresponding to that data column within the table need to be accessed.

neg: XAM it allows applications to store and retrieve their data. XAM stores application data in XSet objects that also contain metadata. The standard XAM API consists of all of the three primary objects and two secondary objects that an application can manipulate to store and retrieve data. The first primary object that applications deal with to store and retrieve data is called an Xset. It is the object that is the container for both the data and the associated metadata. XSet metadata goes beyond the typical system metadata, and adds fields for retention and other data management policies. Applications can also

neg: Rasdaman sciences is EarthServer, six services with a volume of at least 100 terabytes each are being set up for integrated data / metadata retrieval and distributed query processing. Rasdaman Rasdaman ("raster data manager") is an Array DBMS, that is: a Database Management System which adds capabilities for storage and retrieval of massive multi-dimensional arrays, such as sensor, image, simulation, and statistics data. A frequently used synonym to arrays is "raster data", such as in 2-D raster graphics; this actually has motivated the name "rasdaman". However, rasdaman has no limitation in the number of dimensions - it can serve, for example,

neg: Metadata repository policies, and physical characteristics. Ownership, showing the data owner and the application owner. The descriptive characteristics, define the names, types and lengths, and definitions describing business data or business processes. Rules and policies, will define security, data cleanliness, timelines for data, and relationships. Physical characteristics define the origin or source, and physical location. Like building a logical data model for creating a database, a logical meta model can help identify the metadata requirements for business data. The metadata repository will be centralized, decentralized, or distributed. A centralized design means that there is one database for the metadata repository that stores

neg: Metadata in a separate file or field from the described data. A data repository typically stores the metadata "detached" from the data, but can be designed to support embedded metadata approaches. Each option has advantages and disadvantages: Metadata can be stored in either human-readable or binary form. Storing metadata in a human-readable format such as XML can be useful because users can understand and edit it without specialized tools. However, text-based formats are rarely optimized for storage capacity, communication time, or processing speed. A binary metadata format enables efficiency in all these respects, but requires special software to convert the binary

neg: Correlation database Correlation database A correlation database is a database management system (DBMS) that is data-model-independent and designed to efficiently handle unplanned, ad hoc queries in an analytical system environment. Unlike row-oriented relational database management systems, which use a records-based storage approach, or column-oriented databases which use a column-based storage method, a correlation database uses a value-based storage (VBS) architecture in which each unique data value is stored only once and an auto-generated indexing system maintains the context for all values. Because a correlation DBMS stores each unique data value only once, the physical database size is significantly smaller than relational or

neg: IBM BLU Acceleration (about 1,000 records) in this table. This metadata is automatically maintained during insert, update, and delete operations and this is what allows DB2 with BLU Acceleration to automatically detect large sections of data that is not needed during query processing and to effectively ignore it. Conceptually, BLU Acceleration‘s data skipping is similar to the Zone Map technology found in the PureData System for Analytics family. However, unlike Zone Maps, the metadata stored in the synopsis table isn‘t tied to any particular page or extent boundary―instead, it‘s tied to a specific "chunk" of data records. Data skipping can deliver an order

neg: NOVA (filesystem) records the updates to the inode. This allows for increased concurrency in file operations, since different threads can operate on inodes in parallel. Second, the logs do not contain file data, but only metadata updates, resulting in smaller logs. Third, the logs are not stored in physically contiguous memory. Instead, NOVA stores the logs in linked list of 4KB memory pages. NOVA uses the logs to provide atomicity for operations that affect a single file (e.g., writing to a file or modifying its metadata). To do this, NOVA writes a log entry to empty space past the end of the

neg: Fork (file system) forks can be of variable size, possibly even larger than the file's primary data fork. The size of a file is the sum of the sizes of each fork. On file systems without forks, one may instead use multiple separate files that are associated with each other, particularly sidecar files for metadata. However, the connection between these files is not automatically preserved by the file system, and must instead be handled by each program that works on files. Another alternative is a container file, which stores additional data within a given file format, or an archive file, which allows storing

neg: National Information Exchange Model NIEM-conformant. The intent here is to allow use of external standard components exactly as they were defined. Metadata, or data about data, defines information that supports the actual content of XML instances. The metadata feature provides a mechanism for attaching structured properties that describe the pedigree or source (when reported, who reported, how reliable, etc.) of instance data to any data component of the model (type or object, property, association, role, or augmentation) in any namespace. It allows sets of metadata to be extended with additional properties for local requirements and enables metadata properties to be repeated. NIEM has introduced

neg: Apache Empire-db Additionally DBMS independence is achieved through a pluggable driver model. Using references to table and column objects significantly improves compile-time safety and thus reduces the amount of testing. As a positive side effect the IDE's code completion can be used to browse the data model, increases productivity and eliminates the need for other external tools or IDE-plugins. Further the object model also provides safe and easy access to meta-information of the data model such as field data type, maximum field length, whether a field is mandatory and a finite choice of options for a field’s values. Metadata is user-extensible and

neg: Error level analysis In 2007, N. Krawetz denoted this method "error level analysis". Additionally, digital data formats such as JPEG sometimes include metadata describing the specific lossy compression used. If in such data the observed compression artifacts differ from those expected from the given metadata description, then the metadata may not describe the actual compressed data, and thus indicate that the data have been edited. By its nature, data without lossy compression, such as a PNG image, cannot be subjected to error level analysis. Consequently, since editing could have been performed on data without lossy compression with lossy compression applied uniformly to the

neg: C-Store have formed Vertica, a company to commercialize C-Store. C-Store C-Store is a database management system (DBMS) based on a column-oriented DBMS developed by a team at Brown University, Brandeis University, Massachusetts Institute of Technology and the University of Massachusetts Boston including Michael Stonebraker, Stanley Zdonik, and Samuel Madden. The last release was in 2006. With Vertica a commercial fork lives on. C-Store differs from most traditional relational database management system (RDBMS) designs in many ways, primarily in that it stores data by column and not by row, optimizing the database for reading of data rather than writing. C-Store is licensed

neg: C-Store C-Store C-Store is a database management system (DBMS) based on a column-oriented DBMS developed by a team at Brown University, Brandeis University, Massachusetts Institute of Technology and the University of Massachusetts Boston including Michael Stonebraker, Stanley Zdonik, and Samuel Madden. The last release was in 2006. With Vertica a commercial fork lives on. C-Store differs from most traditional relational database management system (RDBMS) designs in many ways, primarily in that it stores data by column and not by row, optimizing the database for reading of data rather than writing. C-Store is licensed under the BSD license. Stonebraker and his colleagues

neg: Column-oriented DBMS the value sex (cardinality of two), then age (cardinality of <150), then name. Columnar compression achieves a reduction in disk space at the expense of efficiency of retrieval. The greater adjacent compression achieved, the more difficult random-access may become, as data might need to be uncompressed to be read. Therefore, column-oriented architectures are sometimes enriched by additional mechanisms aimed at minimizing the need for access to compressed data. Column stores or transposed files have been implemented from the early days of DBMS development. TAXIR was the first application of a column-oriented database storage system with focus on information-retrieval in biology

neg: High-performance Integrated Virtual Environment data deposition back-end allows automatic uploads and downloads of external datasets into HIVE data repositories. The metadata database can be used to maintain specific information about extremely large files ingested into the system (big data) as well as metadata related to computations run on the system. This metadata then allows details of a computational pipeline to be brought up easily in the future in order to validate or replicate experiments. Since the metadata is associated with the computation, it stores the parameters of any computation in the system eliminating manual record keeping. Differentiating HIVE from other object oriented databases is

neg: Correlation database use" of a CDBMS is generally similar to or somewhat faster than alternative structures. Correlation database A correlation database is a database management system (DBMS) that is data-model-independent and designed to efficiently handle unplanned, ad hoc queries in an analytical system environment. Unlike row-oriented relational database management systems, which use a records-based storage approach, or column-oriented databases which use a column-based storage method, a correlation database uses a value-based storage (VBS) architecture in which each unique data value is stored only once and an auto-generated indexing system maintains the context for all values. Because a correlation DBMS stores each unique

neg: Data remanence erasure commands are required, like on a solid-state drive. (In such cases, the operating system will issue the Serial ATA TRIM command or the SCSI UNMAP command to let the drive know to no longer maintain the deleted data.) Instead, they simply remove the file's entry from the file system directory, because this requires less work and is therefore faster, and the contents of the file—the actual data—remain on the storage medium. The data will remain there until the operating system reuses the space for new data. In some systems, enough filesystem metadata are also left behind to enable easy

neg: Array DBMS input images is equivalent to adding the averages of each input array". By replacing the left-hand variant by the right-hand expression, costs shrink from three (costly) array traversals to two array traversals plus one (cheap) scalar operation (see Figure, which uses the rasdaman query language introduced before). In many – if not most – cases where some phenomenon is sampled or simulated the result is a rasterized data set which can conveniently be stored, retrieved, and forwarded as an array. Typically, the array data are ornamented with metadata describing them further; for example, geographically referenced imagery will carry its geographic

neg: Portable Network Graphics Graphic Designer and many others. Some programs bundled with popular operating systems which support PNG include Microsoft's Paint and Apple's Photos/iPhoto and Preview, with the GIMP also often being bundled with popular Linux distributions. Adobe Fireworks (formerly by Macromedia) uses PNG as its native file format, allowing other image editors and preview utilities to view the flattened image. However, Fireworks by default also stores metadata for layers, animation, vector data, text and effects. Such files should not be distributed directly. Fireworks can instead export the image as an optimized PNG without the extra metadata for use on web pages, etc.

neg: Metadata repository though it only reflects the structures consumed from various systems. Repository has additional functionalities compared with registry. Metadata repository not only stores metadata like Metadata registry but also adds relationships with related metadata types. Metadata when related in a flow from its point of entry into organization up to the deliverables is considered as the lineage of that data point. Metadata when related across other related metadata types is called linkages. By providing the relationships to all the metadata points across the organization and maintaining its integrity with an architecture to handle the changes, metadata repository provides the basic material

neg: XKeyscore with data from the following collection systems: In a single, undated slide published by Swedish media in December 2013, the following additional data sources for XKeyscore are mentioned: From these sources, XKeyscore stores "full-take data", which are indexed by plug-ins that extract certain types of metadata (like phone numbers, e-mail addresses, log-ins, and user activity) and index them in metadata tables, which can be queried by analysts. XKeyscore has been integrated with MARINA, which is NSA's database for internet metadata. However, the system continuously gets so much Internet data that it can be stored only for short periods of time.

neg: Polyhedra DBMS active query looks quite like a normal query where some data is retrieved and/or written, but instead the query stays in the database until explicitly aborted. When a change in the data occurs that would alter the result of the query, the application is notified. The CL, which is a fully object-oriented script language that supports encapsulation, information hiding and inheritance, can determine the behaviour of data in the database. This means that methods, private or public, can be associated with data performing operations on them without involving the application." Polyhedra is not a general-purpose DBMS, as the restricted transactional

neg: Metadata repository metadata for all applications business wide. A centralized metadata repository has the same advantages and disadvantages of a centralized database. Easier to manage because all the data is in one database, but the disadvantage is that bottlenecks may occur. A decentralized metadata repository stores metadata in multiple databases, either separated by location and or departments of the business. This makes management of the repository more involved than a centralized metadata repository, but the advantage is that the metadata can be broken down into individual departments. A distributed metadata repository uses a decentralized method, but unlike a decentralized metadata repository the

neg: MPQ (file format) header reserves space to contain format version data. ignores format version data of .mpq compliant files it loads and assumes all are version 1. MPQ archives do not have specific structures to store metadata beyond what is absolutely necessary to access archived files. Instead, the convention is to use regular files whose filename is enclosed by parentheses. Below are known metadata files. In modern MPQ archives, each segment (or sector) of a file can be compressed using a combination of compression algorithms. A header byte is prepended to every compressed sector to indicate which compressions were used. The order in

neg: Entity–attribute–value model and the software generates a basic user interface from this metadata dynamically. The EAV/CR framework, mentioned earlier, was created to address this very situation. Note that an EAV data model is not essential here, but the system designer may consider it an acceptable alternative to creating, say, sixty or more tables containing a total of not more than two thousand rows. Here, because the number of rows per class is so few, efficiency considerations are less important; with the standard indexing by class ID/attribute ID, DBMS optimizers can easily cache the data for a small class in memory when running

neg: Resource fork UFS, and on SMB volumes where Alternate Data Stream support is not enabled. In those cases, macOS stores metadata and resource forks using a technique called AppleDouble, in which the data fork is written as one file, and the resource fork and metadata are written as an entirely separate file preceded by a "._" naming convention. For example: ExampleFile.psd would contain the data fork, and ._ExampleFile.psd would contain the resource fork and metadata. Compatibility problems can arise because macOS will handle storage of resource forks differently, depending on macOS version, settings, and file system type. For example, on an SMB

neg: Oracle metadata each literal underscore should be a double underscore: __ Example The Oracle product Oracle Enterprise Metadata Manager (EMM) is an ISO/IEC 11179 compatible metadata registry. It stores administered metadata in a consistent format that can be used for metadata publishing. In January 2006, EMM was available only through Oracle consulting services. Oracle metadata Oracle Database provides information about all of the tables, views, columns, and procedures in a database. This information about the information is known as metadata. It is stored in two locations: data dictionary tables (accessed via built-in views) and a metadata registry. Other relational database management systems

neg: Nirvana (software) is controlled by three daemons; Metadata, Sync and ILM. The Metadata Daemon can extract metadata automatically from an instrument creating data, from within the file's actual data using predefined and customizable templates and metadata parsing policies, or capturing user input via the GUI or Command Line Interface. The Sync Daemon, running in the background, detects when files are added to, or deleted from, the underlying Storage Resource filesystems. When filesystem changes are observed by the Sync Daemon, the changes are registered and updated in MCAT. The ILM Daemon routinely queries the MCAT and executes actions including migration, replication, or backup

neg: Metadata through software (such as Multicam(LSM) or IPDirector used during the FIFA World Cup or Olympic Games). Metadata that describes geographic objects in electronic storage or format (such as datasets, maps, features, or documents with a geospatial component) has a history dating back to at least 1994 (refer MIT Library page on FGDC Metadata). This class of metadata is described more fully on the geospatial metadata article. Ecological and environmental metadata is intended to document the "who, what, when, where, why, and how" of data collection for a particular study. This typically means which organization or institution collected the data, what

neg: Metadata and Stuart McIntosh in 1967: "In summary then, we have statements in an object language about subject descriptions of data and token codes for the data. We also have statements in a meta language describing the data relationships and transformations, and ought/is relations between norm and data." There are different metadata standards for each different discipline (e.g., museum collections, digital audio files, websites, etc.). Describing the contents and context of data or data files increases its usefulness. For example, a web page may include metadata specifying what software language the page is written in (e.g., HTML), what tools were used

neg: WinFS data), WinFS supports semi-structured data (such as images, which have an unstructured bitstream plus structured metadata) as well as unstructured data (such as files) as well. It stores the unstructured components as files while storing the structured metadata in the structured store. Internally, WinFS uses a relational database to manage data. It does not limit the data to belonging to any particular data model. The WinFS runtime maps the schema to a relational modality, by defining the tables it will store the types in and the primary keys and foreign keys that would be required to represent the relationships. WinFS

neg: Windows Vista networking technologies in a distributed resource management network, which node has what resource needs to be shared. Such information is shared as "Records", which are flooded to all the peers in a graph. Each peer stores the Record to a local database. A Record consists of a header and a body. The body contains data specific to the application that is using the API; the header contains metadata to describe the data in the body as name-value pairs serialized using XML, in addition to author and version information. It can also contain an index of the body data, for fast searching. A

neg: Features new to Windows XP distributed resource management network, which node has what resource needs to be shared. Such information is shared as "Records", which are flooded to all the peers in a graph. Each peer stores the Record to a local database. A Record consists of a header and a body. The body contains data specific to the application that is using the API; the header contains metadata to describe the data in the body as name-value pairs serialized using XML, in addition to author and version information. It can also contain an index of the body data, for fast searching. A node can

neg: Data model the implementation strategy employed by the DBMS. Therefore, the need to define data from a conceptual view has led to the development of semantic data modeling techniques. That is, techniques to define the meaning of data within the context of its interrelationships with other data. As illustrated in the figure. The real world, in terms of resources, ideas, events, etc., are symbolically defined within physical data stores. A semantic data model is an abstraction which defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world. Data architecture is

neg: Journalism source protection means that data on individuals' telecommunication and Internet transactions are collected and stored even when no suspicion of crime has been raised. Some of the data collected under these policies is known as metadata. Metadata is data that defines and describes other data. For the International Organisation for Standardisation standard, metadata is defined as data that defines and describes other data and processes. As the Electronic Frontier Foundation's Peter Eckersley has put it, "Metadata is information about what communications you send and receive, who you talk to, where you are when you talk to them, the length of your conversations,

neg: Object storage associate a limited set of attributes (metadata) with each piece of data. The combination of a key, value, and set of attributes is referred to as an object. Second, object stores are optimized for large amounts of data (hundreds of megabytes or even gigabytes), whereas for key-value stores the value is expected to be relatively small (kilobytes). Finally, object stores usually offer weaker consistency guarantees such as eventual consistency, whereas key-value stores offer strong consistency. Object storage Object storage (also known as object-based storage) is a computer data storage architecture that manages data as objects, as opposed to other storage

neg: Semantic data model the DBMS. Therefore, the need to define data from a conceptual view has led to the development of semantic data modeling techniques. That is, techniques to define the meaning of data within the context of its interrelationships with other data, as illustrated in the figure. The real world, in terms of resources, ideas, events, etc., are symbolically defined within physical data stores. A semantic data model is an abstraction which defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world. According to Klas and Schrefl (1995), the "overall

neg: Zip (file format) does its POSIX port p7zip). When using AES encryption under WinZip, the compression method is always set to 99, with the actual compression method stored in an AES extra data field. In contrast, Strong Encryption Specification stores the compression method in the basic file header segment of Local Header and Central Directory, unless Central Directory Encryption is used to mask/encrypt metadata. There are numerous .ZIP tools available, and numerous .ZIP libraries for various programming environments; licenses used include proprietary and free software. WinZip, WinRAR, Info-ZIP, 7-Zip, PeaZip and B1 Free Archiver are well-known .ZIP tools, available on various platforms. Some

neg: MonetDB contemporary CPUs using demand paging of memory mapped files, and thus departing from traditional DBMS designs involving complex management of large data stores in limited memory. Query recycling is an architecture for reusing the byproducts of the operator-at-a-time paradigm in a column store DBMS. Recycling makes use of the generic idea of storing and reusing the results of expensive computations. Unlike low-level instruction caches, query recycling uses an optimizer to pre-select instructions to cache. The technique is designed to improve query response times and throughput, while working in a self-organizing fashion. The authors from the CWI Database Architectures group, composed

neg: Amiga rigid disk block Amiga rigid disk block In computing, a rigid disk block (RDB) is the block on a hard disk where the Amiga series of computers store the disk's partition and filesystem information. The PC equivalent of the Amiga's RDB is the master boot record (MBR). Unlike its PC equivalent, the RDB doesn't directly contain metadata for each partition. Instead it points to a linked list of partition blocks, which contain the actual partition data. The partition data includes the start, length, filesystem, boot priority, buffer memory type and "flavor", though the latter was never used. Because there is no limitation in

neg: MARINA US persons are not exempt because metadata is not considered data by US law (section 702 of the FISA Amendments Act). MARINA's phone counterpart is MAINWAY. MARINA MARINA is an NSA database and analysis toolset for intercepted Internet metadata (DNI in NSA terminology). The database stores metadata up to a year. According to documents leaked by Edward Snowden: "The Marina metadata application tracks a user's browser experience, gathers contact information/content and develops summaries of target" and "[o]f the more distinguishing features, Marina has the ability to look back on the last 365 days' worth of DNI metadata seen by the

neg: Amiga rigid disk block to be found at block 0. Nearly all Amiga hard disk controllers support the RDB standard, enabling the user to exchange disks between controllers. Amiga rigid disk block In computing, a rigid disk block (RDB) is the block on a hard disk where the Amiga series of computers store the disk's partition and filesystem information. The PC equivalent of the Amiga's RDB is the master boot record (MBR). Unlike its PC equivalent, the RDB doesn't directly contain metadata for each partition. Instead it points to a linked list of partition blocks, which contain the actual partition data. The partition data

neg: Climate and Forecast Metadata Conventions Climate and Forecast Metadata Conventions The Climate and Forecast (CF) metadata conventions are conventions for the description of Earth sciences data, intended to promote the processing and sharing of data files. The metadata defined by the CF conventions are generally included in the same file as the data, thus making the file "self-describing". The conventions provide a definitive description of what the data values found in each netCDF variable represent, and of the spatial and temporal properties of the data, including information about grids, such as grid cell bounds and cell averaging methods. This enables users of files from different

neg: Metadata own requirements for metadata in order to describe not only the source and quality of the data but also what statistical processes were used to create the data, which is of particular importance to the statistical community in order to both validate and improve the process of statistical data production. An additional type of metadata beginning to be more developed is "accessibility metadata". "Accessibility metadata" is not a new concept to libraries; however, advances in universal design have raised its profile. Projects like Cloud4All and GPII identified the lack of common terminologies and models to describe the needs and preferences

neg: WinFS and retrieve information, and allow advanced searches. The applications can then aggregate the data and present the aggregated data to the user. WinFS stores data in relational stores, which are exposed as virtual locations called "stores". A WinFS store is a common repository where any application can store data along with its metadata, relationships and schema. WinFS runtime can apply certain relationships itself; for example, if the values of the "subject" property of a picture and the "name" property of a contact are same, then WinFS can relate the contact with the picture. Relations can also be specified by other

neg: Metadata repository for understanding the complete data flow and their definitions and their impact. Also the important feature is to maintain the version control though this statement for contrasting is open for discussion. These definitions are still evolving, so the accuracy of the definitions needs refinement. Purpose of registry is to define the metadata element and maintained across the organization. and data models and other data management teams refers to the registry for any changes to follow. While Metadata repository sources metadata from various metadata systems in the organizations and reflects what is in the upstream. Repository never acts as an upstream

neg: REDCap researchers begin committing actual patient data. The REDCap design workflow has important limitations. When a project is deployed into production mode, further revisions in database design need to be approved by the informatics core. Furthermore, certain production mode revisions are simply not allowed. For instance, when applying the REDCap calendar tool, production mode revisions to calendar metadata is prohibited, so researchers are advised to be very careful in formulating metadata and event-handlers before committing to the inflexibility of production mode. REDCap REDCap (Research Electronic Data Capture) is a browser-based, metadata-driven EDC software solution and workflow methodology for designing clinical and

neg: Climate and Forecast Metadata Conventions are documented on the conventions web site. As an example of the information provided by CF standard names, the entry for sea-level atmospheric pressure includes: Climate and Forecast Metadata Conventions The Climate and Forecast (CF) metadata conventions are conventions for the description of Earth sciences data, intended to promote the processing and sharing of data files. The metadata defined by the CF conventions are generally included in the same file as the data, thus making the file "self-describing". The conventions provide a definitive description of what the data values found in each netCDF variable represent, and of the spatial and

neg: Storage virtualization packet cracking to snoop on incoming I/O requests and perform the I/O redirection. It is much more difficult to ensure atomic updates of metadata in a switched environment and services requiring fast updates of data and metadata may be limited in switched implementations. In-band, also known as "symmetric", virtualization devices actually sit in the data path between the host and storage. All I/O requests and their data pass through the device. Hosts perform I/O to the virtualization device and never interact with the actual storage device. The virtualization device in turn performs I/O to the storage device. Caching of data,

neg: BioSD BioSD The BioSample Database (BioSD) is a database at European Bioinformatics Institute for the information about the biological samples used in sequencing. It stores submitter-supplied metadata about the biological materials from which data stored in the National Center for Biotechnology Information’s (NCBI) primary data archives are derived. NCBI’s archives hosts data pertaining to diverse types of samples from many species, and as such the BioSample database is similarly diverse. Examples of a BioSample include a primary tissue biopsy, an individual organism or an environmental isolate. The BioSample database captures sample metadata in a structured way by encouraging use of controlled

neg: Multiple instance learning reduces the memory and computational requirements. By mapping each bag to a feature vector of metadata, metadata-based algorithms allow the flexibility of using an arbitrary single-instance algorithm to perform the actual classification task. Future bags are simply mapped (embedded) into the feature space of metadata and labeled by the chosen classifier. Therefore, much of the focus for metadata-based algorithms is on what features or what type of embedding leads to effective classification. Note that some of the previously mentioned algorithms, such as TLC and GMIL could be considered metadata-based. They define two variations of kNN, Bayesian-kNN and citation-kNN, as adaptations

neg: ClickHouse ClickHouse ClickHouse is an open-source column-oriented DBMS (columnar database management system) for online analytical processing (OLAP). ClickHouse was developed by the Russian IT company Yandex for the Yandex.Metrica web analytics service. ClickHouse allows analysis of data that is updated in real time. The system is marketed for high performance. The project was released as open-source software under the Apache 2 license in June 2016. ClickHouse is used by the Yandex.Tank load testing tool. Yandex.Market uses ClickHouse to monitor site accessibility and KPIs. ClickHouse was also implemented at CERN’s LHCb experiment to store and process metadata on 10 billion events with

neg: Oracle Database shared between nodes through the interconnect—the virtual private network. The Oracle DBMS can store and execute stored procedures and functions within itself. PL/SQL (Oracle Corporation's proprietary procedural extension to SQL), or the object-oriented language Java can invoke such code objects and/or provide the programming structures for writing them. The Oracle RDBMS stores data logically in the form of tablespaces and physically in the form of data files ("datafiles"). Tablespaces can contain various types of memory segments, such as Data Segments, Index Segments, etc. Segments in turn comprise one or more extents. Extents comprise groups of contiguous data blocks. Data blocks

neg: Database machine analyzes and stores the data from the front end processor. Back end processors result in higher performance, increasing host main memory, increasing database recovery and security, and decrease of cost to manufacture. The database machine contrasts with a database server, which is a computer in a local area network that holds a database. According to Julie A. McCann, "Currently a DBMS controls the organisation, storage and retrieval of data whilst regulating the security and integrity of the database, it accepts requests for data from the application programs and instructs the operating system (OS) to transfer the appropriate data." An example

neg: Agile Business Intelligence agile methodology and development. Agile BI will play a big part in company’s success as it "emphasizes integration with agile development and innovation". There are couple of factors that influence the success of Business Intelligence Agility. 20% of data is inaccurate and about 50% is inconsistent and these numbers increases with new type of data. Processes need to be re-evaluated and corrected to minimize data entry errors. Often companies have multiple data stores and data is scattered across multiple data stores. "Agility theory emphasizes auto-discovery of each new data source, and automated upgrade of metadata repositories to automatically accommodate the

neg: DataObjects.NET attributes. Even the migration and update logic from older model versions is declared this way. Because the complete model is expressed in standard .NET source code DataObjects.NET integrates seamlessly in revision control and visual class design tools. This code sample declares a persistent codice_1 class with a codice_2 field: Unlike NHibernate DataObjects.net does not need a declared mapping to some kind of database. Instead it generates the needed tables and columns on the database itself. To do this it uses this metadata (the model) to create and modify the database schema on the underlying SQL Server or other DBMS. This

neg: Global surveillance disclosures (2013–present) 97 billion sets of information, about 3 billion data sets originated from U.S. computer networks and around 500 million metadata records were collected from German networks. In August 2013, it was revealed that the Bundesnachrichtendienst (BND) of Germany transfers massive amounts of metadata records to the NSA. "Der Spiegel" disclosed that Germany is the most targeted country of the 27 members of the European Union due to the NSA systematic monitoring and storage of Germany's telephone and Internet connection data. According to the magazine the NSA stores data from around half a billion communications connections in Germany each month. This

neg: Renaissance Computing Institute (CAroliNa Variant Annotation Store) and AnnoBot (Annotation Bot) work together to provide version-controlled annotation and metadata for genomic variant data in order to support up-to-date clinical interpretation of genomic variants and thereby guide clinical decision making. CANVAS is designed as an open source, relational PostgreSQL relational database that stores genomic variant data with associated annotation and metadata. AnnoBot consists of Python modules and software driver code configured to provide automated monitoring and retrieval of external data sources for annotation updates. CHAT (Convergent Haplotype Association Tagging) is a software algorithm that allows for the identification of moderately penetrant genomic variants using

neg: Metadata of older metadata collection is the use by US Census Bureau of what is known as the "Long Form." The Long Form asks questions that are used to create demographic data to find patterns of distribution. Libraries employ metadata in library catalogues, most commonly as part of an Integrated Library Management System. Metadata is obtained by cataloguing resources such as books, periodicals, DVDs, web pages or digital images. This data is stored in the integrated library management system, ILMS, using the MARC metadata standard. The purpose is to direct patrons to the physical or electronic location of items or areas

neg: Software mining mining is closely related to data mining, since existing software artifacts contain enormous business value, key for the evolution of software systems. Knowledge discovery from software systems addresses structure, behavior as well as the data processed by the software system. Instead of mining individual data sets, software mining focuses on metadata, such as database schemas. OMG Knowledge Discovery Metamodel provides an integrated representation to capturing application metadata as part of a holistic existing system metamodel. Another OMG specification, the Common Warehouse Metamodel focuses entirely on mining enterprise metadata. Text-Mining Software Tools enable easy handling of text documents for the purpose

neg: Non-standard RAID levels reconstruction has to traverse the filesystem metadata to determine the actual RAID-Z geometry. This would be impossible if the filesystem and the RAID array were separate products, whereas it becomes feasible when there is an integrated view of the logical and physical structure of the data. Going through the metadata means that ZFS can validate every block against its 256-bit checksum as it goes, whereas traditional RAID products usually cannot do this. In addition to handling whole-disk failures, RAID-Z can also detect and correct silent data corruption, offering "self-healing data": when reading a RAID-Z block, ZFS compares it against its

neg: Microsoft Access Microsoft Access Microsoft Access is a database management system (DBMS) from Microsoft that combines the relational Microsoft Jet Database Engine with a graphical user interface and software-development tools. It is a member of the Microsoft Office suite of applications, included in the Professional and higher editions or sold separately. Microsoft Access stores data in its own format based on the Access Jet Database Engine. It can also import or link directly to data stored in other applications and databases. Software developers, data architects and power users can use Microsoft Access to develop application software. Like other Microsoft Office applications, Access

neg: Data dictionary configuration, documentation, application and users as well as other information relevant to system administration. If a data dictionary system is used only by the designers, users, and administrators and not by the DBMS Software, it is called a "passive data dictionary." Otherwise, it is called an "active data dictionary" or "data dictionary." When a passive data dictionary is updated, it is done so manually and independently from any changes to a DBMS (database) structure. With an active data dictionary, the dictionary is updated first and changes occur in the DBMS automatically as a result. Database users and application developers can

neg: Column-oriented DBMS from a set of columns along with codice_1 pointers back into the original table. An index on the salary column would look something like this: As they store only single pieces of data, rather than entire rows, indexes are generally much smaller than the main table stores. Scanning this smaller set of data reduces the number of disk operations. If the index is heavily used, it can dramatically reduce the time for common operations. However, maintaining indexes adds overhead to the system, especially when new data is written to the database. Records not only need to be stored in the

neg: UVC-based preservation read the metadata schema is needed. A simple solution adopted for the UVC approach is a method for the schema similar to the method for the data: the schema information is stored in an internal representation, and accompanied by a method to decode it. At this point, what will be included in the archive is: the data itself, the metadata, a UVC program to decode the data, and a UVC program to decode the metadata. The UVC method for data archiving can be extended for program archiving. Program archiving involves archiving the behavior and functionality of a program and may

neg: Database tuning prevent read queuing. DBMS tuning refers to tuning of the DBMS and the configuration of the memory and processing resources of the computer running the DBMS. This is typically done through configuring the DBMS, but the resources involved are shared with the host system. Tuning the DBMS can involve setting the recovery interval (time needed to restore the state of data to a particular point in time), assigning parallelism (the breaking up of work from a single query into tasks assigned to different processing resources), and network protocols used to communicate with database consumers. Memory is allocated for data, execution

neg: Column-oriented DBMS precisely access the data it needs to answer a query rather than scanning and discarding unwanted data in rows. Query performance is increased for certain workloads. A relational database management system provides data that represents a two-dimensional table, of columns and rows. For example, a database might have this table: This simple table includes an employee identifier (EmpId), name fields (Lastname and Firstname) and a salary (Salary). This two-dimensional format is an abstraction. In an actual implementation, storage hardware requires the data to be serialized into one form or another. The most expensive operations involving hard disks are seeks. In

neg: RIF-CS RIF-CS The Registry Interchange Format - Collections and Services (RIF-CS) is an XML vocabulary for representing metadata about data collections and related entities based on ISO 2146. It is a machine-readable format to describe metadata about data collections. Similar to how MARC standards are used by library systems to describe books, RIF-CS is used to describe data collections. For example, a RIF-CS record can describe a spreadsheet containing experimental results: it might contain the title, description, creator, keywords, date the experiment was conducted and a URL to obtain the actual spreadsheet. Another RIF-CS record can describe the person who created

neg: Card Transaction Data experiences in stores and online. This encourages the idea of promotional services to be offered to select groups who will take the most advantage from the opportunity. Keeping this type of data anonymous is a challenge for debit and credit card companies who share the information with third parties. Metadata with certain properties, especially a geographical location attached are hard to keep unsourced. Other patterns of behaviour are also unique enough on their own to identify individuals to using metadata, indicating that complete anonymity is impossible. As more information becomes available and is tagged by people's consuming behaviour, this will

neg: Tablespace Tablespace A tablespace is a storage location where the actual data underlying database objects can be kept. It provides a layer of abstraction between physical and logical data, and serves to allocate storage for all DBMS managed segments. (A database segment is a database object which occupies physical space such as table data and indexes.) Once created, a tablespace can be referred to by name when creating database segments. Tablespaces specify only the database storage locations, not the logical database structure, or database schema. For instance, different objects in the same schema may have different underlying tablespaces. Similarly, a tablespace

neg: NOVA (filesystem) also stores two copies of each data structure – the "primary" and the "replica" – and stores them far from one another in memory. Whenever NOVA accesses a metadata structure, it first recomputes the checksum on both the primary and the replica. If either check results in a mismatch, NOVA repairs the damage using the other copy. If neither checksum matches, then the structure is lost and NOVA returns an error. NOVA use RAID 4 to protect file data. It divides each 4KB page into 512-byte strips and stores a parity strip in a dedicated region of persistent memory. It

neg: ZFS create or expand a ZFS filesystem is closer to that of making a new directory than it is to volume manipulation in some other systems. Pools and their associated ZFS file systems can be moved between different platform architectures, including systems implementing different byte orders. The ZFS block pointer format stores filesystem metadata in an endian-adaptive way; individual metadata blocks are written with the native byte order of the system writing the block. When reading, if the stored endianness does not match the endianness of the system, the metadata is byte-swapped in memory. This does not affect the stored data;

neg: Null (SQL) basically adopted by SQL92) this semantic inconsistency is rationalized by arguing that removal of duplicates in set operations happens "at a lower level of detail than equality testing in the evaluation of retrieval operations." The SQL standard does not explicitly define a default sort order for Nulls. Instead, on conforming systems, Nulls can be sorted before or after all data values by using the codice_64 or codice_65 clauses of the codice_66 list, respectively. Not all DBMS vendors implement this functionality, however. Vendors who do not implement this functionality may specify different treatments for Null sorting in the DBMS. Some SQL

neg: ONTAP proxy is a service introduced in ONTAP 9.5 it works in conjunction with NDAS service in a cloud provider. Similarly to FabricPool, NDAS stores data in object format, but unlike FabricPool it stores WAFL metadata in an object storage as well. Information which been transferred from ONTAP system are snapshot deltas, not the entire set of data and already deduplicated & compressed (on volume level). NDAS proxy is basically HTTP with S3 object protocol and few additional API calls to the cloud. NDAS in ONTAP 9.5 works only in a schema with primary ONTAP 9 storage replicating data via Snapmirror

neg: S-RAMP specify much about the repository itself. Instead the specification is written to promote interoperability across all portions of the service lifecycle between Design Time, Run Time and Monitoring systems and tooling. The design adheres to design goals such as the use of existing standards, vendor neutrality and it is driven by use cases. The specification separates out the data model from the bindings that describe the interaction APIs clients use to interact with the repository. An S-RAMP repository stores all SOA content but more importantly metadata about each piece of content. An artifact in S-RAMP is a container for all

neg: MP3 and ID3v2, and the more recently introduced APEv2. These tags are normally embedded at the beginning or end of MP3 files, separate from the actual MP3 frame data. MP3 decoders either extract information from the tags, or just treat them as ignorable, non-MP3 junk data. Playing & editing software often contains tag editing functionality, but there are also tag editor applications dedicated to the purpose. Aside from metadata pertaining to the audio content, tags may also be used for DRM. ReplayGain is a standard for measuring and storing the loudness of an MP3 file (audio normalization) in its metadata tag,

neg: Digital Guardian policies and procedures. This scalable platform provides multiple, independent layers of protection to enable secure data sharing across physical, virtual, mobile and cloud environments. The architecture of the platform has three parts: Digital Guardian allows businesses to host and manage on premise or choose managed security programs. When deployed as a managed service, Digital Guardian does not collect the actual data itself. Rather, it aggregates the metadata about the files and documents and watches for patterns of activity. Metadata is encrypted, hashed and digitally signed before being transferred to Digital Guardian's hosting facilities via FIPS 140-2 certified messaging protocol. Digital

neg: Data model analysis at the conceptual level. The quality of a database application depends critically on its design. To help ensure correctness, clarity, adaptability and productivity, information systems are best specified first at the conceptual level, using concepts and language that people can readily understand. The conceptual design may include data, process and behavioral perspectives, and the actual DBMS used to implement the design might be based on one of many logical data models (relational, hierarchic, network, object-oriented etc.). The Unified Modeling Language (UML) is a standardized general-purpose modeling language in the field of software engineering. It is a graphical language for

neg: File carving File carving File carving is the process of reassembling computer files from fragments in the absence of filesystem metadata. All filesystems contain some metadata that describes the actual file system. At a minimum the following is stored: the hierarchy of folders and files, with names for each. Also stored for each file is the physical address on the hard disk where the file is stored. As explained below, a file might be scattered in fragments at different physical addresses. File carving is the process of trying to recover files without this metadata. This is done by analyzing the raw data

neg: Metadata Metadata Metadata is "data [information] that provides information about other data". Many distinct types of metadata exist, among these descriptive metadata, structural metadata, administrative metadata, reference metadata and statistical metadata Metadata was traditionally used in the card catalogs of libraries until the 1980s, when libraries converted their catalog data to digital databases. In the 2000s, as digital formats were becoming the prevalent way of storing data and information, metadata was also used to describe digital data using metadata standards. The first description of "meta data" for computer systems is purportedly noted by MIT's Center for International Studies experts David Griffel

neg: Metadata registry the following characteristics: Because metadata registries are used to store both semantics (the meaning of a data element) and systems-specific constraints (for example the maximum length of a string) it is important to identify what systems impose these constraints and to document them. For example the maximum length of a string should not change the meaning of a data element. The International Organization for Standardization (ISO) has published standards for a metadata registry called ISO/IEC 11179 and also ISO15000-3 and ISO15000-4 ebXML registry and repository (regrep) EbXML RegRep There are two ISO standards which are commonly referred to as metadata

neg: Metadata discovery Metadata discovery In metadata, metadata discovery (also metadata harvesting) is the process of using automated tools to discover the semantics of a data element in data sets. This process usually ends with a set of mappings between the data source elements and a centralized metadata registry. Metadata discovery is also known as metadata scanning. Data sets may be in a variety of different forms including: There are distinct categories of automated metadata discovery: Semantic matching attempts to use semantics to associate target data with registered data elements. Statistical matching uses statistics about data sources data itself to derive similarities with