International Business Machines Corporation is an American multinational information technology company headquartered in Armonk, New York, with operations in over 170 countries. The company began in 1911, founded in Endicott, New York, as the Computing-Tabulating-Recording Company and was renamed "International Business Machines" in 1924. IBM produces and sells computer hardware and software, provides hosting and consulting services in areas ranging from mainframe computers to nanotechnology. IBM is a major research organization, holding the record for most U. S. patents generated by a business for 26 consecutive years. Inventions by IBM include the automated teller machine, the floppy disk, the hard disk drive, the magnetic stripe card, the relational database, the SQL programming language, the UPC barcode, dynamic random-access memory; the IBM mainframe, exemplified by the System/360, was the dominant computing platform during the 1960s and 1970s. IBM has continually shifted business operations by focusing on higher-value, more profitable markets.
This includes spinning off printer manufacturer Lexmark in 1991 and the sale of personal computer and x86-based server businesses to Lenovo, acquiring companies such as PwC Consulting, SPSS, The Weather Company, Red Hat. In 2014, IBM announced that it would go "fabless", continuing to design semiconductors, but offloading manufacturing to GlobalFoundries. Nicknamed Big Blue, IBM is one of 30 companies included in the Dow Jones Industrial Average and one of the world's largest employers, with over 380,000 employees, known as "IBMers". At least 70% of IBMers are based outside the United States, the country with the largest number of IBMers is India. IBM employees have been awarded five Nobel Prizes, six Turing Awards, ten National Medals of Technology and five National Medals of Science. In the 1880s, technologies emerged that would form the core of International Business Machines. Julius E. Pitrap patented the computing scale in 1885. On June 16, 1911, their four companies were amalgamated in New York State by Charles Ranlett Flint forming a fifth company, the Computing-Tabulating-Recording Company based in Endicott, New York.
The five companies had offices and plants in Endicott and Binghamton, New York. C.. They manufactured machinery for sale and lease, ranging from commercial scales and industrial time recorders and cheese slicers, to tabulators and punched cards. Thomas J. Watson, Sr. fired from the National Cash Register Company by John Henry Patterson, called on Flint and, in 1914, was offered a position at CTR. Watson joined CTR as General Manager 11 months was made President when court cases relating to his time at NCR were resolved. Having learned Patterson's pioneering business practices, Watson proceeded to put the stamp of NCR onto CTR's companies, he implemented sales conventions, "generous sales incentives, a focus on customer service, an insistence on well-groomed, dark-suited salesmen and had an evangelical fervor for instilling company pride and loyalty in every worker". His favorite slogan, "THINK", became a mantra for each company's employees. During Watson's first four years, revenues reached $9 million and the company's operations expanded to Europe, South America and Australia.
Watson never liked the clumsy hyphenated name "Computing-Tabulating-Recording Company" and on February 14, 1924 chose to replace it with the more expansive title "International Business Machines". By 1933 most of the subsidiaries had been merged into one company, IBM. In 1937, IBM's tabulating equipment enabled organizations to process unprecedented amounts of data, its clients including the U. S. Government, during its first effort to maintain the employment records for 26 million people pursuant to the Social Security Act, the tracking of persecuted groups by Hitler's Third Reich through the German subsidiary Dehomag. In 1949, Thomas Watson, Sr. created IBM World Trade Corporation, a subsidiary of IBM focused on foreign operations. In 1952, he stepped down after 40 years at the company helm, his son Thomas Watson, Jr. was named president. In 1956, the company demonstrated the first practical example of artificial intelligence when Arthur L. Samuel of IBM's Poughkeepsie, New York, laboratory programmed an IBM 704 not to play checkers but "learn" from its own experience.
In 1957, the FORTRAN scientific programming language was developed. In 1961, IBM developed the SABRE reservation system for American Airlines and introduced the successful Selectric typewriter. In 1963, IBM employees and computers helped. A year it moved its corporate headquarters from New York City to Armonk, New York; the latter half of the 1960s saw IBM continue its support of space exploration, participating in the 1965 Gemini flights, 1966 Saturn flights and 1969 lunar mission. On April 7, 1964, IBM announced the first computer system family, the IBM System/360, it spanned the complete range of commercial and scientific applications from large to small, allowing companies for the first time to upgrade to models with greater computing capability without having to rewrite their applications. It was followed by the IBM System/370 in 1970. Together the
Extract, transform, load
In computing, transform, load is the general procedure of copying data from one or more sources into a destination system which represents the data differently from the source. The ETL process became a popular concept in the 1970s and is used in data warehousing. Data extraction involves extracting data from heterogeneous sources. A properly designed ETL system extracts data from the source systems, enforces data quality and consistency standards, conforms data so that separate sources can be used together, delivers data in a presentation-ready format so that application developers can build applications and end users can make decisions. Since the data extraction takes time, it is common to execute the three phases in parallel. While the data is being extracted, another transformation process executes while processing the data received and prepares it for loading while the data loading begins without waiting for the completion of the previous phases. ETL systems integrate data from multiple applications developed and supported by different vendors or hosted on separate computer hardware.
The separate systems containing the original data are managed and operated by different employees. For example, a cost accounting system may combine data from payroll and purchasing; the first part of an ETL process involves extracting the data from the source system. In many cases, this represents the most important aspect of ETL, since extracting data sets the stage for the success of subsequent processes. Most data-warehousing projects combine data from different source systems; each separate system may use a different data organization and/or format. Common data-source formats include relational databases, XML, JSON and flat files, but may include non-relational database structures such as Information Management System or other data structures such as Virtual Storage Access Method or Indexed Sequential Access Method, or formats fetched from outside sources by means such as web spidering or screen-scraping; the streaming of the extracted data source and loading on-the-fly to the destination database is another way of performing ETL when no intermediate data storage is required.
In general, the extraction phase aims to convert the data into a single format appropriate for transformation processing. An intrinsic part of the extraction involves data validation to confirm whether the data pulled from the sources has the correct/expected values in a given domain. If the data fails the validation rules, it is rejected or in part; the rejected data is ideally reported back to the source system for further analysis to identify and to rectify the incorrect records. In the data transformation stage, a series of rules or functions are applied to the extracted data in order to prepare it for loading into the end target. An important function of transformation is data cleansing, which aims to pass only "proper" data to the target; the challenge when different systems interact is in the relevant systems' interfacing and communicating. Character sets that may be available in one system may not be so in others. In other cases, one or more of the following transformation types may be required to meet the business and technical needs of the server or data warehouse: Selecting only certain columns to load:.
For example, if the source data has three columns, roll_no, salary the selection may take only roll_no and salary. Or, the selection mechanism may ignore all those records. Translating coded values: Encoding free-form values: Deriving a new calculated value: Sorting or ordering the data based on a list of columns to improve search performance Joining data from multiple sources and deduplicating the data Aggregating Generating surrogate-key values Transposing or pivoting Splitting a column into multiple columns Disaggregating repeating columns Looking up and validating the relevant data from tables or referential files Applying any form of data validation. Depending on the requirements of the organization, this process varies widely; some data warehouses may overwrite existing information with cumulative information. Other data warehouses may add new data in a historical form at regular interv
Mainframe computers or mainframes are computers used by large organizations for critical applications. They are larger and have more processing power than some other classes of computers: minicomputers, servers and personal computers; the term referred to the large cabinets called "main frames" that housed the central processing unit and main memory of early computers. The term was used to distinguish high-end commercial machines from less powerful units. Most large-scale computer system architectures were established in the 1960s, but continue to evolve. Mainframe computers are used as servers. Modern mainframe design is characterized less by raw computational speed and more by: Redundant internal engineering resulting in high reliability and security Extensive input-output facilities with the ability to offload to separate engines Strict backward compatibility with older software High hardware and computational utilization rates through virtualization to support massive throughput. Hot-swapping of hardware, such as processors and memory.
Their high stability and reliability enable these machines to run uninterrupted for long periods of time, with mean time between failures measured in decades. Mainframes have high availability, one of the primary reasons for their longevity, since they are used in applications where downtime would be costly or catastrophic; the term reliability and serviceability is a defining characteristic of mainframe computers. Proper planning and implementation is required to realize these features. In addition, mainframes are more secure than other computer types: the NIST vulnerabilities database, US-CERT, rates traditional mainframes such as IBM Z, Unisys Dorado and Unisys Libra as among the most secure with vulnerabilities in the low single digits as compared with thousands for Windows, UNIX, Linux. Software upgrades require setting up the operating system or portions thereof, are non-disruptive only when using virtualizing facilities such as IBM z/OS and Parallel Sysplex, or Unisys XPCL, which support workload sharing so that one system can take over another's application while it is being refreshed.
In the late 1950s, mainframes had only a rudimentary interactive interface, used sets of punched cards, paper tape, or magnetic tape to transfer data and programs. They operated in batch mode to support back office functions such as payroll and customer billing, most of which were based on repeated tape-based sorting and merging operations followed by line printing to preprinted continuous stationery; when interactive user terminals were introduced, they were used exclusively for applications rather than program development. Typewriter and Teletype devices were common control consoles for system operators through the early 1970s, although supplanted by keyboard/display devices. By the early 1970s, many mainframes acquired interactive user terminals operating as timesharing computers, supporting hundreds of users along with batch processing. Users gained access through keyboard/typewriter terminals and specialized text terminal CRT displays with integral keyboards, or from personal computers equipped with terminal emulation software.
By the 1980s, many mainframes supported graphic display terminals, terminal emulation, but not graphical user interfaces. This form of end-user computing became obsolete in the 1990s due to the advent of personal computers provided with GUIs. After 2000, modern mainframes or phased out classic "green screen" and color display terminal access for end-users in favour of Web-style user interfaces; the infrastructure requirements were drastically reduced during the mid-1990s, when CMOS mainframe designs replaced the older bipolar technology. IBM claimed that its newer mainframes reduced data center energy costs for power and cooling, reduced physical space requirements compared to server farms. Modern mainframes can run multiple different instances of operating systems at the same time; this technique of virtual machines allows applications to run as if they were on physically distinct computers. In this role, a single mainframe can replace higher-functioning hardware services available to conventional servers.
While mainframes pioneered this capability, virtualization is now available on most families of computer systems, though not always to the same degree or level of sophistication. Mainframes can add or hot swap system capacity without disrupting system function, with specificity and granularity to a level of sophistication not available with most server solutions. Modern mainframes, notably the IBM zSeries, System z9 and System z10 servers, offer two levels of virtualization: logical partitions and virtual machines. Many mainframe customers run two machines: one in their primary data center, one in their backup data center—fully active active, or on standby—in case there is a catastrophe affecting the first building. Test, development and production workload for applications and databases can run on a single machine, except for large demands where the capacity of one machine might be limiting; such a two-mainframe installation can support continuous business service, avoiding both planned and unplanned outages.
In practice many customers use multiple mainframes linked either by Parallel Sysplex and shared DASD, or with shared, geographically dispersed storage provided by EMC
A computing platform or digital platform is the environment in which a piece of software is executed. It may be the hardware or the operating system a web browser and associated application programming interfaces, or other underlying software, as long as the program code is executed with it. Computing platforms have different abstraction levels, including a computer architecture, an OS, or runtime libraries. A computing platform is the stage. A platform can be seen both as a constraint on the software development process, in that different platforms provide different functionality and restrictions. For example, an OS may be a platform that abstracts the underlying differences in hardware and provides a generic command for saving files or accessing the network. Platforms may include: Hardware alone, in the case of small embedded systems. Embedded systems can access hardware directly, without an OS. A browser in the case of web-based software; the browser itself runs on a hardware+OS platform, but this is not relevant to software running within the browser.
An application, such as a spreadsheet or word processor, which hosts software written in an application-specific scripting language, such as an Excel macro. This can be extended to writing fully-fledged applications with the Microsoft Office suite as a platform. Software frameworks. Cloud computing and Platform as a Service. Extending the idea of a software framework, these allow application developers to build software out of components that are hosted not by the developer, but by the provider, with internet communication linking them together; the social networking sites Twitter and Facebook are considered development platforms. A virtual machine such as the Java virtual machine or. NET CLR. Applications are compiled into a format similar to machine code, known as bytecode, executed by the VM. A virtualized version of a complete system, including virtualized hardware, OS, storage; these allow, for instance, a typical Windows program to run on. Some architectures have multiple layers, with each layer acting as a platform to the one above it.
In general, a component only has to be adapted to the layer beneath it. For instance, a Java program has to be written to use the Java virtual machine and associated libraries as a platform but does not have to be adapted to run for the Windows, Linux or Macintosh OS platforms. However, the JVM, the layer beneath the application, does have to be built separately for each OS. AmigaOS, AmigaOS 4 FreeBSD, NetBSD, OpenBSD IBM i Linux Microsoft Windows OpenVMS Classic Mac OS macOS OS/2 Solaris Tru64 UNIX VM QNX z/OS Android Bada BlackBerry OS Firefox OS iOS Embedded Linux Palm OS Symbian Tizen WebOS LuneOS Windows Mobile Windows Phone Binary Runtime Environment for Wireless Cocoa Cocoa Touch Common Language Infrastructure Mono. NET Framework Silverlight Flash AIR GNU Java platform Java ME Java SE Java EE JavaFX JavaFX Mobile LiveCode Microsoft XNA Mozilla Prism, XUL and XULRunner Open Web Platform Oracle Database Qt SAP NetWeaver Shockwave Smartface Universal Windows Platform Windows Runtime Vexi Ordered from more common types to less common types: Commodity computing platforms Wintel, that is, Intel x86 or compatible personal computer hardware with Windows operating system Macintosh, custom Apple Inc. hardware and Classic Mac OS and macOS operating systems 68k-based PowerPC-based, now migrated to x86 ARM architecture based mobile devices iPhone smartphones and iPad tablet computers devices running iOS from Apple Gumstix or Raspberry Pi full function miniature computers with Linux Newton devices running the Newton OS from Apple x86 with Unix-like systems such as Linux or BSD variants CP/M computers based on the S-100 bus, maybe the earliest microcomputer platform Video game consoles, any variety 3DO Interactive Multiplayer, licensed to manufacturers Apple Pippin, a multimedia player platform for video game console development RISC processor based machines running Unix variants SPARC architecture computers running Solaris or illumos operating systems DEC Alpha cluster running OpenVMS or Tru64 UNIX Midrange computers with their custom operating systems, such as IBM OS/400 Mainframe computers with their custom operating systems, such as IBM z/OS Supercomputer architectures Cross-platform Platform virtualization Third platform Ryan Sarver: What is a platform
Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial and scientific domains. Data integration appears with increasing frequency as the volume and the need to share existing data explodes, it has become the focus of extensive theoretical work, numerous open problems remain unsolved. Data integration encourages collaboration between internal as well as external users Issues with combining heterogeneous data sources referred to as information silos, under a single query interface have existed for some time. In the early 1980s, computer scientists began designing systems for interoperability of heterogeneous databases; the first data integration system driven by structured metadata was designed at the University of Minnesota in 1991, for the Integrated Public Use Microdata Series. IPUMS used a data warehousing approach, which extracts and loads data from heterogeneous sources into a single view schema so data from different sources become compatible.
By making thousands of population databases interoperable, IPUMS demonstrated the feasibility of large-scale data integration. The data warehouse approach offers a coupled architecture because the data are physically reconciled in a single queryable repository, so it takes little time to resolve queries; the data warehouse approach is less feasible for data sets that are updated, requiring the extract, load process to be continuously re-executed for synchronization. Difficulties arise in constructing data warehouses when one has only a query interface to summary data sources and no access to the full data; this problem emerges when integrating several commercial query services like travel or classified advertisement web applications. As of 2009 the trend in data integration favored loosening the coupling between data and providing a unified query-interface to access real time data over a mediated schema, which allows information to be retrieved directly from original databases; this is consistent with the SOA approach popular in that era.
This approach relies on mappings between the mediated schema and the schema of original sources, transforming a query into specialized queries to match the schema of the original databases. Such mappings can be specified in two ways: as a mapping from entities in the mediated schema to entities in the original sources, or as a mapping from entities in the original sources to the mediated schema; the latter approach requires more sophisticated inferences to resolve a query on the mediated schema, but makes it easier to add new data sources to a mediated schema. As of 2010 some of the work in data integration research concerns the semantic integration problem; this problem addresses not the structuring of the architecture of the integration, but how to resolve semantic conflicts between heterogeneous data sources. For example, if two companies merge their databases, certain concepts and definitions in their respective schemas like "earnings" have different meanings. In one database it may mean profits in dollars, while in the other it might represent the number of sales.
A common strategy for the resolution of such problems involves the use of ontologies which explicitly define schema terms and thus help to resolve semantic conflicts. This approach represents ontology-based data integration. On the other hand, the problem of combining research results from different bioinformatics repositories requires bench-marking of the similarities, computed from different data sources, on a single criterion such as positive predictive value; this enables the data sources to be directly comparable and can be integrated when the natures of experiments are distinct. As of 2011 it was determined that current data modeling methods were imparting data isolation into every data architecture in the form of islands of disparate data and information silos; this data isolation is an unintended artifact of the data modeling methodology that results in the development of disparate data models. Disparate data models, when instantiated as databases, form disparate databases. Enhanced data model methodologies have been developed to eliminate the data isolation artifact and to promote the development of integrated data models.
One enhanced data modeling method recasts data models by augmenting them with structural metadata in the form of standardized data entities. As a result of recasting multiple data models, the set of recast data models will now share one or more commonality relationships that relate the structural metadata now common to these data models. Commonality relationships are a peer-to-peer type of entity relationships that relate the standardized data entities of multiple data models. Multiple data models that contain the same standard data entity may participate in the same commonality relationship; when integrated data models are instantiated as databases and are properly populated from a common set of master data these databases are integrated. Since 2011, data hub approaches have been of greater interest than structured Enterprise Data Warehouses. Since 2013, data lake approaches have risen to the level of Data Hubs; these approaches combine unstructured or varied data into one location, but do not require an maste
Oracle Corporation is an American multinational computer technology corporation headquartered in Redwood Shores, California. The company specializes in developing and marketing database software and technology, cloud engineered systems, enterprise software products — its own brands of database management systems. In 2018, Oracle was the third-largest software maker by revenue, after Alphabet; the company develops and builds tools for database development and systems of middle-tier software, enterprise resource planning software, customer relationship management software, supply chain management software. Larry Ellison co-founded Oracle Corporation in 1977 with Bob Miner and Ed Oates under the name Software Development Laboratories. Ellison took inspiration from the 1970 paper written by Edgar F. Codd on relational database management systems named "A Relational Model of Data for Large Shared Data Banks." He heard about the IBM System R database from an article in the IBM Research Journal provided by Oates.
Ellison wanted to make Oracle's product compatible with System R, but failed to do so as IBM kept the error codes for their DBMS a secret. SDL changed its name to Relational Software, Inc in 1979 again to Oracle Systems Corporation in 1982, to align itself more with its flagship product Oracle Database. At this stage Bob Miner served as the company's senior programmer. On March 12, 1986, the company had its initial public offering. In 1995, Oracle Systems Corporation changed its name to Oracle Corporation named Oracle, but sometimes referred to as Oracle Corporation, the name of the holding company. Part of Oracle Corporation's early success arose from using the C programming language to implement its products; this eased porting to different operating systems. 1979: offers the first commercial SQL RDBMS 1983: offers a VAX-mode database 1984: offers the first database with read-consistency 1986: offers a client-server DBMS 1987: introduces UNIX-based Oracle applications 1988: introduces PL/SQL.
1992: offers full applications implementation methodology 1995: offers the first 64-bit RDBMS 1996: moves towards an open standards-based, web-enabled architecture 1999: offers its first DBMS with XML support 2001: becomes the first to complete 3 terabyte TPC-H world record 2002: offers the first database to pass 15 industry standard security evaluations 2003: introduces what it calls "Enterprise Grid Computing" with Oracle10g 2005: releases its first free database, Oracle Database 10g Express Edition 2006: acquires Siebel Systems 2007: acquires Hyperion Solutions 2008: Smart scans in software improve query-response in HP Oracle Database Machine / Exadata storage 2010: acquires Sun Microsystems 2013: begins use of Oracle 12c, capable of providing cloud services with Oracle Database 2014: acquires Micros Systems 2016: acquires NetSuite Inc. Oracle ranked No. 82 in the 2018 Fortune 500 list of the largest United States corporations by total revenue. According to Bloomberg, Oracle's CEO-to-employee pay ratio is 1,205:1.
The CEO's compensation in 2017 was $108,295,023. Meanwhile, the median employee compensation rate was $89,887. Oracle designs and sells both software and hardware products, as well as offering services that complement them. Many of the products have been added to Oracle's portfolio through acquisitions. Oracle's E-delivery service provides documentation. Oracle Database Release 10: In 2004, Oracle Corporation shipped release 10g as the latest version of Oracle Database. Release 11: Release 11g became the current Oracle Database version in 2007. Oracle Corporation released Oracle Database 11g Release 2 in September 2009; this version was available in four commercial editions—Enterprise Edition, Standard Edition, Standard Edition One, Personal Edition—and in one free edition—the Express Edition. The licensing of these editions shows various restrictions and obligations that were called complex by licensing expert Freirich Florea; the Enterprise Edition, the most expensive of the Database Editions, has the fewest restrictions — but has complex licensing.
Oracle Corporation constrains the Standard Edition and Standard Edition One with more licensing restrictions, in accordance with their lower price. Release 12: Release 12c became available on July 1, 2013. Oracle Corporation has acquired and developed the following additional database technologies: Berkeley DB, which offers embedded database processing Oracle Rdb, a relational database system running on OpenVMS platforms. Oracle acquired Rdb in 1994 from Digital Equipment Corporation. Oracle has since made many enhancements to this product and development continues as of 2008. TimesTen, which features in-memory database operations Oracle Essbase, which continues the Hyperion Essbase tradition of multi-dimensional database management MySQL, a relational database management system licensed under the GNU General Public License developed by MySQL AB Oracle NoSQL Database, a scalable, distributed key-value NoSQL database Oracle Fusion Middleware is a family of middleware
Service-oriented architecture is a style of software design where services are provided to the other components by application components, through a communication protocol over a network. The basic principles of service-oriented architecture are independent of vendors and technologies. A service is a discrete unit of functionality that can be accessed remotely and acted upon and updated independently, such as retrieving a credit card statement online. A service has four properties according to one of many definitions of SOA: It logically represents a business activity with a specified outcome, it is self-contained. It is a black box for its consumers, it may consist of other underlying services. SOA was first termed Service-Based Architecture in 1998 by a team developing integrated foundational management services and business process-type services based upon units of work and using CORBA for inter-process communications. Different services can be used in conjunction to provide the functionality of a large software application, a principle SOA shares with modular programming.
Service-oriented architecture integrates distributed, separately-maintained and -deployed software components. It is enabled by technologies and standards that facilitate components' communication and cooperation over a network over an IP network. In SOA, services use protocols that describe how they pass and parse messages using description metadata; this metadata describes both the functional characteristics of the service and quality-of-service characteristics. Service-oriented architecture aims to allow users to combine large chunks of functionality to form applications which are built purely from existing services and combining them in an ad hoc manner. A service presents a simple interface to the requester that abstracts away the underlying complexity acting as a black box. Further users can access these independent services without any knowledge of their internal implementation; the related buzzword service-orientation promotes loose coupling between services. SOA separates functions into distinct units, or services, which developers make accessible over a network in order to allow users to combine and reuse them in the production of applications.
These services and their corresponding consumers communicate with each other by passing data in a well-defined, shared format, or by coordinating an activity between two or more services. A manifesto was published for service-oriented architecture in October, 2009; this came up with six core values which are listed as follows: Business value is given more importance than technical strategy. Strategic goals are given more importance than project-specific benefits. Intrinsic inter-operability is given more importance than custom integration. Shared services are given more importance than specific-purpose implementations. Flexibility is given more importance than optimization. Evolutionary refinement is given more importance than pursuit of initial perfection. SOA can be seen as part of the continuum which ranges from the older concept of distributed computing and modular programming, through SOA, on to current practices of mashups, SaaS, cloud computing. There are no industry standards relating to the exact composition of a service-oriented architecture, although many industry sources have published their own principles.
Some of these include the following: Standardized service contract Services adhere to a standard communications agreements, as defined collectively by one or more service-description documents within a given set of services. Service reference autonomy The relationship between services is minimized to the level that they are only aware of their existence. Service location transparency Services can be called from anywhere within the network that it is located no matter where it is present. Service longevity Services should be designed to be long lived. Where possible services should avoid forcing consumers to change if they do not require new features, if you call a service today you should be able to call the same service tomorrow. Service abstraction The services act as black boxes, their inner logic is hidden from the consumers. Service autonomy Services are independent and control the functionality they encapsulate, from a Design-time and a run-time perspective. Service statelessness Services are stateless, either return the requested value or give an exception hence minimizing resource use.
Service granularity A principle to ensure services have an adequate scope. The functionality provided by the service to the user must be relevant. Service normalization Services are consolidated to minimize redundancy. In some, this may not be done, These are the cases where performance optimization and aggregation are required. Service composability Services can be used to compose other services. Service discovery Services are supplemented with communicative meta data by which they can be discovered and interpreted. Service reusability Logic is divided into various services. Service encapsulation Many services which were not planned under SOA, may get encapsulated or become a part of SOA; each SOA building block can play any of the three roles: Service provider It creates a web service and provides its information to the service registry. Each provider debates upon a lot of hows and whys like which service to expose, which to give more importance: security or easy availability, what price to offer the service for and many more.
The provider has to decide what category the service should be listed in for a given broker service and what sort of trading partner agreements are required to use the service. Servi