Entry

In our time, without data base management systems, one cannot do practically any organization, especially among quiet ones, as traditionally oriented in interaction with clients. Banks, insurance companies, airlines and other transport companies, supermarket chains, telecommunications and marketing firms, organizations engaged in services and other areas - all stinks collect and collect gigabytes of data about customers, products and services from their databases. The value of such reports is undeniable. Such data bases are called operational or transactional, stink shards are characterized by a great number of small transactions or write-read operations. Computer systems that create the shape of operations and have access to transaction databases are usually called systems of online transaction processing (OLTP - On-Line Transactional Processing) or cloud systems.

Cloud systems are adjusted and optimized for viconannia maximum quantity transactions in short periods of time. Call the okremі operations more or less small and tied to each other. However, a skin record of data that characterizes the interaction with the client (call to the support service, cash transaction, ordering the catalog, looking at the company's Web site then) can new information, And for itself, for the creation of zvіtіv and analysis of the activities of the firm.

A set of analytic functions in oblіkovyh systems sound oblezheniya. Schemes, which are used in OLTP add-ons, make it easier to create simple links, the skeletons of this data are most often divided into anonymous tables, and their aggregation is necessary to combine folding operations. As a rule, try the creation of complex zvіtіv vmagayut great counting strains and produce to waste productivity.

In addition, cloud systems save data that is constantly changing. In the world of choosing transactional totals, the values ​​change quite quickly, so two analyzes carried out at intervals in the sprat of quills can give different results. Most of the analysis takes place after the end of the solar period, otherwise the picture may appear created. In addition, the data necessary for analysis can be collected from some systems.

Deyakі see the analysis of such structural changes, which is unacceptable for the current operational environment. For example, it is necessary to know what will happen if the company shows up new products. On a living basis, such a follow-up cannot be carried out. Also, an effective analysis rarely goes beyond the oblique system.

The systems of support accept the decision to sound to be able to provide data on the data of the aggregate data for different selections from the output set in a manual way for the analysis and analysis of the data. As a rule, such aggregate functions allow a rich (and, also, non-relational) collection of data (sometimes the titles of a hypercube or a metacube), the axis of which is to control the parameters, and the comirks are deposits in them of aggregate data - moreover, such data of tables can be stored in relations. Skin data can be organized as a hierarchy that represents different levels of detail. The leaders of such a model of data koristuvachs can formulate folded water, generate calls, and take a multiplier of data.

At the same time, there was a lot of interest in support systems for adopting solutions, which have become the main area of ​​OLAP (On-Line Analytical Processing, operational analytical processing, operational data analysis), which transforms the "ore" of OLTP systems into ready-made "virib", such as kernels and Analysts can blatantly cheat. This method allows analysts, managers and scientists to "penetrate into the essence" of accumulating data for a small amount of money and convenient access to a wide range of data.

Metoyu term paper view of OLAP technology.

rich analytical processing

main part

1 Basic information about OLAP

The concept of OLAP is based on the principle of rich representation of data. In 1993 the term OLAPvv Edgar Codd. Rozglyanuvshi nedolіki relyatsіynoї modelі, vіn nasampered vkazav on nemozhlivіst "ob'єdnuvati, pereglyadati that analіzuvati danі s Look mnozhinnostі vimіryuvan, tobto nayzrozumіlіshim for corporate analіtikіv way", i viznachiv zagalnі vimogi to OLAP systems, scho rozshiryuyut funktsіonalnіst relyatsіynih database that vklyuchayut bagatovimіrny analіz yak one of its characteristics.

In a great number of publications, the abbreviation OLAP stands for a rich look at the data, and the preservation of the data themselves from a rich database. Seemingly, unbelievably, shards, Codd himself declares that "Relational databases will be the most appropriate technology for collecting corporate data. The need is not in new technologies DB, and, more importantly, in the analysis, to supplement the functions of the basic DBMS and to do the gnuchki, to transfer and automate see different intellectual analysis, the power of OLAP". To produce such a mindset before putting it on the "OLAP or ROLAP" format, which is not correctly understood, ROLAP (relational OLAP) scales on the conceptual level, the whole thing is underlined by the term OLAP functionality. termіna MOLAP For Codd, bagatovimіrne Conceptual uyavlennya (multi-dimensional conceptual view) yavlyaє him mnozhinnu perspective, scho skladaєtsya of dekіlkoh Square vimіryuvan, vzdovzh yakih mozhut Buti proanalіzovanі pevnі sukupnostі danih. eg konsolіdatsії danih scho skladayutsya іz serії poslіdovnih rіvnіv uzagalnennya, de Leather the primordial rіven vіdpovіdaє greater world aggregation of data from vіdpovіdny vimir.

Vikonavets can signify a direct consolidation, which is formed from the equalization of the "company - p_drozdil - vіddіl - servitor". Vimiryuvannya Hour can turn on two direct consolidations - "Rik - Quarter - Month - Day" and "Day - Day", shards of rahunok hour after month and day are insane. In this case, we can still choose a fair amount of bagana level of detail information about skin care. Operation of descent (drilling down) in vіdpovіdaє ruhu in vіd vіdshih shablіv konsolіdatsії to nizhchih; navpaki, operation pіdёmu (rolling up) means Rukh in the lower rіvnіv up to the highest.

Kodd vyznachiv 12 rules, yakim may be satisfied software product OLAP class.

1.2 Opportunities for operational analytical processing

Rich conceptual representation of data (Multi Dimensional Conceptual View). The conceptual representation of data models in OLAP products can be rich in nature, which allows analysts to intuitively operate "slice and dice" analysis, rotation (pivot) and direct consolidation. Transparency. Coristuvach is not guilty of knowing about those, how specific victories are made to save that data collection, how to organize data and stars are taken.

Accessibility. The analyst is guilty of the mother's ability to deviate from the analysis within the framework of the global conceptual scheme, but with any given data, they can be left out of the control of the DBMS, which are left out of the old recession, being tied to the global analytical model. Tobto OLAP tools are guilty of imposing their logical scheme on the physical array of data, vikonuyuchi all transformations, the necessary security of a single, narrow and cisular look corystuvach іnformatsiyu.

Consistent Reporting Performance. With the increase in the number of vimiryuvan and rozmіrіv data bases, analytics are not guilty of any change in productivity. Stability of productivity is necessary for maintaining simplicity and ease of complexity, which is necessary for bringing OLAP to kіntsevogo koristuvach.

Client-server architecture (Client-Server Architecture). Most of the data, requiring operational analytical processing, is stored in mainframe systems, and viewed from personal computers. For this reason alone, the development of OLAP products could be done in the middle of a client-server. The main idea here is that the server component of the OLAP tool is obliged to be intelligent and the mother of the building will be a global conceptual scheme based on the consolidation and consolidation of various logical and physical schemes of corporate databases to ensure the transparency effect.

Equality of vimiriv (Generic Dimensionality). Usі vymіri danih may be equal. Additional characteristics can be given to the world, but the oscalls of all stench are symmetrical, but the additional functionality can be given to the world. The basic structure of the data, the formulas and the formats of the zvіtіv are not liable to overlap with one world.

Dynamic Sparse Matrix Handling. The OLAP tool is responsible for ensuring optimal processing of distributed matrices. The security of access is due to be taken care of independently from the distribution of the middle of the data, but it is a fixed value for the models that can vary the amount of the difference and the cost of the distribution of the data.

Support for a richly secured account in the mode (Multi-User Support). Often, a sprinkling of analysts may need to work at once with one analytical model, or create different models based on corporate data alone. The OLAP tool is responsible for imposing competitive access, ensuring the integrity and protection of data.

Unrestricted Cross-dimensional Operations. Calculating that manipulation of tribute for any number of vimiryuvan is not guilty of harrowing, but to separate whether it is like a vodnosini between the middle of the dans. Reworking, which requires a sufficient appointment, is due to be set functionally with the same formulaic mine.

Intuitive Data Manipulation. Direct reorientation in consolidation, data detailing in columns and rows, aggregation and other manipulations, power structures in the hierarchy directly in consolidation, due to convergence in the most convenient, natural and comfortable interface.

Flexible mechanism for creating calls (Flexible Reporting). Guilty acknowledgment of different ways of visualizing data, so that acknowledgment of guilt should be served in any possible orientation.

Unlimited Dimensions and Aggregation Levels. It is strongly recommended to take at least 15, and more than 20, in the dermal serious OLAP instrument, while surviving in the analytical model.

2 OLAP components

2.1 Server. Client. Internet

OLAP allows you to perform quick and efficient analysis of the great obligations of data. Data is saved from a rich look, which best reflects the natural state of real business data. In addition, OLAP makes it easier for students to capture data quickly and easily. For the same reason, stinks can be drilled down in order to obtain more detailed information.

An OLAP system is made up of anonymous components. At the most advanced level, the system includes a data backend, an OLAP server, and a client. Dzherelo danih є dzherel, from which data are taken for analysis. Data from the dzherel are transferred or copied to the OLAP-server, when they are systematized and prepared for the next generation of requests. Client - the interface of the correspondent to the OLAP server. In this article, the functions of the skin component and the significance of the entire system are described in detail. Jerela. Dzherelom in OLAP systems is a server that supplies data for analysis. Fallen in the field of victorious OLAP-product can be a collection of data, the data base is downgraded, to avenge high data, to collect a table, to collect financial data, or be it a combination of the redeemed. It is very important for an OLAP product to work with the data of different girls. Because of a single format or a single database, in which all external data was stored, it is not suitable for database administrators. In addition, such a pidhyd changes the stiffness and tightness of an OLAP product. Administrators and koristuvachs are aware that OLAP products, which provide more information about data not only from different, but also from rich gerel, seem to be more gnuchkiy and korysnymi, nizh tі, that can be more zhorstkі vimogi.

Server The application part of the OLAP system is the OLAP server. Tsya warehouse vykonuє all work (deposit in the model of the system) and save all the information in your own, until active access is secured. The server architecture is managed by different concepts. Zocrem, the main functional characteristic of an OLAP product is the choice of saving a rich (MMBD, MDDB) and a relational (RDB, RDB) data base. Aggregation/Forward aggregation of data

Shvidka implementation of requests is an imperative for OLAP. One of the basic principles of OLAP is the ability to intuitively manipulate data using swedish knowledge of information. Zagalom, what is more necessary to calculate, to take a fragment of information, then more information is needed. Therefore, in order to save a small hour of the sale of zapіv, fragments of information, which are heard most often, but if at their own expense, the forward aggregation is added. That's why they stink and then they are saved in the database as new data. Like an example of a type of data, which you can razrahuvat zazdalegіd, you can bring zvedenі danі - for example, showings of sales for months, quarters of workers, for those who are officially introduced danim є shodenny pokadniki.

Different postal workers are supplemented by different methods for selecting parameters that allow for forward aggregation and the number of forward counting values. Pidhіd to aggregation vplyvaє one hour and on the data base and for an hour of realization of requests. As more values ​​are calculated, the ability to request the already calculated value is increased, and that hour will be short, so that it will not be possible to collect a large amount for calculation. Prote, yakscho virahuvati all possible values ​​​​- tse not shorter solution- at the time of the growth of the base of data, which is to break up the non-ceramic, that hour of aggregation will be too great. Until then, if numerical values ​​are added to the data base, otherwise the stench changes, the information is liable to be calculated on the forward calculated values, which lie in new data. In this rank, and the renewal of the base can also take a lot of time at times of the great number of numbers in advance of counting the values. Oskіlki zazvichay pіd h aggregatsiї database pratsyuє autonomously, bazhano, schob aggregatsiї buv too trivaly.

Client. Client - those who win for presentation and manipulation of data in the database. The client can be clumsy - look at tables that include such OLAP capabilities, like, for example, wrapping data (pivoting) and burying data (drilling), and being a specialist, but also simple as a hard tool, as if a program was created, it was designed for folding manipulations with data. The Internet is a new form of client. In addition, you are carrying other new technologies; The impersonal Internet solution is strongly challenged for its capabilities in general and as an OLAP solution is a zocrema. Different functional authorities of the skin type of clients are discussed.

Regardless of those that the server is the "backbone" of the OLAP solution, the client is no less important. The server can provide a powerful foundation for easier manipulations with data, but if the client is foldable or low-functional, it can be quick to cope with all the hardships of the hard server. The client is important, so many post-employees maintain their profits exclusively on the client's work. Everything that is connected to the warehouse of these add-ons, is a standard look at the interface, back sing functions that structure, as well as quick solutions for more or less standard situations. For example, popular financial packages. The reason for the creation of financial programs is to allow the specialists of victorists to create primary financial instruments without the need to design the structure of the data base, or the globally accepted form of the name. Call Tool/Call Generator. Call Tool or Ring Generator provides easy access to OLAP data. The stench is simple at vikoristanni graphical interface and allow koristuvachs to create calls to moving objects to the sound using the "drag and drop" method. At that time, as a traditional call generator, it is possible to quickly generate formatted calls, call generators that support OLAP, form actual calls. The end product is a sound that can be buried in the data to the point of detail, wrapping (pivoting) of the sound, support of the hierarchy and other. Add-Ins (additional) spreadsheets.

Today, different forms of analysis of corporate data are being developed for the help of electronic spreadsheets. I have a sensi tse іdealny zasіb svіtіv zvіtіv and revisit data. The analyst can create macros that work directly with the selected data, and the template can be designed in such a way that, if data is entered, the formulas will calculate the correct values, including the need for repeated introduction of simple calculations.

Tim is not less, everything gives a “flat” sound as a result, which means that it is important to look at yoga in different aspects. For example, the diagram displays information for a single hour, let's say, for a month. And if you want to showcase the performances for the day (in contrast, we give for the month), you will need to create a completely new diagram. Next, designate new sets of data, add new marks to the diagram and make impersonal other simple, but laborious changes. In addition, there are a number of areas in which pardons can be allowed, which will change the superstition. If OLAP is added to the table, it becomes possible to create a single diagram, and then allow various manipulations with the method of nadannya koristuvachevi necessary information, not tightening the creation of all possible manifestations. Internet as a client. A new member of the family of OLAP clients and the Internet. Іsnuє impersonal advantages in shaping OLAP-voices via the Internet. The most important thing is the need for a specialist software security access to information. Tse save undertaking a couple of hours and pennies.

Leather internet product specific. Deyakі to clear up the creation of Web-sides, but to think less gnuchkіst. Others allow you to create data declarations, and then save them as static HTML files. All the same, it gives the opportunity to look at data through the Internet, but no more. It is impossible to actively manipulate danim for help.

The main product type is interactive and dynamic, which transforms such products on fully functional tools. Koristuvachs can zdіysnyuvaty in danі, pivoting, obezhennya vymiryuvan, that іn. First of all, choose the best way to implement the Internet, it is important to understand what functional possibilities are affected by a Web solution, and then choose which product is best to implement this functionality.

Programs. Programs - the same type of client, a kind of OLAP data base. The stench is identical to the power supply tools and call generators, we will describe more, but, moreover, the stench contributes to the product wider functionality. The program, as a rule, may be more exhausting, lower the tool for asking.

Rozrobka. Ring in the OLAP postmasters to secure the middle of the development for the creation of personal custom programs. The middle of the development as a whole is a graphical interface that supports the object-oriented development of the add-ons. Until then, more post-masters will provide APIs that can be used to integrate OLAP databases with other add-ons.

2.2 OLAP - Clients

OLAP clients from the installed OLAP machine are installed on the PC of the host. The stench does not forbid the server to calculate, and їm the power of zero administration. Such clients allow coristuvachevy nalashtuvatsya on the basis of data; as a rule, when creating a dictionary, which attaches the physical structure of data to the subject description, we understand fakhivtsyu. After that, the OLAP client wins the request and displays the results in the OLAP table. In this table, in his own line, a koristuvach can manipulate data and take it on the screen or paper hundreds of different letters. OLAP clients, designed for RDBMS work, allow data analysis, which is already in the corporation, for example, stored in the OLTP database. However, for other reasons, it may be cheaper to create collections or showcases of data - in some cases, the programmers of the organization need to create superfluous tables of the "zirka" type in relational databases and data capture procedures. The largest part of the work - writing interfaces with numerous options for requests and calls - is implemented in OLAP clients literally for a few years. Kіntsevy koristuvachev for mastering such a program needs about 30 quills. OLAP clients are supplied by the database vendors themselves, both rich and relational. Tse SAS Corporate Reporter, which is perhaps the best product for its beauty, Oracle Discoverer, MS Pivot Services and Pivot Table and others. A number of software applications for MS OLAP Services are provided as part of the "OLAP in the Mass" campaign sponsored by Microsoft Corporation. As a rule, stinks are reduced versions of the Pivot Table and are licensed for browsing in MS Office or Web browsers. The products of the firms Matryx, Knosys, etc., are the beginnings of simplicity, cheapness and efficiency, which gained great popularity in the West.

3 Classification of OLAP products

3.1 Rich OLAP

In this day and age, there is a large number of products on the market, which in this world will ensure the functionality of OLAP. For a rich conceptual appearance from the side of the interface to the external database, all OLAP products are divided into three classes according to the type of external database.

1. The best systems for operational analytical processing (for example, Arbor Software's Essbase, Oracle's Oracle Express Server) belonged to the MOLAP class, so they could only work with their own rich data bases. The stench is based on patented technologies for rich DBMSs and more expensive ones. Qi systems provide a complete OLAP processing cycle. The stench or turn on, cream the server component, the power of integration of the client interface, or hack to communicate with the koristuvach old programs roboti z spreadsheets. For the maintenance of such systems, a special staff of specialists is needed, as they are engaged in the installation, maintenance of the system, and molding of the appearance of these terminal coristuvachs.

2. Systems of operational analytical processing of relational data (ROLAP) allow representing data stored in a relational database in a rich form, without worrying about converting information to a rich model through an intermediate ball of metadata. This class includes DSS Suite from MicroStrategy, MetaCube from Informix, DecisionSuite from Information Advantage, and others. Software complex InfoVizor, fragmentation in Russia, in the Ivanov State Power Engineering University, also a system of that class. ROLAP-systems are well-adjusted to work with great treasures. Similar to MOLAP systems, the stench of significant costs for servicing by specialists information technologies and transfer the repayment to a rich coristuvachіv work mode.

3. Nareshti, hybrid systems (Hybrid OLAP, HOLAP) have been expanded with the help of modernizing and minimizing the shortcomings, the power of the previous classes. To what class belong Media / MR company Speedware. For the firmness of the retailers, vin poednuє analytic gnuchkіst and swidkіst vіdpovіdі MOLAP with constant access to real data, the authorities of ROLAP.

Krym rehabilitated koshtіv іsnuє є є є є єє їє їє їє іnstrumentі іnstrukіїї ї ї ї ї і ї і ї vyvotіv і zvіtіv іn desktop PCs, supplemented by OLAP functions аbо integrirovanі іz zvnіshnіmi zavom, scho vykonuyut ї ї ї functions. Well-established systems create a selection of data from the weekends, reshape them and place them in a dynamically rich database, which functions at the station of the client of the city of Koristuvach. The main representatives of this class are BusinessObjects of the same name company, BrioQuery by Brio Technology and PowerPlay by Cognos. An overview of current OLAP products is provided in the program.

In specialized DBMS, based on rich data, data is organized over the form of relational tables, and looking at ordering rich arrays:

1) hypercubes (all are stored in the database in the middle due to the mother of the same peacefulness, so that they can be found in the most complete basis of the universes) or

2) polycubes (the skin is changed with a wet set of vimiruvans, and all the changes due to folding are transferred to the internal mechanisms of the system).

The choice of rich databases in systems of operational analytical processing can still be overcome.

1. In different variant rich DBMSs, search and selection of data are significantly better, lower with a rich conceptual look at the relational database, so the rich database is denormalized, avenge distant aggregation indicators, secure access to the optimization.

2. Rich DBMS can be easily handled due to the inclusion of tasks to the information model of various functions, as well as the ob'jective and well-equipped exchange of SQL language.

From the other side, є suttєvі obezhennya.

1. Rich DBMS do not allow working with large data bases. Before that, for denormalization and later aggregation of data from a rich data base, as a rule, it is 2.5-100 times smaller than the amount of detailed data.

2. Bagatomir DBMS, against relational ones, is even more inefficiently victorious against the outer memory. The most important informational hypercube is greatly dispersed, and the data fragments are stored in an ordered way, the insignificant value goes into the selection of the optimal sorting order, which allows organizing data groups as much as possible without interruption. Ale navitt at tsomu vpadku problem virishuєtsya less often. In addition, the optimal order of sorting is not consistent with the order, which is most common in requests. Tom in real systems to be brought to shukati kompromіs mizh svidkodієyu and nadmіrnistyu disk space occupied by the data base.

Otzhe, vikoristannya rich DBMS is true only for such minds.

1. The amount of data for analysis is small (no more than a few gigabytes), so the data aggregation rate is high.

2. A set of informational vimiryuvan is stable (shards of whether a change in their structures may still require a new hypercube).

3. Hour of service of the system on non-regulated drinking is a critical parameter.

4. It is necessary to use a wide variety of folding functions for the calculation of cross-country calculations over the middle hypercubes, including the possibility of writing the functions of a coristuvach.

Without intermediary use of relational databases in systems of operational analytical processing can be such a difference.

1. For most companies, corporate databases are implemented using relational DBMS, and ROLAP tools allow you to analyze them directly. In this case, the connection is not such a critical parameter as in MOLAP mode.

2. In times of change in the size of the plant, if the change to the structure of the change is to be done frequently, ROLAP systems with dynamic appearance of the size will be the optimal solution, but such modifications will not require physical reorganization of the database.

3. Relational DBMS ensures a significant increase in the protection of data and a good possibility of demarcating access rights.

The main lack of ROLAP in por_vnyann_z rich DBMS - less productivity. In order to ensure productivity, based on MOLAP, relational systems require a relay-based data base schema and improved indexes, which are great efforts on the side of database administrators. Only with different mirror-like schemes, the productivity of well-established relational systems can be brought closer to the productivity of systems based on rich data bases.

The description of the star schema (star schema) and the recommendation for її zastosuvannya povnіstyu assigned to work. The idea is based on the fact that there are tables for the skin virus, and all the facts are placed in one table, which is indexed by a multiplier key, we add up the keys to the same virus (Appendix A). The skin of the scheme of the zirki is set, in the terminology of Codd, directly to the consolidation of data from the variant world.

Folding tasks with bagatorіvnevimimi vіrіry mаіє sensation turn up to the extension of the zіrka schema - the suzіr'ya schema (fact constellation schema) and the snіzhinka schema (snowflake schema). In such cases, around the tables of facts are created for the possible cases of aggregation of different worlds (Appendix B). Tse allows you to reach the shortest productivity, but often bring data to the supremacy and to significant complexity in the structure of the database, in which an impersonal table of facts appears.

The increase in the number of fact tables in the data base can be blamed not only on the multiplicity of equals of different worlds, but on the other hand, set up, that in the case of facts there may be different multiplies of worlds. When abstracting from the outside world, the coristuvach is guilty of maximizing the projection of the most complete hypercube, and far from being the result of an elementary subsuming. In this way, with a large number of independent vimiryuvan, it is necessary to substantiate impersonal tables of facts, which can allow the skin to be selected at the time of vimiryuvan, which also leads to an unsustainable victorious memory, to increase the time of occupancy. old dzherel that folding administration.

Frequently violate the problem of expanding the SQL language (GROUP BY CUBE, GROUP BY ROLLUP and GROUP BY GROUPING SETS operators), moreover, a mechanism is proposed to seek a compromise between overworld and swedcode, recommending that fact tables be created not for everyone, but for everyone , the values ​​of the middle ones cannot be removed for additional offensive aggregation of higher fact tables (Appendix B).

For some reason, as a richly modeled model is implemented in a look-and-feel relational data base, following the creation of long and "big" tables of facts and equally small "wide" tables of numbers. The tables of facts contain the numerical values ​​of the middle of the hypercube, and the other tables indicate their multidimensional basis of the number. Part of the information can be taken away for additional dynamic aggregation of data, divided into unsightly normalized structures, if you want to remember what to remember, what to turn on aggregation, with a highly normalized structure of the database, you can increase it more.

Orientation on the submission of rich information for additional sizable relational models allows you to solve the problem of optimizing the selection of different matrices, which sharply stand in front of rich DBMSs (de the problem of variability of special schemes). Wanting to save the skin, the middle of the record is scored, but the values ​​themselves include secondary keys - sent to the tables of variables, unknown values ​​are simply not included to the tables of facts.

Visnovok

Having looked at the power of the robot and the problem of OLAP technology, the companies are blamed for the power, it is clear that they are allowed to choose a product that best suits the needs of the hard worker.

Price like this:

Do you need data? – Data, which are used for analysis, can be at different places. It is possible that the OLAP database can be taken from the corporate Data Store or from the OLTP system. If an OLAP product can already be accessed to some data file, the data categorization and data cleansing process will be delayed.

What are the manipulations of the coristuvach to rob over the tributes? -
Just like a short-timer, having taken away access to the database of data and starting to analyze, it is important to work on the data in an official rank. It may be necessary to use an exhaustive generator of sounds, or it is possible to create and distribute dynamic web pages. At the same time, you can be more coristuvachevi more beautiful than your mother at your own order for a simple and swedish creation of your own dodatkіv.

What kind of impassioned obsyag danikh? - This is the most important official of the database of OLAP data. Relational OLAP-products are designed to operate with greater obligations more quickly, less richly. Even though the data does not require any relational base, a rich product can be won with no less success.

Kim is a koristuvach? - When assigned to an OLAP-system client, it is important that the level of qualification of the clerk is important. It is more convenient for those who are skilled in integrating OLAP with a table, or else to give priority to a specialized addendum. Fallow in the qualification of the koristuvach, the nutrition of the training is violating. Great company You can pay for trainings for koristuvachiv, a company of smaller size can work with them. The client is to blame but in such a way that the koristuvachs felt inspired that they could effectively yogo vicariously.

Today, most of the light companies have switched to using OLAP as the basic technology for providing information to individuals, as they make decisions. Therefore, it is important food, which is necessary to be put, does not lie in the fact that it is necessary to continue the development of electronic spreadsheets as the main platform for the preparation of zvіtnostі, budgeting and forecasting. Companies are responsible for asking themselves if they are ready to stink of competitive advantage, vicarious, inaccurate, irrelevant and incorrect information, first of all, they should look at alternative technologies.

So, in conclusion, it should be noted that the analytical capabilities of OLAP technologies increase the value of data that is stored in the corporate information repository, allowing companies to more effectively interact with their customers.

Glossary

1 Golitsin O.L., Maksimov N.V., Popov I.I. Bazi danih: Headmaster. - M.: FORUM: INFRA-M, 2003. - 352 p.

2 Date K. Introduction to database systems. - M.: Nauka, 2005 - 246 p.

3 Elmanova N.V., Fedorov A.A. Introduction to Microsoft OLAP technology. - M.: Dialogue-MIFI, 2004. - 312 p.

4 Karpova T.S. Data base: models, development, implementation. - St. Petersburg: Peter, 2006. - 304 p.

5 Korovkin S. D., Levenets I. A., Ratmanova I. D., Starikh V. A., Shchavelov L. V. The problem of complex operational analysis of information from data collections // DBMS. - 2005. - No. 5-6. – 47-51 p.

6 Krechetov N., Ivanov P. Data mining products ComputerWeek-Moscow. - 2003. - No. 14-15. – 32-39 p.

7 Przhiyalkovsky U. U. Folded analysis of the data of the great commitment: new perspectives of computerization // DBMS. - 2006. - No. 4. - 71-83 p.

8 Sakharov A. A. The concept of stimulating and implementing information systems, oriented data analysis // DBMS. - 2004. - No. 4. - 55-70 p.

9 Ullman J. Fundamentals of database systems. - M.: Financial statistics, 2003. - 312 p.

10 Hubbard J. Automated design of databases. - M.: Light, 2007. - 294 p.

Korovkin S. D., Levenets I. A., Ratmanova I. D., Starikh V. A., Shchavelov L. V. The problem of complex operational analysis of information from data collections // DBMS. - 2005. - No. 5-6. – 47-51 p.

Ulman J. Fundamentals of database systems. - M.: Financial statistics, 2003. - 312 p.

Barseghyan A.A., Kupriyanov M.S. Data analysis technologies: DataMining, VisualMining, TextMining, Olap. - St. Petersburg: BHV-Petersburg, 2007. - 532 p.

Elmanova N.V., Fedorov A.A. Introduction to Microsoft OLAP technology. - M.: Dialogue-MIFI, 2004. - 312 p.

Date K. Introduction to database systems. - M.: Nauka, 2005 - 246 p.

Golitsina O.L., Maksimov N.V., Popov I.I. Bazi danih: Headmaster. - M.: FORUM: INFRA-M, 2003. - 352p.

Sakharov A. A. The concept of stimulating the implementation of information systems focused on data analysis // DBMS. - 2004. - No. 4. - 55-70 p.

Przhiyalkovsky V.V. Folding analysis of the data of the great commitment: new perspectives of computerization // DBMS - 2006. - No. 4. - 71-83 p.

Zastosuvannya OLAP system allows you to automate the strategic management of the organization. OLAP (Online Analytical Processing - analytical processing of data from a real hour) is an exhaustive technology for processing and processing data. Systems, based on OLAP technology, provide practically unlimited possibilities for easy folding of calls, folding analytical plans, prompting forecasts and scenarios, and developing impersonal options for plans.

Modern OLAP systems appeared on the cob of the 90s, as a result of the development of information systems, a decision was made. The stench is designated for the transformation of different, often divided, data from the basic information. OLAP systems can organize data according to a specific set of criteria. If so, it’s not obov’yazkovo, so the criteria are small enough to read the characteristics.

Svoє zastosuvannya OLAP sistemi znayshli in bagatoh power strategіchnogo upravlіnnya organіzatsієyu: upravlіnnya efektivnіstyu bіznesu, strategіchne planuvannya, byudzhetuvannya, prognozuvannya rozvitku, pіdgotovka fіnansovoї zvіtnostі, analіz robot іmіtatsіyne modelyuvannya zovnіshnogo that vnutrіshnogo seredovischa organіzatsії, zberіgannya danih that zvіtnostі.

Structure of an OLAP system

At the heart of the work of the OLAP system lies the processing of rich arrays of data. Bagatovimіrnі massiv vlashtovanі so that the skin element of the array may have a large number of links with other elements. In order to form a rich array, the OLAP system is responsible for taking input data from other systems (for example, ERP or CRM systems), or through a new input. Koristuvach OLAP system will take the necessary data from the structured person in the right way before his request. Vyhodyachi іz znachennogo order dіy, you can reveal the structure of the OLAP system.

For a wild look, the structure of an OLAP system consists of the following elements:

• database . Database of information for the OLAP robotic system. Looking at the data bases in the form of the OLAP system and the algorithms of the OLAP server. As a rule, relational data bases, rich data bases, data collections, etc.
• OLAP server. Vіn zabezpechuє administrating the rich data structure and interrelationships between the data base and the OLAP systems.
• corystuvac programs . This element of the structure of the OLAP system is responsible for managing the inputs of the data and forming the results of the conversion to the database (calls, graphs, tables and in.)

Depending on the method of organizing, processing and saving data, OLAP systems can be implemented on local computers koristuvachіv or for help seeing the servers.

There are three main ways to save that data collection:

• locally. The data is posted on the computers of the Koristuvachs. Obrobka, analysis and data management are being worked out at local working places. Such a structure of the OLAP system may cause a lot of inconsistencies, leading to data processing security, data protection, and stagnation of rich analysis.
• relational databases. Qi bases of data are victorious at sleeping robot OLAP system with CRM system or ERP system. Data is stored on the servers of these systems as relational databases or data collections. The OLAP server is sent to several databases for the formation of the necessary rich structures and analysis.
• rich basi data. And here the data is organized as a special collection of data on a visible server. All operations with data are sent to the same server, which is the one that converts output data to a different structure. Such structures are called olap cube. Jerelami danich for molding OLAP cubeє relational data bases and/or client files. Data server zdіysnyuє in the front preparation and processing of data. OLAP server works with OLAP cube without direct access to data files (relational databases, client files, etc.).

View OLAP systems

Depending on the data collection method, all OLAP systems can be divided into three main types.

1. ROLAP (Relational OLAP - relational OLAP systems) - this type of OLAP system works with relational databases. Returning to data is directly entered into the relational data base. Data is saved as relational tables. Koristuvachі mayut mozhlivіst zdіysnyuvati rich analysis like in traditional OLAP systems. Tse reach for the account zastosuvannya SQL tools and special requests.

One of the advantages of ROLAP is the ability to effectively implement the Great Obligation of Data. Another advantage of ROLAP is capability efficient processing like numerical, i textual data.

Up to short periods of ROLAP low productivity(Compared to traditional OLAP systems), because Data processing is created by the OLAP server. The other shortcoming is the reduction of functionality through SQL logging.

2. MOLAP (Multidimensional OLAP - rich OLAP systems). This type of OLAP systems is up to traditional systems. Vіdminnіst traditіnіnoі ї OLAP sistemy, vіdіnshіh systems, vіdmіnіє іn podgotovtsі і optimіzії danih. The systems of the system call victorious visions of the server, on which the data is processed in advance. Data are molded in rich arrays - OLAP cubes.

MOLAP systems are the most efficient data processing time, because stinks allow you to easily reorganize and structure the data on the basis of your needs. Analytical tools MOLAP give you the ability to visualize folding patterns. The second advantage of MOLAP is the possibility of a swedish molding and retrieval of results. You need to take care of the help of front molding of OLAP cubes.

Before the MOLAP system is short, there is an exchange of obligatory data and data overhead, tk. On the molding of rich cubes, from various aspects, data must be duplicated.

3. HOLAP (Hybrid OLAP - hybrid OLAP systems). Hybrid OLAP systems - combination of ROLAP and MOLAP systems. In hybrid systems, we tried to combine the two systems: a rich database repository and a relational database manager. HOLAP systems allow you to save a large amount of data in relational tables, and the data that is processed is placed in forward-looking rich OLAP cubes. The advantages of this type of systems are due to the scalability of data, quick data processing and flexible access to the data port.

Іsnuyut Інші types of OLAP systems, ale stink to the bigger world є marketing move of virobnіv, nizh independent type of OLAP system.

Before such views lie:

• WOLAP (Web OLAP). View of the OLAP system with support for the web interface. In these OLAP systems, you can access databases via a web interface.
• DOLAP (Desktop OLAP). This kind of OLAP system allows savants to acquire a data base on a local workspace and work with it locally.
• MobileOLAP. The whole function of OLAP systems, as it allows you to work remotely with the data base, with the use of mobile devices.
• SOLAP (Spatial OLAP). Tsey type of OLAP assignment systems for data processing. Vin vinik as a result of the integration of geographic information systems and OLAP systems. The systems allow processing data not only in alphanumeric format, but also in visual objects and vectors.

Advantages of the OLAP system

Zastosuvannya OLAP system gives the organization the ability to predict and analyze various situations related to the flow of work and development prospects. Qi systems can be added to automation systems equal to business. All the advantages of OLAP systems are uninterrupted in terms of accuracy, reliability and reliability of the data.

The main advantages of the OLAP system are:

• usability of visual information and results of analysis. For the obviousness of the OLAP system, it is possible to easily process the information and find a logical link between the results and the external data. The subjectivity of the results of the analysis is reduced.
• carrying out a multivariate analysis. Zastosuvannya system OLAP allows you to take impersonal scenarios in the development of data on the basis of a set of output data. Using the tools of analysis, it is possible to model situations according to the principle “what will be, what will be”.
• detail management. The details of the presentation of the results may be changed depending on the needs of the coristuvachs. If there is no need to create a folding system for repeating the calculation. It may take away that information, as it is necessary for making a decision.
• Identification of attached deposits. For rahunok pobudovi rich connections, it is possible to show and signify attachment to various situations, which inject virobnicha diyalnist.
• creation of a single platform. For the stability of the OLAP system, it is possible to create a single platform for all processes of predictive analysis in the enterprise. Zokrema, OLAP system data is the basis for budgetary forecasts, sales forecasts, purchasers forecasts, strategic development plans, etc.

In 1993, the founder of the relational approach to the development of databases, Edgar Codd and partners (Edgar Codd, mathematician and IBM fellow), published a paper initiated by the company "Arbor Software" (this year find your company"Hyperion Solutions"), under the name "Security OLAP (operational analytical processing) for core analysts", in which 12 features of OLAP technology were formulated, which were supplemented by the year. These provisions have become the main theme of the new and even promising technology.

Main features of OLAP technology (Basic):

• rich conceptual representation of data;
• intuitive manipulation of data;
• availability and detail of data;
• packet data interpretation against interpretation;
• OLAP analysis models;
• architecture "client-server" (OLAP available from the desktop);
• transparency (clear access to zovnishnіh data);
• rozrakhovan on a rich coristuvachіv pіdtrimka.

Special features (Special):

• processing of unformalized data;
• OLAP results savings: savings on vacation data;
• exclusion of daily values;
• processing of daily values.

Features of the submission of calls (Report):

• flexible molding of zvіtіv;
• standard productivity of calls;
• automatic adjustment of the physical balance of the data.

Dimension Management:

• universality of vimiriv;
• unrestricted quantity of vimiryuvan and equal aggregation;
• I do not need to limit the number of operations between different sizes.

Historically, it turned out so that today the term "OLAP" may not only have a rich look at the data from the side of the endangered coristuvach, but also a rich look at the data from the whole database. The same term is due to the appearance of the independent terms "Relational OLAP" (ROLAP) and "Bagatomirny OLAP" (MOLAP).

OLAP-service is a tool for analyzing the great data exchanges in real-time mode. In conjunction with the OLAP-system, it is possible to make a detailed review of information, to take into account sufficient data and to analyze the analytical operations of detailing, shirring, cross-sectional division, matching in an hour for a rich set of parameters. All work with the OLAP-system is considered in terms of the subject area and allows statistically grounded models of the business situation.

Software OLAP is a tool for the operational analysis of data, as in the case of a shovisch. The main features are those who are focused on the reputation not as a professional in the information technology department, not as an expert statistician, but as a professional in the application management department - the manager of the office, the department, the administration, the manager, the director. Koshti are recognized for solving an analyst with a problem, and not with a computer. On fig. 6.14 readings of an elementary OLAP-cube, which allows you to evaluate data for trials.

A rich OLAP-cube that system of powerful mathematical algorithms in statistical processing allows you to analyze data, no matter how complex, on any hourly intervals.

Rice. 6.14. Elementary OLAP cube

Looming at his own ordering mechanism for manipulating data and visual imagery (Fig. 6.15, Fig. 6.16), the manager looks at the back of the head from the other sides of the data, which may or may not be related to the problem at hand.

Dalí vin zastavlyaє raznі pokaznі poznі vіznі biznі vіzh themselves, namagayuchis vyaviti prihovanі vzaєmozv'yazki; You can look at the data more respectfully, detailing them, for example, spreading them out in warehouses by the hour, by regions or by customers, or, on the other hand, further clarify the given information, so that you can tidy up the necessary details. After that, after the additional module of statistical evaluation and simulation modeling, there will be a number of options for the development of subdivisions, and the most acceptable option will be selected.

Rice. 6.15.

For example, a kerіvnik with a firm, for example, may have a hypothesis that a rozkid increase in assets in various branches of the business to lie in the field of spіvvіdnoshennia in some fakhіvtsіv іz technical and economic education. In order to change this hypothesis, the manager can ask for this information and display it on the graph of the financial situation for those branches, for those who had an increase in assets for the current quarter, the increase in assets decreased in the same quarter by more than 10%, and for those who had 25 more %. Vіn is to blame for the mother's ability to win a simple choice from the proponated menu. If we take away the results, they will clearly fall into two distinct groups, which may become a stimulus for a further re-verification of the hanging hypothesis.

At the present time, the Swiss development has taken off directly, the titles of dynamic simulation (Dynamic Simulation), which implement the FASMI principle throughout the world.

Vikoristovuyuchi dynamic modelling, analyst future model of the business situation, which develops at the hour, for the actual scenario. In this case, the result of such modeling can be a few new business situations, which will give rise to a tree of possible solutions with an assessment of the ability and prospects of the skin.

Rice. 6.16. Analytical analysis, data processing and submission of information

In table 6.3, the characteristics of static and dynamic analysis are shown.

Wash away the high competition and the dynamics of the modern middle ground dictate the promotion of power to business management systems. The development of the theory and practice of management was accompanied by the emergence of new methods, technologies and models focused on improving the efficiency of activity. Methods and models have been adopted by the emergence of analytical systems. The demand for analytical systems in Russia is high. Naytsіkavіshi z at a glance zastosuvannya qі system in the financial sector: banks, insurance business, investment companies. The results of the robotic analytical systems are necessary for us to people who want to develop the company's development: for ceramics, experts, analysts. Analytical systems allow you to change the task of consolidating, zoning, optimizing and forecasting. Until now, there has not been a residual classification of analytical systems, as there is no general system for defining terms that are directly related to this. The informational structure of the enterprise can be represented by a sequence of equalities, which are characterized by their own way of processing and management information, which can have its own function in the management process. In this way, analytical systems will be roztashovuvatisya ієrarchіchno on different levels of infrastructure.

Riven transaction systems

Riven of the Treasury

Riven windows of data

Riven OLAP - systems

Riven analytical programs

OLAP - systems - (OnLine Analytical Processing, analytical processing at the present time) - a technology for complex data analysis. OLAP - a system of zastosovnі there, de є zavdannya analysis of rich factor data. Efficient way to analyze and generate calls. Looked at more data collections, data showcases and OLAP - systems are brought to business intelligence systems (Business Intelligence, BI).

Most of the information-analytical systems, created for the uninterrupted choice of individuals, as they make decisions, appear supernaturally simple in congestion, but zhorstko obrazheniy in functionality. Such static systems are called in the literature Kerivnik Information Systems (ICP) or Executive Information Systems (EIS). The stench of taking revenge on your mind without a face of drinking and, being sufficient for an everyday look, without notice on all food until the obvious data, which can be blamed when a decision is made. As a result of the work of such a system, є rich-sounding sounds, after a recurrent event such as the analyst's, a new series of nutrition is announced. However, the skin of a new request, not transferring during the design of such a system, is guilty of a bunch of formal descriptions, coding by the programmer and only then typing. The hour of reconciliation at such a time may become years that day, which is not always pleasant. In this way, the simplicity of static DSS, yak to actively fight against the greater number of assistants in information-analytical systems, turns into a catastrophic waste of ugliness.

Dynamic DSS, navpak, directing to the processing of non-regulated (ad hoc) requests of analysts to data. One of the best insights into such systems is by looking at E. F. Codd's article, which conceived the concept of OLAP. The work of analysts with these systems is based in an interactive sequence of shaping the demand and seeing their results.

Ale dynamic DSS can work in the field of operational analytical processing (OLAP); Support for the adoption of managerial decisions on the basis of accumulated data can be based on three basic areas.

Sphere of detailed data. This area is filled with more systems, oriented to the search for information. Most relational DBMS do a good job of blaming the problems here. The global standard for the manipulation of relational data is SQL. Info-poshukovі systems, which provide the interface of the end-user in the tasks of searching for detailed information, can win like over the bus and over the other databases of these transactional systems, as well as over the wild data collection.

Sphere of aggregation of indications. A comprehensive look at the data collection in data collections, її aggregation and aggregation, hypercubic imaging and rich analysis and management of systems for operational analytical processing of data (OLAP). Here you can either focus on special rich DBMS or stay within the framework of relational technologies. In another way, late aggregation of data can be collected in a database with a mirror-like look, or aggregation of information can be carried out on the fly during the process of scanning the details of a table of a relational database.

The sphere of laws. Intellectual processing is carried out by the methods of intellectual data analysis (IAD, Data Mining), the main tasks of such searches for functional and logical patterns in the accumulation of information, models and rules, to explain the findings of anomalies and / or predict the development of such processes.

Operational analytical processing of data

The concept of OLAP is based on the principle of rich representation of data. In 1993, Rotsі in Stattі E. F. Codd oving the unbalance of the relatsiino models, Nasampeed on the Messency "Ob'єDnuvati, disturbance to the analization of Danі z point zero zorinnosti Vimіryukovan, Tobto Nazozomіlіlіm for corporate analifikіv in the way relational DBMS and which includes rich analysis as one of its characteristics.

Classification of OLAP products according to the way data is presented.

In this day and age, there is a large number of products on the market, which in this world will ensure the functionality of OLAP. Nearly 30 of the largest entries were reclaimed from the list of the lookout Web server http://www.olapreport.com/. For a rich conceptual appearance from the side of the interface to the external database, all OLAP products are divided into three classes according to the type of external database.

The most advanced analytical processing systems (for example, Arbor Software's Essbase, Oracle's Oracle Express Server) belonged to the MOLAP class, so they could only work with their own rich data bases. The stench is based on patented technologies for rich DBMSs and more expensive ones. Qi systems provide a complete OLAP processing cycle. The stench either includes, the cream of the server component, the power of integration of the client's interface, or the connection for communication with the slickest linking program of work with spreadsheets. For the maintenance of such systems, a special staff of specialists is needed, as they are engaged in the installation, maintenance of the system, and molding of the appearance of these terminal coristuvachs.

Systems of operational analytical processing of relational data (ROLAP) allow representing data that is stored in a relational database in a rich form, without worrying about converting information into a rich model through an intermediate ball of metadata. ROLAP-systems are well-adjusted to work with great treasures. Similar to MOLAP systems, the stench of significant spending on servicing by facsimiles from information technology and transferring insurance to a rich work mode.

Nareshti, hybrid systems (Hybrid OLAP, HOLAP) have been expanded with the help of modernizing and minimizing the shortcomings, the power of the previous classes. To what class belong Media / MR company Speedware. For the firmness of the retailers, vin poednuє analytic gnuchkіst and swidkіst vіdpovіdі MOLAP with constant access to real data, the authorities of ROLAP.

Rich OLAP (MOLAP)

In specialized DBMS, based on rich data, data is organized over the form of relational tables, and looking at ordering rich arrays:

1) hypercubes (all are stored in the database in the middle due to the mother of the same peacefulness, so that they can be found in the most complete basis of the universes) or

2) polycubes (the skin is changed with a wet set of vimiruvans, and all the changes due to folding are transferred to the internal mechanisms of the system).

The choice of rich databases in systems of operational analytical processing can still be overcome.

Different types of rich DBMS searches and selection of data are significantly better, lower with a rich conceptual look at the relational database, so the rich database is denormalized, avenging aggregation indicators and securing access, optimizing access.

Rich DBMS can be easily handled with tasks included to the information model of various functions, even though it is objektivno іsnuyuchi obezhenenya mov SQL to roble vikonannya tsikh zavdannya on the basis of relational DBMS to do foldable, and іnоlіdі d.

From the other side, є suttєvі obezhennya.

Bagatomir DBMS do not allow working with great data bases. Before that, for denormalization and later aggregation of data in a rich database, as a rule, it is (according to Codd's assessment) 2.5-100 times less due to data details.

Bagatomir DBMSs have poor relational memory even more inefficiently. The most important informational hypercube is greatly dispersed, and the data fragments are stored in an ordered way, the insignificant value goes into the selection of the optimal sorting order, which allows organizing data groups as much as possible without interruption. Ale navitt at tsomu vpadku problem virishuєtsya less often. In addition, the optimal order of sorting is not consistent with the order, which is most common in requests. That is why in real systems there is a compromise between the security code and the overworld of the disk space occupied by the data base.

Otzhe, vikoristannya rich DBMS is true only for such minds.

The volume of data for analysis is small (no more than a few gigabytes), so that the rate of data aggregation is high.

The collection of informational vimiryuvan is stable (shards of whether a change in their structures may still require a new hypercube).

Hour of service of the system on non-regulated water supply is a critical parameter.

It is necessary to use a wide variety of folding functions for the calculation of large dimensions over the centers of the hypercube, including the possibility of writing the functions of a coristuvach.

Relational OLAP (ROLAP)

Without intermediary use of relational databases in systems of operational analytical processing can be such a difference.

Most corporate data collections are implemented using relational DBMS, and ROLAP tools allow you to analyze them without intermediary. In this case, the connection is not such a critical parameter as in MOLAP mode.

In times of change in plant diversity, if changes are made to the structure of the change, it is necessary to make frequent updates, ROLAP systems with dynamic manifestations of diversity are optimal solutions, but such modifications do not impair the physical reorganization of the database.

Relational DBMSs ensure a significantly higher cost of data protection and good access rights delimitation.

The main lack of ROLAP in por_vnyann_z rich DBMS - less productivity. In order to ensure productivity, based on MOLAP, relational systems require a relay-based data base schema and improved indexes, which are great efforts on the side of database administrators. Only with different mirror-like schemes, the productivity of well-established relational systems can be brought closer to the productivity of systems based on rich data bases.

The method of course work is the development of OLAP technology, understanding and implementation of the structure.

AT to the current world computer merezhі and enumeration systems allow you to analyze and process large arrays of data.

The great collection of information makes it even more difficult to find a solution, but it also gives you the opportunity to take a richer analysis of the analysis. For the solution of such problems, there are many classes of information systems, as a way of analysis. Such systems are called decision support systems (DSS) (DSS, Decision Support System).

For the purpose of analysis of the DSS, we can accumulate information, using the methods of introducing that collection. Usyi can see three main tasks, which are found in the DSS:

input of data;

· saving data;

· Data analysis.

The introduction of data in the DSS is automatically set by the type of sensors that characterize the middle ground either as a process or as a human operator.

As data input is automatically generated by sensors, data is accumulated after a ready signal, which is due to the appearance of information, or by a cyclical feedback path. If the entry is made by a person, then the stench is to blame to give the coristuvachas a handful for the entry of data, which they check for the correctness of the entry, as well as to collect the necessary charges.

When data is entered overnight by operators, it is necessary to solve the problem of modification and parallel access to the data themselves.

The DSS provides data analytics by looking at the data, tables, graphs for analysis and analysis, and the same systems ensure that the functions of support are adopted to make a decision.

Data entry subsystems, called OLTP (On-linetransactionprocessing), implement operational data processing. For their realization vikoristovuyut primary systems database management (DBMS).

The analysis subsystem can be inspired on the basis of:

· Subsystems of informational-push-by-shock analysis based on relational DBMS and static queries based on SQL language;

· Subsystems of operational analysis. For the implementation of such subsystems, the technology of operational analytical processing of data OLAP is used, like the concept of rich presentation of data;

· Subsystems of intellectual analysis. This subsystem implements methods and algorithms of DataMining.

From the point of view of the corystuvach, OLAP-systems represent the features of a flexible review of information in different views, automatic data retrieval, data aggregation, analysis of analytic operations, aggregation, detailing, and hourly reconciliation. The founders of this OLAP system are solutions with great achievements in the field of data preparation for all types of business events, which transfer data from various retail outlets and different hierarchies, such as sales calls, various forms of budgeting. OLAP-systems can have great advantages of similar manifestations and other forms of data analysis and forecasting.

1.2 Appointment OLAP-systems

The technology of complex multidimensional analysis of data has given the name OLAP. OLAP is a key component of organizing a data warehouse.

OLAP-functionality can be implemented in various ways, both in the simplest way, such as data analysis in office add-ons, and more collapsible - separate analytical systems based on server products.

OLAP (On-LineAnalyticalProcessing) is a technology of operational analytical processing of data, which is a victorious method for collecting, collecting and analyzing rich data and pursuing the goals of the process to make a decision.

The main recognition of OLAP-systems is the support of analytical work, sufficient supply of core-analysts. The method of OLAP-analysis is re-verification of hypotheses that are blamed.