The concept of multidimensional data analysis is closely associated with operational analysis, which is performed by the OLAP systems.

OLAP (on-line Analytical Processing) - Technology of operational analytical data processing using methods and means for collecting, storing and analyzing multidimensional data to support decision-making processes.

The main purpose of OLAP systems - support for analytical activities, arbitrary (often used the term AD-HOC) of analyst users. The goal of OLAP analysis is to check the emerging hypotheses.

At the sources of OLAP technology is the founder of the relational approach E. Codd. In 1993, he published an article entitled "OLAP for analyst users: what should it be." This paper presents the main concepts of operational analytical processing and the following 12 requirements are defined, which must be satisfied with the products allowing operational analytical processing. Tokmakov G.P. Database. Concept of databases, relational data model, SQL languages. P. 51.

The following rules set forth by the code and defining OLAP are listed below.

1. Multidimensionality - the OLAP system at the conceptual level should submit data in the form of a multidimensional model, which simplifies the processes of analysis and perception of information.

2. Transparency - the OLAP system must hide from the user a real implementation of a multidimensional model, a method of organization, sources, processing and storage facilities.

3. Availability - the OLAP system should provide the user with a single, consistent and holistic data model, providing access to data regardless of how and where they are stored.

4. Constant performance when developing reports - the performance of OLAP systems should not be significantly reduced by increasing the number of measurements for which the analysis is performed.

5. Client-server architecture - the OLAP system must be able to work in the "Client-Server" environment, because Most of the data that is required today to be subject to operational analytical processing are stored distributed. The main idea here is that the OLAP tool server component should be sufficiently intelligent and allow us to build a general conceptual scheme based on generalization and consolidation of various logical and physical schemes of corporate databases to ensure transparency effect.

6. Measurement equal rights - the OLAP system must support a multidimensional model in which all measurements are equal. If necessary, additional characteristics can be provided with separate measurements, but this possibility must be provided to any dimension.

7. Dynamic control of racked matrices - the OLAP system should ensure optimal processing of sparse matrices. The access speed should be stored regardless of the location of the data cells and be a constant value for models having a different number of measurements and a different degree of data productivity.

8. Support for multiplayer mode - the OLAP system should provide an opportunity to work to several users together with one analytical model or create various models from uniform data for them. It is possible both reading and record data, so the system should ensure their integrity and safety.

9. Unlimited cross-operations - the OLAP system should ensure the preservation of the functional relations described using a certain formal language between the cells of the hypercube when performing any cut operation, rotation, consolidation or detail operations. The system must independently (automatically) perform the conversion of the set relationship, without requiring the user to redefine them.

10. Intuitive data manipulation - the OLAP system should provide a method for performing operations of cut, rotation, consolidation and detail over a hyperkub without having to make a variety of actions with the interface. Measurements defined in the analytical model must contain all the necessary information to perform the above operations.

11. Flexible reporting capabilities - the OLAP system must support various ways to visualize data, i.e. Reports must be submitted in any possible orientation. Reporting tools must provide synthesized data or information that is the following from the data model in its possible orientation. This means that strings, columns or pages should be shown simultaneously from 0 to n measurements, where N-- the number of measurements of the entire analytical model. In addition, each measurement of the contents shown in one entry, column or page must allow any subset of the elements (values) contained in the dimension in any order.

12. Unlimited dimension and number of aggregation levels - research on the possible number of necessary measurements required in the analytical model showed that up to 19 measurements can be used at the same time. It follows the ultimate recommendation to ensure that the analytical tool can simultaneously provide at least 15, and preferably 20 measurements. Moreover, each of the total dimensions should not be limited by the number of user-defined levels of aggregation levels and consolidation paths.

Additional regulations of the code.

The set of these requirements served as a de facto definition of OLAP, quite often causes various complaints, for example, rules 1, 2, 3, 6 are the requirements, and rules 10, 11 - informalized wishes. Tokmakov G.P. Database. Concept of databases, relational data model, SQL languages. P. 68 Thus, the listed 12 Code requirements do not allow to accurately determine OLAP. In 1995, the code for the list added the following six rules:

13. Batch extraction against interpretation - the OLAP system should equally efficiently provide access to both its own and external data.

14. Support all OLAP-analysis models - the OLAP system must maintain all four data analysis models defined by the code: categorical, interpreting, speculative and stereotypical.

15. Processing of abnormalized data - the OLAP system must be integrated with abnormal data sources. Data modifications made in OLAP medium should not lead to changes in data stored in the source external systems.

16. Saving OLAP results: storing them separately from the source data - an OLAP system operating in the read-write mode, after modifying the source data, the results should be saved separately. In other words, the security of the source data is ensured.

17. The exclusion of missing values-- OLAP-system, presenting these to the user, must discard all the missing values. In other words, missing values \u200b\u200bshould differ from zero values.

18. Processing of missing values \u200b\u200b- the OLAP system must ignore all the missing values \u200b\u200bwithout taking into account their source. This feature is associated with the 17th rule.

In addition, the Codd broke all 18 rules for the next four groups, calling them features. These groups received names in, S, R and D.

The main features (B) include the following rules:

Multidimensional conceptual representation of data (rule 1);

Intuitive data manipulation (rule 10);

Availability (rule 3);

Batch extraction against interpretation (rule 13);

Support for all OLAP analysis models (rule 14);

Architecture "Client-server" (rule 5);

Transparency (rule 2);

Multiplayer support (rule 8)

Special features (s):

Processing of abnormalized data (rule 15);

Saving OLAP results: storing them separately from the source data (rule 16);

Elimination of missing values \u200b\u200b(rule 17);

Processing of missing values \u200b\u200b(rule 18). Features of reporting (R):

Reporting flexibility (rule 11);

Standard report performance (rule 4);

Automatic configuration of the physical layer (modified original rule 7).

Measurement Management (D):

Universality of measurements (rule 6);

Unlimited number of measurements and aggregation levels (rule 12);

Unlimited operations between dimensions (rule 9).

maintenance

Recently, a lot is written about OLAP. It can be said that there is some boom around these technologies. True, for us this boom was somewhat late, but it is connected, of course, with the general situation in the country.

Information systems of the enterprise, as a rule, contain applications intended for complex multidimensional data analysis, their dynamics, trends, and the like. Such an analysis is ultimately intended to promote decision-making. Often these systems are called - solutions support systems.

Solution Support Support Systems usually have the means to provide the user of aggregate data for various samples from the source set in a convenient to perceive and analysis. As a rule, such aggregate functions form a multidimensional (and, therefore, a non-relational) dataset (often called a hypercubus or a metakuch), the axes of which contain parameters, and cells - the aggregate data depending on them - and the same data can be stored in relational tables, but In this case, we are talking about a logical data organization, and not about the physical implementation of their storage). Along every axis, data can be organized in the form of a hierarchy representing various levels of their detail. Thanks to this data model, users can formulate complex queries, generate reports, obtain data subsets.

The technology of comprehensive multidimensional data analysis was named OLAP (On-Line Analytic Processing).

OLAP is the key component of organizing data warehouses.

The OLAP concept was described in 1993 by Edgar Coddo, a well-known database researcher and the author of the relational data model (seeE.F. Codd, S.B. Codd, and c.Salley, providing OLAP (on-line Analytical Processing) to User-Analysts: An It Mandate.TECHNICAL REPORT, 1993).

In 1995, based on the requirements set out by the code, the so-called FASMI test was formulated (Fast Analysis of Shared Multidimensional Information - a quick analysis of shared multidimensional information), which includes the following requirements for multidimensional analysis applications:

· providing the user to the results of the analysis for an acceptable time (usually not more than 5 seconds), even be at the cost of less detailed analysis;

· the ability to implement any logical and statistical analysis characteristic of this application and its saving in an accessible form;

· multiplayer access to data with the support of the relevant locking mechanisms and authorized accessories;

· multidimensional conceptual presentation of data, including full support for hierarchies and multiple hierarchies (this is the key requirement OLAP);

· the ability to refer to any desired information regardless of its volume and storage location.

It should be noted that the OLAP functionality can be implemented in various ways, starting with the simplest means of analyzing data in office applications and ending with distributed analytical systems based on server products.Users can easily consider data on a multidimensional structure to apply to their own tasks.

2. What is OLAP

OLAP - Abbreviation from English on-line Analytical Processing is the name of a non-specific product, but a whole technology. In Russian it is more convenient to call OLAP operational analytical processing. Although in some editions, analytical treatment is also called online, and interactive, however, the adjective "operational" as it cannot more accurately reflects the meaning of the OLAP technology.

Development by the head of management decisions falls into the category of regions of the most difficult automation. However, today there is an opportunity to assist the managers in the development of decisions and, most importantly, to significantly speed up the process of developing solutions, their selection and adoption. To do this, you can use OLAP.

Consider how usually develop solutions.

Historically, it has developed that solutions for automation of operational activities are most developed. We are talking about data transactional systems (OLTP), easier than called operational systems. These systems provide registration of some facts, their short storage and preservation in archives. The basis of such systems provide systems for managing relational databases (RSUBD). The traditional approach is attempts to use already built operational systems to support decision-making. Usually try to build a developed system of requests for the operational system and use the reports received after interpretation directly to support solutions. Reports can be built on a custom base, i.e. The manager requests a report, and on regular when reports are built upon reaching some events or time. For example, the traditional decision-making process may look like: the head goes to a specialist of the information department and shares his question with him. Then the specialist of the information department builds a request to the operational system, receives an electronic report, interprets it and then brings it to the notice of management personnel. Of course, such a scheme provides for some extent supporting decision-making, but it has extremely low efficiency and a huge number of shortcomings. An insignificant amount of data is used to support critical solutions. There are other problems. This process is very slow, since the process of writing requests and interpretation of the electronic report itself is long. He takes many days, while the manager may need to make a decision right now, immediately. If you consider that the manager, after receiving the report, may be interested in another question (let's say, clarifying or requiring data consideration in another section), then this slow cycle must be repeated, and since the process analysis of operational systems will occur iteratively, the time is spent even more. Another problem is the problem of various areas of activity specialist in information technology and leaders who can think in different categories and, as a result, do not understand each other. Then additional clarifying iterations will be required, and this is again the time that is always lacking. Another important problem is the complexity of reports for understanding. The manager has no time to choose the numbers of interest from the report, especially since they may be too much (let's remember huge multi-page reports in which several pages are actually used, and the rest are just in case). We also note that work on interpretation falls most often on specialists of information departments. That is, a competent specialist is distracted by routine and ineffective work on drawing diagrams, etc., which, naturally, cannot be favorable to affect his qualifications. In addition, the presence in the chain of the interpretation of benevolences interested in the intentional distortion of incoming information is not a secret.

The above disadvantages are forced to think about the overall efficiency of the operational system, and the costs associated with its existence, as it turns out that the cost of creating an operational system does not pay for due degree of its efficiency.

In reality, these are not the consequence of the low quality of the operational system or its unsuccessful construction. The roots of problems lie in the fundamental difference between the operational activity, which is automated by the operational system, and the development and decision-making activities. The difference is that the operational systems data are simply recorded on some events that have occurred, facts, but not information in the general sense of the word. Information is something that reduces uncertainty in any area. And very good if the information had reduced uncertainty in the field of preparation of solutions. Regarding the unsuitability for this purpose of operational systems built on RUBD, in due time spoken by E.F. Codd, a man who stood in the 70s at the origins of the technology management systems of relational databases: "Although the relational database management systems are available for users, they have never been considered a means that give powerful synthesis functions, analysis and consolidation (functions called multidimensional data analysis ) ". We are talking about the synthesis of information, to turn the data of operational systems into information and even in qualitative estimates. OLAP allows you to perform such a transformation.

OLAP is based on the idea of \u200b\u200ba multidimensional data model. Human thinking is multidimensional by definition. When a person sets questions, he imposes restrictions, thereby formulating issues in many dimensions, so the analysis process in a multidimensional model is very close to the reality of human thinking. In terms of measurements in a multidimensional model, factors are postponed that affect the activities of the enterprise (for example: time, products, companies, geography, etc.). In this way, hypercube (of course, the name is not very successful, since under the cube usually understand the figure with equal ribs, which, in this case, is far from the case), which is then filled with indicators of enterprise activities (prices, sales, plan, profit, losses and etc.). Filling it can be conducted as real data of operational systems, as well as predicted on the basis of historical data. Measurements of the hypercube can be complex, to be hierarchical, relations can be set between them. In the process of analysis, the user can change the viewing point of view (the so-called logical view change operation), thereby browsing the data in various cuts and allowing specific tasks. Over the cubes can be performed various operations, including forecasting and conditional planning (type analysis, which if "). Moreover, the operations are performed at once over the cubes, i.e. The work, for example, will result in a product-hyperkub, each cell of which is the product of the cells of the corresponding hypercubs faiths. Naturally, it is possible to perform operations over hypercubs having a different number of measurements.

3. History of creating OLAP technology

The idea of \u200b\u200bdata processing on multidimensional arrays is not new. In fact, she dates back to 1962, when Ken Iverson published his book "Programming Language" ("A Programming Language", APL). The first practical implementation of the APL was held in late sixties IBM. APL is a very elegant, mathematically defined language with multidimensional variables and processed operations. It was meant as an original powerful tool for working with multidimensional transformations compared to other practical programming languages.

However, the idea for a long time did not receive massive use, since it did not come to the time of graphic interfaces, printing devices of high quality, and the display of Greek characters required special screens, keyboards and printing devices. Later, English words were sometimes used to replace Greek operators, however fighters for the purity of the APL stopped attempts to popularize their favorite language. APL also absorbed machine resources. In those days, its use required high costs. The programs were very slow and, in addition, the launch itself was very expensive. A lot of memory was required, atther days simply shocking volumes (about 6 MB).

However, the annoyance from these initial errors did not kill the idea. It was used in many business applications of the 70s, 80s. Many of these applications had features of modern analytical processing systems. So, IBM has developed an operating system for APL called VSPC, and some people considered it an ideal environment for personal use until the spreadsheets are universally common.

But the APL was too complicated to use, especially since the inconsistencies between the language itself and the equipment appeared, on which attempts to implement it.

In the 1980s, the APL became available on personal machines, but did not find market use. An alternative was programming multidimensional applications using arrays in other languages. It was a very difficult task even for professional programmers, which forced to wait for the next generation of multidimensional software products.

In 1972, several applied multidimensional software products previously used commercial use: Express. It remains fully rewritten now, however, the original concepts of the 70s stopped being relevant. Today, in the 90s, Express is one of the most popular OLAP technologies, and Oracle (R) will promote it and complement with new features.

More multidimensional products appeared in the 80s. At the beginning of the Decade - the product with the name Stratagem, later called Acumate (today the owner is Kenan Technologies), which is still moved to the beginning of the 90s, but today, unlike Express, is practically not used.

ComShare System W was a multidimensional product of another style. Submitted in 1981, he was the first to be expected to be a big focus on the end user and on the development of financial applications. He will have a lot of concepts that, however, were not well adapted, such as completely non-emerced rules, full-screen viewing and editing of multidimensional data, automatic negotiation and batch integration with relational data. However, ComShare System W was heavy enough for the hardware of that time compared to other products and was less used in the future, sold less and less, and no improvements were made in the product. Although it is also available today on UNIX, it is not a client-server, which does not contribute to increasing his supply in the market of analytical products. In the late 190s, ComShare has released a DOS product, and later for Windows. These products were called Commander Prism and used the same concepts as System W.

Another creative product of the late 80s was called Metaphor. It was intended for professional marketers. He also proposed a lot of new concepts, which only today begin to be widely used: client-server calculations, using a multidimensional model on relational data, object-oriented application development. However, the standard hardware support for the personal machines of those days was not able to work with Metaphor and suppliers were forced to develop their own standards for personal cars and networks. Gradually, Metaphor began to work successfully and on serial personal machines, but the product was performed solely for OS / 2 and had its own graphical user interface.

The then Metaphor concluded the Marketing Alliance with IBM, which was subsequently absorbed. In mid-1994, IBM decided to integrate Metaphor technology (renamed to DIS) with its future technologies and thus stop financing a separate direction, but customers expressed their displeasure and demanded to continue product support. Support was continued for the remaining customers, and IBM rebuilded the product under the new DIS title, which, however, did not make it popular. But creative, innovative Metaphor concepts were not forgotten and visible today in many products.

In the mid-80s, the term EIS was born (Executive Information System - information system of the head). The first product, clearly demonstrated this direction, was Pilot's Command Center. It was a product that allowed to perform joint calculations, what we call today client-server calculations. Since the power of personal computers of the 80s was limited, the product was very "server-centered", but this principle is very popular today. Pilot briefly sold Command Center, but suggested a lot of concepts that can be found in today's OLAP products, including automatic support for temporary intervals, multidimensional client-server calculations and simplified control of the analysis process (mouse, sensitive screens, etc.). Some of these concepts were re-applied later in Pilot Analysis Server.

In the late 80s, spreadsheets were dominant in the market tools providing the analysis to end users. The first multidimensional spreadsheet was represented by Compete. He moved to the market as a very expensive product for specialists, but suppliers did not provide the possibility of capturing the market by this product, and Computer Associates acquired the rights to it along with other products, including SuperCalc and 20/20. The main effect of the purchase of CA Compete was a sharp decline in price for it and removing protection against copying, which naturally contributed to its distribution. However, he was not successful. Compete is based on the SuperCalc 5, but the multidimensional aspect does not move. Old Compete is still sometimes used due to the fact that at one time there were considerable funds.

Lotus was the next one who tried to enter the market of multidimensional spreadsheets with the IMPROV product, which starts on the next machine. This guaranteed at least sales of 1-2-3 will not decline, but when he was released over time, Excel had already had a large market share, which did not allow Lotus to make any changes in the market allocation. Lotus, like CA with Compete, IMPROV moved to the lower part of the market, however, it did not become a condition for successful advancement on the market, and new developments in this area did not receive continuation. It turned out that users of personal computers chose spreadsheets 1-2-3 and are not interested in new multidimensional capabilities if they are not fully compatible with their old tables. Also, the concepts of small, desktop spreadsheets offered as personal applications were not really convenient and did not fit in the present business world. Microsoft (R) went on this path by adding PivotTables (in the Russian edition it is called "summary tables") to Excel. Although few users excel benefited from using this opportunity, it is probably the only fact of widespread use in the world of multidimensional analysis features simply because there are a lot of Excel users in the world.

4. OLAP, ROLAP, MOLAP ...

It is well known that when the code has published its rules for constructing relational DBMS in 1985, they caused a stormy reaction and subsequently reflected strongly at all on the DBMS industry. However, few people know that in 1993 the code has published labor called "OLAP for analyst users: what should it be." It has outlined the basic concepts of operational analytical processing and identified 12 rules to be satisfied with the products that provide the ability to perform operational analytical processing.

Here are these rules (the text of the original is saved if possible):

1. Conceptual multidimensional representation. The user analyst sees the world of the enterprise multidimensional by its nature. Accordingly, the OLAP model must be multidimensional at its base. A multidimensional conceptual scheme or custom representation facilitate modeling and analysis as well, however, as a calculation.

2. Transparency. Regardless of whether the OLAP product is part of the user's tools or not, this fact must be transparent to the user. If OLAP is provided by client-server calculations, this fact also, if possible, must be invisible to the user. OLAP should be provided in the context of a truly open architecture, allowing the user wherever it is, to communicate with the help of an analytical tool with the server. In addition, transparency should be achieved in the interaction of an analytical tool with homogeneous and heterogeneous database environments.

3. Accessibility. The OLAP analyst must be able to perform an analysis based on a common conceptual scheme containing the data of the entire enterprise in the relational database, as well as data from the old inherited databases, on general access methods and on the overall analytical model. This means that OLAP must provide its own logical scheme for access in a heterogeneous database environment and perform appropriate transformations to provide data to the user. Moreover, it is necessary to take care in advance about where and how, and what types of physical data organization will be used. The OLAP system should perform access only to the truly required data, and not apply the general principle of "kitchen funnel", which entails unnecessary input.

4. Constant performance when developing reports. If the number of measurements or database volume increases, the user analyst should not feel any significant degradation in performance. Permanent performance is critical with the support of ease of ease to use and limit the complexity of OLAP. If the user analyst will experience significant differences in performance in accordance with the number of measurements, then it will strive to compensate for these differences in the development strategy, which will cause data representation by other ways, but not those that really need to submit data. The cost of the system bypassing the system to compensate for its inadequacy is not what the analytical products are intended.

5. Client-server architecture. Most of the data that today is required to be subject to operational analytical processing are contained on mainframes with access via PC. This means, therefore, the OLAP products must be able to work in the client-server environment. From this point of view, it is necessary that the server component of the analytical tool is substantially "intellectual" so that different clients can join the server with minimal difficulties and integration programming. Intelligent Server must be able to make mapping and consolidation between inappropriate logical and physical database diagrams. This will provide transparency and constructing a general conceptual, logical and physical scheme.

6. Common multidimensionality. Each measurement should be applied without reference to its structure and operational abilities. Additional operational abilities can be provided with selected measurements, and since measurements are symmetrical, a separately taken function can be provided to any dimension. Basic data structures, formulas and report formats should not be shifted towards any measurement.

7. Dynamic management of sparse matrices. The physical scheme of the OLAP tool must be fully adapted to a specific analytical model for optimal control of racked matrices. For any risen matrix, there is one and only one optimal physical scheme. This scheme provides maximum memory efficiency and the massacreality of the matrix, unless, of course, the entire data set is not placed in memory. Basic physical data of the OLAP tool must be configured to any subset of measurements, in any order, for practical operations with large analytical models. Physical access methods should also dynamically change and contain various types of mechanisms, such as: direct calculations, b-trees and derivatives, hashing, the ability to combine these mechanisms if necessary. Rewards (measured in percentage of empty cells to all possible) is one of the characteristics of data distribution. The inability to regulate the sparseness can make the effectiveness of operations unattainable. If the OLAP tool cannot control and adjust the distribution of the values \u200b\u200bof the analyzed data, the model applying to practicality based on many consolidation paths and measurements can actually be unnecessary and hopeless.

8. Multiplayer support. Often, several analyst users are needed to work in conjunction with one analytical model or create different models from uniform data. Consequently, the OLAP tool must provide joint access capabilities (query and additions), integrity and safety.

9. Unlimited cross-operations. Various levels of convolution and consolidation paths, as a result of their hierarchical nature, represent dependent relations in the OLAP model or annex. Consequently, the tool itself must imply the corresponding calculations and not require the user-analytics to re-identify these calculations and operations. Calculations not as follows from these inherited relations require the definition of various formulas in accordance with some applicable language. Such a language can allow calculations and manipulation with data from any dimensions and not limit the relationship between data cells, not paying attention to the number of total data attributes of specific cells.

10. Intuitive data manipulation. Reorientation of consolidation paths, detailing, consolidation and other manipulations, regulated by consolidation paths, should be applied through a separate effect on the cells of the analytical model, and also should not require the use of the menu system or other multiple actions with the user interface. A view of a user analytics on measurements defined in the analytical model must contain all the necessary information to perform the above actions.

11. Flexible reporting opportunities. Analysis and presentation of data are simple when rows, columns and data cells that will be visually compared with each other, will be near each other or by some logical function that has a place in the enterprise. Reporting tools must provide synthesized data or information that is the following from the data model in its possible orientation. This means that strings, columns or pages should be shown simultaneously from 0 to n measurements, where n is the number of measurements of the entire analytical model. In addition, each measurement of the contents shown in one entry, column or page should also be able to show any subset of elements (values) contained in the dimension in any order.

12. Unlimited dimension and number of aggregation levels. Research on the possible number of necessary measurements required in the analytical model showed that up to 19 measurements can be used at the same time. It follows the ultimate recommendation to ensure that the analytical tool is able to provide at least 15 measurements simultaneously and preferably 20. Moreover, each of the total dimensions should not be limited by the number of user-defined aggregation levels and consolidation paths.

In fact, today the developers of OLAP products follow these rules or at least strive to follow them. These rules can be considered the theoretical basis for operational analytical processing, it is difficult to argue with them. Subsequently, many consequences were derived from the 12 rules, which we, however, will not lead, in order not to complicate the narration.

Let us dwell in more detail on how OLAP products differ in their physical implementation.

As noted above, the OLAP is based on the idea of \u200b\u200bprocessing data on multidimensional structures. When we say OLAP, we mean that the logically structure of these analytical product is multidimensional. Another thing is exactly how it is implemented. There are two main types of analytical processing, which include certain products.

Molap. . Actually multidimensional (Multidimensional) OLAP. The product is based on the non-relational data structure, providing multidimensional storage, processing and presentation of data. Accordingly, the databases are called multidimensional. Products related to this class typically have a multidimensional database server. Data in the analysis process is chosen exclusively from the multidimensional structure. Such a structure is high-performance.

Rolap. . Relational (Relational) OLAP. As is meant by the title, the multidimensional structure in such tools is implemented by relational tables. And the data in the analysis process, respectively, is selected from the relational database by an analytical tool.

Disadvantages and advantages of each approach, in general, are obvious. Multidimensional OLAP provides better performance, but the structures cannot be used to process large amounts of data, since the large dimension requires large hardware resources, and at the same time the permitting of hypercubes can be very high and, therefore, the use of hardware facilities will not be justified. On the contrary, the relational OLAP provides processing on large arrays of stored data, as it is possible to provide more economical storage, but, at the same time, significantly loses in the speed of operation multidimensional. Such reasoning led to the allocation of a new class of analytical instruments - HOLAP. This is hybrid (hybrid) operational analytical processing. Tools of this class allow you to combine both approaches - relational and multidimensional. Access can be carried out both to data of multidimensional databases and to these relational.

There is another fairly exotic type of operational analytical processing - Dolap. This is the "Desktop" (Desktop) OLAP. We are talking about such an analytical processing where the hypercubs are small, the dimension is small, the needs are modest, and for such an analytical processing, a fairly personal machine on the desktop.

Operational analytical processing allows you to significantly simplify and speed up the process of preparing and making decisions with senior personnel. Operational analytical processing is the target of data transformation into information. It is fundamentally different from the traditional decision-making process based, most often, on consideration of structured reports. By analogy, the difference between structured reports and OLAP is such as between riding around the city on the tram and on a personal car. When you are traveling on the tram, it moves along the rails, which does not allow you to well consider remote buildings and especially approach them. On the contrary, riding a personal car gives complete freedom of movement (naturally, traffic rules should be observed). You can drive up to any building and get to those places where trams do not go.

Structured reports are those rails that restrain freedom in the preparation of decisions. OLAP - car for efficient information on information highways.

Application OLAP system allows you to automate the strategic level of organization management. OLAP (Online Analytical Processing - Analytical Processing of Data in Real Time) is a powerful data processing and research technology. Systems built on the basis of OLAP technology provide practically limitless reporting capabilities, the implementation of complex analytical calculations, the construction of forecasts and scenarios, the development of many options for plans.

Full OLAP systems appeared in the early 90s, as the result of the development of information support information systems. They are intended for converting various, often scattered, data, useful information. OLAP systems can arrange data in accordance with some set of criteria. It is not necessary that the criteria have clear characteristics.

OLAP systems have been found in many issues of strategic management organization: business efficiency management, strategic planning, budgeting, development forecasting, preparation of financial statements, work analysis, imitation modeling of the external and internal environment, data storage and reporting.

Structure OLAP Systems

The OLAP system is based on the processing of multidimensional data arrays. Multidimensional arrays are arranged so that each element of the array has many connections with other elements. To form a multidimensional array, the OLAP system should obtain source data from other systems (for example, ERP or CRM system), or through an external input. The OLAP system user receives the necessary data in structured form in accordance with its request. Based on the specified procedure, you can submit to the structure of the OLAP system.

In general, the structure of the OLAP system consists of the following elements:

• database . The database is a source of information for the operation of the OLAP system. The type of database depends on the type of OLAP system and the operation algorithms of the OLAP server. Typically, relational databases are used, multidimensional databases, data warehouses, etc.
• OLAP server. It provides the management of the multidimensional data structure and the relationship between the database and users of the OLAP system.
• custom applications. This element of the OLAP system structure controls user requests and generates the results of accessing the database (reports, graphs, tables, etc.)

Depending on the method of organizing, processing and storing data, OLAP systems can be implemented on local computer computers or using selected servers.

There are three basic ways to store and process data:

• locally. Data is posted on user computers. Processing, analysis and data management is performed on local workplaces. Such a structure of the OLAP system has significant disadvantages associated with the rate of data processing, data security and limited use of multidimensional analysis.
• relational databases. These databases are used when working together by the OLAP system with a CRM system or ERP system. Data is stored on the server of these systems as relational databases or data warehouses. OLAP server refers to these databases to form the necessary multidimensional structures and analysis.
• multidimensional databases. In this case, the data is organized as a special data warehouse on a dedicated server. All data transactions are carried out on this server that converts the initial data into multidimensional structures. Such structures are called OLAP cube. Sources of data for the formation of OLAP cube are relational databases and / or client files. The data server provides preliminary preparation and processing of data. OLAP server works with OLAP cube without direct access to data sources (relational databases, client files, etc.).

Types of OLAP Systems

Depending on the storage and data processing 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. Data appeal is carried out directly to the relational database. Data is stored as relational tables. Users have the ability to carry out multi-dimensional analysis as in traditional OLAP systems. This is achieved by applying SQL tools and special queries.

One of the advantages of ROLAP is the ability to more effectively process a large amount of data. Another advantage of ROLAP is the ability to effectively process both numeric and text data.

The disadvantages of ROLAP include low performance (compared to traditional OLAP systems), because Data processing is performed by the OLAP server. Another disadvantage is the restriction of functionality due to the use of SQL.

2. MOLAP (Multidimensional Olap - multidimensional OLAP systems). This type of OLAP systems refers to traditional systems. The difference between the traditional OLAP system, from other systems, is to preliminarily prepare and optimize the data. These systems, as a rule, use a dedicated server on which the pre-processing of data is performed. The data is formed into multidimensional arrays - OLAP Cuba.

Molap systems are the most effective in data processing, because They allow you to easily reorganize and structure data under various user requests. MOLAP analytical instruments allow you to perform complex calculations. Another advantage of MOLAP is the ability to quickly generate requests and obtain results. This is ensured by pre-formation of OLAP cubes.

The disadvantages of the MOLAP system refers to limit the volumes of data from the data and data redundancy, because To form multidimensional cubes, according to various aspects, data has to duplicate.

3. HOLAP (Hybrid Olap - hybrid OLAP systems). Hybrid OLAP systems are a union of ROLAP and MOLAP systems. In hybrid systems, they tried to combine the benefits of two systems: using multidimensional databases and managing relational databases. HOLAP systems allow you to store a large amount of data in relational tables, and the processed data is placed in pre-constructed multidimensional OLAP cubes. The advantages of this type of systems are to scalable data, quick data processing and flexible access to data sources.

There are other types of OLAP systems, but they are more important than the marketing course of manufacturers than the independent view of the OLAP system.

Such species include:

• WOLAP (Web Olap). View of OLAP systems with web interface support. In these OLAP systems, it is possible to access the databases through the Web interface.
• Dolap (Desktop OLAP). This view of the OLAP system allows users to download to the local workplace database and work with it locally.
• Mobileolap. This is the OLAP system function that allows you to work with a database remotely using mobile devices.
• Solap (Spatial Olap). This type of OLAP systems is designed to process spatial data. It appeared as a result of integrating geographic information systems and OLAP systems. These systems allow you to process data not only in alphanumeric format, but also in the form of visual objects and vectors.

Advantages of OLAP system

The use of OLAP system provides an organization opportunity to predict and analyze various situations related to current activities and development prospects. These systems can be viewed as an addition to the enterprise level automation systems. All advantages of OLAP systems are directly dependent on the accuracy, reliability and volume of the source data.

The main advantages of OLAP systems are:

• consistency of source information and analysis results. If there is an OLAP system, there is always the ability to trace the source of the information and determine the logical connection between the results obtained and the source data. The subjectivity of the analysis results is reduced.
• conducting a multivariate analysis. Application of the OLAP system allows you to get a variety of event development scenarios based on the set of source data. Due to the analysis tools, you can simulate situations on the principle of "what will happen if".
• detail management. Details of the presentation of the results may vary depending on the needs of users. At the same time, there is no need to carry out complex system settings and repeat the calculations. The report may contain exactly the information that is necessary for making decisions.
• detection of hidden dependencies. Due to the construction of multidimensional connections, it is possible to identify and determine hidden dependencies in various processes or situations that affect production activities.
• creating a single platform. By applying the OLAP system, it is possible to create a single platform for all processes of forecasting and analyzing the enterprise. In particular, the data OLAP system are the basis for building budget forecasts, sales forecast, procurement forecast, strategic development plan, etc.

The conditions for high competition and the growing dynamics of the external environment dictate increased requirements for enterprise 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 in turn contributed to the emergence of analytical systems. The demand for analytical systems in Russia is high. Most interesting in terms of application of these systems in the financial sector: banks, insurance business, investment companies. The results of the work of analytical systems are required primarily to people whose decisions depends on the development of the company: managers, experts, analysts. Analytical systems allow you to solve consolidation tasks, reporting, optimization and forecasting. To date, it has not been a final classification of analytical systems, as there is no common system of definitions in terms used in this direction. The information structure of the enterprise can be represented by a sequence of levels, each of which is characterized by its processing and information management method, and has its own function in the management process. Thus, analytical systems will be located hierarchically at different levels of this infrastructure.

Level of transactional systems

Data warehouse level

The level of data showcases

OLAP level - systems

Level of analytical applications

OLAP - Systems - (Online Analytical Processing, Analytical Treatment In the present Time) - are the technology of comprehensive multidimensional data analysis. OLAP - Systems are applicable where there is a task of analyzing multifactor data. There are an effective means of analyzing and generating reports. The above data warehouses, data showcases and OLAP systems refer to business intelligence systems (BUSINESS INTELLIGENCE, BI).

Very often, information and analytical systems created on the direct use of decision-making persons are extremely simple in use, but are rigidly limited in functionality. Such static systems are called in the literature of the information systems of the manager (IPR), or Executive Information Systems (EIS). They contain predefined multiple requests and, being sufficient for everyday review, is unable to respond to all questions to available data that may arise when making decisions. The result of such a system, as a rule, are multi-page reports, after a thorough study of which the analyst has a new series of issues. However, each new request, unforeseen when designing such a system, should be formally described formally, encoded by a programmer and is then executed. Waiting time in this case can make hours and days that is not always acceptable. Thus, the external simplicity of static SPPR, for which most of the customers of information and analytical systems are actively fighting, turns on the catastrophic loss of flexibility.

Dynamic SPPRs, on the contrary, are focused on the processing of non-elected (AD HOC) of analysts to data. The most deeply requirements for such systems reviewed E. F. Codd in the article, which posted the beginning of the concept of OLAP. The work of analysts with these systems is the interactive sequence of querying and studying their results.

But dynamic SPPRs can act not only in the field of operational analytical processing (OLAP); Support for making management decisions based on accumulated data can be performed in three basic areas.

Sphere of detailed data. This is the area of \u200b\u200baction of most systems aimed at finding information. In most cases, relational DBMSs are perfectly coping with tasks arising here. The generally accepted standard of manipulation language with relational data is SQL. Information and search engines that provide the end-user interface in the search tasks of detailed information can be used as add-ons both over separate transaction system databases and over common data storage.

Sphere of aggregated indicators. A comprehensive look at the information collected in the data warehouse, its generalization and aggregation, hypercubic representation and multidimensional analysis are tasks of operational analytical data processing systems (OLAP). Here you can or focus on special multidimensional DBMS, or remain within relational technologies. In the second case, pre-aggregated data can be collected in the database of a star-like type, or the information aggregation can be carried out on the fly in the process of scanning detailed tables of the relational database.

Sphere of patterns. Intelligent processing is performed by the methods of intelligent data analysis (Jaad, Data Mining), the main tasks of which are the search for functional and logical patterns in the accumulated information, the construction of models and rules that explain the found anomalies and / or predict the development of some processes.

Operational analytical data processing

The basis of the concept of OLAP lies the principle of multidimensional data presentation. In 1993, the EF Codd article considered the deficiencies of the relational model, first of all specifying the inability to "combine, view and analyze data from the point of view of the multiplicity of measurements, that is, the most understandable for corporate analysts in the way," and identified general requirements for OLAP systems expanding The functionality of relational DBMS and includes multi-dimensional analysis as one of its characteristics.

Classification of OLAP products according to the data representation method.

Currently, a large number of products are present on the market, which to varying degrees provide OLAP functionality. About 30 most famous are listed in the list of the review Web server http://www.olapreport.com/. Providing a multidimensional conceptual representation by the user interface to the source database, all OLAP products are divided into three classes by type of source database.

The most first operational analytical processing systems (for example, Essbase ARBOR Software, Oracle's Oracle Express Server Company) belonged to the MOLAP class, that is, they could only work with their own multidimensional databases. They are based on proprietary technologies for multidimensional DBMS and are the most expensive. These systems provide a complete OLAP processing cycle. They either include, in addition to the server component, their own integrated client interface is either used to communicate with the user external work programs with spreadsheets. To maintain such systems, a special staff is required by installing, accompanied by system, the formation of data views for end users.

The operational analytical data processing systems (ROLAP) provide data stored in the relational base, in multidimensional form, ensuring the transformation of information into a multidimensional model through the intermediate layer of metadata. Rolap systems are well adapted to work with large storage. Like MOLAP systems, they require considerable service costs for information technology professionals and provide multiplayer operation.

Finally, hybrid systems (Hybrid Olap, Holap) are designed to combine advantages and minimize the shortcomings inherent in previous classes. Speedware Media / MR includes this class. According to developers, it combines analytical flexibility and MOLAP response speed with constant access to real data peculiar to ROLAP.

Multidimensional OLAP (MOLAP)

In specialized DBMS based on multidimensional data presentation, the data is not organized in the form of relational tables, but in the form of ordered multidimensional arrays:

1) hypercubes (all the cells stored in the database must have the same dimension, that is, to be in the maximum full measurement basis) or

2) polycubes (each variable is stored with its own set of measurements, and all the associated complexity of processing is shifted to the internal mechanisms of the system).

The use of multidimensional databases in systems of operational analytical processing has the following advantages.

In the case of using multidimensional DBMS, the search and sample of data is carried out much faster than with a multidimensional conceptual look at the relational database, since the multidimensional database is denormalized, contains pre-aggregated indicators and provides optimized access to the requested cells.

Multidimensional DBMSs easily cope with the tasks of inclusion in the information model of a variety of built-in functions, while objectively existing SQL language restrictions make these tasks based on relational DBMSs quite complicated, and sometimes impossible.

On the other hand, there are significant limitations.

Multidimensional DBMSs do not allow working with large databases. In addition, due to the denormalization and pre-performed aggregation, the amount of data in a multidimensional base, as a rule, corresponds to (by assessing the code) in 2.5-100 times the smaller volume of source detailed data.

Multidimensional DBMSs compared with relational are very inefficiently using external memory. In the overwhelming majority of cases, the information hypercube is strongly rarefied, and since the data is stored in an ordered form, uncertain values \u200b\u200bare deleted only by selecting the optimal sorting order, which allows you to organize data into the maximum continuous groups. But even in this case, the problem is solved only in part. In addition, the sorting procedure is most likely optimal from the point of view of storage, the order of sorting will most likely not coincide with the order that is most often used in queries. Therefore, in real systems, it is necessary to search for a compromise between the speed and redundancy of the disk space occupied by the database.

Consequently, the use of multidimensional DBMS is justified only under the following conditions.

The amount of source data for analysis is not too large (no more than a few gigabytes), that is, the data aggregation level is quite high.

The set of information measurements is stable (since any change in their structure almost always requires a complete hypercube restructuring).

The response time of the system for non-elected requests is the most critical parameter.

A wide use of complex built-in functions is required to perform cross-dimensional calculations over the cells of the hypercube, including the possibility of writing user functions.

Relation OLAP (ROLAP)

Direct use of relational databases in systems of operational analytical processing has the following advantages.

In most cases, corporate data warehouses are implemented by means of relational DBMS, and ROLAP tools make it possible to analyze directly above them. In this case, the storage size is not such a critical parameter as in the case of MOLAP.

In the case of a variable dimension of the task, when changes to the measurement structure have to be made quite often, the rolap system with a dynamic representation of dimension is the best solution, since such modifications do not require physical reorganization of the database.

Relational DBMSs provide a significantly higher level of data protection and good access rights to delimitation.

The main drawback of ROLAP compared to multidimensional DBMS is less performance. To ensure performance comparable to MOLAP, relational systems require a thorough study of the database diagram and index settings, that is, great efforts from the database administrators. Only when using star-shaped schemes, the performance of well-configured relational systems can be approached by the performance of systems based on multidimensional databases.

Online analytical processing, or OLAP is an effective data processing technology, resulting on the basis of huge arrays of all kinds of data output. This is a powerful product that helps access, extract and view information on the PC, analyzing it from different points of view.

OLAP is a tool that provides a strategic position of long-term planning and considers the basic information of operational data to perspective 5, 10 or more. The data is stored in the database with dimension, which is their attribute. Users can view the same data set with different attributes, depending on the analysis objectives.

History OLAP.

OLAP is not a new concept and has been used for decades. In fact, the origin of the technology is tracked since 1962. But the term was invented only in 1993 by the author of the database by Tedododdom, who also installed 12 rules for the product. As in many other applications, the concept was subjected to several stages of evolution.

The history of the OLAP technology itself dates back to 1970, when Express information resources and the first OLAP server were released. They were acquired by Oracle in 1995 and subsequently became the basis of online analytical processing of a multidimensional computing mechanism, which a well-known computer brand provided in its database. In 1992, another well-known online analytical processing product Essbase was released by Arbor Software (purchased Oracle in 2007).

In 1998, Microsoft has released an online analytical MS ANALYSIS SERVICES data processing server. This contributed to the popularity of technology and prompted the development of other products. Today there are several world-famous providers offering OLAP applications, including IBM, SAS, SAP, Essbase, Microsoft, Oracle, Iccube.

Online analytical processing

OLAP is a tool that allows you to make decisions about planned events. Atypical OLAP calculation may be more complicated than just data aggregation. Analytical queries per minute (AQM) are used as a standard standard for comparing the characteristics of various tools. These systems should maximize users from the syntax of complex queries and ensure the consistent response time for all (regardless of how difficult they are).

The following main characteristics of OLAP are exist:

1. Multidimensional data representation.
2. Support for complex computing.
3. Temporary intelligence.

The multidimensional representation provides the basis for analytical processing by means of flexible access to corporate data. It allows users to analyze data in any measurement and at any level of aggregation.

Support for complex calculations is the basis of OLAP software.

Temporary intelligence is used to assess the effectiveness of any analytical application for a certain period of time. For example, this month compared with the past month, this month compared with the same month last year.

Multidimensional data structure

One of the main features of online analytical processing is a multidimensional data structure. Cube can have several measurements. Thanks to such a model, the entire process of intelligent OLAP analysis is simple for managers and managers, since the objects presented in the cells are business objects of the real world. In addition, this data model allows users to process not only structured arrays, but also unstructured and semi-structured. All this makes them especially popular for data analysis and BI applications.

The main characteristics of OLAP systems:

1. Use multidimensional data analysis methods.
2. Provide advanced database support.
3. Create easy-to-use end-user interfaces.
4. Support the client / server architecture.

One of the main components of the OLAP concepts is the server on the client side. In addition to aggregation and pre-processing of data from the relational base, it provides advanced calculation and recording parameters, additional functions, basic advanced requests and other functions.

Depending on the example of the application selected by the user, various data models and tools are available, including real-time notification, the function for using scripts "What, if", optimization and complex OLAP reports.

Cubic form

The concept is based on a cubic form. The location of the data in it shows how OLAP adheres to the principle of multidimensional analysis, as a result of which the data structure is created for quick and effective analysis.

OLAP cube is also called "hypercub." It is described as consisting of numerical facts (measures), classified by facets (measurements). Dimensions belong to attributes that define a business problem. Simply put, the measurement is a label describing the measure. For example, in sales reports, the sales will be sales, and the size will include a period of sales, sellers, a product or service, as well as a sales region. In reporting on industrial operations, the measure may be general production costs and products. Dimensions will be the date or production time, the stage of production or phase, even workers involved in the production process.

OLAP-cube data is the cornerstone of the system. The data in Cuba is organized using either a star or snowflake schemes. The center has a table of facts containing aggregates (measures). It is associated with a number of measurement tables containing information about measures. Dimensions describe how these measures can be analyzed. If the cube contains more than three dimensions, it is often called hypercubus.

One of the main functions belonging to Cuba is its static character that implies that the cube cannot be changed after its development. Consequently, the process of assembling the cube and the data model settings is a decisive step towards the appropriate data processing in the OLAP architecture.

Data Combining

The use of aggregations is the main reason for which requests are processed much faster in OLAP tools (compared to OLTP). Aggregations are reports of data that were previously calculated during their processing. All members stored in OLAP measurement tables determine requests that the cube can get.

In Cuba, the accumulation of information is stored in cells whose coordinates are specified by specific sizes. The number of units that may contain a cube depends on all possible combinations of measurement elements. Therefore, a typical cube in the application may contain an extremely large number of aggregates. The preliminary calculation will be performed only for key units that are distributed throughout Analytical Cuba online analytics. This will significantly reduce the time required to determine any aggregations when performing a query in the data model.

There are also two options related to aggregations with which you can increase the performance of the finished cube: create a cache aggregation of capabilities and use aggregation based on user query analysis.

Principle of operation

Typically, the analysis of operational information obtained from transactions can be performed using a simple spreadsheet (data values \u200b\u200bare presented in lines and columns). This is good, given the two-dimensional nature of the data. In the case of OLAP there are differences, which is associated with a multidimensional data array. Since they are often obtained from different sources, the spreadsheet may not always effectively process them.

The cube solves this problem, as well as ensures the operation of the OLAP data storage of the data is logical and ordered. Business collects data from numerous sources and is presented in different formats, such as text files, multimedia files, Excel spreadsheets, Access databases, and even OLTP databases.

All data are collected in the repository, filled directly from sources. It has untreated information obtained from OLTP and other sources, will be cleaned from any erroneous, incomplete and inconsistent transactions.

After cleaning and converting the information will be stored in the relational database. It will then be downloaded to a multidimensional OLAP server (or OLAP cube) for analysis. End users responsible for business applications, intelligent data analysis and other business operations will access the information they need from OLAP cube.

Advantages of the array model

OLAP is a tool providing quick queries performance, which is achieved due to optimized storage, multidimensional indexing and caching, which refers to a significant advantage of the system. In addition, the benefits are:

1. Smaller data on disk.
2. Automated calculation of higher data units.
3. The array models provide natural indexation.
4. Effective data extraction is achieved due to pre-structuring.
5. Compactness for low dimension data sets.

The disadvantages of OLAP include the fact that some solutions (processing step) can be rather long, especially with large amounts of information. It is usually fixed by performing only incremental processing (the data that has been changed) is being studied.

Main analytical operations

Convolution (Roll-Up / Drill-Up) is also known as "Consolidation". Cutting includes collecting all data that can be obtained and calculating all in one or more dimensions. Most often, this may require the use of mathematical formula. As an OLAP example, you can consider a retail chain with outlets in different cities. To determine the models and foresee future sales trends, data about them from all points "rolled" to the main sales department of the company for consolidation and calculation.

Disclosure (Drill-DowN). This is the opposite of coagulation. The process begins with a large data set, and then divided into its smaller parts, thereby allowing users to view the details. In the example with the retail network analyst will analyze sales data and view individual brands or products that are considered bestsellers in each of the outlets in different cities.

Section (Slice and Dice). This process is when analytical operations include two actions: output a specific set of data from OLAP cube ("cutting" aspect of the analysis) and view it from different points of view or corners. This can happen when all the data of the outlets are obtained and entered into the hypercub. Analyst carries out of the OLAP Cube a set of data related to sales. Further, it will be viewed when analyzing sales of individual units in each region. At this time, other users can focus on assessing the economic efficiency of sales or evaluating the effectiveness of the marketing and advertising campaign.

Turn (Pivot). It turns the data axis to ensure the replacement of information presentation.

Database varieties

In principle, this is a typical OLAP cube that implements the analytical processing of multidimensional data using the OLAP Cube or any cube of data so that the analytical process can add dimensions as needed. Any information loaded into a multidimensional database will be stored or archived and can be caused when it is necessary.

	Value
Relational OLAP (ROLAP)	ROLAP is an extended DBMS together with multidimensional data display to perform a standard relational operation.
Multidimensional OLAP (MOLAP)	Molap - implements work in multidimensional data
Hybrid online analytical processing (HOLAP)	In the HOLAP approach, aggregated final values \u200b\u200bare stored in a multidimensional database, and detailed information is stored in the relational base. This provides both the efficiency of the ROLAP model and the productivity of the MOLAP model.
OLAP Desk (Dolap)	In Desktop OLAP, the user downloads part of the data from the database locally or on its desktop and analyzes it. DOLAP is relatively cheaper for deployment, because it offers very little functionality compared to other OLAP systems
Web OLAP (WOLAP)	Web OLAP is an OLAP system available through a web browser. WOLAP is a three-level architecture. It consists of three components: client, intermediate software and database server
Mobile OLAP.	Mobile OLAP helps users receive and analyze OLAP data using their mobile devices.
Spatial Olap.	SolaP is created to facilitate control of both spatial and non-spatial data in the geographic information system (GIS)

There are less well-known OLAP systems or technologies, but these are the main, which currently use major corporations, business structures and even the government.

OLAP Tools

Tools for online analytical processing are very well presented on the Internet in the form of both paid and free versions.

The most popular of them:

1. Dundas Bi from Dundas Data Visualization is a browser-based platform for business analysts and data visualization, which includes integrated information panels, OLAP reports and data analytics.
2. Yellowfin - Business Analytics Platform, which is a single integrated solution developed for companies of various industries and scales. This system is configured for enterprises in accounting, advertising, agriculture.
3. CLICDATA is a solution for business analysts (BI), intended for use in the main enterprises of small and medium-sized businesses. The tool allows end users to create reports and information panels. Board is created to combine business analytics, corporate efficiency management and is a full-featured system that serves the company's secondary and corporate level.
4. DOMO is a cloud business management pack that is combined with multiple data sources, including spreadsheets, databases, social networks and any existing cloudy or local software solution.
5. INETSOFT STYLE INTELLIGENCE is a software platform for business analysts that allows users to create information panels, visual OLAP analysis technology and reports using the Mashup mechanism.
6. BIRST from Infor Company is a networking solution for business analysts and analysis, which combines ideas of various commands and helps make informed decisions. The tool allows decentralized users to enlarge the corporate command model.
7. Halo is a comprehensive supply chain management system and business analysts that helps in business planning and forecasting stocks for supply chain management. The system uses data from all sources - large, small and intermediate.
8. Chartio is a cloud solution for business analysts, which provides founders, business groups, analytics of data and groups of products tools of the organization for everyday work.
9. Exago Bi is a web solution intended for implementation in a web application. The implementation of Exago BI allows companies of all sizes to provide its customers with special, operational and interactive reporting.

Impact on business

The user will find OLAP in most business applications in different industries. An analysis is used not only to business, but also other stakeholders.

Some of its most common applications include:

1. Marketing OLAP analysis of data.
2. Financial reporting, which covers sales and expenses, budgeting and financial planning.
3. Business process management.
4. Sales analysis.
5. Marketing databases.

The industries continue to grow, which means that soon users will see more OLAP applications. Multidimensional adapted processing provides more dynamic analysis. It is for this reason that these OLAP systems and technologies are used to evaluate the scripts "that, if" and alternative business scenarios.