A rule known to everyone applies here. primary school: red is bad!

So now you know how to create, edit, edit, and review markup for your business address and location using Schema.org. If you use a more convenient method or have any questions, feel free to leave a message below. I'm crazy about the new markup features and would be happy to answer any questions or help solve problems.

From the editor

Please note, gentlemen, the article is from 2012. How much water has flown under the bridge since then, how many trends have changed, how many new tools and opportunities have appeared.

Do you need a developer who is not only up to date with all the innovations, but also knows how to look beyond the horizon?

We recommend contacting the participants in the objective and independent rating of web studios!

Every enterprise has many different databases that are replenished from structured data sources. Structured data is data that is entered into databases in a specific form, for example, Excel tables, with strictly defined fields. A set of enterprise databases is called in English literature Enterprise Data Warehouse (EDW) - literally “data warehouse”. I have not yet come across an analogue of this term in Russian-language literature, so let’s call it “enterprise data warehouse.” For beauty we will use English abbreviation E.D.W.

Structured data sources are applications that capture data from various transactions. For example, this could be CDRs in the operator’s network, notifications of network failures (trouble tickets), financial transactions on bank accounts, ER (Enterprise Resource Planning) system data, application programs, and etc.

Business intelligence BI (Business Intelligence) is a data processing component. This various applications, tools and utilities that allow you to analyze the data collected in the EDW and make decisions based on it. These are systems for generating operational reports, selective queries, OLAP applications(On-Line Analytical Processing), so-called “disruptive analytics”, predictive analysis and data visualization systems. Simply put, the manager must see the business process in an easy-to-read form, preferably graphic and animated, in order to quickly make decisions. optimal solutions. The first law of business: correct solution– this is a decision made on time. If the right decision for yesterday is made today, it is not a fact that it is still correct.

What to do if the data sources are unstructured, heterogeneous, obtained from different sources? How will they work with them? analytical systems? Try using your mouse to select several cells with data in Excel spreadsheet and paste into simple text editor(for example, Notepad) and you will see what “unstructured data” is. Examples of unstructured data: Email, information from social networks, XML data, video, audio and image files, GPS data, satellite images, sensor data, web logs, movement data mobile subscriber in handover, RFID tags, PDF documents...

To store such information in data processing centers (DPCs), distributed file system Hadoop, HDFS (Hadoop Distributed File System). HDFS can store all types of data: structured, unstructured and semi-structured.

Applications Big Data for business analytics - a component not only of processing, but also with data, both structured and not. They include applications, tools and utilities that help analyze large volumes of data and make decisions based on data from Hadoop and other non-relational storage systems. It does not include traditional BI analytics applications, nor extension tools for Hadoop itself.

In addition, an important component of Hadoop is the MapReduce system. It is designed to manage resources and data processing in Hadoop to ensure storage reliability and optimized data placement in geographically distributed data centers. The MapReduce system consists of two main components - Map, which distributes duplicate blocks of unstructured data across various nodes of the storage system (for the purpose of reliable storage of information), and Reduce - a component for removing identical data, both in order to reduce the required total storage volume and increase correctness subsequent actions on the data. MapReduce is notable for the fact that it processes data where it is stored (i.e. in HDFS), instead of moving it somewhere for processing, and then writing the results somewhere else, which is usually done in conventional EDW . MapReduce also has a built-in data recovery system, i.e. if one storage node fails, MapReduce always knows where to go for a copy of the lost data.

Although the data processing speed of MapReduce is an order of magnitude superior traditional methods processing with “extraction” of data, however, due to the incomparably large volumes of data (that’s why they are Big Data), MapReduce usually uses parallel processing data streams (batch mode). With Hadoop 2.0, resource management is a separate functionality (called YARN), so MapReduce is no longer a bottleneck in Big Data.

The transition to Big Data systems does not mean that traditional EDW should be scrapped. Instead, they can be used together to take advantage of both and extract new business value from their synergies.

What is all this for?

There is a widespread opinion among consumers of IT and telecom equipment that all these spectacular foreign word and letter combinations - Cloud Computing, Big Data and various other IMS with softswitches are invented by cunning equipment suppliers in order to maintain their margins. That is, to sell, sell and sell new developments. Otherwise, the sales plan will not be fulfilled and Bill Jobs Chambers will say “ah-ah-ah.” And “the bonus for the quarter was covered.”

Therefore, let's talk about the need for all this and trends.

Probably many have not yet forgotten scary virus H1N1 flu. There were fears that it could be even stronger than the Spanish flu of 1918, when the number of victims was in the tens of millions. Although doctors were supposed to regularly report about increasing cases of diseases (and they did report them), the analysis of this information was delayed by 1-2 weeks. And the people themselves applied, as a rule, 3-5 days after the onset of the disease. That is, measures were taken according to by and large, backdating.

The dependence of the value of information on time usually takes the form of a U-shaped curve.

Information is most valuable either immediately after it is received (for acceptance operational decisions), or after some time (for trend analysis).

Google, which stores many years of search history, decided to analyze the 50 million most popular queries from the sites of previous influenza epidemics, and compare them with medical statistics during these epidemics. A system was developed to establish a correlation between the frequency of certain queries and 40-50 typical queries were found. The correlation coefficient reached 97%.

In 2009, it was possible to avoid the serious consequences of the H1N1 epidemic, precisely because the data was obtained immediately, and not after 1-2 weeks, when the clinics in the epidemic areas would no longer be crowded. This was perhaps the very first use of big data technology, although it was not called that at the time.

It is well known that the price of an air ticket is very unpredictable and depends on many factors. I recently found myself in a situation where I could buy the same economy class ticket from the same airline to the same city in two possible options. For a flight leaving in the evening in three hours, a ticket cost 12 thousand rubles, and for the early morning tomorrow– 1500 rubles. I repeat, there is one airline and even the aircraft on both flights are of the same type. Typically, the price of a ticket becomes more expensive the closer the departure time. There are many other factors that influence the price of a ticket - once a booking agent explained to me the essence of this host of fares, but I still didn’t understand anything. There may be cases when the price of a ticket, on the contrary, falls if, as the departure date approaches, there are many unsold seats, in the event of any promotions, etc.

Once Upon a Time, Oren Encioni, Program Director artificial intelligence at Washington State University, getting ready to fly to his brother's wedding. Since weddings are usually planned in advance, he bought the ticket immediately, long before departure. The ticket was indeed inexpensive, much cheaper than usual when he bought a ticket for an urgent business trip. During the flight, he boasted to his neighbor how cheaply he managed to buy a ticket. It turned out that the neighbor’s ticket was even cheaper, and he bought it later. Out of frustration, Mr. Encioni conducted an impromptu sociological survey right in the cabin about ticket prices and dates of purchase. Most passengers paid less than Encioni, and almost all bought tickets later than Encioni. It was very strange. And Enzioni, as a professional, decided to tackle this problem.

Having acquired a sample of 12 thousand transactions on the website of one of the travel agencies, he created a model for predicting prices for air tickets. The system analyzed only prices and dates, without taking into account any factors. Only “what” and “how much”, without analyzing “why”. The output was a predictive probability of a decrease or increase in the price of a flight, based on the history of price changes for other flights. As a result, the scientist founded a small consulting firm called Farecast (play on words: Fare - fare, price; Forecast - forecast) to forecast prices for air tickets, based on a large database of flight bookings, which, of course, did not give 100% accuracy (which indicated in user agreement), but with a reasonable degree of probability she could answer the question of whether to buy a ticket right now or wait. To further protect against lawsuits, the system also provided a “self-trust score” something like this: “There is an 83.65% chance that the ticket price will be lower in three days.”

Then Farecast was bought by Microsoft for several billion dollars and integrated its model into its Bing search engine. (And, as is most often the case with Microsoft, nothing more is heard about this functionality, since few people use this Bing, and those who use it know nothing about this function).

These two examples show how social benefits and economic benefits can be achieved through Big Data analytics.

What exactly is Big Data?

There is no strict definition for “big data”. As technologies emerged for working with large volumes of data, for which the memory of one computer was no longer enough and had to be stored somewhere (MapReduce, Apache Hadoop), it became possible to operate much larger volumes of data than before. In this case, the data could be unstructured.

This makes it possible to abandon the restrictions of the so-called. “representative samples” from which larger conclusions can be drawn. The analysis of causality is replaced by the analysis of simple correlations: it is not “why” that is analyzed, but “what” and “how much”. This fundamentally changes established approaches to how to make decisions and analyze a situation.

On stock markets Tens of billions of transactions occur every day, of which about two-thirds of trades are resolved using computer algorithms based mathematical models using huge amounts of data.

Back in 2000, the amount of digitized information accounted for only 25% of the total amount of information in the world. Currently, the amount of information stored in the world is on the order of zettabytes, of which non-digital information accounts for less than 2%.

According to historians, from 1453 to 1503 (over 50 years) about 8 million books were printed. This is more than all the handwritten books written by scribes since the Nativity of Christ. In other words, it took 50 years to approximately double the information stock. Today this happens every three days.

To understand the value of “big data” and how it works, let’s give a simple example. Before the invention of photography, it took from several hours to several days or even weeks to draw a portrait of a person. In this case, the artist made a certain number of strokes or strokes, the number of which (to achieve a “portrait likeness”) can be measured in hundreds and thousands. At the same time, it was important HOW to draw, how to apply paint, how to shade, etc. With the invention of photography, the number of “grains” in analog photography, or the number of “pixels” in digital photography, changed by several orders of magnitude, and HOW to arrange them does not matter to us - it’s up to us the camera does this.

However, the result is essentially the same – an image of a person. But there are also differences. In a handwritten portrait, the accuracy of the likeness is very relative and depends on the “vision” of the artist; distortion of proportions, addition of shades and details, which are in the “original”, i.e., are inevitable. in a human face, there was none. The photograph accurately and scrupulously conveys the “WHAT”, leaving the “HOW” in the background.

With some allegory, we can say that photography is Big Data for a handwritten portrait.

And now we will record every human movement at strictly defined and fairly small time intervals. It will turn out to be a movie. Film is “big data” in relation to photography. We increased the amount of data and processed it accordingly, resulting in a new quality – a moving image. By changing the quantity, adding a processing algorithm, we get a new quality.

Now the video images themselves serve as food for computer systems Big Data.

As the scale of processed data increases, new opportunities appear that are not available when processing smaller data volumes. Google predicts flu epidemics no worse, and much faster, than official medical statistics. To do this, it requires a thorough analysis of hundreds of billions of source data, as a result of which it provides an answer much faster than official sources.

Well, briefly about two more aspects of big data.

Accuracy.

Big Data systems can analyze huge amounts of data, and in some cases - all data, and NOT samples. Using all the data, we get a more accurate result and can see nuances that are not available with limited sampling. However, in this case one has to be content with a general idea, and not an understanding of the phenomenon up to the smallest details. However, inaccuracies at the micro level allow large quantities data allow you to make discoveries at the macro level.

Causality.

We are used to looking for reasons in everything. This is, in fact, what scientific analysis is based on. In the world of big data, causality is not so important. More important are the correlations between the data, which can give necessary knowledge. Correlations cannot answer the question “why”, but they do a good job of predicting “what” will happen if certain correlations are discovered. And most often this is exactly what is required.

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Page 1

Structured data in Ada can be used in the form of arrays and records. In addition, structured data in Ada can be accessed using pointers. The use of arrays with unspecified boundaries makes it possible to parameterize arrays and use subroutines that use variable-sized arrays as parameters.

Processing complex structured data in external memory is a distinctive property of all YAPZ. But in addition to this, ordinary variables are also needed. That's why registers and stacks are introduced in PILOT/2.

Many procedures representing structured data have interesting and sometimes useful property: from them other possible representations can be formed. So, for example, list view 2 logically follows from list view 1, and the first of them could be equipped with instructions using appropriate control directives that would allow the second view to be output. In this context, list view 2 would behave like a normal set of procedures producing output. This ability of logical statements to simultaneously function as both ordinary procedures and representations of data structures shows that any supposed distinction between procedures and data is essentially pragmatic, and concerns only the use of these resources and not their inherent attributes.

The components of an array represent structured data of the same type. An array combines data with the same properties. In contrast to arrays, the components of a direct (Cartesian) product can have Various types. The direct (Cartesian) product, like an array, is one of the basic structured data types, and is also called a record or structure.

Knowledge is well-structured data, or data about data, or metadata.

In term representation, structured data is formed using functional symbols that allow its constituent parts to be assembled into groups. So, for example, the list (10 20 30) could be represented by the term 10.20.30. NIL, in which each dot functor groups the element to its left with the tail of the list to its right. Both constants and structured terms can be considered as essentially passive objects, intended for processing by procedures.

The ALTOP technology, created on the basis of work on software ACS. This development includes original tools for compiling initial descriptions, discussed in sections 2.4 and 2.5, and a design methodology (see Chap.

Thus, the structured data class represents data / for which storage requires the creation of fixed sets of formats. Databases storing such data are formatted with a deterministic schema, oriented towards preliminary fixation and classification of objects external environment, the precise statement of properties and relationships described in a database from a pre-created set of fixed formats.

A database is a collection of structured data.

Various techniques and methods are used to place structured data in linear memory structures. As a rule, such data is presented in the form of lists, and the efficiency of search and other characteristics of data processing systems directly depend on their organization.

Names in programs are used to denote simple variables, structured data, structured data elements, constants, subroutines, operations, statement labels, formal parameters, and other program elements. Names can be uncompounded or compound.

The language language is based on non-operator means of describing hierarchically structured data. It uniquely determines the trajectory of movement and access to the database. In addition, languages ​​have tools similar to procedural programming languages.

A formalized questionnaire designed for processing and recording structured data.

In this terminology, a database can be defined as a collection of specially structured data and connections between their elements, segments and logical records. Construction of databases in this understanding is possible only for information objects, which have properties common to the whole class. If necessary, provide information base objects with individual properties, it is advisable to build unstructured data bases that allow recording information in natural language.

Almost all webmasters believe that by adding their site to search engine indexing through the appropriate services, they ensure it full promotion in this search engine. But it was not there.

Google suggests creating structured data. This service significantly improves search results and also increases the visitor’s likelihood of finding the material they need. This, in turn, improves the demand for your resource, and gives your address to those users who really need it.

How structured data works

In order to understand how this works and what benefits it will bring to you, as usual, let’s turn to a live example.

Imagine that a passerby named User is a person looking for chocolate wafers. In the example, there is also a foreign passerby Google name and candy stores called Site and Site2.

A passerby asks Google where I can buy chocolate wafers. And he shows him the way to the Site1 store. But in the Site1 store there are only vanilla wafers, and chocolate ones are sold in Site2. Google obviously found out about the assortment of stores, but due to the fact that he is a foreigner, he did not understand everything correctly.

A passerby can, of course, buy vanilla wafers, because they are also waffles. But he will not be as satisfied as he would be if he bought chocolate ones. Therefore, he will leave a bad review about the Site1 store, and is unlikely to come here again, and he will probably never know about Site2 at all.

So, from the example it is clear that search engine Google is an uncomprehending foreigner when it collects information about your site. And not all the data that seems obvious to a person will be assessed correctly by Google. Therefore, errors may sometimes occur.

Structured data is markup on site pages that becomes more understandable to Google, and the search engine will return your site more correctly. This tool is free. How can you miss the opportunity to take advantage of it?

How to get started with Google structured data

To start working with structured data, you should go to the appropriate Google service, and there load the site page into the wizard. In this wizard, you can assign explanatory labels that will be clear to Google.
It's the same as making a sign on the Site2 store on native language foreigner Google, that they sell chocolate wafers. Then he definitely won't make a mistake.

After assigning all the tags, you will receive an html code that you can download and save on your website.
Everything is simple down to the basics, and the return will be good.

Topic 4.7

Algorithm programming
formation and processing
one-dimensional arrays

Structured Data

Often it is necessary to process not single data, but a collection of data of the same type. For example, task tabulating function , which consists of obtaining a sequence of values given function for several argument values. To intermediately store each value of the received data, you need to declare your own variable with a unique name.

Referring to each sequence variable by name turns into a long string of similar operations with each variable. Program code becomes difficult to see. Such a program requires a lot of memory to accommodate.

For elimination mentioned problems Algorithmic languages ​​use structured data. The simplest structured data is data sets .

Array is a set of variables of the same type ( array elements ). All variables have the same name, and to access a specific element of the array, an additional identifier is used - its serial number (index), which starts from 0.

In addition to arrays in programming for plotting efficient algorithms Other standard data structures can be used, data structures such as stacks, queues, related lists and others.

Along with standard data structures, user-defined data structures can be used. These data structures are defined by object-oriented programming tools using classes .

4.7.2. Tools for describing and working with one-dimensional
data sets

Array– a sequence of variables of the same type, united by a common name . For example: one-dimensional array a(9) consists of 10 elements with common name a: a(0), a(1), a(2), a(3),..., a(9), ordered by index i , which takes values ​​from 0 to 9:

An array is declared in a VB program in the same way as simple variables are declared. If the array is declared local , it can only be used in the procedure in which it is declared. If the array is declared as global , it can be used anywhere in the program.

When declaring an array, the declaration statement must include the following information:

· array name – name (identifier) ​​that is used to represent the array in the program;

· data type – the data type that the array elements have;

· dimension (rank) – the number of dimensions of the declared array (i.e. the number of indexes when declared; one-dimensional arrays have one dimension);

· amount of elements – the number of elements that will be contained in the array.

Let's look at examples of some array descriptions:

In these examples the following arrays are declared:

· one-dimensional array d, consisting of 31 elements of type Integer with indices from 0 to 30;

· one-dimensional array a, consisting of 11 elements of type Double with indices from 0 to 10;

· two-dimensional array b, consisting of 14x11=151 elements of type Single with indexes on rows from 0 to 13 and columns from 0 to 10.

Please note that the value lower limit array inVBthere can only be 0 .

Thus, the array consists of elements that can be accessed using indexes . When accessing array elements indices are written following the name in parentheses and can be any valid integer expression. For example, d(24), a(2*i+1).

Note that the number of indices indicates the size of the array. So, in the example above, the array dimension is a(10) equal to one. Array b(2,3) has dimension 2.

Unlike dimensions , array size is the number of elements in the array. In our example, the size of the array is a(10) equals 11.

Before using an array in a program, it must be declared using the operator Dim, and assign specific values ​​to the array elements. Operator Dim allocates memory space computer to place array elements, zeros elements of numeric arrays or fills elements of string arrays empty lines ("""").

As for simple types, data, when declaring arrays that are structured data types, there are two ways to allocate memory: static – at the compilation stage before executing the program, and dynamic – during program execution. By default, an array whose boundaries are specified by constant expressions is considered static. Memory to accommodate such an array is allocated at the compilation stage and retained for the entire execution period.

You can fill array elements with specific values ​​using input array element values ​​using the operator assignments or using initialization array elements.

Initialization array elements is an element-by-element assignment of a value in an array declaration statement. In this case, the size of the array is not specified in parentheses after the array name, but is determined implicitly by the size of the list of values. The list of values ​​starts with the element at index 0 and is enclosed in curly brackets , For example:

It should be noted that regardless of specific task, algorithms for generating and processing arrays are usually built using regular cyclic structures:

To make it easier to work with arrays in procedures, the built-in function is used to determine the upper bound of the array
Bound(ArrayName).

This function returns (determines) the number of the last element of the array and allows you to process arrays in procedures without passing the number of array elements as a parameter. For example,

Additionally, you can use the method to determine the upper bound of a one-dimensional array GetUpperBound(). Since the array is one-dimensional, the value 0 should be specified in parentheses. For example:

In addition, it is known that the keyword ByVal specifies passing an array argument by value, and the keyword ByRef indicates that the array argument is passed by reference. Note that if the keywords ByVal or ByRef are omitted, the array argument is passed by reference.

Thus, when describing the formal parameters of any procedure after ArrayNameyou must always include empty round brackets, since they indicate that this parameter is a one-dimensional array.

Note that there are no parentheses after the array name, which is the actual parameter.

As you know, passing arguments by value (using keyword ByVal) causes VB to pass a copy of the data to the procedure. Therefore, you should not pass arrays by value unless you really need to.