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Введение 4

1 Searching-insertion-borrowing functions in Database Applications 6

1.1 The principles and characteristics of the Structural Organization of the Databases 6

1.2 Standard Procedures for Manipulating Data at OLTP - tasks 14

1.3 "Searching" requirements for user interfaces 18

2 Инструментальные средства и стандартные интерфейсы поиска 23

2.1 Базовые классы поиска: поиск по маске и контекстный поиск 23

2.2 Типовая организация поисковых интерфейсов 27

3 Интегрированные интерфейсы поиска: вопросы организации, спецификации и встраивания в приложениях баз данных 33

3.1 Элементы «пошагового» интерфейса 33

3.2 Процесс поиска 37

3.3 Оценка эффективности 44

Заключение 47

Список источников 49

Приложение А. Графический материал 54

Введение

В эпоху компьютерных технологий развитие информационных систем предъявляет повышенные требования к процессу разработки систем, достоверности и полноте информации, без которой практически невозможна эффективная работоспособность любого приложения базы данных.

Что же касается вышеупомянутых систем, то здесь происходит повсеместное внедрение новых продуктов и готовых решений по оптимизации процесса обработки данных. С каждым днем появляются новые возможности конструирования приложений, но современные требования и принципы, выдвигаемые разработчиками и пользователями, намного опережают существующие интерфейсы. Как следствие – появление необходимости комплексного подхода к решению данной проблемы.

Проблематика разработки инновационных интерфейсов находит множество обсуждений среди разработчиков систем баз данных. В основном это связано с необходимостью унификации процессов обработки информации.

Основной задачей подобных процессов является обеспечение комфортных условий пребывания в среде системы хранения информации. Разработка методов экономии времени способствует повышению производительности. Работа над модернизацией приложения в целом является результатом модернизации каждого отдельного элемента системы.

Большинство разработчиков преследуют цель быстрого завершения возложенной на них задачи с наименьшими временными затратами. Другими словами, при экономии времени в первую очередь страдает качество готового продукта, что непременно сказывается на общей картине предлагаемых на рынке решений.

В условиях усиливающейся конкуренции среди проектировщиков программных продуктов возникают новые типы и комбинации интерфейсов, призванные поднять на новый уровень рынок информационных услуг.

Все вышесказанное подчеркивает актуальность выбранной темы дипломной работы и необходимость создания нового интерфейса для приложений баз данных.

Целью данной работы является разработка новых решений в области обработки данных с использованием поисковых интерфейсов. Особое внимание будет уделено анализу современного состояния подобных систем, а также сравнению показателей производительности новых интерфейсных решений, интегрирующих в себе функции поиска, заимствования и вставки данных.

В качестве объектов проектирования выбраны пользовательские интерфейсы, а предмет составляют решения по интеграции отдельных интерфейсов в один структурированный интерфейсный класс.

Перед началом процесса разработки системы было изучено достаточное для выполнения задания информации из различных источников. Основными являются научные труды российских и зарубежных ученых в области разработки приложений баз данных, тематические статьи и руководства пользования прикладными программами разработки интерфейсов.

Практическая значимость проекта разработки данного приложения заключается в возможности внедрения готового продукта в системы баз данных с целью получения дополнительных показателей производительности и эффективности.

1 Searching-insertion-borrowing functions in Database Applications

1.1 The principles and characteristics of the Structural Organization of the Databases

The data domains can encompass a wide variety of data models to identify where there are some identifiers (markers), namely, the criteria. The function of the criteria is not only a choice, but also assistance in building the optimal models reducing the risks of getting low-quality samples to zero.

Every single data model is able to combine a certain set of criteria; for their recognition, the mechanism of bringing them to a common form is required.

Assessment of the quality of the database is usually not built on the basis of criteria, and several fundamental principles. One of them - a principle irredundancy (minimum redundancy).

In an effort to abandon the multiple storage of the same data only in one table called "universal relationship", the difficulties of inserting, deleting, and modifying the data arise. The solution to this problem has led to the formation of the structure which has the name "data model" consisting of a set of tables. In such tables, the data should be arranged in accordance with the initially predetermined rules whose observance guarantees the absence of the further problems.

Most of these rules communicate the potentially realizable functional dependencies between the attributes of relationships. The dependencies that do not meet those requirements should be excluded. Exclusion of such links leads to decomposition of the table where they were revealed and the appearance of the new relationships.

The term ‘normalization’ was created to describe the final desired result under those rules. It is considered that a fully normalized relationship is non-redundant.

Complete abandonment from redundancy is accompanied by a negative effect - considerably increases the time for search and placement of the data scattered across different tables.

Nevertheless, it is possible to bring the data to a reasonable redundancy at which storage of duplicates and even calculated values can be made.

The next principle - the principle of compactness. Compactness is a collection of database elements expressed in the presence of free and white space.

In connection with the statutory requirements for the principle of compactness, some vulnerabilities are formed which, in addition, appear to be its characteristics. One is the memory overrun and the other is the reduced query performance data. A more obvious example of this is the current trend of information service development: the performance of processes is growing, cheaper memory, and its available volumes are constantly increasing, and demand is increasing at the same time. Here are some of them:

• The customer wants to track the growing number of the data including graphics;

• For the purposes of consistency, the local databases are combined into an integrated database;

• The requirement for the length of performing the certain requests tends to a minimum, for example, banking transactions;

• The analysis involves larger and larger amounts of information.

The problem of compactness in such systems can be successfully solved, but every change in the structural integrity of the compact will certainly impact on the performance of all applications. It follows that the introduction of the new methods of working in these structures requires the use of multi-criteria optimization covering all possible measures to improve the basic system, but not only a single principle.

The next principle - the principle of completeness. The data model must be complete. This means that it must fix the data reflecting the location of the properties and condition of the entities constituting the subject field at the arbitrary points in time.

Hence, the database can be complete if the structure allows storing locations, properties, and state of entities at the arbitrary points in time.

The dynamics of the entities in this respect are the random moments of time which have been mentioned since the dynamics in relation to entities is just a change of their locations, properties, or conditions that occur at the discrete points in time.

Unlike the other principles, completeness is not difficult to be expressed quantitatively. This criterion is equal not to the absolute values but the relative ones, and it is the function of time.

The next principle - the principle of single-task identification of entities. Each entity domain is required to be uniquely identified. This requirement is one of the basic requirements whose presence cannot only allow talking about the optimal performance, but also the efficiency of the system as a whole. This is mainly due to the fact that not every entity can get a unique name. Each domain can encompass countless entities and to find anything comparable in size would have been problematic.

Unlike the semantic identification, the digital identification is possible and is successfully applied. It is known that the set of integers is infinite and it is typically used to assign the entities with the unique digital identifier in the current database.

In fact, no data domains require absolutely for all of their entities to have the unique names.

Most of the entities are consolidated in the group and the names are assigned only to them. At that, the semantic identification does not end. Some semantic load takes on the names that represent the types of entities, and some - attributes that reflect the classification attributes and properties-attributes. In this connection, it is more correct to raise the question of the magnitude of the semantic identification of all entities.

In this principle, there are several important consequences for the construction of these models. As part of the mode, one can distinguish a special category of the objects that will only store information of the names: the name, surname, patronymic name, the names of streets, cities, planets, solar system, educational disciplines, etc. These objects have a simple structural organization represented only by two attributes: name and numerical identifier of the actual name.

Another consequence of the principle of identification is Pattern, Sample, and Instance-objects that appear in the process of building a unified system of semantic identification and performing the function of reference data objects.

The next principle - the principle of adaptability (flexibility). Any data model is originally based on the principle of the referenced object in which at least improves the system should be made adjustments content. From this it follows that in changing the data model, the data domains will also be subject to changes. Flexibility domains depend on the research subject.

The objects of changes in the data model are its tables, links, and attributes. Technologically, today to make some adjustments, including the addition, modification, or removal of the element in the model is very simple. All DBMS provide the convenient and understandable tools for such manipulation.

The issue of adaptability arises precisely because of the lack of independence of the database. And since they are open parts of information systems and domains, they are already considered to be necessity than a regular condition.

Actually, the adaptability and flexibility of data model should be considered from the point of view of assessing the consequences of changes made to the model. You need to know on what components of the data domains and to what extent will reflect these changes.

Regarding the quality, the focus can be made on the track of only two characteristics – processing speed and data reliability. However, the criterion of the quality itself is complex in the construction since both performance and reliability are manifested already in the process of solving the functional problems of the data domain; and by no means, all depends on how the data are arranged.

Further are such concepts as the critical and noncritical changes. Any changes force to make adjustments in the information system component as the software. The logic of the program is affected most seriously by the actions of decomposition, integration, and removal of structures (tables) of the database. And these are the tables that are already used by the programs. The data are entered and read out there. On the contrary, inclusion of the new attributes, establishment of the new connections, addition of the tables, and a number of other actions that do refer to the above category are not so critical.

Hence, adaptability principle could be specified as follows. Already existing structures of these models should not be subjected to critical upgrades. They should remain stable.

The problem of elasticity of organizing the data is extremely rarely raised despite the fact that the overwhelming majority of specialists who design and use the data face it: database administrators, application programmers, and problem originators.

The next principle - the principle of processing speed. Organizing the data should be built so that the access to the data would require the least amount of time spent. The problem of the processing speed should be considered from the standpoint of the conversion rate to the individual database segments –to its individual tables or groups of tables. Some table will be applied very often and others will be used rarely. Over time, the conversion rate distribution between the tables will change. There will also change the frequency distribution of appeals to the individual tables because these frequencies are dependent on time.

Hence, based on the activity of only one model, to judge the performance of temporary access to the data stored in its structures is difficult. Moreover, the problem is complex, i.e., it has no specific solutions. Optimizing the processing spped requires an appropriate set of measures.

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