in the development of application-specific database approaches that are specific to each area or project.
Given the different levels of knowledge accumulation in different areas, we do not expect application-specific databases in knowledge management systems to be used for all kinds of data. Just imagine if in a data-driven knowledge management system you could only find a database or query that fits the application. This may seem in some cases too simple, and sometimes too naive. When dealing with multiple data systems for knowledge management, we expect databases or query engines of different levels of complexity to work together. This may have led to the creation of multiple databases and query engines, resulting in semantic heterogeneity.
Nowadays, as more and more databases are being developed from specific databases on the same topic, it may be necessary to define new datasets (samples) for each database or database query. Some solutions exist, such as classifying metadata fields in databases and databases for different collections. But the challenge is to use existing databases as often as possible, not create new databases for different purposes.
Another good example of semantic heterogeneity is the multitude of software platforms and data processing engines used for web services. Each platform and database has its own way of displaying data. It is important not to use different data sources for different web applications, but to find a way to reconcile different data sources with different web applications. While data sources, data management, applications and systems are heterogeneous, we need a database that provides all the data we need when different applications or systems are required. And as new platforms and databases are developed, semantic heterogeneity can be expected to remain a key feature of data analysis systems.
Data Discovery
The complexity of various databases and data engines is often hidden from the end user. In many cases, if a data user is not familiar with data sources, data management systems, and data analysis, they are likely to be unable to find the data they need. Data discovery tools used by data scientists in an enterprise provide a more consistent view of data across applications and data sources and are used to discover data sources and data management systems. Therefore, data discovery tools designed to discover data sources and data management systems must be able to integrate with all systems used to create data. In addition, any tool should be able to link the data discovery tool with other data analysis tools or data management systems.
Конец ознакомительного фрагмента.
Текст предоставлен ООО «ЛитРес».
Прочитайте эту книгу целиком, купив полную легальную версию на ЛитРес.
Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.
