130 research outputs found
Semantic Data Management in Data Lakes
In recent years, data lakes emerged as away to manage large amounts of
heterogeneous data for modern data analytics. One way to prevent data lakes
from turning into inoperable data swamps is semantic data management. Some
approaches propose the linkage of metadata to knowledge graphs based on the
Linked Data principles to provide more meaning and semantics to the data in the
lake. Such a semantic layer may be utilized not only for data management but
also to tackle the problem of data integration from heterogeneous sources, in
order to make data access more expressive and interoperable. In this survey, we
review recent approaches with a specific focus on the application within data
lake systems and scalability to Big Data. We classify the approaches into (i)
basic semantic data management, (ii) semantic modeling approaches for enriching
metadata in data lakes, and (iii) methods for ontologybased data access. In
each category, we cover the main techniques and their background, and compare
latest research. Finally, we point out challenges for future work in this
research area, which needs a closer integration of Big Data and Semantic Web
technologies
ΠΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡ Π΅ΠΌ ΡΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ
Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΡΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΈ ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΈ. ΠΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ ΠΌΠ°ΡΠ΅ΒΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² Π³ΠΎΠ΄ΠΎΠ²ΡΡ
ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Π½ΡΡ
Π·Π°ΡΡΠ°Ρ. ΠΠ°ΡΡΠ΄Ρ Ρ ΡΡΠΈΠΌ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΎΠ΄Π½Π° ΠΈΠ· Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΎΠ΄Π½ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡ
Π΅ΠΌ ΡΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ. ΠΡΠΌΠ΅ΡΠ°ΡΡΡΡ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΡΡΠ΄Π½ΠΎΡΡΠΈ Π² ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ
ΠΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠΉ ΠΈΠ· Π·Π°ΡΠ°Π±ΠΎΡΠ½ΠΎΠΉ ΠΏΠ»Π°ΡΡ
Π Π΄Π°Π½Π½ΠΎΠΉ Π½Π°ΡΡΠ½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΏΠΎΡΡΠ°Π²Π»Π΅Π½Π° Π·Π°Π΄Π°ΡΠ° Π²ΡΡΡΠ½ΠΈΡΡ, ΠΊΠ°ΠΊ ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΡΡΡΡΡ ΡΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ ΠΈΠ· Π·Π°ΡΠ°Π±ΠΎΡΠ½ΠΎΠΉ ΠΏΠ»Π°ΡΡ, ΠΎΡΠΌΠ΅ΡΠΈΡΡ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΠΌΠΎΠΌΠ΅Π½ΡΡ Π΄Π»Ρ ΡΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ. Π’Π°ΠΊΠΆΠ΅ Π² ΡΡΠ°ΡΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΡΠΈΡΡΠ°ΡΠΈΡ, ΠΊΠΎΠ³Π΄Π° ΠΎΠ΄ΠΈΠ½ Π²ΠΈΠ΄ ΡΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠΉ ΠΎΡΠ½ΠΎΡΠΈΡΡΡ ΠΊ ΡΠ°Π·Π½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ, Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ Π°Π»ΠΈΠΌΠ΅Π½ΡΠΎΠ²
Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΡΡΡΠΎΠΈΡΠ΅Π»ΡΡΡΠ²Π° ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π½Π°ΠΊΠ»ΠΎΠ½Π½ΠΎ-Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΠΎΠΉ ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ Π³Π»ΡΠ±ΠΈΠ½ΠΎΠΉ 2757 ΠΌΠ΅ΡΡΠΎΠ² Π½Π° Π‘ΡΠ΅Π΄Π½Π΅-ΠΡΡΠΎΠ»ΡΡΠΊΠΎΠΌ Π½Π΅ΡΡΡΠ½ΠΎΠΌ ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΠΈ (Π’ΠΎΠΌΡΠΊΠ°Ρ ΠΎΠ±Π»Π°ΡΡΡ)
ΠΠ±ΡΠ΅ΠΊΡΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΏΡΠΎΠ΅ΠΊΡ Π½Π° ΡΡΡΠΎΠΈΡΠ΅Π»ΡΡΡΠ²ΠΎ ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π½Π°ΠΊΠ»ΠΎΠ½Π½ΠΎ-Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΠΎΠΉ ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ. Π¦Π΅Π»Ρ ΡΠ°Π±ΠΎΡΡ β ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΡΠΎΠΈΡΠ΅Π»ΡΡΡΠ²Π° ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ Π³Π»ΡΠ±ΠΈΠ½ΠΎΠΉ 2757 ΠΌ Π½Π° Π‘ΡΠ΅Π΄Π½Π΅-ΠΡΡΠΎΠ»ΡΡΠΊΠΎΠΌ ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΠΈ.
Π ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈΡΡ ΡΠ°ΡΡΡΡΡ ΠΈ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΡΠΈΠΈΠ½ΡΠ΅ΡΠ²Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠΈΠ»Ρ ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ, ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΈΠΈ, ΡΠ³Π»ΡΠ±Π»Π΅Π½ΠΈΡ ΠΈ Π·Π°ΠΊΠ°Π½ΡΠΈΠ²Π°Π½ΠΈΡ ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ. Π Π°Π±ΠΎΡΠ° Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° ΠΏΠΎ Π³Π΅ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π°ΠΌ Π‘ΡΠ΅Π΄Π½Π΅-ΠΡΡΠΎΠ»ΡΡΠΊΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΡ. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ»ΡΡΠ΅Π½ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΏΡΠΎΠ΅ΠΊΡ Π½Π° ΡΠΎΠΎΡΡΠΆΠ΅Π½ΠΈΠ΅ ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ Π³Π»ΡΠ±ΠΈΠ½ΠΎΠΉ 2757 ΠΌΠ΅ΡΡΠΎΠ².The object of the study is a technological project for the construction of an operational directional well.
The aim of the work is the design of the construction of a production well depth of 2757 m at the Sredne-Nyurolsky field. In the course of the study, calculations and justification of the five-interval well profile, construction, deepening and completion of the well were performed. The work was carried out according to the geological materials of the Sredne-Nyurolsky deposit. As a result of the research, a technical design for the construction of a well depth of 2,757 meters was obtained
"ΠΠ΅Π³ΡΡΠ²ΠΎ ΠΎΡ ΡΠ²ΠΎΠ±ΠΎΠ΄Ρ" ΡΠΎΠ²Π΅ΡΡΠΊΠΈΡ Π±ΡΠ±ΠΈΠ±ΡΠΌΠ΅ΡΠΎΠ²: ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΎ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠΌ ΠΈ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ Π±Π»Π°Π³ΠΎΠΏΠΎΠ»ΡΡΠΈΠΈ ΠΏΠΎΡΠ»Π΅Π²ΠΎΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ Π² Π‘Π‘Π‘Π
ΠΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ ΡΠΎΡΠΈΠΎΠΊΡΠ»ΡΡΡΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ±ΠΈΠ±ΡΠΌΠ΅ΡΠΎΠ² - ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΠ»Π΅Π²ΠΎΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ - Π½Π΅ ΡΠ΅ΡΡΡΡ ΡΠ²ΠΎΠ΅ΠΉ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΠΈ. Π ΡΡΠ°ΡΡΠ΅ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΡΡΡΡ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΎ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠΌ ΠΈ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ Π±Π»Π°Π³ΠΎΠΏΠΎΠ»ΡΡΠΈΠΈ ΠΏΠΎΡΠ»Π΅Π²ΠΎΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ Π² Π‘Π‘Π‘Π ΡΠΊΠ²ΠΎΠ·Ρ ΠΏΡΠΈΠ·ΠΌΡ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΠΉ ΠΏΠΎΡΡΠ΅Π±ΠΈΡΠ΅Π»ΡΡΠΊΠΈΡ
ΠΈΠ΄Π΅Π°Π»ΠΎΠ², ΡΠΎΡΠΈΠ°Π»ΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅Π½Π½ΠΎΡΡΠ΅ΠΉ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠΉ ΠΎ ΠΏΡΠ΅ΡΡΠΈΠΆΠ½ΡΡ
Π²ΠΈΠ΄Π°Ρ
Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠΌ ΡΡΠ°ΡΡΡΠ΅. ΠΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ²ΡΡ Π±Π°Π·Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΈ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΡΠΎΠ²Π΅ΡΡΠΊΠΎΠΉ Ρ
ΡΠ΄ΠΎΠΆΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΊΠΈΠ½Π΅ΠΌΠ°ΡΠΎΠ³ΡΠ°ΡΠΈΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠ΅ ΠΏΡΠΎΡΠ»Π΅Π΄ΠΈΡΡ ΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈ ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½Π½ΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ° Π±ΡΠ±ΠΈΠ±ΡΠΌΠ΅ΡΠΎΠ² Π² Π·Π°ΠΏΠ°Π΄Π½ΠΎΠ΅Π²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΈΡ
ΡΡΡΠ°Π½Π°Ρ
ΠΈ Π² Π‘Π‘Π‘Π
Repository Support for Data Warehouse Evolution
Data warehouses are complex systems consisting of many components which store highlyaggregated data for decision support. Due to the role of the data warehouses in the daily business work of an enterprise, the requirements for the design and the implementation are dynamic and subjective. Therefore, data warehouse design is a continuous process which has to reflect the changing environment of a data warehouse, i.e. the data warehouse must evolve in reaction to the enterprise's evolution. Based on existing meta models for the architecture and quality of a data warehouse, we propose in this paper a data warehouse process model to capture the dynamics of a data warehouse. The evolution of a data warehouse is represented as a special process and the evolution operators are linked to the corresponding architecture components and quality factors they affect. We show the application of our model on schema evolution in data warehouses and its consequences on data warehouse ..
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