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MANI KANTA LOKESH
MEGHANA KORADA
PART-1
1.Describe briefly the concept of non-strict hierarchies. What is a typical problem in such settings?
Non-Strict When a dimension has many-to-many relationships, this occurs. Absent or non-online data
When a lower level in a dimension can exist without matching data in the higher level to roll-up to, a
hierarchy is created. Absent coverage Also referred to as ragged dimensions or unbalanced hierarchies.
Non-strict hierarchies, or partial hierarchies, in which elements or entities can belong to more than one
category or level at the same time and where there are no rigid, exclusive links between categories. To put
it another way, objects in a non-strict hierarchy may have more than one parent or fall under more than
one category.
Misunderstanding and confusion risk if it's unclear to whom to report. reduced sense of accountability
because workers could have multiple supervisors. danger of power disputes developing in the absence of
a clear framework.
Uncertainty regarding an item's hierarchy rank is a common issue in non-strict hierarchical environments.
This may make it difficult to organize, categorize, and find information or objects. Take into account, for
instance, a loose hierarchy for online retail goods, in which a "smartphone" might fall under more than
one category, such as "Electronics," "Mobile Devices," or "Communication." This makes it challenging to
assign the item to a single, clear category, which can make user navigation and search more challenging.
Non-strict hierarchies are frequently seen in a variety of real-world situations, including file system
organization, e-commerce product classification, and news website article classification. Additional
tactics like tagging, keywords, and search algorithms may be used to solve the difficulties in such
contexts and enhance the hierarchy's organization and item retrieval.
https://www.nibusinessinfo.co.uk/content/flat-organisational-structure#:~:text=The%20common%20disad
vantages%20of%20a,absence%20of%20a%20formal%20system
https://cyberleninka.org/article/n/1023245.pdf
MANI KANTA LOKESH
MEGHANA KORADA
Explain briefly the constellation logical schema of DW.
Fact Constellation Schema describes a logical structure of data warehouse or data mart. Fact Constellation
Schema can design with a collection of de-normalized FACT, Shared, and Conformed Dimension tables.
Fact Constellation Schema is a sophisticated database design that is difficult to summarize information.
Drill-down, roll-up, and pivoting are just a few of the online analytical processing techniques it supports.
Both a summary and a detailed format are being processed for the historical data. OLAP is applied to data
marts or warehouses. The main goal of OLAP is to facilitate the ad hoc querying required to support DSS.
The OLAP application is based on the multidimensional view of data. OLAP is not a data structure or
schema; rather, it is an operational view. Because OLAP systems are complicated, a multidimensional
representation of the data is necessary.
In a Constellation Schema:
Fact Tables: Similar to a star schema, the fact tables are located in the middle and contain
numerical, quantitative data or measurements, like sales revenue or quantities sold. Fact tables
and multiple dimension tables are interconnected.
Dimension Tables: Dimension tables, like the star schema, are used to store descriptive attributes
that give the measures in the fact tables context. These characteristics support data grouping,
filtering, and analysis. Compared to star schema dimension tables, constellation schema
dimension tables may be more standardized.
Constellation of Dimension Tables: In contrast to the star schema, a constellation schema enables
direct relationships between dimension tables and numerous fact tables. More intricate data
relationships and analytical capabilities are made possible by this flexibility. It also permits
common dimensions to be shared amongst various fact tables.
Similar to the snowflake schema, dimension tables in a constellation schema can be further
normalized by dividing them into hierarchies or sub-dimensions, which results in a more complex
and effective structure. This can be useful for managing big, intricate data warehouses and helps
cut down on data redundancy.
https://www.javatpoint.com/data-warehouse-what-is-fact-constellation-schema#:~:text=Fact%20Constella
tion%20Schema%20describes%20a,is%20difficult%20to%20summarize%20information
.
https://www.geeksforgeeks.org/fact-constellation-in-data-warehouse-modelling/
MANI KANTA LOKESH
MEGHANA KORADA
3. Assume that you have a generalized hierarchy in which the split-paths are again merging at some level.
What happens to the key from that level when MultiDim is translated into a logical (say, snowflake)
model.
When you have a generalized hierarchy in which the split-paths are merging at some level, the translation
of such a hierarchy into a logical data model, such as a snowflake schema, involves certain
considerations:
Merging Levels: In a generalized hierarchy, multiple paths may converge or merge at a certain
level. In a snowflake schema, this means that the attributes corresponding to those merging paths
should be represented by a single dimension table at that level. This helps in reducing redundancy
and maintaining data consistency.
Snowflaking: If the hierarchy is complex and has many attributes or sub-levels, you may apply
the snowflaking technique to normalize the dimension tables. This involves breaking down
dimension tables into sub-dimensions or hierarchies, which can help with data management and
reduce redundancy.
Primary Keys: When multiple paths merge in the hierarchy, you'll need to decide how to handle
primary keys in the logical model. The primary key of the dimension table at the merging level
should be a combination of attributes that uniquely identify the level. These attributes should be
chosen carefully to ensure data integrity and consistency.
Relationships: In the snowflake schema, relationships between dimension tables and fact tables
are typically one-to-many. Ensure that the relationships between fact tables and dimension tables
reflect the merged paths in the hierarchy correctly.
Additional Tables: Depending on the complexity of the hierarchy and the need for different
attributes or hierarchies, you may create additional dimension tables or sub-dimensions to capture
the unique characteristics of each path within the merged hierarchy.
https://www.integrate.io/blog/snowflake-schemas-vs-star-schemas-what-are-they-and-how-are-they-differ
ent/
https://www.sciencedirect.com/topics/computer-science/generalization-hierarchy
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