Early DBMS have following problems

Real World entities are not represented properly
because normalization leads to relations that do not correspond to real world

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Relation is the only construct that is used to
represent data and data relationships.

Early DBMS have only a fixed set of operations
and it cannot be extended

Recursive queries are not supported if the depth
level of recursion is not known.

Transactions are short lived and concurrency
controls are not suited for long lived transactions.

Most of the DMLs are effected by computational
incompleteness. This limitation can be overcome by embedding SQL in higher
level 3GLs but it creates impedance mismatch.

Schema changes are difficult.

Early DBMS are poor at navigational access.  



Limitations in quality consideration

quality explains the condition of a data set of qualitative or quantitative
variable. Data quality is high if the data is fit for use in planning, decision
making and operations. Another measure of quality is the correctness of representation
a real life object. Increasing Size of data introduce another measure for data
quality which is data consistency.

Different aspects of Data Quality are:



Update status


Consistency across data sources


Appropriate presentation





Future and emerging aspects of quality in DBMS Design

Trends come and go, but many new concepts for database
management are not flavor-of-the-month fads but have staying power, as well as
the power to transform organizations.


What are the current trends in database management and how
can you best take advantage of them to benefit your organization? In a
nutshell, the current trends we’ve found are:


    Databases that
bridge SQL/NoSQL

    Databases in the
cloud/Platform as a Service


    An increased focus
on security