In and steadiness and propose new models to


In this paper the researchers are trying to
focus on five research areas:

Infrastructure of the
scalable big/fast data:

Because of increasing use of declarative
languages due to the advent of big data there is a need for effective query
optimization; query execution; progress monitors by integrating various other
fields like machine learning, datamining. Researchers should also focus of
controlling specialized processors. As the co-design of software and hardware
plays a major role in data machines. Both server and network storages should be
considered to learn to control the new storage technologies. Persisted data’s
index should be schema-on-read rather than schema-on write. The focus should
also be on developing more schema-on-read languages and tools. Should rethink
about data currency and steadiness and propose new models to help progress
robust applications. Scalability should also incorporate new measurement criteria’s
that include ownership; end-to-end processing speed; brittleness and usability.

Handling the diversities
in data management:

Focus should be on parallel processing and also
on data sets which are unable to assimilate in main memory. To help data
analysts examine data through various platforms. Researchers should also be
able to handle data from disconnected devices. Handling data diversity both in
programming and systems should be developed.

Understanding the data
and its processing from one end to the other:

Incorporating data integration onto end-to-end results.

Various different tools should be developed to handle diverse raw data’s
available to knowledge and those also should be able to manipulate the knowledge.

Explanation; filtering and various other techniques become very crucial in
every step. Use of more and more knowledge bases would lead to the development
of knowledge centers. So focus should be here in order to help in the
management of knowledge centers.

Cloud services:

Here the challenges are elasticity; data
replication; system administration and tuning; data sharing; multitenancy; Service
level agreements; hybrid clouds

Managing various roles
of people in data life cycle:

Building platforms that allow people to share
and curate data easily. Developing interfaces for data consumers to visualize,
query and navigate through data. Build tools to produce, change, share and
traverse through data.


Apart from the above discussed problems; there
are some others which does not fall under the above categories and they are
changing the way the database education is taught and research is going on.

Helping in improving the big data and data science fields.