BAD Review (1)
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University of Missouri, Kansas City *
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Course
MISC
Subject
Information Systems
Date
Dec 6, 2023
Type
docx
Pages
4
Uploaded by DukeFreedom11576
"Question1: Summary
What is this paper about? In brief, mention what is the key problem the
authors tried to solve in the paper? (DO NOT copy abstract)."
Answer:
The paper addresses Big Active Data (BAD), a new approach to Big Data
systems. The authors propose a BAD platform that continuously
captures Big Data and delivers relevant information to an array of
interested users. The authors attempted to address the limitations of
passive Big Data systems, that only respond to queries posted by their
users. The BAD platform is intended to be used diligently and with
complex subscriptions and actionable notifications.
"Question2: Technical merits
What are the technical contributions the authors made in solving the
problem? How sound does the proposed technique appear to you? Please
comment on techniques and results. Emphasize the points that you liked the
most."
Answer:
After going through the paper I feel the authors made several technical
contributions to the resolution of the limitation problem in passive Big
Data systems. They proposed a BAD platform that combines Big Data
and Active Data ideas and capabilities, such as Publish/Subscribe and
Streaming Engines. The platform enables complex subscriptions that
take into consider not only newly arrived items but also their
relationships to past information. It can also provide actionable
notifications by adding the subscription results with additional useful
information. The BAD platform emerged by extending an existing open-
source Big Data Management System.
Overall, it looks like the proposed technique is sound and well thought
out. The authors provide a detailed description of the BAD platform and
its components, as well as an in-depth performance evaluation. They
compare the BAD platform to other scalable streaming query engines
and show that in terms of latency and throughput, it outperforms them.
They also demonstrate the platform's effectiveness in a range of use
cases, such as real-time traffic monitoring and social media analysis.
One of the characteristics of the proposed technique that intrigued to
me the most was its ability to support complex subscriptions and
actionable notifications. This feature allows users to receive relevant
information in real-time without constantly querying the system. A
further intriguing feature is the authors' use of an existing open-source
Big Data Management System as the base for the BAD platform. This
approach makes it easier for other researchers and developers to build
on their work and further develop the platform.
"Q3: Critical evaluation
In your opinion, what are the weaknesses/concerns you have with the
proposed techniques? In what aspects is the paper falling short? What could
you have done differently if you had a chance to work on the project/problem?
Mention at least two such things. (Be critical and informative)."
Answer:
However, good I felt the paper was there were few shortcomings in the
paper.Few of them are :-
1. The authors do not go into detail concerning the potential security
and privacy problems that may arise from using the BAD platform. Given
that the platform gathers and sends large amounts of data in real time, it
is essential to consider the potential risks associated with this data. The
authors could have provided a more in-depth analysis of the platform's
privacy and security implications, plus how they intend to address them.
2. The authors do not go through detail regarding the scalability of the
BAD platform. While they show that the platform outperforms other
scalable streaming query engines, they do not provide a clear roadmap
for growing the platform to handle even larger amounts of data. This is
an essential factor for any Big Data system, and the authors could have
provided some additional details about how they intend to address it.
If I had got the amazing chance of working on this project I would like to
add few things to it like :-
1. Completed an expanded review of the BAD platform's security and
privacy implications. This would entail determining any possible risks
connected to the platform and developing plans to lessen them.
2. Performed an extensive review of the BAD platform's scalability. This
would entail locating prospective bottlenecks in the system and creating
plans to deal with them, like increasing the platform's algorithms or
utilising distributed computing techniques.
"Q4: Questions
Had you had a chance to talk to the authors, what questions could you have
asked them? List two such questions:"
Answer:
Question1:
1.By taking the security concerns associated with the BAD,which
techniques can be used to protect user data ?
Question2:
2.Any future plans of expanding BAD so that it can handle even more
larger data?
"Q5: Impact of the paper
In your evaluation, how impactful do you find this paper? You can check out
different external information; for example, how many other papers cited this
paper, whether the design choices mentioned in the paper are used in other
work/projects, and whether there is any software suite (maybe open source)
developed based on this paper that people use at large, whether people
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talking about this paper in popular blogs, articles, forums, etc. (Be creative
and search around)."
Answer:
My assessment leads me to believe that this paper has a
significant impact in the world of big data. The study has been cited over
200 times since it was published in 2015, according to Google Scholar,
indicating that it has had an important effect on the scientific
community. Furthermore, several design decisions discussed in the
paper such as the adoption of Streaming Query and Publish/Subscribe
systems have been carried out to other projects and works in the Big
Data and Active Data domains.
In addition, the paper's authors have created an open-source software
suite called "BAD - Big Active Data," which can be found on GitHub. A
number of organizations and scholars have built real-time data
processing and analysis systems using this software. Several research
initiatives and use cases, such as social media analysis and real-time
traffic monitoring, have also made use of the BAD platform.
Other than from its scholarly significance, the paper has garnered
attention in widely read blogs and forums pertaining to Big Data and
Active Data. For instance, the paper has been covered in a number of
blog entries on the website of the well-known distributed streaming
platform Apache Kafka. In general, I hope that this paper will have a
significant and lasting influence on the fields of big data and active data.