adplus-dvertising
frame-decoration

Question

Why might traditional Workflow Management Systems (WfMS) be inadequate for processing Big Data in real time?

a.

They are highly scalable and efficient.

b.

They lack support for programing languages like Java and JavaScript.

c.

They use a "store-then-process" paradigm.

d.

They can handle complex event processing (CEP).

Posted under Big Data Computing

Answer: (c).They use a "store-then-process" paradigm. Explanation:Traditional Workflow Management Systems (WfMS) are inadequate for processing Big Data in real time because they use a "store-then-process" paradigm, which may not meet the high data flow and timing requirements of some Big Data applications.

Engage with the Community - Add Your Comment

Confused About the Answer? Ask for Details Here.

Know the Explanation? Add it Here.

Q. Why might traditional Workflow Management Systems (WfMS) be inadequate for processing Big Data in real time?

Similar Questions

Discover Related MCQs

Q. What is the main characteristic of complex event processing (CEP) in the context of Big Data?

Q. How does the Large Hadron Collider (LHC) handle the massive amount of data it generates?

Q. Why is the cloud paradigm considered a desirable feature in Big Data solutions?

Q. What is a limitation of using public cloud systems for extensive computations on large volumes of data?

Q. Which project allows experiments on interlinked cluster systems for Big Data solutions?

Q. What does the term "self-healing" refer to in the context of Big Data systems?

Q. How can a system achieve self-healing in the event of a server or node failure?

Q. What happens when a node/storage fails in a cluster with self-healing capabilities?

Q. What is the BASE property in the context of NoSQL databases?

Q. What is the primary goal of the CAP theorem in distributed storage systems?

Q. Why is it challenging to implement a fault-tolerant BASE architecture for Big Data management?

Q. How does the CAP theorem impact the design of Big Data solutions?

Q. What role does the concept of "eventual consistency" play in the CAP theorem?

Q. What characterizes Big Data solutions in terms of data management aspects?

Q. What is the trend in data structures for Big Data?

Q. According to the CAP Theorem, what are the three main features that a distributed storage system can provide?

Q. What does the property of "consistency" mean in the context of the CAP Theorem?

Q. How does the CAP Theorem impact Big Data solutions?

Q. What properties are described by the ACID paradigm in relational databases?

Q. What is one of the primary challenges associated with the database size in Big Data problems?