Describe data stream mining and how it is used
What will be an ideal response?
Data stream mining, as an enabling technology for stream analytics, is the process of extracting novel patterns and knowledge structures from continuous, rapid data records. A data stream is a continuous flow of ordered sequence of instances that in many applications of data stream mining can be read/processed only once or a small number of times using limited computing and storage capabilities. Examples of data streams include sensor data, computer network traffic, phone conversations, ATM transactions, web searches, and financial data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery. In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream.
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Indicate whether the statement is true or false
The unit cost
A) is the total product costs divided by the number of units produced B) includes period costs C) is the total prime costs divided by the number of units produced D) is the total conversion costs divided by the number of units produced
Typically, a team is characterized by four qualities. Which quality below is not included?
a. Size b. Limitations c. Personalities d. Diversity
Standard material costs, standard labor costs, and standard overhead costs can be obtained from standard cost tables published by the Institute of Management Accountants.
Answer the following statement true (T) or false (F)