This case study has explored search and content analytics technologies, uses, and challenges as they exist today. Search is grounded in the belief that it is possible to find documents that match a query more or less accurately, and that the more the search engine understands about each indexed document in terms of what it is about, who it is by$,$ and where and when it was published, the better our matches will be$.$ Search is document$-$centric, and the process is closed off from the user. It is generally stateless, so that each successive query in an online search session is considered a separate event. Today's search technologies are precursors to tomorrow's answer machines. In contrast to search engines, answer machines are question and questioner centric. Answer machines try to understand the question and the motivation or intent behind the question before they match an information request to a collection of data. Focusing on understanding the question before responding is new for information systems, but it's what humans do when they exchange information with each other. This is a big step forward in online information seeking: answer machines recognize that even if two queries are the same, the two users making those queries may have different information needs. Answer machines represent the next era of information access and analysis systems, and they embody the five large trends explored above: probabilistic computing, adaptive learning, big data, complex integrated information platforms, and contextually aware, conversational systems. Answer machines enlarge and adapt each of these trends to solve the specific challenges of information access and analysis in complex, information$-$rich environments.

Question $1$

$[15$ Marks$]$

In re$-$engineering, the search document content analysis plays a major role. Analyse the five phases of SDLC in creating a better version of the answering machine.