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The DBMS applications are becoming increasingly popular in the scientific community to support the interactive exploration of huge data. A result of heavy usage has also lead to a lot of tavles generated in the warehouse. This has tremendously increased the need for recommendation system & various tools for the user as user is not capable to explore such huge data by using Structured Query language. This motivates to turn towards the idea of query recommendation system which will assist the technical & non-technical users to generate top-N query recommendations as per their needs. In this survey paper, the focus is given on opportunities that exist in domain of query recommendation, brief description of recommendation outline and techniques. Along with this, the terminology to identify the similarity and comparison of fragment based approach & matrix factorization approach is also explored. The Database Management Systems are used to organize, maintain and retrieve the huge amount of data. To acquire the essential data the user need to interact with the database by taking the support of declarative Structured Query Language (SQL). Most of times, it is observed that user faces the difficulties in interacting with the database by using SQL query language as the database have hundreds of schema's and thousands of attributes. This problem has been solved with the support of Database Management Applications such as SAP, Easy Query, and Microsoft Access. These applications involve the user interaction and manual editing of the SQL query components but when the user is unacquainted about the underlying database structure then it becomes difficult for the user to formulate the query. To formulate the query, the research work has been done (1) and that results into the query assistant tool which helps the user to either formulate the query or to recommend the relevant query items based on formulated query. This work is the reflection of the web recommendation techniques those are used in gaining the on-line marketing intelligence. The web recommendation techniques and algorithms have their strong relation with the Information Retrieval Science (IR). In IR, the recommendation techniques and algorithms are used to retrieve the most relevant top K documents as per the formulated query. Most of times the Business Verticals have their own off-line database and the user will be interested in exploration of the database and faces the difficulties. Therefore, it may also possible to extend the idea specified by the authors (1) to provide the Query Recommendation Assistant Tool that enable the user to formulate and refine the SQL query. This take off to cataloged view on how to make the imperative use of IR and the web recommendation techniques to generate and recommend the top K relevant query objects by identifying the similarities among the SQL query fragments. This becomes the novel approach that handles crucial responsibility in proposing the assistant tool to retrieve data over the database. The rest of the paper is organized as follows: section-2 describes the related work and the section-3 concentrates on recommendation outline. The section-4 describes the recommendation techniques and similarity metrics those have been used in finding the similarity among the query fragments. The section-5 focuses on terminology used to calculate weights of items required for finding similarity. The section-6 states the comparison between the fragment based approach and matrix factorization approach.

A Survey Report on the Novel Approach on Use of Recommendation Outline in Query Recommender System