Keyword searching and browsing in databases using BANKS

TLDR

The growing Web has increased users needing database access without schema knowledge, and even simple query languages are too complex for them. The authors present BANKS, a keyword‑based search system for relational databases that also supports data and schema browsing, and they propose an efficient heuristic for finding and ranking results. BANKS represents tuples as graph nodes linked by foreign keys and other relationships, models answers as rooted trees connecting keyword‑matching tuples, and ranks them using proximity and node prestige based on inlinks, with users interacting via keyword input, hyperlinks, and controls. BANKS allows users to retrieve information easily without schema knowledge or complex queries.

Abstract

With the growth of the Web, there has been a rapid increase in the number of users who need to access online databases without having a detailed knowledge of the schema or of query languages; even relatively simple query languages designed for non-experts are too complicated for them. We describe BANKS, a system which enables keyword-based search on relational databases, together with data and schema browsing. BANKS enables users to extract information in a simple manner without any knowledge of the schema or any need for writing complex queries. A user can get information by typing a few keywords, following hyperlinks, and interacting with controls on the displayed results. BANKS models tuples as nodes in a graph, connected by links induced by foreign key and other relationships. Answers to a query are modeled as rooted trees connecting tuples that match individual keywords in the query. Answers are ranked using a notion of proximity coupled with a notion of prestige of nodes based on inlinks, similar to techniques developed for Web search. We present an efficient heuristic algorithm for finding and ranking query results.