Efficient IR-Style Keyword Search over Relational Databases

By Vagelis Hristidis, Luis Gravano, Yannis Papakonstantinou. Very Large Data Bases Conference, 2003

653 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/15c71e9d0b467796ecc0519fe64203a4a79f76e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Alon Y. Halevy
2. David Maier
3. M. Franklin
4. Lu Qin
5. Xuemin Lin
6. S. Rudolph
7. S. Sudarshan
8. Jeffrey Xu Yu

In Information Systems

1. Alon Y. Halevy
2. M. Franklin
3. David Maier
4. Xuemin Lin
5. Lu Qin
6. S. Rudolph
7. S. Sudarshan
8. Jeffrey Xu Yu

In Signal Processing

1. Lu Qin
2. Xuemin Lin
3. Jianhua Feng
4. Guoliang Li
5. M. Franklin
6. S. Rudolph
7. S. Sudarshan
8. Jeffrey Xu Yu

Citations in the corpus

• EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data (2008, background): “DBXplorer[1], DISCOVER-I[15], DISCOVER-II[13], BANKSI [2] and BANKS-II [17] are systems built on top of relational databases.”
• Keyword Search in Relational Databases: A Survey (2010, methodology): “In DISCOVER-II, three algorithms are proposed to get top-k MTJNTs, namely, the Sparse algorithm, the Single-Pipelined algorithm, and the Global-Pipelined algorithm.”
• Spark: top-k keyword query in relational databases (2007, methodology): “Among them, [15] first incorporates state-of-the-art IR ranking formula to address the retrieval effectiveness issue.”
• Principles of dataspace systems (2006, background): “Some work has already been done on ranking answers in the context of keyword queries over relational databases and over XML documents [3, 4, 9, 30, 33], and on finding relevant information sources in large collections of formally described sources [41].”
• Bidirectional Expansion For Keyword Search on Graph Databases (2005, methodology): “In this section we study the quality of answers and the execution time of Bidirectional search with respect to two versions of Backward expanding search, and the Sparse algorithm from [8].”
• Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data (2009, background): “Much work has been carried out on keyword search over tree-structured data (e.g. [6], [7], [8], [9], [10], [11], [12], [13]) as well as graph-structured data (e.g. [2], [14], [1]).”
• Finding Top-k Min-Cost Connected Trees in Databases (2007, background): “The recent studies on supporting IR style queries in RDBMS include DBXPlore [1], IR-Style [17], DISCOVER [18], ObjectRank [4], BANKS-I [6], and BANKS-II [25].”