Automatically Extracting Structure from Free Text Addresses.

By Vinayak R. Borkar, Kaustubh Deshmukh, Sunita Sarawagi. 2000

313 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fe3e6b3810c018c95f4284b903126be57204e3a6

How researchers see this paper

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

Citations in the corpus

Issues in data stream management (2003, background): “Similarly, a pipelined hash join [ 66 , 69] is a non-blocking join operator, which builds hash tables on-the-y for each of the participating relations.”
Exploiting Punctuation Semantics in Continuous Data Streams (2003, methodology): “For example, the XJoin implementation [ 26 ] might be able to use this kind of punctuation to determine which partition can be written to disk.”
Maximizing the Output Rate of Multi-Way Join Queries over Streaming Information Sources (2003, methodology): “Prior work has answered that the traditional approach is not sufficient, and has addressed the problem of streaming inputs by retaining binary execution trees but by replacing blocking operators with streaming symmetric operators [ 11 , 14], possibly coupled with dynamic reorganization of the execut”
Models and issues in data stream systems (2002, methodology): “This is the approach used in the XJoin algorithm [ 88 ].”
Evaluating window joins over unbounded streams (2003, unknown): “To rectify the situation XJoin was introduced [ 8 ].”
Fjording the stream: an architecture for queries over streaming sensor data (2002, methodology): “Such operators have been discussed in detail in adaptive query processing systems such as Xjoin [ 26 ], Tukwila [14], Eddy [3] and Ripple Joins [9].”
Processing Sliding Window Multi-Joins in Continuous Queries over Data Streams (2003, methodology): “Relevant work on join processing over unbounded streams includes non-blocking binary join algorithms such as the XJoin [ 27 ], which is a variant of the Symmetric Hash Join [30], and the Ripple Join [18].”
Interactive Information Extraction with Constrained Conditional Random Fields (2004, unknown): “An interactive form filling system is quite different from the batch processing of data, such as for warehouse data cleaning ( Borkar, Deshmukh, & Sarawagi 2000 ).”
Chain: operator scheduling for memory minimization in data stream systems (2003, background): “changing arrival characteristics of the data, similar to [1, 29 , 31].”
Conceptual modeling for ETL processes (2002, background): “Finally, apart from the commercial ETL tools [1, 7, 9, 21, 25] there also exist research efforts including [3, 10, 20, 23, 26, 27, 34, 35, 36].”
Continuously adaptive continuous queries over streams (2002, background): “Adaptive systems such as XJoin, Query Scrambling, and Tukwila [ 26 , 27, 10] demonstrated the importance of pipelined operators to adaptivity.”
SINA: scalable incremental processing of continuous queries in spatio-temporal databases (2004, background): “Similar ideas for in-memory hash-based join have been studied in the context of nonblocking join algorithms, e.g., the symmetric hash join [30], XJoin [ 29 ], and the hash-merge join [18].”
The cougar approach to in-network query processing in sensor networks (2002, methodology): “Examples include work on memory-adaptive sorting and hashing [13, 28, 30, 34, 53, 54], and online aggregation algorithms [15, 18, 39, 48 ].”