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Semistructured data

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Peter BunemanAuthors Info & Claims

PODS '97: Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems

Pages 117 - 121

https://doi.org/10.1145/263661.263675

Published: 01 May 1997 Publication History

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References

[1]

Serge Abiteboul. Querying semi-structured data, In Proceedings of IGDT, Jan 1997.

Digital Library

Google Scholar

[2]

Serge Abiteboul, Sophie Cluet, Vassills Ohristophidcs, Toys Milo, and JdrSme Simdon. Querying documents in object databases. In Journal of Digital Librarlcs, volume 1:1, 1997.

Crossref

Google Scholar

[3]

Serge Abiteboul, Sophie Cluet, and Tova Milo, Querying and updating the file. In Proceedings of 19th international Conference on Very Large Databases, pages 73-84, Dublin, Ireland, 1993.

Digital Library

Google Scholar

[4]

Serge Abiteboul, Roy Goldman, Jason Mcttugh, Vasills Vassalos, and Yue Zhuge. Views for semistructurcd data. Technical report, Stanford University, 1977,

Google Scholar

[5]

Serge Abiteboul, Dallan Quass, Jason McHugh, Jennifer Widom, and Janet L. Weiner. The lord query language for semistructured data. In Journal of Dig. ital Libraries, volume 1:1, 1997. To appear. See http://ww~-db, stanford, edu/pub/papers/.

Google Scholar

[6]

Serge Abiteboul and Victor Vianu. Queries and computation on the web. In Proceedings of ICDT, Jan 1997.

Digital Library

Google Scholar

[7]

Gustavo O. Arocena, Alberto O. Mendelzon, and George A. Mihaila. Applications of a Web query language. In Proe. 6th. Int'l. WWW Conf., April 1997. In press.

Digital Library

Google Scholar

[8]

P. Buneman, S. Davidson, Mary Fernandez, and D. Suciu. Adding structure to unstructured data. in Proceedings of IGDT, January 1997.

Digital Library

Google Scholar

[9]

P. Buneman, S.B. Davidson, K. Hart, C. Overton, and L. Wong. A data transformation system for biological data sources. In Proceedings of VLDB, Sept 1995.

Digital Library

Google Scholar

[10]

Peter Buneman, Susan Davidson, Gerd ttillebrand, and Dan Suciu. A query language and optimization tcch. niques for unstructured data. In Proceedings of A CM. SIGMOD International Conference on Management of Data, pages 505-516, Montreal, Canada, June 1996.

Digital Library

Google Scholar

[11]

Peter Buneman, Susan Davidson, and Dan Suciu. Pro. gramming constructs for unstructured data. In Proceed. ings of 5th International Workshop on Database Programming Languages, Gubbio, Italy, September 1995. To appear.

Digital Library

Google Scholar

[12]

Peter Buneman, Shamim Naqvi, Val Tannen, and Lirasoon Wong. Principles of programming with complex objects and collection types. Theoretical Computer Science, 149(1):3--48, September 1995.

Digital Library

Google Scholar

[13]

R. G. G. Cattell, editor. The Object Database Standard: ODMG-93. Morgan Kaufmann, San Mateoj California, 1996.

Digital Library

Google Scholar

[14]

Sophie Cluet and Claude Delobel. A general framework for the optimization of object oriented queries. In M. Stonebraker, editor, Proceedings A GM-SIGMOD international Conference on Management of Data, pages 383-392, San Diego, California, June 1992.

Digital Library

Google Scholar

[15]

Sophie Cluet and Guido Moerkotte. Query processing in the sehemaless and semistructured context. Technical report, INRIA, 1997.

Google Scholar

[16]

Mariano P. Consens and Alberto O. Mendelzon. Expressing structural hypertext queries in graphlog. In Proc. 2nd. A CM Conference on Hypertext, pages 269- 292, Pittsburgh, November 1989.

Digital Library

Google Scholar

[17]

Susan B. Davidson, Christian Overton, Val Tanhen, and Limsoon Wong. Biokleisli: A digital library for biomedical researchers. In Journal of Digital Libraries, volume 1:1, November 1996. See http://~w, cis. upenn, edu/ db.

Google Scholar

[18]

Mary Fernandez, Daniela Florescu, Jaewoo Kang, Alon Levy, and Dan Suciu. STRUDEL: A Web Site Managemeat System. In Proceedings of AGM-SIGMOD International Conference on Management of Data, Tuscon, May 1997.

Digital Library

Google Scholar

[19]

Mary Fernandez, Lucian Pops, and Suciu Dan. A structure based approach to querying semistructured data, 1997. Manuscript available from http : //www .research. att. com/inf o/{ruff, suciu}.

Google Scholar

[20]

Mary Fernandez and Dan Suciu. Query optimizations for semi-structured data using graph schemas, 1996. Manuscript available from http ://www .research. att. com/inf o/{ruff, suciu}.

Google Scholar

[21]

H. Garcia-Molina, Y. Papakonstaatinou, D. Quass, A. Rajaraman, Y. Sagiv, 3. Ullman, and 3. Widom. The tsimmis approach to mediation: Data models and languages. In Proceedings of Second INternational Workshop on Next Generation Information Technologies and Systems, pages 185-193, June 1995.

Google Scholar

[22]

Roy Goldman and :Iennifer Widom. Dataguides: Enabling query formulation and optimization in semistructured databases. Technical report, Stanford, 1977.

Google Scholar

[23]

The internet movie database, http://us.imdb.com/.

Google Scholar

[24]

M. Kifer, W. Kim, and Y. Sagiv. Querying objectoriented databases. In M. Stonebraker, editor, Proceedings A CM-SIGMOD International Conference on Management of Data, pages 393-402, San Diego, California, June 1992.

Digital Library

Google Scholar

[25]

Anthony Kosky. Observational properties of databases with object identity. Technical Report MS-CIS-95-20, University of Pennsylvania, 1995.

Google Scholar

[26]

Laks V.S. Lakshmanaa, Fereidoon Sadri, and Iyer N. Subramanian. A declarative language for querying and restructuring the world-wide-web. In Post-IGDE IEEE Workshop on Research Issues in Data Engineering (RIDE-NDS'96), New Orleans, February 1996. See also ~tp://ftp. cs. concordia, ca/pub/inks/papers.

Digital Library

Google Scholar

[27]

J. McHugh, S. Abiteboul, R. Goldman, D. Quass, and J. Widom. Lore: A database management system for semistructured data. Technical report, Stanford University Database Group, February 1997.

Google Scholar

[28]

J. McHugh and J. Widom. Integrating dynamicallyfetched external information into a dbms for semistructured data. Technical report, Stanford University, 1997.

Google Scholar

[29]

Alberto O. Mendehon, George A. Mihaila, and Tova Milo. Querying the World Wide Web. In Proc. PDIS '96, pages 80-91, December 1996. Available as tp. db. toronto, edu/pub/pap ers/pdis96, ps. gz.

Digital Library

Google Scholar

[30]

Alberto O. Mendelzon and Tova Milo. Formal models of web queries. In Proc. PODS '97, May 1997. In press, available in ~tp. db. toronto, edu/pub/papers/pods97MM.ps.

Digital Library

Google Scholar

[31]

S. Nestorov, J Ullman, Weiner J, and S. Chawathe. Representative objects: Concise representations of semistructured hierarchical data. In Proceedings of the Thirteenth International Conference on Data Engineering, Birmingham, England, April 1997.

Digital Library

Google Scholar

[32]

Y. Papakonstaatinou, S. Abiteboul, and H. Garcia- Molina. Object fusion in mediator systems. In Proc 22nd. VLDB conference, September 1996.

Digital Library

Google Scholar

[33]

Yannis Papakonstaatinou, Hector Gaxcia-Molina, and Jennifer Widom. Object exchange across hetezogenous information sources. In Proceedings of IEEE International Conference on Data Engineering, pages 251-260, March 1995.

Digital Library

Google Scholar

[34]

D. Quass, A. Rjaraman, Y. Sagiv, and J. Ullman. Querying semistructured heterogeneous information. In Proceedings of the Fourth International Conference on Deductive and Object-oriented Databases, pages 319- 344, dec 1995.

Digital Library

Google Scholar

[35]

Dan Suciu. Query decomposition for unstructured query languages. In Proc 22nd. VLDB conference, September 1996.

Google Scholar

[36]

Jean Thierry-Mieg and Richard Durbin. ACeDB--'. A C. elegans Database: Syntactic definitions for the ACeDB data base manager, 1992.

Google Scholar

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Index Terms

Semistructured data
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
    2. Query languages
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query languages (principles)

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Published In

PODS '97: Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems

May 1997

272 pages

ISBN:0897919106

DOI:10.1145/263661

Chairmen:
Alberto Mendelzon
Univ. of Toronto, Toronto, Ont., Canada
,
Z. Meral Özsoyoglu
Case Western Reserve Univ., Cleveland, OH

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 1997

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SIGMOD/PODS97

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SIGMOD

SIGMOD/PODS97: Joint ACM SIGMOD International Conference on Management of Data and ACM Symposium on Principles of Database Systems

May 11 - 15, 1997

Arizona, Tucson, USA

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PODS '97 Paper Acceptance Rate 23 of 118 submissions, 19%;

Overall Acceptance Rate 642 of 2,707 submissions, 24%

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https://doi.org/10.1109/ICDE60146.2024.00395
Ronqui ROliveira TDa Silva ABrandão CDa Silva RHelouani WLara TFrancisco E(2023)COVID-19 EMERGENCY AID: HOW THE BRAZILIAN GOVERNMENT USED SOCIAL BIG DATA ANALYTICS TO GIVE ECONOMIC SUPPORT AND PROTECT VULNERABLE CITIZENSRevista Contemporânea10.56083/RCV3N8-1433:8(12537-12560)Online publication date: 23-Aug-2023
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