LIVE: A Lineage-Supported Versioned DBMS

This paper presents LIVE, a complete DBMS designed for applications with many stored derived relations, and with a need for simple versioning capabilities when base data is modified. Target applications include, for example, scientific data management and data integration. A key feature of LIVE is the use of lineage (provenance) to support modifications and versioning in this environment. In our system, lineage significantly facilitates both: (1) efficient propagation of modifications from base to derived data; and (2) efficient execution of a wide class of queries over versioned, derived data. LIVE is fully implemented; detailed experimental results are presented that validate our techniques.

[1]  Serge Abiteboul,et al.  Update Semantics for Incomplete Databases , 1985, VLDB.

[2]  C. J. Date,et al.  Temporal data and the relational model , 2002 .

[3]  Helmut Seidl,et al.  Exact XML Type Checking in Polynomial Time , 2007, ICDT.

[4]  Serge Abiteboul,et al.  Foundations of Databases , 1994 .

[5]  Keishi Tajima,et al.  Archiving scientific data , 2002, SIGMOD '02.

[6]  Nick Roussopoulos,et al.  View indexing in relational databases , 1982, TODS.

[7]  Dan Suciu,et al.  Efficient query evaluation on probabilistic databases , 2004, The VLDB Journal.

[8]  Laura M. Haas,et al.  Beauty and the Beast: The Theory and Practice of Information Integration , 2007, ICDT.

[9]  Arthur M. Keller,et al.  Approaches for updating databases with incomplete information and nulls , 1984, 1984 IEEE First International Conference on Data Engineering.

[10]  Serge Abiteboul,et al.  On the representation and querying of sets of possible worlds , 1987, SIGMOD '87.

[11]  Frank Wm. Tompa,et al.  Efficiently updating materialized views , 1986, SIGMOD '86.

[12]  Jennifer Widom,et al.  Working Models for Uncertain Data , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[13]  T. S. Jayram,et al.  OLAP over uncertain and imprecise data , 2007, The VLDB Journal.

[14]  Sunil Prabhakar,et al.  U-DBMS: A Database System for Managing Constantly-Evolving Data , 2005, VLDB.

[15]  Jennifer Widom,et al.  Deriving Production Rules for Incremental View Maintenance , 1991, VLDB.

[16]  Dan Olteanu,et al.  Fast and Simple Relational Processing of Uncertain Data , 2007, 2008 IEEE 24th International Conference on Data Engineering.

[17]  Christopher Ré,et al.  Efficient Top-k Query Evaluation on Probabilistic Data , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[18]  Adriane Chapman,et al.  Issues in Building Practical Provenance Systems , 2007, IEEE Data Eng. Bull..

[19]  Christopher Ré,et al.  MYSTIQ: a system for finding more answers by using probabilities , 2005, SIGMOD '05.

[20]  Nick Roussopoulos,et al.  ADMS: A Testbed for Incremental Access Methods , 1993, IEEE Trans. Knowl. Data Eng..

[21]  James Cheney,et al.  Provenance management in curated databases , 2006, SIGMOD Conference.

[22]  Mohamed F. Mokbel,et al.  Immortal DB: transaction time support for SQL server , 2005, SIGMOD '05.

[23]  Christopher Ré,et al.  Approximate lineage for probabilistic databases , 2008, Proc. VLDB Endow..

[24]  Jennifer Widom,et al.  Trio: A System for Integrated Management of Data, Accuracy, and Lineage , 2004, CIDR.

[25]  Wang Chiew Tan,et al.  DBNotes: a post-it system for relational databases based on provenance , 2005, SIGMOD '05.

[26]  Marianne Winslett,et al.  A model-based approach to updating databases with incomplete information , 1988, TODS.

[27]  Michael Stonebraker,et al.  Supporting fine-grained data lineage in a database visualization environment , 1997, Proceedings 13th International Conference on Data Engineering.

[28]  Stephen J. Hegner Specification and implementation of programs for updating incomplete information databases , 1987, PODS '87.

[29]  Dan Olteanu,et al.  MayBMS: Managing Incomplete Information with Probabilistic World-Set Decompositions , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[30]  Tomasz Imielinski,et al.  Incomplete Information in Relational Databases , 1984, JACM.

[31]  Umeshwar Dayal,et al.  On the Updatability of Relational Views , 1978, VLDB.

[32]  Norbert Fuhr,et al.  A Probabilistic NF2 Relational Algebra for Imprecision in Databases , 1997 .

[33]  Parag Agrawal,et al.  Trio-One: Layering Uncertainty and Lineage on a Conventional DBMS (Demo) , 2007, CIDR.

[34]  Jennifer Widom,et al.  ULDBs: databases with uncertainty and lineage , 2006, VLDB.

[35]  Inderpal Singh Mumick,et al.  Maintenance of Materialized Views: Problems, Techniques, and Applications , 1999, IEEE Data Eng. Bull..

[36]  Jennifer Widom,et al.  Exploiting Lineage for Confidence Computation in Uncertain and Probabilistic Databases , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[37]  Laks V. S. Lakshmanan,et al.  ProbView: a flexible probabilistic database system , 1997, TODS.

[38]  Wang Chiew Tan Provenance in Databases: Past, Current, and Future , 2007, IEEE Data Eng. Bull..

[39]  Richard T. Snodgrass,et al.  Developing Time-Oriented Database Applications in SQL , 1999 .

[40]  Val Tannen,et al.  Models for Incomplete and Probabilistic Information , 2006, IEEE Data Eng. Bull..

[41]  Michael Pittarelli,et al.  The Theory of Probabilistic Databases , 1987, VLDB.

[42]  Val Tannen,et al.  Provenance semirings , 2007, PODS.

[43]  Jennifer Widom,et al.  Performance Issues in Incremental Warehouse Maintenance , 2000, VLDB.

[44]  Jennifer Widom,et al.  Lineage tracing for general data warehouse transformations , 2003, The VLDB Journal.

[45]  Prithviraj Sen,et al.  Representing and Querying Correlated Tuples in Probabilistic Databases , 2007, 2007 IEEE 23rd International Conference on Data Engineering.