%0 Conference Paper %1 Beckmann:2006:ERS:1129754.1129918 %A Beckmann, Jennifer L. %A Halverson, Alan %A Krishnamurthy, Rajasekar %A Naughton, Jeffrey F. %B Proceedings of the 22nd International Conference on Data Engineering %C Washington, DC, USA %D 2006 %I IEEE Computer Society %K datasets rdbms sparse xml %P 58-- %R 10.1109/ICDE.2006.67 %T Extending RDBMSs To Support Sparse Datasets Using An Interpreted Attribute Storage Format %U http://dx.doi.org/10.1109/ICDE.2006.67 %X "Sparse" data, in which relations have many attributes that are null for most tuples, presents a challenge for relational database management systems. If one uses the normal "horizontal" schema to store such data sets in any of the three leading commercial RDBMS, the result is tables that occupy vast amounts of storage, most of which is devoted to nulls. If one attempts to avoid this storage blowup by using a "vertical" schema, the storage utilization is indeed better, but query performance is orders of magnitude slower for certain classes of queries. In this paper, we argue that the proper way to handle sparse data is not to use a vertical schema, but rather to extend the RDBMS tuple storage format to allow the representation of sparse attributes as interpreted fields. The addition of interpreted storage allows for efficient and transparent querying of sparse data, uniform access to all attributes, and schema scalability. We show, through an implementation in PostgreSQL, that the interpreted storage approach dominates in query efficiency and ease-of-use over the current horizontal storage and vertical schema approaches over a wide range of queries and sparse data sets. %@ 0-7695-2570-9

@inproceedings{Beckmann:2006:ERS:1129754.1129918, abstract = {"Sparse" data, in which relations have many attributes that are null for most tuples, presents a challenge for relational database management systems. If one uses the normal "horizontal" schema to store such data sets in any of the three leading commercial RDBMS, the result is tables that occupy vast amounts of storage, most of which is devoted to nulls. If one attempts to avoid this storage blowup by using a "vertical" schema, the storage utilization is indeed better, but query performance is orders of magnitude slower for certain classes of queries. In this paper, we argue that the proper way to handle sparse data is not to use a vertical schema, but rather to extend the RDBMS tuple storage format to allow the representation of sparse attributes as interpreted fields. The addition of interpreted storage allows for efficient and transparent querying of sparse data, uniform access to all attributes, and schema scalability. We show, through an implementation in PostgreSQL, that the interpreted storage approach dominates in query efficiency and ease-of-use over the current horizontal storage and vertical schema approaches over a wide range of queries and sparse data sets.}, acmid = {1129918}, added-at = {2012-05-07T13:27:39.000+0200}, address = {Washington, DC, USA}, author = {Beckmann, Jennifer L. and Halverson, Alan and Krishnamurthy, Rajasekar and Naughton, Jeffrey F.}, biburl = {https://www.bibsonomy.org/bibtex/2bacb6766270ac5cc5c44164e216aa5ad/sac}, booktitle = {Proceedings of the 22nd International Conference on Data Engineering}, description = {Extending RDBMSs To Support Sparse Datasets Using An Interpreted Attribute Storage Format}, doi = {10.1109/ICDE.2006.67}, interhash = {69bc090d280ef2377bd9ffd53934fddd}, intrahash = {bacb6766270ac5cc5c44164e216aa5ad}, isbn = {0-7695-2570-9}, keywords = {datasets rdbms sparse xml}, pages = {58--}, publisher = {IEEE Computer Society}, series = {ICDE '06}, timestamp = {2012-05-07T13:27:39.000+0200}, title = {Extending RDBMSs To Support Sparse Datasets Using An Interpreted Attribute Storage Format}, url = {http://dx.doi.org/10.1109/ICDE.2006.67}, year = 2006 }