@article {14734,
	title = {Serializing C intermediate representations for efficient and portable parsing},
	journal = {Software: Practice and Experience},
	volume = {40},
	year = {2010},
	month = {2010/03/01/},
	pages = {225 - 238},
	abstract = {C static analysis tools often use intermediate representations (IRs) that organize program data in a simple, well-structured manner. However, the C parsers that create IRs are slow, and because they are difficult to write, only a few implementations exist, limiting the languages in which a C static analysis can be written. To solve these problems, we investigate two language-independent, on-disk representations of C IRs: one using XML and the other using an Internet standard binary encoding called eXternal Data Representation (XDR). We benchmark the parsing speeds of both options, finding the XML to be about a factor of 2 slower than parsing C and the XDR over 6 times faster. Furthermore, we show that the XML files are far too large at 19 times the size of C source code, whereas XDR is only 2.2 times the C size. We also demonstrate the portability of our XDR system by presenting a C source code querying tool in Ruby. Our solution and the insights we gained from building it will be useful to analysis authors and other clients of C IRs. We have made our software freely available for download at http://www.cs.umd.edu/projects/PL/scil/. Copyright {\textcopyright} 2010 John Wiley\&Sons, Ltd.},
	keywords = {C, intermediate representations, parsing, static analysis, XDR, XML},
	isbn = {1097-024X},
	doi = {10.1002/spe.954},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/spe.954/abstract},
	author = {Meister,Jeffrey A and Foster, Jeffrey S. and Hicks, Michael W.}
}
@conference {18454,
	title = {Pictorial query trees for query specification in image databases},
	booktitle = {Fourteenth International Conference on Pattern Recognition, 1998. Proceedings},
	volume = {1},
	year = {1998},
	month = {1998/08//},
	pages = {919-921 vol.1 - 919-921 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A technique that enables specifying complex queries in image databases using pictorial query trees is presented. The leaves of a pictorial query tree correspond to individual pictorial queries that specify which objects should appear in the target images as well as how many occurrences of each object are required. In addition, the minimum required certainty of matching between query-image objects and database-image objects, as well as spatial constraints that specify bounds on the distance between objects and the relative direction between them are also specified. Internal nodes in the query tree represent logical operations (AND, OR, XOR) and their negations on the set of pictorial queries (or subtrees) represented by its children. The syntax of query trees is described. Algorithms for processing individual pictorial queries and for parsing and computing the overall result of a pictorial query tree are outlined},
	keywords = {Automation, complex queries, Computer science, content-based retrieval, Database systems, database-image objects, Educational institutions, Electrical capacitance tomography, grammars, Image databases, Image matching, parsing, pictorial query trees, Postal services, query specification, query-image objects, spatial constraints, syntax, visual databases},
	isbn = {0-8186-8512-3},
	doi = {10.1109/ICPR.1998.711383},
	author = {Soffer,A. and Samet, Hanan and Zotkin,Dmitry N}
}
