The next 700 data description languages

The next 700 data description languages (2006)

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by Kathleen Fisher , Yitzhak Mandelbaum , David Walker

Venue:	In ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
Citations:	27 - 8 self

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Datum	Value	Source
TITLE	The next 700 data description languages	INFERENCE
AUTHOR NAME	Kathleen Fisher	SVM HeaderParse 0.2
AUTHOR AFFIL	; AT&T Labs Research; and	SVM HeaderParse 0.2
AUTHOR NAME	Yitzhak Mandelbaum	SVM HeaderParse 0.2
AUTHOR AFFIL	; AT&T Labs Research; and	SVM HeaderParse 0.2
AUTHOR NAME	David Walker	SVM HeaderParse 0.2
AUTHOR AFFIL	Princeton University	SVM HeaderParse 0.2
ABSTRACT	governmental, scientific, and private data. Because they have been standardized and are widely used, many reliable, efficient, and convenient tools for processing data in these formats are readily available. For instance, your favorite programming language undoubtedly has libraries for parsing XML and HTML as well as reading and transforming images in JPEG or movies in MPEG. Query engines are available for querying XML documents. Widely-used applications like Microsoft Word and Excel automatically translate documents between HTML and other standard formats. In short, life is good when working with standard data formats. In an ideal world, all data would be in such formats. In reality, however, we are not nearly so fortunate. An ad hoc data format is any non-standard data format. Typically, such formats do not have parsing, querying, analysis, or transformation tools readily available. Every day, network administrators, financial analysts, computer scientists, biologists, chemists, astronomers, and physicists deal with ad hoc data in a myriad of complex formats. Figure 1 gives a partial sense of the range and pervasiveness of such data. Since off-the-shelf tools for processing these ad hoc data formats do not exist or are not readily available, talented scientists, data analysts, and programmers must waste their time on low-level chores like parsing and format translation to extract the valuable information they need from their data.	SVM HeaderParse 0.2
YEAR	2006	INFERENCE
VENUE	In ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	2--15	INFERENCE
CITATIONS	34 found	ParsCit 1.0