Segmenting Tables via Indexing of Value Cells by Table Headers

Seth, Sharad; Nagy, George

doi:10.1109/icdar.2013.181

Cited by 18 publications

(16 citation statements)

References 15 publications

(17 reference statements)

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“…For those data sets the syntactical structure of the content is more important, as also shown by Cortez et al [5]. Seth et al [19] proposed a reliable approach for table normalization based on sequential circuit analysis, which does not require background knowledge, but only succeeds on tables with unique access path to data cells.…”

Section: Related Workmentioning

confidence: 92%

Multi-Hypothesis CSV Parsing

Döhmen

Mühleisen

Boncz

2017

Proceedings of the 29th International Conference on Scientific and Statistical Database Management

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Comma Separated Value (CSV) files are commonly used to represent data. CSV is a very simple format, yet we show that it gives rise to a surprisingly large amount of ambiguities in its parsing and interpretation. We summarize the state-of-the-art in CSV parsers, which typically make a linear series of parsing and interpretation decisions, such that any wrong decision at an earlier stage can negatively affect all downstream decisions. Since computation time is much less scarce than human time, we propose to turn CSV parsing into a ranking problem. Our quality-oriented multi-hypothesis CSV parsing approach generates several concurrent hypotheses about dialect, table structure, etc. and ranks these hypotheses based on quality features of the resulting table. This approach makes it possible to create an advanced CSV parser that makes many different decisions, yet keeps the overall parser code a simple plug-in infrastructure. The complex interactions between these decisions are taken care of by searching the hypothesis space rather than by having to program these many interactions in code. We show that our approach leads to better parsing results than the state of the art and facilitates the parsing of large corpora of heterogeneous CSV files.

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Section: Related Workmentioning

confidence: 92%

Multi-Hypothesis CSV Parsing

Döhmen

Mühleisen

Boncz

2017

Proceedings of the 29th International Conference on Scientific and Statistical Database Management

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“…Attempts at segmentation using table grammars-syntactic pattern recognition-did not give acceptable results either [ 10 , 11 ]. However, segmentation based on indexing, even though more primitive than our current method, resulted in 98.5% accuracy [12]. The indexing property is fundamental and deserves to be incorporated in any [13,14] Importing and querying visual tables in a Data Base Management System (DBMS) was originally proposed for scanned paper tables [15], and much later for Web tables [16].…”

Section: Previous Workmentioning

confidence: 99%

Transforming Web Tables to a Relational Database

Embley

Seth

Nagy

2014

2014 22nd International Conference on Pattern Recognition

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Abstract-HTML tables represent a significant fraction of web data. The often complex headers of such tables are determined accurately using their indexing property. Isolated headers are factored to extract hierarchical categories. Web tables are then transformed into a canonical form and imported into a relational database. The proposed processing allows for the formulation of arbitrary SQL queries over the collection of induced relational tables.

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“…VeriClick, an interactive tool for table segmentation and ground-truthing, was described in [61]. We introduced algorithmic table segmentation, based on the fundamental indexing property, in [62]. Some other conference reports of our experiments on various aspects of table processing are cited in the above publications.…”

Section: Our Earlier Workmentioning

confidence: 99%

“…In addition to the already-mentioned IEA/AIE'11 [60] and ICDAR'13 [62] papers, three precursors to this article have recently appeared in conference proceedings. At the 2014 Document Analysis Systems workshop, we reported on our initial, automatic end-to-end conversion of web tables to relational databases [63].…”

Section: Our Earlier Workmentioning

confidence: 99%

Converting heterogeneous statistical tables on the web to searchable databases

Embley

Krishnamoorthy

Nagy

et al. 2016

IJDAR

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Much of the world's quantitative data resides in scattered web tables. For a meaningful role in Big Data analytics, the facts reported in these tables must be brought into a uniform framework. Based on a formalization of header-indexed tables, we proffer an algorithmic solution to end-to-end table processing for a large class of humanreadable tables. The proposed algorithms transform headerindexed tables to a category table format that maps easily to a variety of industry-standard data stores for query processing. The algorithms segment table regions based on the unique indexing of the data region by header paths, classify table cells, and factor header category structures of two-dimensional as well as the less common multidimensional tables. Experimental evaluations substantiate the algorithmic approach to processing heterogeneous tables. As demonstrable results, the algorithms generate queryable relational database tables and semantic-web triple stores. Application of our algorithms to 400 web tables randomly selected from diverse sources shows that the algorithmic solution automates end-to-end table processing. document analysis table segmentation table analysis table header factoring end-to-end table processing· table headers queries over table data ___________________________________________ Keywords

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Segmenting Tables via Indexing of Value Cells by Table Headers

Cited by 18 publications

References 15 publications

Multi-Hypothesis CSV Parsing

Multi-Hypothesis CSV Parsing

Transforming Web Tables to a Relational Database

Converting heterogeneous statistical tables on the web to searchable databases

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