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Table 2. Input document shapes.

in NEXSORT: Sorting XML in external memory

by Adam Silberstein, Jun Yang 2004

"... In PAGE 11: ... Within each docu- ment, the fan-out is near uniform across all elements. Table2 summaries the characteristics of these docu- ments. Figure 7 plots the running times of NEXSORT and external merge sort with 4 MB of main memory.... ..."

Cited by 1

Table 2. File sizes for input documents.

in Efficient schema-based revalidation of XML

by Mukund Raghavachari, Oded Shmueli 2004

"... In PAGE 16: ... We vary the number of item elements from 2 to 1000. Table2 lists the file size of each document. Figure 3a plots the time taken to validate the docu- ment versus the number of item elements in the document for both the modified and the unmodified Xerces validators for the first experiment.... ..."

Cited by 5

Table 2. Activities, input and output documents of the Organization Process Input documents Activities Output documents

in A Culture-Centered Multilevel Software Process Cycle Model

by Silvia T. Acuna, Mabel del V. Sosa, Santiago Estero

Table 3: Summary Of The Various Types Of Queries Made On Different Input Documents

in RELATIONAL VIEWS OF XML FOR THE SEMANTIC WEB

by Shruti Atre

"... In PAGE 9: ...able 2: Summary Of The Input Files Used As Examples In This Chapter..................................82 Table3... In PAGE 92: ... The various input files used were from different sources. Table3 summarizes the input files used in our experiments and the different types of queries that were posed on them. Table 3: Summary Of The Various Types Of Queries Made On Different Input Documents ... ..."

Table 4 Summarization systems from medical documents Input Purpose Output Method Evaluation

in unknown title

by unknown authors 2005

Cited by 10

Table 4 Summarization systems from medical documents Input Purpose Output Method Evaluation

in Summary

by Stergos Afantenos A, Vangelis Karkaletsis A, Panagiotis Stamatopoulos B

"... In PAGE 18: ... The agents remove items that do not meet their rele- vance criteria. Table4 summarizes the main features of the projects/systems presented in Summarization tech- niques in the medical domain. 5.... ..."

Table 3: Some features of text segmentation algorithms: input type of the documents (binary, gray-level) and working resolution, layout structure, searched text regions, assumptions and limitations, advantages.

in Geometric layout analysis techniques for document image understanding: a review

by R. Cattoni, T. Coianiz, S. Messelodi, C. M. Modena 1998

Cited by 28

Table 3: Some features of text segmentation algorithms: input type of the documents (binary, gray-level) and working resolution, layout structure, searched text regions, assumptions and limitations, advantages.

in Geometric Layout Analysis Techniques for Document Image Understanding: a Review

by R. Cattoni, T. Coianiz, S. Messelodi, C. M. Modena 1998

Cited by 28

Table II. The TRSM nonhierarchical clustering algorithm. Input The set D of documents and the number K of clusters Result K overlapping clusters of D associatedwith cluster membership of each document

in Nonhierarchical document clustering based on a tolerance rough set model

by Tu Bao Ho, Ngoc Binh Nguyen 2002

Cited by 4

Table 3: Parameters for the case of stitching document of unknown length. DB indicates the number or columns in the input images and CW the number of rows.

by unknown authors

"... In PAGE 9: ... Figure 3 shows some results of stitching flatbed-scanned images, including a US Legal-sized document, a brochure with mixed text and graphics and another one with a rotated second portion. Table3 gives the parameters uses by the algorithm for stitching oversized document of unknown length, where CW and DB are the pixel height and the width, respectively, of the input image and CH C7BYBY is an average offset arising from the scanning process (e.g.... ..."

Results 1 - 10 of 27,872

Tools

Table 2. Input document shapes.

Table 2. File sizes for input documents.

Table 2. Activities, input and output documents of the Organization Process Input documents Activities Output documents

Table 3: Summary Of The Various Types Of Queries Made On Different Input Documents

Table 4 Summarization systems from medical documents Input Purpose Output Method Evaluation

Table 4 Summarization systems from medical documents Input Purpose Output Method Evaluation

Table 3: Some features of text segmentation algorithms: input type of the documents (binary, gray-level) and working resolution, layout structure, searched text regions, assumptions and limitations, advantages.

Table 3: Some features of text segmentation algorithms: input type of the documents (binary, gray-level) and working resolution, layout structure, searched text regions, assumptions and limitations, advantages.

Table II. The TRSM nonhierarchical clustering algorithm. Input The set D of documents and the number K of clusters Result K overlapping clusters of D associatedwith cluster membership of each document

Table 3: Parameters for the case of stitching document of unknown length. DB indicates the number or columns in the input images and CW the number of rows.