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2 results on '"Fulls electrònics"'

1. XLIndy

Author

Maik Thiele, Nico Luettig, Dana Kuban, Dominik Olwig, Elvis Koci, Wolfgang Lehner, Oscar Romero, Julius Gonsior, Universitat Politècnica de Catalunya. Doctorat Erasmus Mundus en Tecnologies de la Informació per a la Intel·ligència Empresarial, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació, and Universitat Politècnica de Catalunya. IMP - Information Modeling and Processing

Subjects
Informàtica::Intel·ligència artificial::Aprenentatge automàtic [Àrees temàtiques de la UPC], Information extraction, Spreadsheets, Computer science, Layout inference, Annotation, Informàtica::Sistemes d'informació [Àrees temàtiques de la UPC], Inference, 02 engineering and technology, Add-in, computer.software_genre, Bottleneck, Interactive, 020204 information systems, Machine learning, Aprenentatge automàtic, 0202 electrical engineering, electronic engineering, information engineering, Information retrieval, Excel, computer.programming_language, Recuperació de la informació, Microsoft excel, 020207 software engineering, Python (programming language), JSON, Electronic spreadsheets, Table recognition, computer, Fulls electrònics
Abstract

Over the years, spreadsheets have established their presence in many domains, including business, government, and science. However, challenges arise due to spreadsheets being partially-structured and carrying implicit (visual and textual) information. This translates into a bottleneck, when it comes to automatic analysis and extraction of information. Therefore, we present XLIndy, a Microsoft Excel add-in with a machine learning back-end, written in Python. It showcases our novel methods for layout inference and table recognition in spreadsheets. For a selected task and method, users can visually inspect the results, change configurations, and compare different runs. This enables iterative fine-tuning. Additionally, users can manually revise the predicted layout and tables, and subsequently save them as annotations. The latter is used to measure performance and (re-)train classifiers. Finally, data in the recognized tables can be extracted for further processing. XLIndy supports several standard formats, such as CSV and JSON.

Published
2019
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2. Table identification and reconstruction in spreadsheets

Author

Maik Thiele, Elvis Koci, Wolfgang Lehner, Oscar Romero, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació, and Universitat Politècnica de Catalunya. MPI - Modelització i Processament de la Informació

Subjects
Identification, Computer science, Layout, Informàtica::Sistemes d'informació [Àrees temàtiques de la UPC], Data transformation, 02 engineering and technology, Disk formatting, 020204 information systems, Computer graphics (images), Header, 0202 electrical engineering, electronic engineering, information engineering, Grid, Information retrieval, Negocis -- Informàtica, Tabular, Document, Visualization, Metadata, Identification (information), Recognition, Business -- Data processing, Spreadsheet, Table (database), 020201 artificial intelligence & image processing, Electronic spreadsheets, Heuristics, Table, Fulls electrònics
Abstract

Spreadsheets are one of the most successful content generation tools, used in almost every enterprise to perform data transformation, visualization, and analysis. The high degree of freedom provided by these tools results in very complex sheets, intermingling the actual data with formatting, formulas, layout artifacts, and textual metadata. To unlock the wealth of data contained in spreadsheets, a human analyst will often have to understand and transform the data manually. To overcome this cumbersome process, we propose a framework that is able to automatically infer the structure and extract the data from these documents in a canonical form. In this paper, we describe our heuristics-based method for discovering tables in spreadsheets, given that each cell is classified as either header, attribute, metadata, data, or derived. Experimental results on a real-world dataset of 439 worksheets (858 tables) show that our approach is feasible and effectively identifies tables within partially structured spreadsheets.

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