Spreadsheet-based complex data transformation

Hung, Vu; Benatallah, Boualem; Saint-Paul, Regis

doi:10.1145/2063576.2063829

Cited by 26 publications

(17 citation statements)

References 11 publications

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“…Recent studies [1,2,8,11] attempted to transform spreadsheet data into the relational model, making further integration among spreadsheets possible. Some extraction systems require explicit sheet-specific user-provided rules [2,11], which might yield good results for a single spreadsheet. But they are not feasible for our setting: the corpus is large and users are not aware of the target spreadsheets to be processed ahead of time.…”

Section: Introductionmentioning

confidence: 99%

Automatic web spreadsheet data extraction

Chen

Cafarella

2013

Proceedings of the 3rd International Workshop on Semantic Search Over the Web

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Spreadsheets contain a huge amount of high-value data but do not observe a standard data model and thus are difficult to integrate. A large number of data integration tools exist, but they generally can only work on relational data. Existing systems for extracting relational data from spreadsheets are too labor intensive to support ad-hoc integration tasks, in which the correct extraction target is only learned during the course of user interaction.This paper introduces a system that automatically extracts relational data from spreadsheets, thereby enabling relational spreadsheet integration. The resulting integrated relational data can be queried directly or can be translated into RDF triples. When compared to standard techniques for spreadsheet data extraction on a set of 100 random Web spreadsheets, the system reduces the amount of human labor by 72% to 92%. In addition to the system design, we present the results of a general survey of more than 400,000 spreadsheets we downloaded from the Web, giving a novel view of how users organize their data in spreadsheets.

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Section: Introductionmentioning

confidence: 99%

Automatic web spreadsheet data extraction

Chen

Cafarella

2013

Proceedings of the 3rd International Workshop on Semantic Search Over the Web

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“…The DIOM framework differs significantly from other systems [1,6] by exploiting both explicit and implicit categories of knowledge to ensure the correct extraction and integration of the semantics contained in spreadsheets.…”

Section: Contributionsmentioning

confidence: 99%

“…Second, the rule-based approach requires the user to explicitly specify the transformation in the form of conversion rules [1]. The approach is flexible in that the rules could be applied to a variety of spreadsheets.…”

Section: Contributionsmentioning

confidence: 99%

“…Unfortunately, while progress has been made on integration [1][2][3], it remains challenging and labor-intensive to integrate data of the rich variety required to answer complex societal questions. A large amount of information collected from these web sites is retrieved in the form of spreadsheets.…”

Section: Introductionmentioning

confidence: 99%

“…They would thus require users to have an in-depth knowledge of the data and to learn mapping languages [4] and operators [5]. Others [1,2] require users to specify explicit conversion rules that can be difficult and time consuming for the user to compose. At the other end of the spectrum, efforts are under way to automate various aspects of the data extraction from web spreadsheets [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Using entity identification and classification for automated integration of spatial-temporal data

Ahsan¹,

Neamtu²,

Rundensteiner³

2016

Int. J. DNE

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Big data, crucial to answering economic, social, and political questions facing our society, tend to be diverse and distributed through various sites across the Internet. The creation of tools to integrate and analyze such data is of paramount interest. Yet the automation of these processes continues to be a great challenge. Our work rests on the observation that a great number of public data sources in domains ranging from economic to demographic, although of complex structure, often share key similarities, namely the presence of the Time and Location. Our proposed Data Integration through Object Modeling framework or DIOM tackles the critical problem of automating data integration from a variety of public websites by abstracting key features of multi-dimensional tables and interpreting them in the context of knowledge-centered Unified Spatial Temporal Model. Our classification-driven extractors are trained to identify and classify entities from both structured and unstructured parts of spreadsheets. The unstructured part contained in titles, headers, and footers reveals critical information, so-called Implicit Knowledge, crucial to the correct interpretation of data. Our experimental results on real world datasets from heterogeneous public data sources show increased accuracy by 25% compared to state-of-the-art approaches.

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EXACT: A tool for comprehending VBA-based Excel spreadsheet applications

Amalfitano

Simone

Fasolino

et al. 2016

J. Softw. Evol. and Proc.

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Spreadsheet applications are widely adopted by millions of end users from several application domains and provide strategic support to many business, scientific, industrial, and organizational processes. These applications are usually developed by rapid application development processes, exploiting host scripting languages allowing the basic spreadsheets to provide complex functionality, business rules, and user interfaces. Several factors complicate the comprehension of these applications because they are usually developed and maintained by end users without specific software engineering skills, grow over time, are not adequately documented, and do not present explicit separation between data, business logic, and user interface layers. This paper presents a reverse engineering tool intended to support the comprehension of Excel spreadsheet applications developed using the Visual Basic for Application programming language. The tool has been implemented as an add-in that extends the Excel working environment by providing analysis and visualization features. It is able to extract information about the elements composing the analyzed Excel spreadsheet application, the functionality it exposes through its user interface, and the dependencies among its cells. This information is provided by means of interactive views. The validity of the tool has been assessed by a qualitative case study performed with professional end users from an automotive industrial domain.

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Spreadsheet-based complex data transformation

Cited by 26 publications

References 11 publications

Automatic web spreadsheet data extraction

Automatic web spreadsheet data extraction

Using entity identification and classification for automated integration of spatial-temporal data

EXACT: A tool for comprehending VBA-based Excel spreadsheet applications

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