Documents

DOI

Spreadsheets are popular end-user computing applications and one reason behind their popularity is that they offer a large degree of freedom to their users regarding the way they can structure their data. However, this flexibility also makes spreadsheets difficult to understand. Textual documentation can address this issue, yet for supporting automatic generation of textual documentation, an important pre-requisite is to extract metadata inside spreadsheets. It is a challenge though, to distinguish between data and metadata due to the lack of universally accepted structural patterns in spreadsheets. Two existing approaches for automatic extraction of spreadsheet metadata were not evaluated on large datasets consisting of user inputs. Hence in this paper, we describe the collection of a large number of user responses regarding identification of spreadsheet metadata from participants of a MOOC. We describe the use of this large dataset to understand how users identify metadata in spreadsheets, and to evaluate two existing approaches of automatic metadata extraction from spreadsheets. The results provide us with directions to follow in order to improve metadata extraction approaches, obtained from insights about user perception of metadata. We also understand what type of spreadsheet patterns the existing approaches perform well and on what type poorly, and thus which problem areas to focus on in order to improve.
Original languageEnglish
Title of host publication2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)
EditorsA. Jiu
Place of PublicationLos Alamitos, CA
PublisherIEEE society
Pages135-145
Number of pages11
Volume2
ISBN (Print)978-1-5090-1855-0
DOIs
StatePublished - 2016
EventSANER 2016 - Osaka, Japan

Conference

ConferenceSANER 2016
CountryJapan
CityOsaka
Period14/03/1618/03/16

    Research areas

  • computer aided instruction, meta data, personal computing, spreadsheet programs, text analysis, MOOC participants, automatic spreadsheet metadata extraction, automatic textual documentation generation, data structure, end-user computing applications, metadata user perception, spreadsheet patterns, Computers, Conferences, Data mining, Documentation, Metadata, Reliability, Software, Empirical evaluation, MOOC, Meta-data extraction, Spreadsheet, User-study

ID: 7607036