Spreadsheets are widely used within companies and often form the basis for business decisions. Numerous cases are known where incorrect information in spreadsheets has lead to incorrect decisions. Such cases underline the relevance of research on the professional use of spreadsheets. Recently a new dataset became available for research, containing over 15.000 business spreadsheets that were extracted from the Enron E-mail Archive. With this dataset, we 1) aim to obtain a thorough understanding of the characteristics of spreadsheets used within companies, and 2) compare the characteristics of the Enron spreadsheets with the EUSES corpus which is the existing state of the art set of spreadsheets that is frequently used in spreadsheet studies. Our analysis shows that 1) the majority of spreadsheets are not large in terms of worksheets and formulas, do not have a high degree of coupling, and their formulas are relatively simple; 2) the spreadsheets from the EUSES corpus are, with respect to the measured characteristics, quite similar to the Enron spreadsheets.
Most related to our efforts is of course the work of Hermans and Murphy-Hill @cite_10 which initially presented the Enron spreadsheet corpus. The authors performed a preliminary analysis of some basic characteristics (like number of worksheets, cells, and formulas) of these spreadsheets. While Hermans and Murphy-Hill present a first overview of the spreadsheets, we, in this paper, dive deeper and added additional metrics to measure the degree of coupling and gain more insight in the actual use of functions. Also, for every metric we compared the Enron spreadsheets with the EUSES corpus. By using the Wilcoxon-Mann-Whitney test and calculating the Cliff's delta for the different metrics we answer the question of representativeness of the EUSES corpus.
@cite_10
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Spreadsheets are used extensively in business processes around the world and as such, a topic of research interest. Over the past few years, many spreadsheet studies have been performed on the EUSES spreadsheet corpus. While this corpus has served the spreadsheet community well, the spreadsheets it contains are mainly gathered with search engines and as such do not represent spreadsheets used in companies. This paper presents a new dataset, extracted for the Enron Email Archive, containing over 15,000 spreadsheets used within the Enron Corporation. In addition to the spreadsheets, we also present an analysis of the associated emails, where we look into spreadsheet specific email behavior. Our analysis shows that 1) 24 of Enron spreadsheets with at least one formula contain an Excel error, 2) there is little diversity in the functions used in spreadsheets: 76 of spreadsheets in the presented corpus only use the same 15 functions and, 3) the spreadsheets are substantially more smelly than the EUSES corpus, especially in terms of long calculation chains. Regarding the emails, we observe that spreadsheets 1) are a frequent topic of email conversation with 10 of emails either sending or referring spreadsheets and 2) the emails are frequently discussing errors in and updates to spreadsheets. A large set of email messages, the Enron corpus, was made public during the legal investigation concerning the Enron corporation. This dataset, along with a thorough explanation of its origin, is available at http: www-2.cs.cmu.edu enron . This paper provides a brief introduction and analysis of the dataset. The raw Enron corpus contains 619,446 messages belonging to 158 users. We cleaned the corpus before this analysis by removing certain folders from each user, such as “discussion_threads”. These folders were present for most users, and did not appear to be used directly by the users, but rather were computer generated. Many, such as “all_documents”, also contained large numbers of duplicate emails, which were already present in the users’ other folders. Our goal in this paper is to analyze the suitability of this corpus for exploring how to classify messages as organized by a human, so these folders would have likely been misleading. The Enron email corpus is appealing to researchers because it represents a rich temporal record of internal communication within a large, real-world organization facing a severe and survival-threatening crisis. We describe how we enhanced the original corpus database and present findings from our investigation undertaken with a social network analytic perspective. We explore the dynamics of the structure and properties of the organizational communication network, as well as the characteristics and patterns of communicative behavior of the employees from different organizational levels. We found that during the crisis period, communication among employees became more diverse with respect to established contacts and formal roles. Also during the crisis period, previously disconnected employees began to engage in mutual communication, so that interpersonal communication was intensified and spread through the network, bypassing formal chains of communication. The findings of this study provide valuable insight into a real-world organizational crisis, which may be further used for validating or developing theories and dynamic models of organizational crises; thereby leading to a better understanding of the underlying causes of, and response to, organization failure. This paper reports our implementation of the Virtual Corpus approach to deriving ngram statistics for ngrams of any length from large-scale corpora based on the suffix array data structure. In order to enable the VC to accommodate corpora with a vocabulary of different size, we first convert corpus tokens into integer codes. To accelerate the processing, we employ a bucket-radixsort for sorting the VC indices (or pointers, each of which represents a sequence of corpus tokens from its position to the end of the corpus). The time complexity of the sorting algorithm is of O(N log n N) in token code comparisons.