{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/102483"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/102483","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards unifying spreadsheets with databases for ad-hoc interactive data management at scale","abstract":"We are witnessing the increasing availability of data across a spectrum of domains, necessitating the interactive ad-hoc management and analysis of this data, in order to put it to use. Unfortunately, interactive ad-hoc management of very large datasets presents a host of challenges, ranging from performance to interface usability. This thesis introduces a new research direction of manipulation of large datasets using an interactive interface and makes several steps towards this direction. In particular, we develop DataSpread, a tool that enables users to work with arbitrary large datasets via a direct manipulation interface. DataSpread holistically unifies spreadsheets and relational databases to leverage the benefits of both. However, this holistic integration is not trivial due to the differences in the architecture and ideologies of the two paradigms: spreadsheets and databases. We have built a prototype of DataSpread, which, in addition to motivating the underlying challenges, demonstrates the feasibility and usefulness of this holistic integration. We focus on the following challenges encountered while developing DataSpread. (i) Representation—here, we address the challenges of flexibly representing ad-hoc spreadsheet data within a relational database; (ii) Indexing—here, we develop indexing data structures for supporting and maintaining access by position; (iii) Formula Computation—here, we introduce an asynchronous formula computation framework that addresses the challenge of ensuring consistency and interactivity at the same time; and (iv) Organization—here, we develop a framework to best organize data based on a workload, e.g., queries specified on the spreadsheet interface.","abstract_html":"We are witnessing the increasing availability of data across a spectrum of domains, necessitating the interactive ad-hoc management and analysis of this data, in order to put it to use. Unfortunately, interactive ad-hoc management of very large datasets presents a host of challenges, ranging from performance to interface usability. This thesis introduces a new research direction of manipulation of large datasets using an interactive interface and makes several steps towards this direction. In particular, we develop DataSpread, a tool that enables users to work with arbitrary large datasets via a direct manipulation interface. DataSpread holistically unifies spreadsheets and relational databases to leverage the benefits of both. However, this holistic integration is not trivial due to the differences in the architecture and ideologies of the two paradigms: spreadsheets and databases. We have built a prototype of DataSpread, which, in addition to motivating the underlying challenges, demonstrates the feasibility and usefulness of this holistic integration. We focus on the following challenges encountered while developing DataSpread. (i) Representation—here, we address the challenges of flexibly representing ad-hoc spreadsheet data within a relational database; (ii) Indexing—here, we develop indexing data structures for supporting and maintaining access by position; (iii) Formula Computation—here, we introduce an asynchronous formula computation framework that addresses the challenge of ensuring consistency and interactivity at the same time; and (iv) Organization—here, we develop a framework to best organize data based on a workload, e.g., queries specified on the spreadsheet interface.","abstract_has_math":false,"creators":["Bendre, Mangesh"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Parameswaran, Aditya","Chang, Kevin","Zhai, ChengXiang","Nandi, Arnab"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-02-06T19:36:36Z","date_published":"2019-02-06T19:36:36Z","updated_at":"2026-07-22T22:24:42Z","subjects":["DataSpread","spreadsheets","asynchronous","scalability","big-data management"],"languages":["en"],"rights":["Copyright 2018 Mangesh Bendre"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/102483","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Parameswaran, Aditya","Chang, Kevin","Zhai, ChengXiang","Nandi, Arnab"]},{"key":"dc:creator","label":"Author","values":["Bendre, Mangesh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-02-06T19:36:36Z","2018-12-05","2018-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["DataSpread","spreadsheets","asynchronous","scalability","big-data management"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Mangesh Bendre"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/102483"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We are witnessing the increasing availability of data across a spectrum of domains, necessitating the interactive ad-hoc management and analysis of this data, in order to put it to use. Unfortunately, interactive ad-hoc management of very large datasets presents a host of challenges, ranging from performance to interface usability. This thesis introduces a new research direction of manipulation of large datasets using an interactive interface and makes several steps towards this direction. In particular, we develop DataSpread, a tool that enables users to work with arbitrary large datasets via a direct manipulation interface. DataSpread holistically unifies spreadsheets and relational databases to leverage the benefits of both. However, this holistic integration is not trivial due to the differences in the architecture and ideologies of the two paradigms: spreadsheets and databases. We have built a prototype of DataSpread, which, in addition to motivating the underlying challenges, demonstrates the feasibility and usefulness of this holistic integration. We focus on the following challenges encountered while developing DataSpread. (i) Representation—here, we address the challenges of flexibly representing ad-hoc spreadsheet data within a relational database; (ii) Indexing—here, we develop indexing data structures for supporting and maintaining access by position; (iii) Formula Computation—here, we introduce an asynchronous formula computation framework that addresses the challenge of ensuring consistency and interactivity at the same time; and (iv) Organization—here, we develop a framework to best organize data based on a workload, e.g., queries specified on the spreadsheet interface.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Mangesh Bendre, accepted the attached license on 2018-12-05 at 13:40.","The student, Mangesh Bendre, submitted this Dissertation for approval on 2018-12-05 at 14:11.","This Dissertation was approved for publication on 2018-12-05 at 15:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13221 on 2019-02-05 at 11:15:14","Made available in DSpace on 2019-02-06T19:36:36Z (GMT). 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This thesis introduces a new research direction of manipulation of large datasets using an interactive interface and makes several steps towards this direction. In particular, we develop DataSpread, a tool that enables users to work with arbitrary large datasets via a direct manipulation interface. DataSpread holistically unifies spreadsheets and relational databases to leverage the benefits of both. However, this holistic integration is not trivial due to the differences in the architecture and ideologies of the two paradigms: spreadsheets and databases. We have built a prototype of DataSpread, which, in addition to motivating the underlying challenges, demonstrates the feasibility and usefulness of this holistic integration. We focus on the following challenges encountered while developing DataSpread. (i) Representation—here, we address the challenges of flexibly representing ad-hoc spreadsheet data within a relational database; (ii) Indexing—here, we develop indexing data structures for supporting and maintaining access by position; (iii) Formula Computation—here, we introduce an asynchronous formula computation framework that addresses the challenge of ensuring consistency and interactivity at the same time; and (iv) Organization—here, we develop a framework to best organize data based on a workload, e.g., queries specified on the spreadsheet interface.","Submission original under an indefinite embargo labeled 'Open Access'. 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