TY - CONF
T1 - Caravan
T2 - 6th Biennial Conference on Innovative Data Systems Research, CIDR 2013
AU - Deutch, Daniel
AU - Ives, Zachary
AU - Milo, Tova
AU - Tannen, Val
N1 - Funding Information:
We thank the reviewers for their helpful comments. This research was funded in part by NSF grants IIS-1217798 and CNS-1065130, by the Binational (US-Israel) Science Foundation, by the Israeli Ministry of Science, and by the European Research Council under the European Community?s Seventh Framework Programme (FP7/2007-2013) / ERC grant MoDaS, agreement 291071. Val Tannen is grateful to EPFL for the excellent sabbatical support received.
Funding Information:
We thank the reviewers for their helpful comments. This research was funded in part by NSF grants IIS-1217798 and CNS-1065130, by the Binational (US-Israel) Science Foundation, by the Israeli Ministry of Science, and by the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013) / ERC grant MoDaS, agreement 291071. Val Tannen is grateful to EPFL for the excellent sabbatical support received.
Publisher Copyright:
© 2013 Conference on Innovative Data Systems Research (CIDR). All rights reserved.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Problems of what-if analysis (such as hypothetical deletions, insertions, and modifications) over complex analysis queries are increasingly commonplace, e.g., in forming a business strategy or looking for causal relationships in science. Here, data analysts are typically interested only in task-specific views of the data, and they expect to be able to interactively manipulate the data in a natural and seamless way — possibly on a phone or tablet, and possibly via a spreadsheet or similar interface without having to carry the full machinery of a DBMS. The Caravan system enables what-if analysis: fast, lightweight, interactive exploration of alternative answers, within views computed over large-scale distributed data sources. Our novel approach is based on creating dedicated provisioned autonomous representations, or PARs. PARs are compiled out of the data, initial analysis queries and user-specified what-if scenarios. They allow rapid evaluation of what-if scenarios without accessing the original data or performing complex query operations. Importantly, the size of PARs is governed by the parameters of the what-if analysis and is proportional to the size of the initial query answer rather than the typically much larger source data. Consequently, many what-if analysis tasks performed through PAR evaluations can be done autonomously, on limited-resource devices. We describe our model and architecture, demonstrate preliminary performance results, and present several open implementation and optimization issues.
AB - Problems of what-if analysis (such as hypothetical deletions, insertions, and modifications) over complex analysis queries are increasingly commonplace, e.g., in forming a business strategy or looking for causal relationships in science. Here, data analysts are typically interested only in task-specific views of the data, and they expect to be able to interactively manipulate the data in a natural and seamless way — possibly on a phone or tablet, and possibly via a spreadsheet or similar interface without having to carry the full machinery of a DBMS. The Caravan system enables what-if analysis: fast, lightweight, interactive exploration of alternative answers, within views computed over large-scale distributed data sources. Our novel approach is based on creating dedicated provisioned autonomous representations, or PARs. PARs are compiled out of the data, initial analysis queries and user-specified what-if scenarios. They allow rapid evaluation of what-if scenarios without accessing the original data or performing complex query operations. Importantly, the size of PARs is governed by the parameters of the what-if analysis and is proportional to the size of the initial query answer rather than the typically much larger source data. Consequently, many what-if analysis tasks performed through PAR evaluations can be done autonomously, on limited-resource devices. We describe our model and architecture, demonstrate preliminary performance results, and present several open implementation and optimization issues.
UR - http://www.scopus.com/inward/record.url?scp=85084012892&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:85084012892
Y2 - 6 January 2013 through 9 January 2013
ER -