research-article

How to quantify the impact of lossy transformations on change detection

Authors:

Adrian Englhardt,

Klemens BöhmAuthors Info & Claims

SSDBM '15: Proceedings of the 27th International Conference on Scientific and Statistical Database Management

Article No.: 17, Pages 1 - 11

https://doi.org/10.1145/2791347.2791371

Published: 29 June 2015 Publication History

Abstract

To ease the proliferation of big data, it frequently is transformed, be it by compression, be it by anonymization. Such transformations however modify characteristics of the data, such as changes in the case of time series. Changes however are important for subsequent analyses. The impact of those modifications depends on the application scenario, and quantifying it is far from trivial. This is because a transformation can shift or modify existing changes or introduce new ones. In this paper, we propose MILTON, a flexible and robust Measure for quantifying the Impact of Lossy Transformations on subsequent change detectiON. MILTON is applicable to any lossy transformation technique on time-series data and to any general-purpose change-detection approach. We have evaluated it with three real-world use cases. Our evaluation shows that MILTON allows to quantify the impact of lossy transformations and to choose the best one from a class of transformation techniques for a given application scenario.

References

[1]

G. Acs and C. Castelluccia. I have a dream!(differentially private smart metering). In Information Hiding, 2011.

Digital Library

[2]

S. Barker, A. Mishra, D. Irwin, E. Cecchet, P. Shenoy, and J. Albrecht. Smart*: An open data set and tools for enabling research in sustainable homes. SustKDD Workshop on Data Mining Applications in Sustainability, 2012.

[3]

D. J. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In KDD workshop, 1994.

Digital Library

[4]

A. Bifet and R. Gavaldà. Learning from Time-Changing Data with Adaptive Windowing, chapter 42.

[5]

W. L. Bircher and L. K. John. Complete system power estimation using processor performance events. IEEE Transactions on Computers, 61:563--577, 2012.

Digital Library

[6]

E. Buchmann, K. Böhm, T. Burghardt, and S. Kessler. Re-identification of smart meter data. Personal and Ubiquitous Computing, 17:653--662, 2013.

Digital Library

[7]

E. Buchmann, S. Kessler, P. Jochem, and K. Böhm. The costs of privacy in local energy markets. In IEEE Conference on Business Informatics, 2013.

Digital Library

[8]

C. Buragohain, N. Shrivastava, and S. Suri. Space efficient streaming algorithms for the maximum error histogram. In IEEE International Conference on Data Engineering, 2007.

[9]

C. Dwork. Differential privacy. In Automata, languages and programming. Springer, 2006.

Digital Library

[10]

P. Efros, E. Buchmann, and K. Böhm. FRESCO: A framework to estimate the energy consumption of computers. In IEEE Conference on Business Informatics, 2014.

Digital Library

[11]

F. Eichinger, P. Efros, S. Karnouskos, and K. Böhm. A time-series compression technique and its application to the smart grid. The VLDB Journal, 24:193--218, 2015.

Digital Library

[12]

F. Eichinger, D. Pathmaperuma, H. Vogt, and E. Müller. Data analysis challenges in the future energy domain. Computational Intelligent Data Analysis for Sustainable Development, 2012.

[13]

H. Elmeleegy, A. K. Elmagarmid, E. Cecchet, W. G. Aref, and W. Zwaenepoel. Online piece-wise linear approximation of numerical streams with precision guarantees. VLDB Endowment, 2:145--156, 2009.

Digital Library

[14]

X. Fan, W.-D. Weber, and L. A. Barroso. Power provisioning for a warehouse-sized computer. In Annual International Symposium on Computer Architecture, 2007.

Digital Library

[15]

B. C. M. Fung, K. Wang, R. Chen, and P. S. Yu. Privacy-preserving data publishing: A survey of recent developments. ACM Computing Surveys, 42:14:1--14:53, 2010.

Digital Library

[16]

R. Ge, X. Feng, S. Song, H.-C. Chang, D. Li, and K. Cameron. Powerpack: Energy profiling and analysis of high-performance systems and applications. IEEE Transactions on Parallel and Distributed Systems, 21:658--671, 2010.

Digital Library

[17]

V. Guralnik and J. Srivastava. Event detection from time series data. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999.

Digital Library

[18]

N. Q. V. Hung, H. Jeung, and K. Aberer. An evaluation of model-based approaches to sensor data compression. IEEE Transactions on Knowledge and Data Engineering, 25:2434--2447, 2013.

Digital Library

[19]

A. Kansal, F. Zhao, J. Liu, N. Kothari, and A. A. Bhattacharya. Virtual machine power metering and provisioning. In ACM Symposium on Cloud Computing, 2010.

Digital Library

[20]

E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra. Locally adaptive dimensionality reduction for indexing large time series databases. ACM SIGMOD Record, 30:151--162, 2001.

Digital Library

[21]

J. Z. Kolter and M. J. Johnson. Redd: A public data set for energy disaggregation research. In SIGKDD Workshop on Data Mining Applications in Sustainability, 2011.

[22]

L. Latecki, Q. Wang, S. Koknar-Tezel, and V. Megalooikonomou. Optimal subsequence bijection. In IEEE International Conference on Data Mining, 2007.

Digital Library

[23]

I. Lazaridis and S. Mehrotra. Capturing sensor-generated time series with quality guarantees. In International Conference on Data Engineering, 2003.

[24]

K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Workload-aware anonymization. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006.

Digital Library

[25]

S. Liu, M. Yamada, N. Collier, and M. Sugiyama. Change-point detection in time-series data by relative density-ratio estimation. Neural Networks, 43:72--83, 2013.

Digital Library

[26]

A. Molina-Markham, P. Shenoy, K. Fu, E. Cecchet, and D. Irwin. Private memoirs of a smart meter. In ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, 2010.

Digital Library

[27]

A. Noureddine, A. Bourdon, R. Rouvoy, and L. Seinturier. Runtime monitoring of software energy hotspots. In IEEE/ACM International Conference on Automated Software Engineering, 2012.

Digital Library

[28]

E. S. Page. Continuous inspection schemes. Biometrika, 41:100--115, 1954.

[29]

S. Papadimitriou, F. Li, G. Kollios, and P. S. Yu. Time series compressibility and privacy. In International Conference on Very Large Data Bases, 2007.

Digital Library

[30]

T. G. Papaioannou, M. Riahi, and K. Aberer. Towards online multi-model approximation of time series. In IEEE International Conference on Mobile Data Management, 2011.

Digital Library

[31]

M. Poess and R. O. Nambiar. Power based performance and capacity estimation models for enterprise information systems. IEEE Data Engineering Bulletin, 34:34--49, 2011.

[32]

S. Rajagopalan, L. Sankar, S. Mohajer, and H. Poor. Smart meter privacy: A utility-privacy framework. In IEEE International Conference on Smart Grid Communications, 2011.

[33]

R. Ramanathan, R. Engle, C. W. Granger, F. Vahid-Araghi, and C. Brace. Short-run forecasts of electricity loads and peaks. Cambridge University Press, 2001.

[34]

C. A. Ratanamahatana and E. Keogh. Three myths about dynamic time warping data mining. In SIAM International Conference on Data Mining, 2005.

[35]

G. Ristanoski, W. Liu, and J. Bailey. A time-dependent enhanced support vector machine for time series regression. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013.

Digital Library

[36]

S. Rivoire, P. Ranganathan, and C. Kozyrakis. A comparison of high-level full-system power models. HotPower, 8:3--3, 2008.

Digital Library

[37]

Y. Saatçi, R. D. Turner, and C. E. Rasmussen. Gaussian process change point models. In International Conference on Machine Learning, 2010.

[38]

X. Song, M. Wu, C. Jermaine, and S. Ranka. Statistical change detection for multi-dimensional data. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007.

Digital Library

[39]

J. Takeuchi and K. Yamanishi. A unifying framework for detecting outliers and change points from time series. IEEE Transactions on Knowledge and Data Engineering, 2006.

Digital Library

[40]

Y. Tao and M. T. Ozsu. Mining data streams with periodically changing distributions. In ACM Conference on Information and Knowledge Management, 2009.

Digital Library

[41]

M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, and E. Keogh. Indexing multi-dimensional time-series with support for multiple distance measures. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.

Digital Library

[42]

W. Vereecken et al. Overall ICT Footprint and Green Communication Technologies. In International Symposium on Communications, Control and Signal Processing, 2010.

[43]

Y. Wang and T. Pavlidis. Optimal correspondence of string subsequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:1080--1087, 1990.

Digital Library

[44]

Watts up? Meters. https://www.wattsupmeters.com, Accessed 22 March 2015.

Cited By

Hollmig GHorne MLeimkühler SSchöll FStrunk CEnglhardt AEfros PBuchmann EBöhm K(2017)An evaluation of combinations of lossy compression and change-detection approaches for time-series dataInformation Systems10.1016/j.is.2016.11.00165:C(65-77)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.is.2016.11.001

Index Terms

How to quantify the impact of lossy transformations on change detection
1. Applied computing
  1. Physical sciences and engineering
    1. Mathematics and statistics
2. Information systems

Recommendations

Change-driven model transformations

In this paper, we investigate change-driven model transformations, a novel class of transformations, which are directly triggered by complex model changes carried out by arbitrary transactions on the model (e.g. editing operation, transformation, etc). ...
Bidirectional and Change Propagating Model Transformations in MDE: Bidirectional and Change Propagating Model Transformations by means of the Janus Transformation Language (JTL)
Translating ATL Model Transformations to Algebraic Graph Transformations
Proceedings of the 8th International Conference on Theory and Practice of Model Transformations - Volume 9152

Analyzing and reasoning on model transformations has become very relevant for various applications such as ensuring the correctness of transformations. ATL is a model transformation language with rich semantics and a focus on usability, making its ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

SSDBM '15: Proceedings of the 27th International Conference on Scientific and Statistical Database Management

June 2015

390 pages

ISBN:9781450337090

DOI:10.1145/2791347

Editors:
Amarnath Gupta
University of California San Diego
,
Susan Rathbun
University of California San Diego

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

SSDBM 2015

SSDBM 2015: International Conference on Scientific and Statistical Database Management

June 29 - July 1, 2015

California, La Jolla

Acceptance Rates

Overall Acceptance Rate 56 of 146 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
81
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hollmig GHorne MLeimkühler SSchöll FStrunk CEnglhardt AEfros PBuchmann EBöhm K(2017)An evaluation of combinations of lossy compression and change-detection approaches for time-series dataInformation Systems10.1016/j.is.2016.11.00165:C(65-77)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.is.2016.11.001

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten