@inproceedings{vkistowski2015modeling,
  abstract = {Today’s system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly vari- able load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load inten- sity descriptions also do not sufficiently capture concrete pattern load profile variations over time.
To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.},
  author = {v. Kistowski, Jóakim and Nikolas, Herbst. and Zoller, Daniel and Kounev, Samuel and Hotho, Andreas},
  booktitle = {Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)},
  interhash = {9f0be929d7bcc057c778f6b44e73cf4c},
  intrahash = {f449d3cf35941636f96d72aaf620a275},
  title = {Modeling and Extracting Load Intensity Profiles},
  year = 2015
}

@inproceedings{1390343,
  address = {New York, NY, USA},
  author = {Wong, Tak-Lam and Lam, Wai and Wong, Tik-Shun},
  booktitle = {SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval},
  doi = {http://doi.acm.org/10.1145/1390334.1390343},
  interhash = {acb8410f8feae2a3eaa6839e7a077361},
  intrahash = {e02ce18885b3ab54256b628ddb920658},
  isbn = {978-1-60558-164-4},
  location = {Singapore, Singapore},
  pages = {35--42},
  publisher = {ACM},
  title = {An unsupervised framework for extracting and normalizing product attributes from multiple web sites},
  url = {http://portal.acm.org/citation.cfm?id=1390343},
  year = 2008
}

@inproceedings{arasugarciamolina03,
  author = {Arasu, Arvind and Garcia-Molina, Hector},
  booktitle = {Proceedings of the 2003 ACM SIGMOD International Conference on               Management of Data, San Diego, California, USA, June 9-12, 2003},
  editor = {Halevy, Alon Y. and Ives, Zachary G. and Doan, AnHai},
  interhash = {4d5c1e9aa3fda480e5945c4c0778ba14},
  intrahash = {884b6d51ca600b501cf93a59ba71ccd4},
  isbn = {1-58113-634-X},
  pages = {337-348},
  publisher = {ACM},
  title = {Extracting Structured Data from Web Pages},
  year = 2003
}

@inproceedings{SilkFromSow,
  address = {New York, NY},
  author = {Pirolli, Ramana Rao Peter},
  booktitle = {Proc. {ACM} Conf. Human Factors in Computing Systems, {CHI}},
  interhash = {4b04f73f97fa83c290df092515487e65},
  intrahash = {2f6dcc06ec9d1974f9f57612ffeeccfe},
  location = {Santa Barbara, CA},
  pages = {118--125},
  publisher = {ACM Press},
  title = {Silk from a Sow's Ear: Extracting Usable Structures from the Web},
  url = {citeseer.nj.nec.com/article/pirolli96silk.html},
  year = 1996
}