Publication 2014 Issue No. 3 - March Abstract - Discovering the Top-k Unexplained Sequences in Time-Stamped Observation Data
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Discovering the Top-k Unexplained Sequences in Time-Stamped Observation Data
March 2014 (vol. 26 no. 3)
pp. 577-594
 ASCII Text x Massimiliano Albanese, Cristian Molinaro, Fabio Persia, Antonio Picariello, V.S. Subrahmanian, "Discovering the Top-k Unexplained Sequences in Time-Stamped Observation Data," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 3, pp. 577-594, March, 2014.
 BibTex x @article{ 10.1109/TKDE.2013.33,author = {Massimiliano Albanese and Cristian Molinaro and Fabio Persia and Antonio Picariello and V.S. Subrahmanian},title = {Discovering the Top-k Unexplained Sequences in Time-Stamped Observation Data},journal ={IEEE Transactions on Knowledge and Data Engineering},volume = {26},number = {3},issn = {1041-4347},year = {2014},pages = {577-594},doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.33},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on Knowledge and Data EngineeringTI - Discovering the Top-k Unexplained Sequences in Time-Stamped Observation DataIS - 3SN - 1041-4347SP577EP594EPD - 577-594A1 - Massimiliano Albanese, A1 - Cristian Molinaro, A1 - Fabio Persia, A1 - Antonio Picariello, A1 - V.S. Subrahmanian, PY - 2014KW - computing methodologiesKW - Knowledge representation formalisms and methodsKW - artificial intelligenceKW - computing methodologiesKW - knowledge base managementKW - knowledge representation formalisms and methodsKW - artificial intelligenceVL - 26JA - IEEE Transactions on Knowledge and Data EngineeringER -
Massimiliano Albanese, George Mason University, Fairfax
Cristian Molinaro, Università della Calabria
Fabio Persia, Universita di Napoli, Napoli
Antonio Picariello, Universita di Napoli, Napoli
V.S. Subrahmanian, University of Maryland, College Park
There are numerous applications where we wish to discover unexpected activities in a sequence of time-stamped observation data--for instance, we may want to detect inexplicable events in transactions at a website or in video of an airport tarmac. In this paper, we start with a known set $({\cal A})$ of activities (both innocuous and dangerous) that we wish to monitor. However, in addition, we wish to identify "unexplained" subsequences in an observation sequence that are poorly explained (e.g., because they may contain occurrences of activities that have never been seen or anticipated before, i.e., they are not in $({\cal A})$). We formally define the probability that a sequence of observations is unexplained (totally or partially) w.r.t. $({\cal A})$. We develop efficient algorithms to identify the top-$(k)$ Totally and partially unexplained sequences w.r.t. $({\cal A})$. These algorithms leverage theorems that enable us to speed up the search for totally/partially unexplained sequences. We describe experiments using real-world video and cyber-security data sets showing that our approach works well in practice in terms of both running time and accuracy.
Index Terms:
computing methodologies,Knowledge representation formalisms and methods,artificial intelligence,computing methodologies,knowledge base management,knowledge representation formalisms and methods,artificial intelligence
Citation:
Massimiliano Albanese, Cristian Molinaro, Fabio Persia, Antonio Picariello, V.S. Subrahmanian, "Discovering the Top-k Unexplained Sequences in Time-Stamped Observation Data," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 3, pp. 577-594, March 2014, doi:10.1109/TKDE.2013.33