#### Filter Results:

- Full text PDF available (72)

#### Publication Year

2002

2017

- This year (4)
- Last 5 years (29)
- Last 10 years (57)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

- Michail Vlachos, Dimitrios Gunopulos, George Kollios
- ICDE
- 2002

We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize non-metric similarity functions based on the Longest Common Subsequence (LCSS), which are very robust to noise… (More)

Although most time-series data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for a single index structure that can support multiple distance measures. Our specific area of interest is the efficient retrieval and analysis of trajectory similarities. Trajectory datasets are very… (More)

- Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Sang-Hee Lee, Michail Vlachos
- VLDB
- 2006

The matching of two-dimensional shapes is an important problem with applications in domains as diverse as biometrics, industry, medicine and anthropology. The distance measure used must be invariant to many distortions, including scale, offset, noise, partial occlusion, etc. Most of these distortions are relatively easy to handle, either in the… (More)

- Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh
- The VLDB Journal
- 2004

While most time series data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for an index structure that can support multiple distance measures. Our specific area of interest is the efficient retrieval and analysis of similar trajectories. Trajectory datasets are very common in… (More)

- Michail Vlachos, Christopher Meek, Zografoula Vagena, Dimitrios Gunopulos
- SIGMOD Conference
- 2004

We present several methods for mining knowledge from the query logs of the MSN search engine. Using the query logs, we build a time series for each query word or phrase (e.g., 'Thanksgiving' or 'Christmas gifts') where the elements of the time series are the number of times that a query is issued on a day. All of the methods we describe use sequences of… (More)

- Michail Vlachos, Philip S. Yu, Vittorio Castelli
- SDM
- 2005

This work motivates the need for more flexible structural similarity measures between time-series sequences, which are based on the extraction of important periodic features. Specifically, we present non-parametric methods for accurate periodicity detection and we introduce new periodic distance measures for time-series sequences. The goal of these tools… (More)

- Francesco Fusco, Marc Ph. Stoecklin, Michail Vlachos
- PVLDB
- 2010

The ever-increasing number of intrusions in public and commercial networks has created the need for high-speed archival solutions that continuously store streaming network data to enable forensic analysis and auditing. However, “turning back the clock” for post-attack analyses is not a trivial task. The first major challenge is that the solution has to… (More)

We present data representations, distance measures and organizational structures for fast and efficient retrieval of similar shapes in image databases. Using the Hough Transform we extract shape signatures that correspond to important features of an image. The new shape descriptor is robust against line discontinuities and takes into consideration not only… (More)

We present a novel anytime version of partitional clustering algorithm, such as k-Means and EM, for time series. The algorithm works by leveraging off the multi-resolution property of wavelets. The dilemma of choosing the initial centers is mitigated by initializing the centers at each approximation level, using the final centers returned by the coarser… (More)

- Tsuyoshi Idé, Spiros Papadimitriou, Michail Vlachos
- Seventh IEEE International Conference on Data…
- 2007

This paper addresses the task of change analysis of correlated multi-sensor systems. The goal of change analysis is to compute the anomaly score of each sensor when we know that the system has some potential difference from a reference state. Examples include validating the proper performance of various car sensors in the automobile industry. We solve this… (More)