#### Filter Results:

- Full text PDF available (23)

#### Publication Year

2003

2017

- This year (1)
- Last 5 years (19)
- Last 10 years (30)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data, apart… (More)

- Huiping Cao, Nikos Mamoulis, David Wai-Lok Cheung
- Fifth IEEE International Conference on Data…
- 2005

Many applications track the movement of mobile objects, which can be represented as sequences of timestamped locations. Given such a spatiotemporal series, we study the problem of discovering sequential patterns, which are routes frequently followed by the object. Sequential pattern mining algorithms for transaction data are not directly applicable for this… (More)

- Huiping Cao, Nikos Mamoulis, David Wai-Lok Cheung
- IEEE Transactions on Knowledge and Data…
- 2007

In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data could… (More)

- Huiping Cao, Nikos Mamoulis, David Wai-Lok Cheung
- Sixth International Conference on Data Mining…
- 2006

Given a collection of trajectories of moving objects with different types (e.g., pumas, deers, vultures, etc.), we introduce the problem of discovering collocation episodes in them (e.g., if a puma is moving near a deer, then a vulture is also going to move close to the same deer with high probability within the next 3 minutes). Collocation episodes catch… (More)

- Huiping Cao, David Wai-Lok Cheung, Nikos Mamoulis
- PAKDD
- 2004

The problem of partial periodic pattern mining in a discrete data sequence is to find subsequences that appear periodically and frequently in the data sequence. Two essential subproblems are the efficient mining of frequent patterns and the automatic discovery of periods that correspond to these patterns. Previous methods for this problem in event sequence… (More)

- Huiping Cao, Yan Qi, K. Selçuk Candan, Maria Luisa Sapino
- EDBT
- 2010

Feedback process has been used extensively in document-centric applications, such as text retrieval and multimedia retrieval. Recently, there have been efforts to apply feedback to semi-structured XML document collections as well. In this paper, we note that feedback can also be an effective tool for exploring (through result ranking and query refinement)… (More)

Widespread placement and high data sampling rate of current generation of Phasor Measurement Units (PMUs) in Wide Area Monitoring Systems (WAMS) result in huge amount of data to be analyzed and stored, making efficient storage of such data a priority. This paper presents a generalized compression technique that utilizes the inherent correlation within PMU… (More)

- Huiping Cao, Shan Wang, Lingwei Li
- Inf. Sci.
- 2003