Querying imprecise data in moving object environments

By Reynold Cheng, Dmitri V. Kalashnikov, Sunil Prabhakar. IEEE Transactions on Knowledge and Data Engineering, 2003

391 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Geography, Planning and Development, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d25f720872f17cd06edd1b1d935aab3c40bf34ff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Philip S. Yu
2. Charu C. Aggarwal
3. Mohamed F. Mokbel
4. Reynold Cheng
5. Walid G. Aref
6. Sunil Prabhakar
7. Mohamed A. Soliman
8. Ihab F. Ilyas

In Geography, Planning and Development

1. Mohamed F. Mokbel
2. Yu Zheng
3. Walid G. Aref
4. Chi-Yin Chow
5. Philip S. Yu
6. Xing Xie

In Signal Processing

1. Charu C. Aggarwal
2. Mohamed F. Mokbel
3. Philip S. Yu
4. Chi-Yin Chow
5. Walid G. Aref
6. Reynold Cheng
7. Yu Zheng
8. Sunil Prabhakar

Citations in the corpus

• A Survey of Uncertain Data Algorithms and Applications (2009, background): “Another example of such a database is the Orion project [25], [28] which presents query processing and indexing techniques in order to manage uncertainty over continuous intervals.”
• Data integration with uncertainty (2007, background): “There has been a flurry of activity around probabilistic and uncertain databases lately [4, 24, 5, 3].”
• Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data (2004, background): “The probabilistic uncertainty model for two-dimensional moving objects is discussed in [8].”
• The new Casper: query processing for location services without compromising privacy (2006, methodology): “Figure 17a shows the performance for groups of k from [1-10] till [40-50] while Figure 17b gives the same experiment but for larger values of k up to [ 150-200 150-200 ].”
• Top-k Query Processing in Uncertain Databases (2007, background): “Efficient processing of uncertain (probabilistic) data is a crucial requirement in different domains including sensor networks [18] [22] [8], moving objects tracking [7] [9] and data cleaning [12] [3].”
• Evaluating probabilistic queries over imprecise data (2003, background): “the specified range. Cheng et. al [ 3 ] considered the problem”
• Trajectory Data Mining (2015, background): “Recent approaches use independent probability density functions at each point of time [24], or stochastic processes [85][109][30][74] (e.”