Towards effective spatial data mining : uncertainty, condensity and privacy
- Publication Type:
- Thesis
- Issue Date:
- 2017
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Spatial data mining (SDM) is a process of knowledge discovery that the observing data is related to geographical information. It has become an important data mining task due to the explosive growth and pervasive use of spatial data. It is more difficult to extract interesting and useful patterns from spatial datasets due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. Although existing methods can handle the spatial mining task properly, as the arrival of the big data era, new challenges for SDM are arising.
Firstly, traditional SDM methods usually focus on deterministic datasets, where spatial events occur affirmatively at precise locations. However, the inherent uncertainty of spatial data makes the mining process more difficult. Classical spatial data mining algorithms are no longer applicable or need delicate modification. Secondly, traditional SDM frameworks produce an exponential number of patterns, which makes it hard for users to understand or apply. To solve the condensity issue, novel techniques such as summarization or representation must be carefully investigated. Thirdly, spatial data usually involves an individual’s location information, which incurs location privacy problem. It would be a challenge to protect location privacy with enhanced data security and improved resulting accuracy.
To address the uncertainty issue, we study the problem of discovering co-location patterns in the context of continuously distributed uncertain data, namely Probabilistic Co-location Patterns Mining (PCPM). We develop an effective probabilistic co-location mining framework integrated with optimization strategies to address the challenges.
To address the condensity issue, we investigate the problem of Representative Co-location Patterns Mining (RCPM). We define a new measure to quantify the distance between co-location patterns, and develop two efficient algorithms for summarization.
To address the privacy issue, we solve the problem of protecting Location Privacy in Spatial Crowdsourcing (LPSC). We propose a secure spatial crowdsourcing framework based on encryption, and devise a novel secure indexing technique for efficient querying.
The experimental results demonstrate the effectiveness and efficiency of our proposed solutions. The methods and techniques used in solving concrete SDM tasks can also be applied or extended to other SDM scenarios.
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