71,580 research outputs found
Collaborative Concealment of Spatio-Temporal Mobile Sequential Patterns
Recent advances in communication and information technology such as the increasing accuracy of GPS technology and the portability of wireless communication devices coat the way for Location Based Services LBS Based on the data collected from the location aware mobile devices data mining techniques are used to meet the quality requirements of expected services The efficient management of moving object databases has gained much interest in recent years due to the development of mobile communication and positioning technologies A typical way of representing moving objects is to use the trajectories Much work has focused on the topics of indexing query processing and data mining of moving object trajectories but little attention has been paid to the preservation of privacy in this setting The major contribution of this paper is to provide privacy to the users of Location Based Services along with capturing interesting user s behavior pattern by broaden the ideas presented in the datamining-literatur
Location Query Based on Moving Behaviors
[[abstract]]The importance of location prediction is rapidly increasing with the current trend of database applications in mobile
computing environment. However, current personal communication services network could only provide currently
maintained location information of non-idle mobile terminals. Pertinent researches predict the future location based on
tangent velocity approaches, which require mobile terminals to spend lots of precious electronic power to sense and then
measure a sequence of positions for predicting the future tangent velocity, and the prediction is effective only within a short
range of time. In this study, we propose an approach to predict future locations of mobile terminals based on the moving
behaviors mined from their long-term moving history. Location prediction based on moving behavior requires no power
consumption for position measurement, and the prediction results are effective for a long time without requiring the
queried clients to be non-idle. With the help of moving behavior, we propose several location prediction operators for
location query. Finally, we demonstrate the accuracy of the location query operators through simulation statistics. The
experimental results show that the predictions are accurate enough for regular moving mobile terminals.
r 2006 Elsevier B.V. All rights reserved
Towards a Scalable Dynamic Spatial Database System
With the rise of GPS-enabled smartphones and other similar mobile devices,
massive amounts of location data are available. However, no scalable solutions
for soft real-time spatial queries on large sets of moving objects have yet
emerged. In this paper we explore and measure the limits of actual algorithms
and implementations regarding different application scenarios. And finally we
propose a novel distributed architecture to solve the scalability issues.Comment: (2012
A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters
Keyword-based web queries with local intent retrieve web content that is
relevant to supplied keywords and that represent points of interest that are
near the query location. Two broad categories of such queries exist. The first
encompasses queries that retrieve single spatial web objects that each satisfy
the query arguments. Most proposals belong to this category. The second
category, to which this paper's proposal belongs, encompasses queries that
support exploratory user behavior and retrieve sets of objects that represent
regions of space that may be of interest to the user. Specifically, the paper
proposes a new type of query, namely the top-k spatial textual clusters (k-STC)
query that returns the top-k clusters that (i) are located the closest to a
given query location, (ii) contain the most relevant objects with regard to
given query keywords, and (iii) have an object density that exceeds a given
threshold. To compute this query, we propose a basic algorithm that relies on
on-line density-based clustering and exploits an early stop condition. To
improve the response time, we design an advanced approach that includes three
techniques: (i) an object skipping rule, (ii) spatially gridded posting lists,
and (iii) a fast range query algorithm. An empirical study on real data
demonstrates that the paper's proposals offer scalability and are capable of
excellent performance
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