Results 1 - 10
of
47
Indexing Spatio-Temporal Trajectories with Chebyshev Polynomials
- Proc. 2004 SIGMOD, toappear
"... In this thesis, we investigate the subject of indexing large collections of spatiotemporal trajectories for similarity matching. Our proposed technique is to first mitigate the dimensionality curse problem by approximating each trajectory with a low order polynomial-like curve, and then incorporate ..."
Abstract
-
Cited by 83 (0 self)
- Add to MetaCart
In this thesis, we investigate the subject of indexing large collections of spatiotemporal trajectories for similarity matching. Our proposed technique is to first mitigate the dimensionality curse problem by approximating each trajectory with a low order polynomial-like curve, and then incorporate a multidimensional index into the reduced space of polynomial coefficients. There are many possible ways to choose the polynomial, including Fourier transforms, splines, non-linear regressions, etc. Some of these possibilities have indeed been studied before. We hypothesize that one of the best approaches is the polynomial that minimizes the maximum deviation from the true value, which is called the minimax polynomial. Minimax approximation is particularly meaningful for indexing because in a branch-and-bound search (i.e., for finding nearest neighbours), the smaller the maximum deviation, the more pruning opportunities there exist. In general, among all the polynomials of the same degree, the optimal minimax polynomial is very hard to compute. However, it has been shown that the Chebyshev approximation is almost identical to the optimal minimax polynomial, and is easy to compute [32]. Thus, we shall explore how to use
Mining frequent spatio-temporal sequential patterns
- In ICDM
, 2005
"... Many applications track the movement of mobile objects, which can be represented as sequences of timestamped locations. Given such a spatio-temporal series, we study the problem of discovering sequential patterns, which are routes frequently followed by the object. Sequential pattern mining algorith ..."
Abstract
-
Cited by 40 (0 self)
- Add to MetaCart
(Show Context)
Many applications track the movement of mobile objects, which can be represented as sequences of timestamped locations. Given such a spatio-temporal 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 setting. The challenges to address are (i) the fuzziness of locations in patterns, and (ii) the identification of non-explicit pattern instances. In this paper, we define pattern elements as spatial regions around frequent line segments. Our method first transforms the original sequence into a list of sequence segments, and detects frequent regions in a heuristic way. Then, we propose algorithms to find patterns by employing a newly proposed substring tree structure and improving Apriori technique. A performance evaluation demonstrates the effectiveness and efficiency of our approach. 1
Mining, Indexing, and Querying Historical Spatiotemporal Data
, 2004
"... 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 ..."
Abstract
-
Cited by 30 (1 self)
- Add to MetaCart
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 from unveiling important information to the data analyst, can facilitate data management substantially. Based on this observation, we propose a framework that analyzes, manages, and queries object movements that follow such patterns. We define the spatiotemporal periodic pattern mining problem and propose an effective and fast mining algorithm for retrieving maximal periodic patterns. We also devise a novel, specialized index structure that can benefit from the discovered patterns to support more efficient execution of spatiotemporal queries. We evaluate our methods experimentally using datasets with object trajectories that exhibit periodicity.
Discovery of periodic patterns in spatiotemporal sequences
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (TKDE
, 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 discover ..."
Abstract
-
Cited by 26 (0 self)
- Add to MetaCart
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 unveil important information to the data analyst. Existing approaches for discovering periodic patterns focus on symbol sequences. However, these methods cannot directly be applied to a spatiotemporal sequence because of the fuzziness of spatial locations in the sequence. In this paper, we define the problem of mining periodic patterns in spatiotemporal data and propose an effective and efficient algorithm for retrieving maximal periodic patterns. In addition, we study two interesting variants of the problem. The first is the retrieval of periodic patterns that are frequent only during a continuous subinterval of the whole history. The second problem is the discovery of periodic patterns, whose instances may be shifted or distorted. We demonstrate how our mining technique can be adapted for these variants. Finally, we present a comprehensive experimental evaluation, where we show the effectiveness and efficiency of the proposed techniques.
Traffic Density-Based Discovery of Hot Routes in Road Networks
, 2007
"... Finding hot routes (traffic flow patterns) in a road network is an important problem. They are beneficial to city planners, police departments, real estate developers, and many others. Knowing the hot routes allows the city to better direct traffic or analyze congestion causes. In the past, this p ..."
Abstract
-
Cited by 26 (2 self)
- Add to MetaCart
(Show Context)
Finding hot routes (traffic flow patterns) in a road network is an important problem. They are beneficial to city planners, police departments, real estate developers, and many others. Knowing the hot routes allows the city to better direct traffic or analyze congestion causes. In the past, this problem has largely been addressed with domain knowledge of city. But in recent years, detailed information about vehicles in the road network have become available. With the development and adoption of RFID and other location sensors, an enormous amount of moving object trajectories are being collected and can be used towards finding hot routes. This is a challenging problem due to the complex nature of the data. If objects traveled in organized clusters, it would be straightforward to use a clustering algorithm to find the hot routes. But, in the real world, objects move in unpredictable ways. Variations in speed, time, route, and other factors cause them to travel in rather fleeting “clusters. ” These properties make the problem difficult for a naive approach. To this end, we propose a new density-based algorithm named FlowScan. Instead of clustering the moving objects, road segments are clustered based on the density of common traffic they share. We implemented FlowScan and tested it under various conditions. Our experiments show that the system is both efficient and effective at discovering hot routes.
Discovering frequent arrangements of temporal intervals
- In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM’05
, 2005
"... In this paper we study a new problem in temporal pattern mining: discovering frequent arrangements of temporal intervals. We assume that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine arrangements of event intervals that appear frequen ..."
Abstract
-
Cited by 25 (5 self)
- Add to MetaCart
(Show Context)
In this paper we study a new problem in temporal pattern mining: discovering frequent arrangements of temporal intervals. We assume that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine arrangements of event intervals that appear frequently in the database. There are many applications where these type of patterns can be useful, including data network, scientific, and financial applications. Efficient methods to find frequent arrangements of temporal intervals using both breadth first and depth first search techniques are described. The performance of the proposed algorithms is evaluated and compared with other approaches on real datasets (American Sign Language streams and network data) and large synthetic datasets.
Mining spatio-temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases
- of Lecture Notes in Computer Science
, 2006
"... Abstract. As mobile devices proliferate and networks become more locationaware, the corresponding growth in spatio-temporal data will demand analysis techniques to mine patterns that take into account the semantics of such data. Association Rule Mining has been one of the more extensively studied da ..."
Abstract
-
Cited by 22 (3 self)
- Add to MetaCart
(Show Context)
Abstract. As mobile devices proliferate and networks become more locationaware, the corresponding growth in spatio-temporal data will demand analysis techniques to mine patterns that take into account the semantics of such data. Association Rule Mining has been one of the more extensively studied data mining techniques, but it considers discrete transactional data (supermarket or sequential). Most attempts to apply this technique to spatial-temporal domains maps the data to transactions, thus losing the spatio-temporal characteristics. We provide a comprehensive definition of spatio-temporal association rules (STARs) that describe how objects move between regions over time. We define support in the spatio-temporal domain to effectively deal with the semantics of such data. We also introduce other patterns that are useful for mobility data; stationary regions and high traffic regions. The latter consists of sources, sinks and thoroughfares. These patterns describe important temporal characteristics of regions and we show that they can be considered as special STARs. We provide efficient algorithms to find these patterns by exploiting several pruning properties 1. 1
Mining Long Sharable Patterns in Trajectories of Moving Objects
- IN PROC. OF STDBM
, 2006
"... The efficient analysis of spatio–temporal data, generated by moving objects, is an essential requirement for intelligent location–based services. Spatiotemporal rules can be found by constructing spatio–temporal baskets, from which traditional association rule mining methods can discover spatio–tem ..."
Abstract
-
Cited by 16 (4 self)
- Add to MetaCart
(Show Context)
The efficient analysis of spatio–temporal data, generated by moving objects, is an essential requirement for intelligent location–based services. Spatiotemporal rules can be found by constructing spatio–temporal baskets, from which traditional association rule mining methods can discover spatio–temporal rules. When the items in the baskets are spatio–temporal identifiers and are derived from trajectories of moving objects, the discovered rules represent frequently travelled routes. For some applications, e.g., an intelligent ridesharing application, these frequent routes are only interesting if they are long and sharable, i.e., can potentially be shared by several users. This paper presents a database projection based method for efficiently extracting such long, sharable frequent routes. The method prunes the search space by making use of the minimum length and sharable requirements and avoids the generation of the exponential number of sub–routes of long routes. Considering alternative modelling options for trajectories, leads to the development of two effective variants of the method. SQL–based implementations are described, and extensive experiments on both real life – and large–scale synthetic data show the effectiveness of the method and its variants.
On the Spatiotemporal Burstiness of Terms
"... Thousands of documents are made available to the users via the web on a daily basis. One of the most extensively studied problems in the context of such document streams is burst identification. Given a term t, a burst is generally exhibited when an unusually high frequency is observed for t. While ..."
Abstract
-
Cited by 14 (1 self)
- Add to MetaCart
(Show Context)
Thousands of documents are made available to the users via the web on a daily basis. One of the most extensively studied problems in the context of such document streams is burst identification. Given a term t, a burst is generally exhibited when an unusually high frequency is observed for t. While spatial and temporal burstiness have been studied individually in the past, our work is the first to simultaneously track and measure spatiotemporal term burstiness. In addition, we use the mined burstiness information toward an efficient document-search engine: given a user’s query of terms, our engine returns a ranked list of documents discussing influential events with a strong spatiotemporal impact. We demonstrate the efficiency of our methods with an extensive experimental evaluation on real and synthetic datasets. 1.
H.: Extracting mobility statistics from indexed spatio-temporal datasets
- In: STDBM
, 2004
"... With the recent progress of spatial information technologies and mobile computing technologies, spatio-temporal databases that store information of moving objects have gained a lot of research interests. In this paper, we propose an algorithm to extract mobility statistics from indexed spatiotempora ..."
Abstract
-
Cited by 13 (1 self)
- Add to MetaCart
(Show Context)
With the recent progress of spatial information technologies and mobile computing technologies, spatio-temporal databases that store information of moving objects have gained a lot of research interests. In this paper, we propose an algorithm to extract mobility statistics from indexed spatiotemporal datasets for interactive analysis of huge collections of moving object trajectories. We focus on mobility statistics called the Markov transition probability, which is based on a cell-based organization of a target space and the Markov chain model. The algorithm computes the specified Markov transition probabilities efficiently with the help of an Rtree spatial index. It reduces the statistics computation task to a kind of constraint satisfaction problem and uses internal structure of an R-tree in an efficient manner. 1
